Debian Med Project
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Summary
Biology
Debian Med bioinformatics packages

This metapackage will install Debian packages for use in molecular biology, structural biology and other biological sciences.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Med to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Med mailing list

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Debian Med Biology packages

Official Debian packages with high relevance

abacas
close gaps in genomic alignments from short reads
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ABACAS (Algorithm Based Automatic Contiguation of Assembled Sequences) intends to rapidly contiguate (align, order, orientate), visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence.

ABACAS uses MUMmer to find alignment positions and identify syntenies of assembled contigs against the reference. The output is then processed to generate a pseudomolecule taking overlapping contigs and gaps in to account. ABACAS generates a comparison file that can be used to visualize ordered and oriented contigs in ACT. Synteny is represented by red bars where colour intensity decreases with lower values of percent identity between comparable blocks. Information on contigs such as the orientation, percent identity, coverage and overlap with other contigs can also be visualized by loading the outputted feature file on ACT.

The package is enhanced by the following packages: abacas-examples
Please cite: Samuel Assefa, Thomas M. Keane, Thomas D. Otto, Chris Newbold and Matthew Berriman: ABACAS: algorithm-based automatic contiguation of assembled sequences. (PubMed,eprint) Bioinformatics 25(15):1968-1969 (2009)
Topics: Probes and primers
abpoa
adaptive banded Partial Order Alignment
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abPOA is an extended version of Partial Order Alignment (POA) that performs adaptive banded dynamic programming (DP) with an SIMD implementation. abPOA can perform multiple sequence alignment (MSA) on a set of input sequences and generate a consensus sequence by applying the heaviest bundling algorithm to the final alignment graph.

abPOA can generate high-quality consensus sequences from error-prone long reads and offer significant speed improvement over existing tools.

abPOA supports three alignment modes (global, local, extension) and flexible scoring schemes that allow linear, affine and convex gap penalties. It right now supports SSE2/SSE4.1/AVX2 vectorization.

For more information please refer to the paper1 published in Bioinformatics.

Please cite: Yan Gao, Yongzhuang Liu, Yanmei Ma, Bo Liu, Yadong Wang and Yi Xing: abPOA: an SIMD-based C library for fast partial order alignment using adaptive band. Bioinformatics 37(15):2209–2211 (2021)
abyss
de novo, parallel, sequence assembler for short reads
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ABySS is a de novo, parallel, sequence assembler that is designed for short reads. It may be used to assemble genome or transcriptome sequence data. Parallelization is achieved using MPI, OpenMP and pthread.

Please cite: Shaun D. Jackman, Benjamin P. Vandervalk, Hamid Mohamadi, Justin Chu, Sarah Yeo, S. Austin Hammond, Golnaz Jahesh, Hamza Khan, Lauren Coombe, Rene L. Warren and İnanç Birol: "ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter". (PubMed,eprint) Genome Research 27(5):768-777 (2017)
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Topics: Sequence assembly
acedb-other
retrieval of DNA or protein sequences
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This package collects all those smallish applications that acedb collects under its 'other' target of its Makefile.

efetch: presumably short for 'entry fetch' collects sequence information from common DNA and protein databases.

Please cite: L. D. Stein and J. Thierry-Mieg: AceDB: a genome database management system. Computing in Science and Engineering 1(3):44-52 (1999)
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adapterremoval
rapid adapter trimming, identification, and read merging of gene sequences
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This program searches for and removes remnant adapter sequences from High- Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into (longer) consensus sequences. Additionally, the AdapterRemoval may be used to recover a consensus adapter sequence for paired-ended data, for which this information is not available.

The package is enhanced by the following packages: multiqc
Please cite: Mikkel Schubert, Stinus Lindgreen and Ludovic Orlando: AdapterRemoval v2: rapid adapter trimming, identification, and read merging. (PubMed,eprint) BMC Research Notes 9:88 (2016)
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adun-core
Molecular Simulator
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Adun is a biomolecular simulator that also includes data management and analysis capabilities. It was developed at the Computational Biophysics and Biochemistry Laboratory, a part of the Research Unit on Biomedical Informatics of the UPF.

This package contains the AdunCore program and the Adun server. If you want the graphical UI frontend, install the adun.app package.

Please cite: Michael A. Johnston, Ignacio Fdez. Galván and Jordi Villà-Freixa: Framework-based design of a new all-purpose molecular simulation application: The Adun simulator. (PubMed) J. Comp. Chem. 26(15):1647-1659 (2005)
aegean
integrated genome analysis toolkit
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The AEGeAn Toolkit is designed for the Analysis and Evaluation of Genome Annotations. The toolkit includes a variety of analysis programs, e.g. for comparing distinct sets of gene structure annotations (ParsEval), computation of gene loci (LocusPocus) and more.

Please cite: Daniel S Standage and Volker P Brendel: ParsEval: parallel comparison and analysis of gene structure annotations.. (PubMed,eprint) BMC Bioinformatics 13(1):187 (2012)
Topics: Sequencing
aevol
digital genetics model to run Evolution Experiments in silico
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Aevol is a digital genetics model: populations of digital organisms are subjected to a process of selection and variation, which creates a Darwinian dynamics.

By modifying the characteristics of selection (e.g. population size, type of environment, environmental variations) or variation (e.g. mutation rates, chromosomal rearrangement rates, types of rearrangements, horizontal transfer), one can study experimentally the impact of these parameters on the structure of the evolved organisms. In particular, since Aevol integrates a precise and realistic model of the genome, it allows for the study of structural variations of the genome (e.g. number of genes, synteny, proportion of coding sequences).

The simulation platform comes along with a set of tools for analysing phylogenies and measuring many characteristics of the organisms and populations along evolution.

Please cite: Dusan Misevic, Antoine Frenoy, David P. Parsons and Francois Taddei: Effects of public good properties on the evolution of cooperation. (eprint) :218-225 (2012)
alien-hunter
Interpolated Variable Order Motifs to identify horizontally acquired DNA
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Alien_hunter is an application for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated Variable Order Motifs (IVOMs). An IVOM approach exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted regions. The predictions (embl format) can be automatically loaded into Artemis genome viewer freely available at: http://www.sanger.ac.uk/Software/Artemis/.

Please cite: Georgios S. Vernikos and Julian Parkhill: Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands. (PubMed,eprint) Bioinformatics 22(18):2196-2203 (2006)
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alter-sequence-alignment
genomic sequences ALignment Transformation EnviRonment
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ALTER (ALignment Transformation EnviRonment) is a tool to transform between multiple sequence alignment formats. ALTER focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats.

Please cite: Daniel Glez-Peña, Daniel Gómez-Blanco, Miguel Reboiro-Jato, Florentino Fdez-Riverola and David Posada: ALTER: program-oriented conversion of DNA and protein alignments". (PubMed,eprint) Nucl. Acids Res. 38(suppl 2):W14-W18 (2010)
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altree
program to perform phylogeny-based association and localization analysis
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ALTree was designed to perform association detection and localization of susceptibility sites using haplotype phylogenetic trees: first, it allows the detection of an association between a candidate gene and a disease, and second, it enables to make hypothesis about the susceptibility loci.

Please cite: Claire Bardel, Vincent Danjean and Emmanuelle Genin: ALTree: association detection and localization of susceptibility sites using haplotype phylogenetic trees. (PubMed,eprint) Bioinformatics 22(11):1402-1403 (2006)
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amap-align
Protein multiple alignment by sequence annealing
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AMAP is a command line tool to perform multiple alignment of peptidic sequences. It utilizes posterior decoding, and a sequence-annealing alignment, instead of the traditional progressive alignment method. It is the only alignment program that allows one to control the sensitivity / specificity tradeoff. It is based on the ProbCons source code, but uses alignment metric accuracy and eliminates the consistency transformation.

The Java visualisation tool of AMAP 2.2 is not yet packaged in Debian.

Please cite: Ariel S. Schwartz and Lior Pachter: Multiple alignment by sequence annealing. (eprint) Bioinformatics 23(2):e24-e29 (2007)
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Remark of Debian Med team: Dead upstream

The homepage of this project vanished as well as the Download area. An old unmaintained version remained at code.google.com. Please drop the maintainer a note if you have any news of this project.

ampliconnoise
removal of noise from 454 sequenced PCR amplicons
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AmpliconNoise is a package of applications to clean up high-throughput sequence data. It consists of three main parts:

Pyronoise - does flowgram-based clustering to spot misreads SeqNoise - removes PCR point mutations Perseus - removes PCR chimeras without the need for a set of reference sequences

Previously there was a standalone "Pyronoise" by the same authors and this package includes an updated version. There is also a "Denoiser" in Qiime which is related but distinct.

Please cite: Christopher Quince, Anders Lanzen, Russell J Davenport and Peter J Turnbaugh: Removing Noise From Pyrosequenced Amplicons. (PubMed,eprint) BMC Bioinformatics 12:38 (2011)
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Topics: Sequencing
andi
Efficient Estimation of Evolutionary Distances
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This is the andi program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because andi does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.

Please cite: Bernhard Haubold, Fabian Klötzl and Peter Pfaffelhuber: andi: Fast and accurate estimation of evolutionary distances between closely related genomes. (PubMed,eprint) Bioinformatics 31(8):1169-1175 (2015)
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Topics: Phylogenetics
anfo
Short Read Aligner/Mapper from MPG
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Anfo is a mapper in the spirit of Soap/Maq/Bowtie, but its implementation takes more after BLAST/BLAT. It's most useful for the alignment of sequencing reads where the DNA sequence is somehow modified (think ancient DNA or bisulphite treatment) and/or there is more divergence between sample and reference than what fast mappers will handle gracefully (say the reference genome is missing and a related species is used instead).

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Topics: Sequencing
any2fasta
convert various sequence formats to FASTA
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Established tools like readseq and seqret from EMBOSS, both create mangled IDs containing | or . characters, and there is no way to fix this behaviour. This resultes in inconsitences between .gbk and .fna versions of files in pipelines.

This script uses only core Perl modules, has no other dependencies like Bioperl or Biopython, and runs very quickly.

It supports the following input formats:

 1. Genbank flat file, typically .gb, .gbk, .gbff (starts with LOCUS)
 2. EMBL flat file, typically .embl, (starts with ID)
 3. GFF with sequence, typically .gff, .gff3 (starts with ##gff)
 4. FASTA DNA, typically .fasta, .fa, .fna, .ffn (starts with >)
 5. FASTQ DNA, typically .fastq, .fq (starts with @)
 6. CLUSTAL alignments, typically .clw, .clu (starts with CLUSTAL or MUSCLE)
 7. STOCKHOLM alignments, typically .sth (starts with # STOCKHOLM)
 8. GFA assembly graph, typically .gfa (starts with ^[A-Z]\t)

Files may be compressed with:

 1. gzip, typically .gz
 2. bzip2, typically .bz2
 3. zip, typically .zip
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aragorn
tRNA and tmRNA detection in nucleotide sequences
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The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base-paired cloverleaf. tmRNA genes are identified using a modified version of the BRUCE program.

Please cite: Dean Laslett and Bjorn Canback: ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. (PubMed,eprint) Nucleic Acids Research 32(1):11-16 (2004)
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Topics: Functional, regulatory and non-coding RNA
arden
specificity control for read alignments using an artificial reference
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ARDEN (Artificial Reference Driven Estimation of false positives in NGS data) is a novel benchmark that estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It allows the computation of error rates specifically for a dataset and the construction of a ROC-curve. Thereby, it can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation.

Please cite: Sven H. Giese, Franziska Zickmann and Bernhard Y. Renard: Specificity control for read alignments using an artificial reference genome-guided false discovery rate. (PubMed,eprint) Bioinformatics 30(1):9-16 (2013)
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Topics: Sequencing
ariba
Antibiotic Resistance Identification By Assembly
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ARIBA is a tool that identifies antibiotic resistance genes by running local assemblies. The input is a FASTA file of reference genes and paired sequencing reads. ARIBA reports which of the reference genes were found, plus detailed information on the quality of the assemblies and any variants between the sequencing reads and the reference genes.

Please cite: Martin Hunt, Alison E. Mather, Leonor Sanchez-Buso, Andrew J. Page, Julian Parkhill, Jacqueline A. Keane and Simon R. Harris: ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. (PubMed,eprint) Microbial Genomics 3 (2017)
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art-nextgen-simulation-tools
simulation tools to generate synthetic next-generation sequencing reads
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ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Please cite: Weichun Huang, Leping Li, Jason R. Myers and Gabor T. Marth: ART: a next-generation sequencing read simulator. (PubMed,eprint) Bioinformatics 28(4):593-594 (2012)
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artemis
genome browser and annotation tool
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Artemis is a genome browser and annotation tool that allows visualisation of sequence features, next generation data and the results of analyses within the context of the sequence, and also its six-frame translation.

This package includes the Artemis genome browser, the Artemis Comparison Tool (ACT), and the DNAplotter and BamView utilities.

Please cite: Tim Carver, Simon R. Harris, Matthew Berriman, Julian Parkhill and Jacqueline A. McQuillan: Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. (PubMed,eprint) Bioinformatics 28(4):464-469 (2012)
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Topics: Genomics
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artfastqgenerator
outputs artificial FASTQ files derived from a reference genome
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ArtificialFastqGenerator takes the reference genome (in FASTA format) as input and outputs artificial FASTQ files in the Sanger format. It can accept Phred base quality scores from existing FASTQ files, and use them to simulate sequencing errors. Since the artificial FASTQs are derived from the reference genome, the reference genome provides a gold-standard for calling variants (Single Nucleotide Polymorphisms (SNPs) and insertions and deletions (indels)). This enables evaluation of a Next Generation Sequencing (NGS) analysis pipeline which aligns reads to the reference genome and then calls the variants.

Please cite: Matthew Frampton and Richard Houlston: Generation of Artificial FASTQ Files to Evaluate the Performance of Next-Generation Sequencing Pipelines. (PubMed,eprint) PLOSone 7(11):e49110 (2012)
assembly-stats
get assembly statistics from FASTA and FASTQ files
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Get statistics from a list of files.

Detection of FASTA or FASTQ format of each file is automatic from the file contents, so file names and extensions are irrelevant.

The default output format is human readable. You can change the output format and ignore sequences shorter than a given length.

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assemblytics
detect and analyze structural variants from a genome assembly
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Assemblytics incorporates a unique anchor filtering approach to increase robustness to repetitive elements, and identifies six classes of variants based on their distinct alignment signatures. Assemblytics can be applied both to comparing aberrant genomes, such as human cancers, to a reference, or to identify differences between related species.

Please cite: Maria Nattestad and Michael C. Schatz: Assemblytics: a web analytics tool for the detection of variants from an assembly. (PubMed) Bioinformatics 32(19):3021-3023 (2016)
atac
genome assembly-to-assembly comparison
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atac computes a one-to-one pairwise alignment of large DNA sequences. It first finds the unique k-mers in each sequence, chains them to larger blocks, and fills in spaces between blocks. It was written primarily to transfer annotations between different assemblies of the human genome.

The output is a set of ungapped 'matches', and a set of gapped 'runs' formed from the matches. Each match or run associates one sequence with the other sequence. The association is 'unique', in that there is no other (sizeable) associations for either sequence. Thus, large repeats and duplications are not present in the output - they appear as unmapped regions.

Though the output is always pairwise, atac can cache intermediate results to speed a comparisons of multiple sequences.

This package is part of the Kmer suite.

The package is enhanced by the following packages: kmer-examples
Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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ataqv
ATAC-seq QC and visualization
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A toolkit for measuring and comparing ATAC-seq results, made in the Parker lab at the University of Michigan. They wrote it to help understand how well their ATAC-seq assays had worked, and to make it easier to spot differences that might be caused by library prep or sequencing.

Please cite: Peter Orchard, Yasuhiro Kyono, John Hensley, Jacob O. Kitzman and Stephen C.J. Parker: Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv. (eprint) Cell Systems 10(3):2405-4712 (2020)
atropos
NGS read trimming tool that is specific, sensitive, and speedy
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Atropos is tool for specific, sensitive, and speedy trimming of NGS reads. It is a fork of the venerable Cutadapt read trimmer, with the primary improvements being:

  1. Multi-threading support, including an extremely fast "parallel
     write" mode.
  2. Implementation of a new insert alignment-based trimming algorithm
     for paired-end reads that is substantially more sensitive and
     specific than the original Cutadapt adapter alignment-based
     algorithm. This algorithm can also correct mismatches between the
     overlapping portions of the reads.
  3. Options for trimming specific types of data (miRNA, bisulfite-seq).
  4. A new command ('detect') that will detect adapter sequences and
     other potential contaminants.
  5. A new command ('error') that will estimate the sequencing error
     rate, which helps to select the appropriate adapter- and quality-
     trimming parameter values.
  6. A new command ('qc') that generates read statistics similar to
     FastQC. The trim command can also compute read statistics both
     before and after trimming (using the '--stats' option).
  7. Improved summary reports, including support for serialization
     formats (JSON, YAML, pickle), support for user-defined templates
     (via the optional Jinja2 dependency), and integration with MultiQC.
  8. The ability to merge overlapping reads (this is experimental and
     the functionality is limited).
  9. The ability to write the summary report and log messages to
     separate files.
 10. The ability to read SAM/BAM files and read/write interleaved
     FASTQ files.
 11. Direct trimming of reads from an SRA accession.
 12. A progress bar, and other minor usability enhancements.
Please cite: John P. Didion, Marcel Martin and Francis S. Collins: Atropos: specific, sensitive, and speedy trimming of sequencing reads. (PubMed,eprint) PeerJ 5:e3720 (2017)
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augur
pipeline components for real-time virus analysis
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The nextstrain project is an attempt to make flexible informatic pipelines and visualization tools to track ongoing pathogen evolution as sequence data emerges. The nextstrain project derives from nextflu, which was specific to influenza evolution.

nextstrain is comprised of three components:

  • fauna: database and IO scripts for sequence and serological data
  • augur: informatic pipelines to conduct inferences from raw data
  • auspice: web app to visualize resulting inferences
augustus
gene prediction in eukaryotic genomes
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AUGUSTUS is a software for gene prediction in eukaryotic genomic sequences that is based on a generalized hidden Markov model (HMM), a probabilistic model of a sequence and its gene structure. After learning gene structures from a reference annotation, AUGUSTUS uses the HMM to recognize genes in a new sequence and annotates it with the regions of identified genes. External hints, e.g. from RNA sequencing, EST or protein alignments etc. can be used to guide and improve the gene finding process. The result is the set of most likely gene structures that comply with all given user constraints, if such gene structures exist. AUGUSTUS already includes prebuilt HMMs for many species, as well as scripts to train custom models using annotated genomes.

Please cite: Stefanie König, Lars Romoth, Lizzy Gerischer and Mario Stanke: Simultaneous gene finding in multiple genomes. (PubMed,eprint) Bioinformatics 32(22):3388-3395 (2016)
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Topics: Gene transcripts; Gene and protein families
autodock
analysis of ligand binding to protein structure
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AutoDock is a prime representative of the programs addressing the simulation of the docking of fairly small chemical ligands to rather big protein receptors. Earlier versions had all flexibility in the ligands while the protein was kept rather ridgid. This latest version 4 also allows for a flexibility of selected sidechains of surface residues, i.e., takes the rotamers into account.

The AutoDock program performs the docking of the ligand to a set of grids describing the target protein. AutoGrid pre-calculates these grids.

The package is enhanced by the following packages: autogrid
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autodock-vina
docking of small molecules to proteins
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AutoDock Vina is a program to support drug discovery, molecular docking and virtual screening of compound libraries. It offers multi-core capability, high performance and enhanced accuracy and ease of use.

The same institute also developed autodock, which is widely used.

O. Trott, A. J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, Journal of Computational Chemistry 31 (2010) 455-461

Please cite: Oleg Trott and Arthur J. Olson: AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. (eprint) Journal of Computational Chemistry 31(2):455-461 (2010)
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autogrid
pre-calculate binding of ligands to their receptor
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The AutoDockSuite addresses the molecular analysis of the docking of a smaller chemical compounds to their receptors of known three-dimensional structure.

The AutoGrid program performs pre-calculations for the docking of a ligand to a set of grids that describe the effect that the protein has on point charges. The effect of these forces on the ligand is then analysed by the AutoDock program.

avogadro
Molecular Graphics and Modelling System
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Avogadro is a molecular graphics and modelling system targeted at molecules and biomolecules. It can visualize properties like molecular orbitals or electrostatic potentials and features an intuitive molecular builder.

Features include:

  • Molecular modeller with automatic force-field based geometry optimization
  • Molecular Mechanics including constraints and conformer searches
  • Visualization of molecular orbitals and general isosurfaces
  • Visualization of vibrations and plotting of vibrational spectra
  • Support for crystallographic unit cells
  • Input generation for the Gaussian, GAMESS and MOLPRO quantum chemistry packages
  • Flexible plugin architecture and Python scripting

File formats Avogadro can read include PDB, XYZ, CML, CIF, Molden, as well as Gaussian, GAMESS and MOLPRO output.

Please cite: Marcus D Hanwell, Donald E Curtis, David C Lonie, Tim Vandermeersch, Eva Zurek and Geoffrey R Hutchison: Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. (eprint) J. Cheminf. 4:17 (2012)
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axe-demultiplexer
Trie-based DNA sequencing read demultiplexer
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Axe very rapidly selects the optimal barcode present in a sequence read, even in the presence of sequencing errors. The algorithm is able to handle combinatorial barcoding, barcodes of differing length, and several mismatches per barcode.

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baitfisher
software package for designing hybrid enrichment probes
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The BaitFisher package consists of two programs: BaitFisher and BaitFilter.

BaitFisher was been designed to construct hybrid enrichment baits from multiple sequence alignments (MSAs) or annotated features in MSAs. The main goal of BaitFisher is to avoid redundancy in the construction of baits by designing fewer baits in conserved regions of the MSAs and designing more baits in variable regions. This makes use of the fact that hybrid enrichment baits can differ to some extends from the target region, which they should capture in the enrichment procedure. By specifying the allowed distance between baits and the sequences in the MSAs the user can control the allowed bait-to-target distance and the degree of reduction in the number of baits that are designed. See the BaitFisher paper for details.

BaitFilter was designed (i) to determine whether baits bind unspecifically to a reference genome, (ii) to filter baits that only have partial length matches to a reference genome, (iii) to determine the optimal bait region in a MSA and to convert baits to a format that can be uploaded at a bait constructing company. The optimal bait region can be the most conserved region in the MSA or the region with the highest number of sequences without gaps or ambiguous nucleotides.

Please cite: Christoph Mayer, Manuela Sann, Alexander Donath, Martin Meixner, Lars Podsiadlowski, Ralph S. Peters, Malte Petersen, Karen Meusemann, Karsten Liere, Johann-Wolfgang Wägele, Bernhard Misof, Christoph Bleidorn, Michael Ohl and Oliver Niehuis: BaitFisher: A Software Package for Multispecies Target DNA Enrichment Probe Design. (PubMed,eprint) Mol. Biol. Evol. 33(7):1875-1886 (2016)
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bali-phy
Bayesian Inference of Alignment and Phylogeny
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BAli-Phy estimates multiple sequence alignments and evolutionary trees from unaligned DNA, amino acid, or codon sequences. BAli-Phy uses MCMC to estimate evolutionary trees, positive selection, and branch lengths while averaging over alternative alignments. BAli-Phy can display alignment ambiguity graphically in an alignment uncertainty (AU) plot.

BAli-Phy can also estimate phylogenies from a fixed alignment (like MrBayes and BEAST) using substitution models like GTR+gamma. BAli-Phy automatically estimates relative rates for each gene.

Please cite: Benjamin D. Redelings and Marc A. Suchard: Joint Bayesian Estimation of Alignment and Phylogeny. (PubMed,eprint) Systematic Biology 54(3):401-418 (2005)
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ballview
free molecular modeling and molecular graphics tool
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BALLView provides fast OpenGL-based visualization of molecular structures, molecular mechanics methods (minimization, MD simulation using the AMBER, CHARMM, and MMFF94 force fields), calculation and visualization of electrostatic properties (FDPB) and molecular editing features.

BALLView can be considered a graphical user interface on the basis of BALL (Biochemical Algorithms Library) with a focus on the most common demands of protein chemists and biophysicists in particular. It is developed in the groups of Hans-Peter Lenhof (Saarland University, Saarbruecken, Germany) and Oliver Kohlbacher (University of Tuebingen, Germany). BALL is an application framework in C++ that has been specifically designed for rapid software development in Molecular Modeling and Computational Molecular Biology. It provides an extensive set of data structures as well as classes for Molecular Mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization.

Please cite: Andreas Moll, Andreas Hildebrandt, Hans-Peter Lenhof and Oliver Kohlbacher: BALLView: a tool for research and education in molecular modeling. (PubMed,eprint) Bioinformatics 22(3):365-366 (2006)
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bamclipper
Remove gene-specific primer sequences from SAM/BAM alignments
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Remove gene-specific primer sequences from SAM/BAM alignments of PCR amplicons by soft-clipping.

bamclipper.sh soft-clips gene-specific primers from BAM alignment file based on genomic coordinates of primer pairs in BEDPE format.

Please cite: Chun Hang Au, Dona N Ho, Ava Kwong, Tsun Leung Chan and Edmond S K Ma: BAMClipper: removing primers from alignments to minimize false-negative mutations in amplicon next-generation sequencing. (PubMed,eprint) Scientific Reports 7(1):1567 (2017)
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bamkit
tools for common BAM file manipulations
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This package provides some Python3 tools for common BAM file manipulations.

bamtools
toolkit for manipulating BAM (genome alignment) files
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BamTools facilitates research analysis and data management using BAM files. It copes with the enormous amount of data produced by current sequencing technologies that is typically stored in compressed, binary formats that are not easily handled by the text-based parsers commonly used in bioinformatics research.

BamTools provides both a C++ API for BAM file support as well as a command-line toolkit.

This is the bamtools command-line toolkit.

Available bamtools commands:

 convert  Converts between BAM and a number of other formats
 count    Prints number of alignments in BAM file(s)
 coverage Prints coverage statistics from the input BAM file
 filter   Filters BAM file(s) by user-specified criteria
 header   Prints BAM header information
 index    Generates index for BAM file
 merge    Merge multiple BAM files into single file
 random   Select random alignments from existing BAM file(s), intended more
          as a testing tool.
 resolve  Resolves paired-end reads (marking the IsProperPair flag as needed)
 revert   Removes duplicate marks and restores original base qualities
 sort     Sorts the BAM file according to some criteria
 split    Splits a BAM file on user-specified property, creating a new BAM
          output file for each value found
 stats    Prints some basic statistics from input BAM file(s)
The package is enhanced by the following packages: multiqc
Please cite: Derek W. Barnett, Erik K. Garrison, Aaron R. Quinlan, Michael P. Stromberg and Gabor T. Marth: BamTools: a C++ API and toolkit for analyzing and managing BAM files. (PubMed,eprint) Bioinformatics 27(12):1691-2 (2011)
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bandage
Bioinformatics Application for Navigating De novo Assembly Graphs Easily
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Bandage is a GUI program that allows users to interact with the assembly graphs made by de novo assemblers such as Velvet, SPAdes, MEGAHIT and others.

De novo assembly graphs contain not only assembled contigs but also the connections between those contigs, which were previously not easily accessible. Bandage visualises assembly graphs, with connections, using graph layout algorithms. Nodes in the drawn graph, which represent contigs, can be automatically labelled with their ID, length or depth. Users can interact with the graph by moving, labelling and colouring nodes. Sequence information can also be extracted directly from the graph viewer. By displaying connections between contigs, Bandage opens up new possibilities for analysing and improving de novo assemblies that are not possible by looking at contigs alone.

More information and download links are on the Bandage website: rrwick.github.io/Bandage

The package is relevant to the field of genome assembly.

The package is enhanced by the following packages: bandage-examples
Please cite: Ryan R. Wick, Mark B. Schultz, Justin Zobel and Kathryn E. Holt: Bandage: interactive visualisation of de novo genome assemblies. (PubMed,eprint) Bioinformatics 31(20):3350-3352 (2015)
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barrnap
rapid ribosomal RNA prediction
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Barrnap (BAsic Rapid Ribosomal RNA Predictor) predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

It takes FASTA DNA sequence as input, and writes GFF3 as output. It uses the NHMMER tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style. Multithreading is supported and one can expect roughly linear speed-ups with more CPUs.

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Topics: Functional, regulatory and non-coding RNA
bbmap
BBTools genomic aligner and other tools for short sequences
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The BBTools are a collection of small programs to solve recurrent tasks for the creative handling of short biological RNA/DNA sequences. This suite may be best known for its mapper, which is also the name of the project on sourceforge, but several tools have been added over time. All tools are multi-threaded, implemented platform-independently in Java:

BBMap: Short read aligner for DNA and RNA-seq data. Capable of handling arbitrarily large genomes with millions of scaffolds. Handles Illumina, PacBio, 454, and other reads; very high sensitivity and tolerant of errors and numerous large indels.

BBNorm: Kmer-based error-correction and normalization tool.

Dedupe: Simplifies assemblies by removing duplicate or contained subsequences that share a target percent identity.

Reformat: Reformats reads between fasta/fastq/scarf/fasta+qual/sam, interleaved/paired, and ASCII-33/64, at over 500 MB/s.

BBDuk: Filters, trims, or masks reads with kmer matches to an artifact/contaminant file.

The package is enhanced by the following packages: multiqc
Please cite: Brian Bushnell, Jonathan Rood and Esther Singer: BBMerge – Accurate paired shotgun read merging via overlap. (PubMed,eprint) PLOS One (2017)
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bcalm
de Bruijn compaction in low memory
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A bioinformatics tool for constructing the compacted de Bruijn graph from sequencing data.

This is the parallel version of the BCALM software using gatb-core library.

Please cite: Rayan Chikhi, Antoine Limasset and Paul Medvedev: Compacting de Bruijn graphs from sequencing data quickly and in low memory.. (eprint) Bioinformatics 32(12):208 (2016)
bcftools
genomic variant calling and manipulation of VCF/BCF files
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BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

The package is enhanced by the following packages: multiqc
Please cite: Petr Danecek and Shane A. McCarthy: BCFtools/csq: Haplotype-aware variant consequences. (2016)
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beads
2-DE electrophoresis gel image spot detection
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Beads is a program for spot detection on 2-D gel images. It is based on an analogy with beads flowing uphill on the surface of the gel image and on the analysis of their paths (Langella & Zivy, 2008).

Please cite: Olivier Langella and Michel Zivy: A method based on bead flows for spot detection on 2-D gel images. (PubMed) Proteomics 8(23-24):4914-8 (2008)
beagle
Genotype calling, genotype phasing and imputation of ungenotyped markers
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Beagle performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection. Genotypic imputation works on phased haplotypes using a Li and Stephens haplotype frequency model. Beagle also implements the Refined IBD algorithm for detecting homozygosity-by-descent (HBD) and identity-by-descent (IBD) segments.

The package is enhanced by the following packages: beagle-doc
Please cite: Sharon R. Browning and Brian L. Browning: Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering. (eprint) The American Journal of Human Genetics 81(5):1084-1097 (2007)
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beast-mcmc
Bayesian MCMC phylogenetic inference
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BEAST is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. Included is a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

The package is enhanced by the following packages: beast-mcmc-doc beast-mcmc-examples
Please cite: Alexei J Drummond and Andrew Rambaut: BEAST: Bayesian evolutionary analysis by sampling trees. (PubMed,eprint) BMC Evol Biol 8(7):214 (2007)
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beast2-mcmc
Bayesian MCMC phylogenetic inference
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BEAST is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. Included is a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

This is no new upstream version of beast-mcmc (1.x) but rather a rewritten version.

The package is enhanced by the following packages: beast2-mcmc-doc beast2-mcmc-examples
Please cite: Remco Bouckaert, Joseph Heled, Denise Kühnert, Tim Vaughan, Chieh-Hsi Wu, Dong Xie, Marc A. Suchard, Andrew Rambaut and Alexei J. Drummond: BEAST 2: A Software Platform for Bayesian Evolutionary Analysis. (PubMed,eprint) PLoS Comput Biol 10(4):e1003537 (2014)
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bedops
high-performance genomic feature operations
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BEDOPS is a suite of tools to address common questions raised in genomic studies, mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

Please cite: Shane Neph, M. Scott Kuehn, Alex P. Reynolds, Eric Haugen, Robert E. Thurman, Audra K. Johnson, Eric Rynes, Matthew T. Maurano, Jeff Vierstra, Sean Thomas, Richard Sandstrom, Richard Humbert and John A. Stamatoyannopoulos: BEDOPS: high-performance genomic feature operations. (PubMed,eprint) 28(14):1919-1920 (2012)
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bedtools
suite of utilities for comparing genomic features
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The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. Using BEDTools, one can develop sophisticated pipelines that answer complicated research questions by streaming several BEDTools together.

The groupBy utility is distributed in the filo package.

Please cite: Aaron R. Quinlan and Ira M. Hall: BEDTools: a flexible suite of utilities for comparing genomic features. (PubMed,eprint) Bioinformatics 26(6):841-842 (2010)
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belvu
multiple sequence alignment viewer and phylogenetic tool
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Belvu is a multiple sequence alignment viewer and phylogenetic tool with an extensive set of user-configurable modes to color residues.

  • View multiple sequence alignments.
  • Residues can be coloured by conservation, with user-configurable cutoffs and colours.
  • Residues can be coloured by residue type (user-configurable).
  • Colour schemes can be imported or exported.
  • Swissprot (or PIR) entries can be fetched by double clicking.
  • The position in the alignment can be easily tracked.
  • Manual deletion of rows and columns.
  • Automatic editing of rows and columns based on customisable criteria:
    • removal of all-gap columns;
    • removal of all gaps;
    • removal of redundant sequences;
    • removal of a column by a user-specified percentage of gaps;
    • filtering of sequences by percent identity;
    • removal of sequences by a user-specified percentage of gaps;
    • removal of partial sequences (those starting or ending with gaps); and
    • removal of columns by conservation (with user-specified upper/lower cutoffs).
  • The alignment can be saved in Stockholm, Selex, MSF or FASTA format.
  • Distance matrices between sequences can be generated using a variety of distance metrics.
  • Distance matrices can be imported or exported.
  • Phylogenetic trees can be constructed based on various distance-based tree reconstruction algorithms.
  • Trees can be saved in New Hampshire format.
  • Belvu can perform bootstrap phylogenetic reconstruction.
Please cite: Gemma Barson and Ed Griffiths: SeqTools: visual tools for manual analysis of sequence alignments. (PubMed,eprint) BMC Research Notes 9:39 (2016)
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berkeley-express
Streaming quantification for high-throughput sequencing
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eXpress is a streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data. It is based on an online-EM algorithm that results in space (memory) requirements proportional to the total size of the target sequences and time requirements that are proportional to the number of sampled fragments. Thus, in applications such as RNA-Seq, eXpress can accurately quantify much larger samples than other currently available tools greatly reducing computing infrastructure requirements. eXpress can be used to build lightweight high-throughput sequencing processing pipelines when coupled with a streaming aligner (such as Bowtie), as output can be piped directly into eXpress, effectively eliminating the need to store read alignments in memory or on disk.

In an analysis of the performance of eXpress for RNA-Seq data, it was observed that this efficiency does not come at a cost of accuracy. eXpress is more accurate than other available tools, even when limited to smaller datasets that do not require such efficiency. Moreover, like the Cufflinks program, eXpress can be used to estimate transcript abundances in multi-isoform genes. eXpress is also able to resolve multi-mappings of reads across gene families, and does not require a reference genome so that it can be used in conjunction with de novo assemblers such as Trinity, Oases, or Trans-ABySS. The underlying model is based on previously described probabilistic models developed for RNA-Seq but is applicable to other settings where target sequences are sampled, and includes parameters for fragment length distributions, errors in reads, and sequence-specific fragment bias.

eXpress can be used to resolve ambiguous mappings in other high-throughput sequencing based applications. The only required inputs to eXpress are a set of target sequences and a set of sequenced fragments multiply-aligned to them. While these target sequences will often be gene isoforms, they need not be. Haplotypes can be used as the reference for allele-specific expression analysis, binding regions for ChIP-Seq, or target genomes in metagenomics experiments. eXpress is useful in any analysis where reads multi-map to sequences that differ in abundance.

Please cite: Adam Roberts and Lior Pachter: Streaming fragment assignment for real-time analysis of sequencing experiments. (PubMed) Nature Methods 10(1):71–73 (2013)
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bifrost
parallel construction, indexing and querying of de Bruijn graphs
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Bifrost is a command-line tool for sequencing that features a broad range of functions, such as indexing, editing, and querying the graph, and includes a graph coloring method that maps each k-mer of the graph to the genomes it occurs in.

  • Build, index, color and query the compacted de Bruijn graph
  • No need to build the uncompacted de Bruijn graph
  • Reads or assembled genomes as input
  • Output graph in GFA (can be visualized with Bandage), FASTA or binary
  • Graph cleaning: short tip clipping, etc.
  • Multi-threaded
  • No parameters to estimate with other tools
  • Exact or approximate k-mer search of queries
Please cite: Guillaume Holley and Páll Melsted: Bifrost – Highly parallel construction and indexing of colored and compacted de Bruijn graphs. (PubMed,eprint) bioRxiv 21(1):249 (2020)
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bio-eagle
Haplotype phasing within a genotyped cohort or using a phased reference panel
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Eagle estimates haplotype phase either within a genotyped cohort or using a phased reference panel. The basic idea of the Eagle1 algorithm is to harness identity-by-descent among distant relatives—which is pervasive at very large sample sizes but rare among smaller numbers of samples—to rapidly call phase using a fast scoring approach. In contrast, the Eagle2 algorithm analyzes a full probabilistic model similar to the diploid Li-Stephens model used by previous HMM-based methods.

Please note: The executable was renamed to bio-eagle because of a name clash. Please read more about this in /usr/share/doc/bio-eagle/README.Debian.

The package is enhanced by the following packages: bio-eagle-examples
Please cite: Po-Ru Loh, Pier Francesco Palamara and Alkes L Price: Fast and accurate long-range phasing in a UK Biobank cohort. Nature Genetics (2016)
bio-rainbow
clustering and assembling short reads for bioinformatics
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Efficient tool for clustering and assembling short reads, especially for RAD.

Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top-down manner. long a guided tree, it iteratively merges sibling leaves in a bottom-up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, it is shown that Rainbow is more competent than the other tools in dealing with RAD-seq data.

Please cite: Zechen Chong, Jue Ruan and Chung-I. Wu: Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads.. (PubMed) Bioinformatics 28(21):2732-2737 (2012)
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bio-tradis
analyse the output from TraDIS analyses of genomic sequences
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Bio-Tradis contains a set of tools to analyse the output from TraDIS analyses.

The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools.

Please note: You need to manually install BioConductor Edger which can not be distributed by Debian in recent version since it is using non-distributable code locfit.

Please cite: Lars Barquist, Matthew Mayho, Carla Cummins, Amy K. Cain, Christine J. Boinett, Andrew J. Page, Gemma C. Langridge, Michael A. Quail, Jacqueline A. Keane and Julian Parkhill: The TraDIS toolkit: sequencing and analysis for dense transposon mutant libraries. (PubMed,eprint) Bioinformatics 32(7):1109-1111 (2016)
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bio-vcf
domain specific language (DSL) for processing the VCF format
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Bio-vcf provides a domain specific language (DSL) for processing the VCF format. Record named fields can be queried with regular expressions, e.g.

 sample.dp>20 and rec.filter !~ /LowQD/ and rec.tumor.bcount[rec.alt]>4

Bio-vcf is a new generation VCF parser, filter and converter. Bio-vcf is not only very fast for genome-wide (WGS) data, it also comes with a really nice filtering, evaluation and rewrite language and it can output any type of textual data, including VCF header and contents in RDF and JSON.

bioawk
extension of awk for biological sequence analysis
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Bioawk is an extension to Brian Kernighan's awk, adding the support of several common biological data formats, including optionally gzip'ed BED, GFF, SAM, VCF, FASTA/Q and TAB-delimited formats with column names. It also adds a few built-in functions and an command line option to use TAB as the input/output delimiter. When the new functionality is not used, bioawk is intended to behave exactly the same as the original BWK awk.

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biobambam2
tools for early stage alignment file processing
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This package contains some tools for processing BAM files, including

  bamsormadup:  parallel sorting and duplicate marking
  bamcollate2:  reads BAM and writes BAM reordered such that alignment
                or collated by query name
  bammarkduplicates: reads BAM and writes BAM with duplicate alignments
                marked using the BAM flags field
  bammaskflags: reads BAM and writes BAM while masking (removing) bits
                from the flags column
  bamrecompress: reads BAM and writes BAM with a defined compression
                 setting. This tool is capable of multi-threading.
  bamsort:       reads BAM and writes BAM resorted by coordinates or
                 query name
  bamtofastq:    reads BAM and writes FastQ; output can be collated
                 or uncollated by query name
The package is enhanced by the following packages: multiqc
Please cite: German Tischler and Steven Leonard: biobambam: tools for read pair collation based algorithms on BAM files. (eprint) Source Code Biol Med. 9:13 (2014)
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biosyntax
Syntax Highlighting for Computational Biology (metapackage)
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Syntax highlighting for computational biology to bring you intuitively close to your data. BioSyntax supports .sam, .flagstat, .vcf, .fasta, .fastq, .faidx , .clustal, .pdb, .gtf, .bed files & more.

This is a metapackage depending on all bioSyntax plugins.

Please cite: Artem Babaian, Anicet Ebou, Alyssa Fegen, Ho Yin Jeffrey Kam, German E Novakovsky, Jasper Wong, Dylan Aïssi and Li Yao: bioSyntax: syntax highlighting for computational biology. BMC Bioinformatics 19(303) (2018)
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bitseq
Bayesian Inference of Transcripts from Sequencing Data
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BitSeq is an application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions. An advantage of this approach is the ability to account for both technical uncertainty and intrinsic biological variance in order to avoid false DE calls. The technical contribution to the uncertainty comes both from finite read-depth and the possibly ambiguous mapping of reads to multiple transcripts.

Please cite: James Hensman, Panagiotis Papastamoulis, Peter Glaus, Antti Honkela and Magnus Rattray: Fast and accurate approximate inference of transcript expression from RNA-seq data. (PubMed,eprint) Bioinformatics 31(24):3881-9 (2015)
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blasr
mapping single-molecule sequencing reads
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Basic local alignment with successive refinement (BLASR) is a method for mapping single-molecule sequencing reads against a reference genome. Such reads are thousands of bases long, with divergence between them and the genome being dominated by insertion and deletion error.

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blixem
interactive browser of sequence alignments
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Blixem is an interactive browser of sequence alignments that have been stacked up in a "master-slave" multiple alignment; it is not a 'true' multiple alignment but a 'one-to-many' alignment.

  • Overview section showing the positions of genes and alignments around the alignment window
  • Detail section showing the actual alignment of protein or nucleotide sequences to the genomic DNA sequence.
  • View alignments against both strands of the reference sequence.
  • View sequences in nucleotide or protein mode; in protein mode, Blixem will display the three-frame translation of the reference sequence.
  • Residues are highlighted in different colours depending on whether they are an exact match, conserved substitution or mismatch.
  • Gapped alignments are supported, with insertions and deletions being highlighted in the match sequence.
  • Matches can be sorted and filtered.
  • SNPs and other variations can be highlighted in the reference sequence.
  • Poly(A) tails can be displayed and poly(A) signals highlighted in the reference sequence.
Please cite: Gemma Barson and Ed Griffiths: SeqTools: visual tools for manual analysis of sequence alignments. (PubMed,eprint) BMC Research Notes 9:39 (2016)
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bolt-lmm
Efficient large cohorts genome-wide Bayesian mixed-model association testing
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The BOLT-LMM software package currently consists of two main algorithms, the BOLT-LMM algorithm for mixed model association testing, and the BOLT-REML algorithm for variance components analysis (i.e., partitioning of SNP-heritability and estimation of genetic correlations).

The BOLT-LMM algorithm computes statistics for testing association between phenotype and genotypes using a linear mixed model. By default, BOLT-LMM assumes a Bayesian mixture-of-normals prior for the random effect attributed to SNPs other than the one being tested. This model generalizes the standard infinitesimal mixed model used by previous mixed model association methods, providing an opportunity for increased power to detect associations while controlling false positives. Additionally, BOLT-LMM applies algorithmic advances to compute mixed model association statistics much faster than eigendecomposition-based methods, both when using the Bayesian mixture model and when specialized to standard mixed model association.

The BOLT-REML algorithm estimates heritability explained by genotyped SNPs and genetic correlations among multiple traits measured on the same set of individuals. BOLT-REML applies variance components analysis to perform these tasks, supporting both multi-component modeling to partition SNP-heritability and multi-trait modeling to estimate correlations. BOLT-REML applies a Monte Carlo algorithm that is much faster than eigendecomposition-based methods for variance components analysis at large sample sizes.

The package is enhanced by the following packages: bolt-lmm-example
Please cite: Po-Ru Loh, George Tucker, Brendan K Bulik-Sullivan, Bjarni J Vilhjálmsson, Hilary K Finucane, Rany M Salem, Daniel I Chasman, Paul M Ridker, Benjamin M Neale, Bonnie Berger, Nick Patterson and Alkes L Price: Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nature Genetics (2015)
bowtie
Ultrafast memory-efficient short read aligner
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This package addresses the problem to interpret the results from the latest (2010) DNA sequencing technologies. Those will yield fairly short stretches and those cannot be interpreted directly. It is the challenge for tools like Bowtie to give a chromosomal location to the short stretches of DNA sequenced per run.

Bowtie aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: typically about 2.2 GB for the human genome (2.9 GB for paired-end).

The package is enhanced by the following packages: bowtie-examples multiqc
Please cite: Ben Langmead, Cole Trapnell, Mihai Pop and Steven L Salzberg: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. (eprint) Genome Biology 10:R25 (2009)
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Topics: Genomics
bowtie2
ultrafast memory-efficient short read aligner
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is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes.

Bowtie 2 indexes the genome with an FM Index to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 GB. Bowtie 2 supports gapped, local, and paired-end alignment modes

The package is enhanced by the following packages: bowtie2-examples multiqc
Please cite: Ben Langmead and Steven L Salzberg: Fast gapped-read alignment with Bowtie 2. (PubMed) Nature Methods 9:357–359 (2012)
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Topics: Genomics
boxshade
Pretty-printing of multiple sequence alignments
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Boxshade is a program for creating good looking printouts from multiple-aligned protein or DNA sequences. The program does not perform the alignment by itself and requires as input a file that was created by a multiple alignment program or manually edited with respective tools.

Boxshade reads multiple-aligned sequences from either PILEUP-MSF, CLUSTAL-ALN, MALIGNED-data and ESEE-save files (limited to a maximum of 150 sequences with up to 10000 elements each). Various kinds of shading can be applied to identical/similar residues. Output is written to screen or to a file in the following formats: ANSI/VT100, PS/EPS, RTF, HPGL, ReGIS, LJ250-printer, ASCII, xFIG, PICT, HTML

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bppphyview
Bio++ Phylogenetic Viewer
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A phylogenetic tree editor developed using Bio++ and Qt. Phyview allows one to visualize, edit, print and output phylogenetic trees and associated data.

bppsuite
Bio++ program suite
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The Bio++ Program Suite is a package of programs using the Bio++ libraries and dedicated to Phylogenetics and Molecular Evolution. All programs are independent, but can be combined to perform rather complex analyses. These programs use the interface helper tools of the libraries, and hence share the same syntax. They also have several options in common, which may also be shared by third-party software.

The following programs are included:

  • BppML for maximum likelihood analysis,
  • BppSeqGen for sequences simulation,
  • BppAncestor for ancestral states reconstruction,
  • BppDist for distance methods,
  • BppPars for parsimony analysis,
  • BppSeqMan for file conversion and sequence manipulation,
  • BppConsense for building consensus tree and computing bootstrap values,
  • BppReRoot for tree rerooting.
  • BppTreeDraw for tree drawing.
  • BppAlnScore for comparing alignments and computing alignment scores.
  • BppMixedLikelihoods for computing site per site likelihoods of components of mixture models.
  • BppPopGen for population genetics analyses.
The package is enhanced by the following packages: bppsuite-examples
brig
BLAST Ring Image Generator
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BRIG can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data.

  • Images show similarity between a central reference sequence and other sequences as concentric rings.
  • BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
  • Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
  • Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
  • BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously
Please cite: Nabil-Fareed Alikhan, Nicola K Petty, Nouri L Ben Zakour and Scott A Beatson: BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. (PubMed,eprint) BMC Genomics 12:402 (2011)
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btllib-tools
Bioinformatics Technology Lab common code library tools
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Bioinformatics Technology Lab common code library in C++ with Python wrappers.

This package contains the tool indexlr.

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busco
benchmarking sets of universal single-copy orthologs
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Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (BUSCO).

  • Automated selection of lineages issued from https://www.orthodb.org/
  • Automated download of all necessary files and datasets to conduct a run
  • Use prodigal for non-eukaryotic genomes
The package is enhanced by the following packages: multiqc
Please cite: Mathieu Seppey, Mosè Manni and Evgeny M. Zdobnov: BUSCO: Assessing Genome Assembly and Annotation Completeness. (PubMed) Methods Mol Biol. 1962:227-245 (2019)
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bustools
program for manipulating BUS files for single cell RNA-Seq datasets
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This package contains BUStools program, it can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices

Please cite: Páll Melsted, A. Sina Booeshaghi, Fan Gao, Eduardo Beltrame, Lambda Lu, Kristján Eldjárn Hjorleifsson, Jase Gehring and Lior Pachter: Modular and efficient pre-processing of single-cell RNA-seq.. BioRxiv :673285 (2019)
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bwa
Burrows-Wheeler Aligner
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BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

Please cite: Heng Li and Richard Durbin: Fast and accurate short read alignment with Burrows-Wheeler transform. (PubMed,eprint) Bioinformatics 25(14):1754-1760 (2009)
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canu
single molecule sequence assembler for genomes
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Canu is a fork of the Celera Assembler, designed for high-noise single-molecule sequencing (such as the PacBio RS II or Oxford Nanopore MinION).

Canu is a hierarchical assembly pipeline which runs in four steps:

  • Detect overlaps in high-noise sequences using MHAP
  • Generate corrected sequence consensus
  • Trim corrected sequences
  • Assemble trimmed corrected sequences
Please cite: Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller and Adam M. Phillippy: Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation.. Genome Res. (2017)
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Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
cassiopee
index and search tool in genomic sequences
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Cassiopee index and search library C implementation. It is a complete rewrite of the ruby Cassiopee gem. It scans an input genomic sequence (dna/rna/protein) and search for a subsequence with exact match or allowing substitutions (Hamming distance) and/or insertion/deletions.

This package contains the cassiopee and cassiopeeknife tools.

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cat-bat
taxonomic classification of contigs and metagenome-assembled genomes (MAGs)
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Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formatted appropriately.

Please cite: F. A. Bastiaan von Meijenfeldt, Ksenia Arkhipova, Diego D. Cambuy, Felipe H. Coutinho and Bas E. Dutilh: Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. (PubMed,eprint) Genome Biology 20(1):217 (2019)
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cct
visually comparing bacterial, plasmid, chloroplast, or mitochondrial sequences
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The CGView Comparison Tool (CCT) is a package for visually comparing bacterial, plasmid, chloroplast, or mitochondrial sequences of interest to existing genomes or sequence collections. The comparisons are conducted using BLAST, and the BLAST results are presented in the form of graphical maps that can also show sequence features, gene and protein names, COG category assignments, and sequence composition characteristics. CCT can generate maps in a variety of sizes, including 400 Megapixel maps suitable for posters. Comparisons can be conducted within a particular species or genus, or all available genomes can be used. The entire map creation process, from downloading sequences to redrawing zoomed maps, can be completed easily using scripts included with the CCT. User-defined features or analysis results can be included on maps, and maps can be extensively customized. To simplify program setup, a CCT virtual machine that includes all dependencies preinstalled is available. Detailed tutorials illustrating the use of CCT are included with the CCT documentation.

Please cite: Jason R Grant, Adriano S Arantes and Paul Stothard: Comparing thousands of circular genomes using the CGView Comparison Tool. (PubMed,eprint) BMC Genomics 13:202 (2012)
cd-hit
suite of programs designed to quickly group sequences
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cd-hit contains a number of programs designed to quickly group sequences. cd-hit groups proteins into clusters that meet a user-defined similarity threshold. cd-hit-est is similar to cd-hit, but designed to group nucleotide sequences (without introns). cd-hit-est-2d is similar to cd-hit-2d but designed to compare two nucleotide datasets. A number of other related programs are also in this package. Please see the cd-hit user manual, also part of this package, for further information.

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Topics: Genomics
cdbfasta
Constant DataBase indexing and retrieval tools for multi-FASTA files
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CDB (Constant DataBase) can be used for creating indices for quick retrieval of any particular sequences from large multi-FASTA files. It has the option to compress data records in order to save space.

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centrifuge
rapid and memory-efficient system for classification of DNA sequences
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Centrifuge is a very rapid and memory-efficient system for the classification of DNA sequences from microbial samples, with better sensitivity than and comparable accuracy to other leading systems. The system uses a novel indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (e.g., 4.3 GB for ~4,100 bacterial genomes) yet provides very fast classification speed, allowing it to process a typical DNA sequencing run within an hour. Together these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers.

Please cite: Daehwan Kim, Li Song, Florian P. Breitwieser and Steven L. Salzberg: Centrifuge: rapid and sensitive classification of metagenomic sequences. (PubMed,eprint) Genome Research 26(12):1721-1729 (2016)
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cgview
Circular Genome Viewer
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CGView is a Java package for generating high quality, zoomable maps of circular genomes. Its primary purpose is to serve as a component of sequence annotation pipelines, as a means of generating visual output suitable for the web. Feature information and rendering options are supplied to the program using an XML file, a tab delimited file, or an NCBI ptt file. CGView converts the input into a graphical map (PNG, JPG, or Scalable Vector Graphics format), complete with labels, a title, legends, and footnotes. In addition to the default full view map, the program can generate a series of hyperlinked maps showing expanded views. The linked maps can be explored using any web browser, allowing rapid genome browsing, and facilitating data sharing. The feature labels in maps can be hyperlinked to external resources, allowing CGView maps to be integrated with existing web site content or databases.

In addition to the CGView application, an API is available for generating maps from within other Java applications, using the cgview package.

CGView can be used for any of the following:

  • Bacterial genome visualization and browsing - CGView can be incorporated into bacterial genome annotation pipelines, as a means of generating web content for data visualization and navigation. The PNG and image map content does not require Java applets or special browser plugins.
  • Genome poster generation - CGView can generate poster-sized images of circular genomes in rasterized image formats or in Scalable Vector Graphics format.
  • Sequence analysis visualization - CGView can be used to display the output of sequence analysis programs in a circular context.

CGView features:

  • Images can be generated in PNG, JPG, or SVG format. See the CGView gallery.
  • Static or interactive maps can be generated. The interactive maps make use of standard PNG images and HTML image maps. Scalable Vector Graphics output is included in the interactive maps (see example).
  • The XML input allows complete control over the appearance of the map.
  • Tab delimited input files and NCBI ptt files can be used as an alternative to the XML format.
  • The CGView API can be used to incorporate CGView into Java applications.
  • The CGView applet can be used to incorporate zoomable maps into web pages (see example).
  • The CGView Server can be used to generate maps online.
Please cite: Paul Stothard and David S. Wishart: Circular genome visualization and exploration using CGView. (PubMed,eprint) Bioinformatics 21(4):537-539 (2004)
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changeo
Repertoire clonal assignment toolkit (Python 3)
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Change-O is a collection of tools for processing the output of V(D)J alignment tools, assigning clonal clusters to immunoglobulin (Ig) sequences, and reconstructing germline sequences.

Dramatic improvements in high-throughput sequencing technologies now enable large-scale characterization of Ig repertoires, defined as the collection of trans-membrane antigen-receptor proteins located on the surface of B cells and T cells. Change-O is a suite of utilities to facilitate advanced analysis of Ig and TCR sequences following germline segment assignment. Change-O handles output from IMGT/HighV-QUEST and IgBLAST, and provides a wide variety of clustering methods for assigning clonal groups to Ig sequences. Record sorting, grouping, and various database manipulation operations are also included.

This package installs the library for Python 3.

Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Link to publication (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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chimeraslayer
detects likely chimeras in PCR amplified DNA
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ChimeraSlayer is a chimeric sequence detection utility, compatible with near-full length Sanger sequences and shorter 454-FLX sequences (~500bp).

Chimera Slayer involves the following series of steps that operate to flag chimeric 16S rRNA sequences:

 1. the ends of a query sequence are searched against an included
    database of reference chimera-free 16S sequences to identify potential
    parents of a chimera
 2. candidate parents of a chimera are selected as those that form a
    branched best scoring alignment to the NAST-formatted query sequence
 3. the NAST alignment of the query sequence is improved in a
    ‘chimera-aware’ profile-based NAST realignment to the selected
    reference parent sequences
 4. an evolutionary framework is used to flag query sequences found to
    exhibit greater sequence homology to an in silico chimera formed
    between any two of the selected reference parent sequences.

To run Chimera Slayer, you need NAST-formatted sequences generated by the nast-ier utility.

ChimeraSlayer is part of the microbiomeutil suite.

The package is enhanced by the following packages: microbiomeutil-data
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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chromhmm
Chromatin state discovery and characterization
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ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

The package is enhanced by the following packages: chromhmm-example
Please cite: Jason Ernst and Manolis Kellis: ChromHMM: automating chromatin-state discovery and characterization. (eprint) Nature Methods 9(3):215-216 (2012)
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chromimpute
Large-scale systematic epigenome imputation
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ChromImpute takes an existing compendium of epigenomic data and uses it to predict signal tracks for mark-sample combinations not experimentally mapped or to generate a potentially more robust version of data sets that have been mapped experimentally. ChromImpute bases its predictions on features from signal tracks of other marks that have been mapped in the target sample and the target mark in other samples with these features combined using an ensemble of regression trees.

Please cite: Jason Ernst and Manolis Kellis: Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. (eprint) Nature Biotechnology 33(4):364-376 (2015)
cif-tools
Suite of tools to manipulate, validate and query mmCIF files
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This package contains a suite of tools for the manipulation of mmCIF files.

The structure of macro molecules is nowadays recorded in mmCIF files. Until recently however the ancient PDB file format was used by many programs but that format has since long been deprecated.

This package provides two tools, pdb2cif and cif2pdb, that can convert files from one format into the other, provided that data fits of course.

Other tools are cif-validate, cif-grep, cif-diff, cif-merge and mmCQL. The latter can be used to manipulate an mmCIF file as if it were a SQL like database using SELECT, UPDATE, INSERT and DELETE commands.

This package depends on libcifpp.

circlator
circularize genome assemblies
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Circlator is a tool to automate assembly circularization for bacterial and small eukaryotic genomes and produce accurate linear representations of circular sequences.

Please cite: Martin Hunt, Nishadi De Silva, Thomas D. Otto, Julian Parkhill, Jacqueline A. Keane and Simon R. Harris: Circlator: automated circularization of genome assemblies using long sequencing reads. (PubMed) Genome Biology 29(16):294 (2015)
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circos
plotter for visualizing data
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Circos visualizes data in a circular layout — ideal for exploring relationships between objects or positions, and creating highly informative publication-quality graphics.

This package provides the Circos plotting engine, which is command-line driven (like gnuplot) and fully scriptable.

Please cite: Martin I Krzywinski, Jacqueline E Schein, Inanc Birol, Joseph Connors, Randy Gascoyne, Doug Horsman, Steven J Jones and Marco A Marra: Circos: An information aesthetic for comparative genomics. (PubMed,eprint) Genome Research 19(9):1639-45 (2009)
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clearcut
extremely efficient phylogenetic tree reconstruction
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Clearcut is the reference implementation for the Relaxed Neighbor Joining (RNJ) algorithm by J. Evans, L. Sheneman, and J. Foster from the Initiative for Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho.

Please cite: Jason Evans, Luke Sheneman and James A. Foster: Relaxed Neighbor-Joining: A Fast Distance-Based Phylogenetic Tree Construction Method. (PubMed) J. Mol. Evol. 62(6):785-792 (2006)
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clonalframe
inference of bacterial microevolution using multilocus sequence data
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ClonalFrame identifies the clonal relationships between the members of a sample, while also estimating the chromosomal position of homologous recombination events that have disrupted the clonal inheritance.

ClonalFrame can be applied to any kind of sequence data, from a single fragment of DNA to whole genomes. It is well suited for the analysis of MLST data, where 7 gene fragments have been sequenced, but becomes progressively more powerful as the sequenced regions increase in length and number up to whole genomes. However, it requires the sequences to be aligned. If you have genomic data that is not aligned, it is recommend to use Mauve which produces alignment of whole bacterial genomes in exactly the format required for analysis with ClonalFrame.

Please cite: Xavier Didelot and Daniel Falush: Inference of Bacterial Microevolution Using Multilocus Sequence Data. (PubMed,eprint) Genetics Advance 175:1251-1266 (2006)
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clonalframeml
Efficient Inference of Recombination in Whole Bacterial Genomes
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ClonalFrameML is a software package that performs efficient inference of recombination in bacterial genomes. ClonalFrameML was created by Xavier Didelot and Daniel Wilson. ClonalFrameML can be applied to any type of aligned sequence data, but is especially aimed at analysis of whole genome sequences. It is able to compare hundreds of whole genomes in a matter of hours on a standard Desktop computer. There are three main outputs from a run of ClonalFrameML: a phylogeny with branch lengths corrected to account for recombination, an estimation of the key parameters of the recombination process, and a genomic map of where recombination took place for each branch of the phylogeny.

ClonalFrameML is a maximum likelihood implementation of the Bayesian software ClonalFrame which was previously described by Didelot and Falush (2007). The recombination model underpinning ClonalFrameML is exactly the same as for ClonalFrame, but this new implementation is a lot faster, is able to deal with much larger genomic dataset, and does not suffer from MCMC convergence issues

Please cite: Xavier Didelot and Daniel J. Wilson: ClonalFrameML: Efficient Inference of Recombination in Whole Bacterial Genomes. (PubMed,eprint) PLoS Comput Biology 11(2):e1004041 (2015)
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clonalorigin
inference of homologous recombination in bacteria using whole genome sequences
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Bacteria, unlike us, can reproduce on their own. They do however have mechanisms that transfer DNA between organisms, a process more formally known as recombination. The mechanisms by which recombination takes place have been studied extensively in the laboratory but much remains to be understood concerning how, when and where recombination takes place within natural populations of bacteria and how it helps them to adapt to new environments. ClonalOrigin performs a comparative analysis of the sequences of a sample of bacterial genomes in order to reconstruct the recombination events that have taken place in their ancestry.

Please cite: Xavier Didelot, Daniel Lawson, Aaron Darling and Daniel Falush: Inference of Homologous Recombination in Bacteria Using Whole-Genome Sequences. (PubMed,eprint) Genetics 186(4):1435-1449 (2010)
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clustalo
General-purpose multiple sequence alignment program for proteins
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Clustal Omega is a general-purpose multiple sequence alignment (MSA) program, primarily for amino-acid sequences. It produces high quality MSAs and is capable of handling data sets of hundreds of thousands of sequences in reasonable time, using multiple processors where available.

Please cite: Fabian Sievers, Andreas Wilm, David Dineen, Toby J Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez, Hamish McWilliam, Michael Remmert, Johannes Söding, Julie D Thompson and Desmond G Higgins: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. (PubMed,eprint) Molecular Systems Biology 7:539 (2011)
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clustalw
global multiple nucleotide or peptide sequence alignment
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This program performs an alignment of multiple nucleotide or amino acid sequences. It recognizes the format of input sequences and whether the sequences are nucleic acid (DNA/RNA) or amino acid (proteins). The output format may be selected from in various formats for multiple alignments such as Phylip or FASTA. Clustal W is very well accepted.

The output of Clustal W can be edited manually but preferably with an alignment editor like SeaView or within its companion Clustal X. When building a model from your alignment, this can be applied for improved database searches. The Debian package hmmer creates such in form of an HMM.

The package is enhanced by the following packages: clustalw-mpi
Please cite: M. A. Larkin, G. Blackshields, N. P. Brown, R. Chenna, P. A. McGettigan, H. McWilliam, F. Valentin, I.M. Wallace, A. Wilm, R. Lopez, J. D. Thompson, T. J. Gibson and D. G. Higgins: Clustal W and Clustal X version 2.0. (PubMed,eprint) Bioinformatics 23(21):2947-2948 (2007)
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Topics: Sequence analysis
clustalx
Multiple alignment of nucleic acid and protein sequences (graphical interface)
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This package offers a GUI interface for the Clustal multiple sequence alignment program. It provides an integrated environment for performing multiple sequence- and profile-alignments to analyse the results. The sequence alignment is displayed in a window on the screen. A versatile coloring scheme has been incorporated to highlight conserved features in the alignment. For professional presentations, one should use the texshade LaTeX package or boxshade.

The pull-down menus at the top of the window allow you to select all the options required for traditional multiple sequence and profile alignment. You can cut-and-paste sequences to change the order of the alignment; you can select a subset of sequences to be aligned; you can select a sub-range of the alignment to be realigned and inserted back into the original alignment.

An alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted.

Please cite: M.A. Larkin, G. Blackshields, N.P. Brown, R. Chenna, P.A. McGettigan, H. McWilliam, F. Valentin, I.M. Wallace, A. Wilm, R. Lopez, J.D. Thompson, T.J. Gibson and D.G. Higgins: Clustal W and Clustal X version 2.0. (PubMed,eprint) Bioinformatics 23(21):2947-2948 (2007)
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cnvkit
Copy number variant detection from targeted DNA sequencing
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A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from targeted DNA sequencing. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina and Ion Torrent.

Please cite: Eric Talevich, A. Hunter Shain, Thomas Botton and Boris C. Bastian: CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. (PubMed,eprint) PLOS 12(4):e1004873 (2016)
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codonw
Correspondence Analysis of Codon Usage
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CodonW is a package for codon usage analysis. It was designed to simplify Multivariate Analysis (MVA) of codon usage. The MVA method employed in CodonW is correspondence analysis (COA) (the most popular MVA method for codon usage analysis). CodonW can generate a COA for codon usage, relative synonymous codon usage or amino acid usage. Additional analyses of codon usage include investigation of optimal codons, codon and dinucleotide bias, and/or base composition. CodonW analyses sequences encoded by genetic codes other than the universal code.

Please cite: Paul M. Sharp, Elizabeth Bailes, Russell J. Grocock, John F. Peden and R. Elizabeth Sockett: Variation in the strength of selected codon usage bias among bacteria.. (PubMed,eprint) Nucleic Acids Research 33(4):1141-1153 (2005)
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Topics: Sequence composition, complexity and repeats
comet-ms
Tandem mass spectrometry (MS/MS) search engine
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Comet is an open source tandem mass spectrometry (MS/MS) sequence database search engine. It identifies peptides by searching MS/MS spectra against sequences present in protein sequence databases.

This package ships a binary that does MS/MS database searches. Supported input formats are mzXML, mzML, and ms2 files. Supported output formats are .out, SQT, and pepXML.

Please cite: Jimmy K. Eng, Tahmina A. Jahan and Michael R. Hoopmann: Comet: an open source tandem mass spectrometry sequence database search tool. (PubMed) Proteomics 13(1) (2012)
concavity
predictor of protein ligand binding sites from structure and conservation
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ConCavity predicts protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

ConCavity takes as input a PDB format protein structure and optionally files that characterize the evolutionary sequence conservation of the chains in the structure file.

The following result files are produced by default:

  • Residue ligand binding predictions for each chain (*.scores).
  • Residue ligand binding predictions in a PDB format file (residue scores placed in the temp. factor field, *_residue.pdb).
  • Pocket prediction locations in a DX format file (*.dx).
  • PyMOL script to visualize the predictions (*.pml).
The package is enhanced by the following packages: conservation-code
Please cite: John A. Capra, Roman A. Laskowski, Janet M. Thornton, Mona Singh and Thomas A. Funkhouser: Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure. (PubMed) PLoS Computational Biology 5(12):e1000585 (2009)
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conservation-code
protein sequence conservation scoring tool
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This package provides score_conservation(1), a tool to score protein sequence conservation.

The following conservation scoring methods are implemented:

  • sum of pairs
  • weighted sum of pairs
  • Shannon entropy
  • Shannon entropy with property groupings (Mirny and Shakhnovich 1995, Valdar and Thornton 2001)
  • relative entropy with property groupings (Williamson 1995)
  • von Neumann entropy (Caffrey et al 2004)
  • relative entropy (Samudrala and Wang 2006)
  • Jensen-Shannon divergence (Capra and Singh 2007)

A window-based extension that incorporates the estimated conservation of sequentially adjacent residues into the score for each column is also given. This window approach can be applied to any of the conservation scoring methods.

The program accepts alignments in the CLUSTAL and FASTA formats.

The sequence-specific output can be used as the conservation input for concavity.

Conservation is highly predictive in identifying catalytic sites and residues near bound ligands.

Please cite: John A. Capra and Mona Singh: Predicting functionally important residues from sequence conservation. (PubMed) Bioinformatics 23(15):1875-82 (2007)
covtobed
convert the coverage track from a BAM file into a BED file
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Reads one (or more) alignment files (sorted BAM) and prints a BED with the coverage. It will join consecutive bases with the same coverage, and can be used to only print a BED file with the regions having a specific coverage range.

Please cite: Giovanni Birolo and Andrea Telatin: covtobed: a simple and fast tool to extract coverage tracks from BAM files. Journal of Open Source Software 5(47):2119 (2020)
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crac
integrated RNA-Seq read analysis
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CRAC is a tool to analyze High Throughput Sequencing (HTS) data in comparison to a reference genome. It is intended for transcriptomic and genomic sequencing reads. More precisely, with transcriptomic reads as input, it predicts point mutations, indels, splice junction, and chimeric RNAs (ie, non colinear splice junctions). CRAC can also output positions and nature of sequence error that it detects in the reads. CRAC uses a genome index. This index must be computed before running the read analysis. For this sake, use the command "crac-index" on your genome files. You can then process the reads using the command crac. See the man page of CRAC (help file) by typing "man crac". CRAC requires large amount of main memory on your computer. For processing against the Human genome, say 50 million reads of 100 nucleotide each, CRAC requires about 40 gigabytes of main memory. Check whether the system of your computing server is equipped with sufficient amount of memory before launching an analysis.

Please cite: Eliseos J. Mucaki, Natasha G. Caminsky, Ami M. Perri, Ruipeng Lu, Alain Laederach, Matthew Halvorsen, Joan H. M. Knoll and Peter K. Rogan: A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. (PubMed) BMS Medical Genomics 9:19 (2016)
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csb
Computational Structural Biology Toolbox (CSB)
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Computational Structural Biology Toolbox (CSB) is a Python class library for reading, storing and analyzing biomolecular structures in a variety of formats with rich support for statistical analyses.

CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice.

This package contains some user executable tools.

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ctffind
fast and accurate defocus estimation from electron micrographs
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This is a widely-used program for the estimation of objective lens defocus parameters from transmission electron micrographs. Defocus parameters are estimated by fitting a model of the microscope's contrast transfer function (CTF) to an image's amplitude spectrum.

Please cite: Alexis Rohou and Nikolaus Grigorieff: CTFFIND4: Fast and accurate defocus estimation from electron micrographs. (PubMed) Journal of Structural Biology 192(2):216-221 (2015)
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cutadapt
Clean biological sequences from high-throughput sequencing reads
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Cutadapt helps with biological sequence clean tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

This package contains the user interface.

The package is enhanced by the following packages: multiqc
Please cite: Marcel Martin: Cutadapt removes adapter sequences from high-throughput sequencing reads. (eprint) EMBnet.journal 17(1):10-12 (2015)
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cutesv
comprehensive discovery of structural variations of genomic sequences
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Long-read sequencing enables the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high sensitivity and performance simultaneously due to the complex SV characteristics implied by noisy long reads.

cuteSV is a sensitive, fast and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. Benchmarks on real Pacific Biosciences (PacBio) and Oxford Nanopore Technology (ONT) datasets demonstrate that cuteSV has better yields and scalability than state-of-the-art tools.

Please cite: Tao Jiang, Yongzhuang Liu, Yue Jiang, Junyi Li, Yan Gao, Zhe Cui, Yadong Liu, Bo Liu and Yadong Wang: Long-read-based human genomic structural variation detection with cuteSV. (PubMed,eprint) Genome Biology 21(1):189 (2020)
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daligner
local alignment discovery between long nucleotide sequencing reads
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These tools permit one to find all significant local alignments between reads encoded in a Dazzler database. The assumption is that the reads are from a Pacific Biosciences RS II long read sequencer. That is, the reads are long and noisy, up to 15% on average.

Please cite: Gene Myers: Efficient Local Alignment Discovery amongst Noisy Long Reads. 8701:52-67 (2014)
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damapper
long read to reference genome mapping tool
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Recognised as the Damapper Library, this is a long read to reference genome mapping command line tool.

For a given reference database 'X' and read block 'Y', damapper produces the single file 'Y.X.las'. Each output file is sorted in order of the A-reads, and if a match is a chain of local alignments, then the LA's in the chain occur in increasing order of A-coordinates.

HPC.damapper writes a UNIX shell script to the standard output that maps every read in blocks to of database to a reference sequence . If is missing then only the single block is mapped, and if is also missing then all blocks of the database are mapped.

This package contains the damapper and HPC.damapper binaries.

datamash
statistics tool for command-line interface
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GNU Datamash is a command-line program which performs basic numeric, textual and statistical operations on input textual data files. It is designed to be portable and reliable, and aid researchers to easily automate analysis pipelines, without writing code or even short scripts.

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dawg
simulate the evolution of recombinant DNA sequences
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DNA Assembly with Gaps (Dawg) is an application designed to simulate the evolution of recombinant DNA sequences in continuous time based on the robust general time reversible model with gamma and invariant rate heterogeneity and a novel length-dependent model of gap formation. The application accepts phylogenies in Newick format and can return the sequence of any node, allowing for the exact evolutionary history to be recorded at the discretion of users. Dawg records the gap history of every lineage to produce the true alignment in the output. Many options are available to allow users to customize their simulations and results.

Please cite: Reed A. Cartwright: DNA assembly with gaps (Dawg): simulating sequence evolution. (PubMed,eprint) Bioinformatics 21(Suppl 3):iii31-iii38 (2005)
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dazzdb
manage nucleotide sequencing read data
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To facilitate the multiple phases of the dazzler assembler, all the read data is organized into what is effectively a database of the reads and their meta-information. The design goals for this data base are as follows:

  • The database stores the source Pacbio read information in such a way that it can re-create the original input data, thus permitting a user to remove the (effectively redundant) source files. This avoids duplicating the same data, once in the source file and once in the database.
  • The data base can be built up incrementally, that is new sequence data can be added to the data base over time.
  • The data base flexibly allows one to store any meta-data desired for reads. This is accomplished with the concept of tracks that implementors can add as they need them.
  • The data is held in a compressed form equivalent to the .dexta and .dexqv files of the data extraction module. Both the .fasta and .quiva information for each read is held in the data base and can be recreated from it. The .quiva information can be added separately and later on if desired.
  • To facilitate job parallel, cluster operation of the phases of the assembler, the database has a concept of a current partitioning in which all the reads that are over a given length and optionally unique to a well, are divided up into blocks containing roughly a given number of bases, except possibly the last block which may have a short count. Often programs can be run on blocks or pairs of blocks and each such job is reasonably well balanced as the blocks are all the same size. One must be careful about changing the partition during an assembly as doing so can void the structural validity of any interim block-based results.
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deblur
deconvolution for Illumina amplicon sequencing
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Deblur is a greedy deconvolution algorithm for amplicon sequencing based on Illumina Miseq/Hiseq error profiles. The authors recommend using Deblur via the QIIME2 plugin q2-deblur. Examples of its use can be found within the plugin itself. However, Deblur itself does not depend on QIIME2.

The input to Deblur workflow is a directory of FASTA or FASTQ files (1 per sample) or a single demultiplexed FASTA or FASTQ file. These files can be gzip'd. The output directory will contain three BIOM tables in which the observation IDs are the Deblurred sequences. The outputs are contingent on the reference databases used and a more focused discussion on them is in the subsequent README section titled "Positive and Negative Filtering." The output files are as follows:

  • reference-hit.biom : contains only Deblurred reads matching the positive filtering database. By default, a reference composed of 16S sequences is used, and this resulting table will contain only those reads which recruit at a coarse level to it will be retained. Reads are also filtered against the negative reference, which by default will remove any read which appears to be PhiX or adapter.

  • reference-hit.seqs.fa : a fasta file containing all the sequences in reference-hit.biom

  • reference-non-hit.biom : contains only Deblurred reads that did not align to the positive filtering database. Negative filtering is also appied to this table, so by default, PhiX and adapter are removed.

  • reference-non-hit.seqs.fa : a fasta file containing all the sequences in reference-non-hit.biom

  • all.biom : contains all Deblurred reads. This file represents the union of the "reference-hit.biom" and "reference-non-hit.biom" tables.

  • all.seqs.fa : a fasta file containing all the sequences in all.biom

Deblur uses two types of filtering on the sequences:

  • Negative mode - removes known artifact sequences (i.e. sequences aligning to PhiX or Adapter with >=95% identity and coverage).

  • Positive mode - keeps only sequences similar to a reference database (by default known 16S sequences). SortMeRNA is used, and any sequence with an e-value <= 10 is retained. Deblur also outputs a BIOM table without this positive filtering step (named all.biom).

The FASTA files for both of these filtering steps can be supplied via the --neg-ref-fp and --pos-ref-fp options. By default, the negative database is composed of PhiX and adapter sequence and the positive database of known 16S sequences.

Deblur uses negative mode filtering to remove known artifact (i.e. PhiX and Adapter sequences) prior to denoising. The output of Deblur contains three files: all.biom, which includes all sOTUs, reference-hit.biom, which contains the output of positive filtering of the sOTUs (default only sOTUs similar to 16S sequences), and reference-non-hit.biom, which contains only sOTUs failing the positive filtering (default only non-16S sOTUs).

deepnano
alternative basecaller for MinION reads of genomic sequences
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DeepNano is alternative basecaller for Oxford Nanopore MinION reads based on deep recurrent neural networks.

Currently it works with SQK-MAP-006 and SQK-MAP-005 chemistry and as a postprocessor for Metrichor.

Please cite: Vladimír Boža, Broňa Brejová and Tomáš Vinař: DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads. PLOS one (2017)
Remark of Debian Med team: There is no intend to keep continue the existing packaging since

the program nanocall seems to serve the intended purpose better

delly
Structural variant discovery by read analysis
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Delly performs Structural variant discovery by integrated paired-end and split-read analysis. It discovers, genotypes and visualizes deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends, split-reads and read-depth to sensitively and accurately delineate genomic rearrangements throughout the genome.

Please cite: Tobias Rausch, Thomas Zichner, Andreas Schlattl, Adrian M. Stuetz, Vladimir Benes and Jan O. Korbel: DELLY: structural variant discovery by integrated paired-end and split-read analysis.. Bioinformatics 28:i333-i339 (2012)
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density-fitness
Calculates per-residue electron density scores
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The program density-fitness calculates electron density metrics, for main- (includes Cβ atom) and side-chain atoms of individual residues.

For this calculation, the program uses the structure model in either PDB or mmCIF format and the electron density from the 2mFo-DFc and mFo-DFc maps. If these maps are not readily available, the MTZ file and model can be used to calculate maps clipper. Density-fitness support both X-ray and electron diffraction data.

This program is essentially a reimplementation of edstats, a program available from the CCP4 suite. However, the output now contains only the RSR, SRSR and RSCC fields as in edstats with the addition of EDIAm and OPIA and no longer requires pre-calculated map coefficients.

Please cite: I. J. Tickle: Statistical quality indicators for electron-density maps. Acta Cryst. (D68):454-467 (2012)
dextractor
(d)extractor and compression command library
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Dextractor commands allow one to pull exactly and only the information needed for assembly and reconstruction from the source HDF5 files produced by the PacBio RS II sequencer, or from the source BAM files produced by the PacBio Sequel sequencer.

For each of the three extracted file types -- fasta, quiva, and arrow -- the library contains commands to compress the given file type, and to decompress it, which is a reversible process delivering the original uncompressed file. The compressed .fasta files, with the extension .dexta, consume 1/4 byte per base. The compressed .quiva files, with the extension .dexqv, consume 1.5 bytes per base on average, and the compressed .arrow files, with the extension .dexar, consume 1/4 byte per base

For more information, please view the available documentation at https://github.com/thegenemyers/DEXTRACTOR

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dialign
Segment-based multiple sequence alignment
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DIALIGN2 is a command line tool to perform multiple alignment of protein or DNA sequences. It constructs alignments from gapfree pairs of similar segments of the sequences. This scoring scheme for alignments is the basic difference between DIALIGN and other global or local alignment methods. Note that DIALIGN does not employ any kind of gap penalty.

Please cite: Burkhard Morgenstern: DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment. (PubMed,eprint) Bioinformatics 15(3):211-218 (1999)
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dialign-tx
Segment-based multiple sequence alignment
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DIALIGN-TX is a command line tool to perform multiple alignment of protein or DNA sequences. It is a complete reimplementation of the segment-base approach including several new improvements and heuristics that significantly enhance the quality of the output alignments compared to DIALIGN 2.2 and DIALIGN-T. For pairwise alignment, DIALIGN-TX uses a fragment-chaining algorithm that favours chains of low-scoring local alignments over isolated high-scoring fragments. For multiple alignment, DIALIGN-TX uses an improved greedy procedure that is less sensitive to spurious local sequence similarities.

The package is enhanced by the following packages: dialign-tx-data
Please cite: Amarendran R. Subramanian, Michael Kaufmann and Burkhard Morgenstern: DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment. (PubMed) Algorithms for Molecular Biology 3(1):6 (2008)
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diamond-aligner
accelerated BLAST compatible local sequence aligner
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DIAMOND is a sequence aligner for protein and translated DNA searches and functions as a drop-in replacement for the NCBI BLAST software tools. It is suitable for protein-protein search as well as DNA-protein search on short reads and longer sequences including contigs and assemblies, providing a speedup of BLAST ranging up to x20,000.

Please cite: Benjamin Buchfink, Chao Xie and Daniel H Huson: Fast and sensitive protein alignment using DIAMOND. (PubMed) Nature methods 12(1):59-60 (2015)
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discosnp
discovering Single Nucleotide Polymorphism from raw set(s) of reads
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Software discoSnp is designed for discovering Single Nucleotide Polymorphism (SNP) from raw set(s) of reads obtained with Next Generation Sequencers (NGS).

Note that number of input read sets is not constrained, it can be one, two, or more. Note also that no other data as reference genome or annotations are needed.

The software is composed by two modules. First module, kissnp2, detects SNPs from read sets. A second module, kissreads, enhance the kissnp2 results by computing per read set and for each found SNP:

 1) its mean read coverage
 2) the (phred) quality of reads generating the polymorphism.

This program is superseded by DiscoSnp++.

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disulfinder
cysteines disulfide bonding state and connectivity predictor
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'disulfinder' is for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Disulfide bridges play a major role in the stabilization of the folding process for several proteins. Prediction of disulfide bridges from sequence alone is therefore useful for the study of structural and functional properties of specific proteins. In addition, knowledge about the disulfide bonding state of cysteines may help the experimental structure determination process and may be useful in other genomic annotation tasks.

'disulfinder' predicts disulfide patterns in two computational stages: (1) the disulfide bonding state of each cysteine is predicted by a BRNN-SVM binary classifier; (2) cysteines that are known to participate in the formation of bridges are paired by a Recursive Neural Network to obtain a connectivity pattern.

Please cite: Alessio Ceroni, Andrea Passerini, Alessandro Vullo and Paolo Frasconi: DISULFIND: a disulfide bonding state and cysteine connectivity prediction server. (PubMed) Nucleic Acids Res 34(Web Server issue):W177-181 (2006)
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dnaclust
tool for clustering millions of short DNA sequences
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dnaclust is a tool for clustering large number of short DNA sequences. The clusters are created in such a way that the "radius" of each clusters is no more than the specified threshold.

The input sequences to be clustered should be in Fasta format. The id of each sequence is based on the first word of the seqeunce in the Fasta format. The first word is the prefix of the header up to the first occurrence of white space characters in the header.

Please cite: Mohammadreza Ghodsi, Bo Liu and Mihai Pop: DNACLUST: accurate and efficient clustering of phylogenetic marker genes. (PubMed,eprint) BMC Bioinformatics 12:271 (2011)
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dnarrange
Method to find rearrangements in long DNA reads relative to a genome seq
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This package provides utilities to align the reads to the genome, find rearrangements and draw pictures of rearranged groups

dotter
detailed comparison of two genomic sequences
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Dotter is a graphical dot-matrix program for detailed comparison of two sequences.

  • Every residue in one sequence is compared to every residue in the other, and a matrix of scores is calculated.
  • One sequence is plotted on the x-axis and the other on the y-axis.
  • Noise is filtered out so that alignments appear as diagonal lines.
  • Pairwise scores are averaged over a sliding window to make the score matrix more intelligible.
  • The averaged score matrix forms a three-dimensional landscape, with the two sequences in two dimensions and the height of the peaks in the third. This landscape is projected onto two dimensions using a grey-scale image - the darker grey of a peak, the higher the score is.
  • The contrast and threshold of the grey-scale image can be adjusted interactively, without having to recalculate the score matrix.
  • An Alignment Tool is provided to examine the sequence alignment that the grey-scale image represents.
  • Known high-scoring pairs can be loaded from a GFF file and overlaid onto the plot.
  • Gene models can be loaded from GFF and displayed alongside the relevant axis.
  • Compare a sequence against itself to find internal repeats.
  • Find overlaps between multiple sequences by making a dot-plot of all sequences versus themselves.
  • Run Dotter in batch mode to create large, time-consuming dot-plots as a background process.
Please cite: Gemma Barson and Ed Griffiths: SeqTools: visual tools for manual analysis of sequence alignments. (PubMed,eprint) BMC Research Notes 9:39 (2016)
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drop-seq-tools
analyzing Drop-seq data
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This software provide for core computational analysis of Drop-seq data, which shows you how to transform raw sequence data into an expression measurement for each gene in each individual cell.

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dssp
protein secondary structure assignment based on 3D structure
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DSSP is an application you use to assign the secondary structure of a protein based on its solved three dimensional (3D) structure.

This version (4.2) of DSSP is a rewrite that writes annotated mmCIF files by default but can still produce the older dssp format. New is also the support of PP helices.

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dwgsim
short sequencing read simulator
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DWGSIM simulates short sequencing reads from modern sequencing platforms. DWGSIM generates base error rates using a parametric model, allowing a more realisic error profile. It was originally developed for use in evaluating short read aligners.

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e-mem
Efficient computation of Maximal Exact Matches for very large genomes
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E-MEM enables efficient computation of Maximal Exact Matches (MEMs) that does not use full text indexes. The algorithm uses much less space and is highly amenable to parallelization. It can compute all MEMs of minimum length 100 between the whole human and mouse genomes on a 12 core machine in 10 min and 2 GB of memory; the required memory can be as low as 600 MB. It can run efficiently genomes of any size. Extensive testing and comparison with currently best algorithms is provided.

Mummer has many different scripts where one of the key program is MEM computation. In all the scripts, the MEM computation program can be replaced with e-mem with ease for better performance.

Please cite: Nilesh Khiste and Lucian Ilie: E-MEM: efficient computation of maximal exact matches for very large genomes. (PubMed,eprint) Bioinformatics 31(4):509-514 (2015)
ea-utils
command-line tools for processing biological sequencing data
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Ea-utils provides a set of command-line tools for processing biological sequencing data, barcode demultiplexing, adapter trimming, etc.

Primarily written to support an Illumina based pipeline - but should work with any FASTQs.

Main Tools are:

  • fastq-mcf Scans a sequence file for adapters, and, based on a log-scaled threshold, determines a set of clipping parameters and performs clipping. Also does skewing detection and quality filtering.

  • fastq-multx Demultiplexes a fastq. Capable of auto-determining barcode id's based on a master set fields. Keeps multiple reads in-sync during demultiplexing. Can verify that the reads are in-sync as well, and fail if they're not.

  • fastq-join Similar to audy's stitch program, but in C, more efficient and supports some automatic benchmarking and tuning. It uses the same "squared distance for anchored alignment" as other tools.

  • varcall Takes a pileup and calculates variants in a more easily parameterized manner than some other tools.

Please cite: Erik Aronesty: Comparison of Sequencing Utility Programs. (eprint) The Open Bioinformatics Journal 7:1-8 (2013)
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ecopcr
estimate PCR barcode primers quality
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DNA barcoding is a tool for characterizing the species origin using a short sequence from a standard position and agreed upon position in the genome. To be used as a DNA barcode, a genome locus should vary among individuals of the same species only to a minor degree and it should vary among species very quickly. From a practical point of view, a barcode locus should be flanked by two conserved regions to design PCR primers. Several manually discovered barcode loci like COI, rbcL, 18S, 16S and 23S rDNA, or trnH-ps are routinely used today, but no objective function has been described to measure their quality in terms of universality (barcode coverage, Bc ) or in terms of taxonomical discrimination capacity (barcode specificity, Bs ).

ecoPCR is an electronic PCR software developed by LECA and Helix-Project. It helps to estimate Barcode primers quality. In conjunction with OBITools you can postprocess ecoPCR output to compute barcode coverage and barcode specificity. New barcode primers can be developed using the ecoPrimers software

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edtsurf
triangulated mesh surfaces for protein structures
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EDTSurf is a open source program to construct triangulated surfaces for macromolecules. It generates three major macromolecular surfaces: van der Waals surface, solvent-accessible surface and molecular surface (solvent-excluded surface). EDTsurf also identifies cavities which are inside of macromolecules.

Please cite: Dong Xu and Yang Zhang: Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform.. (PubMed,eprint) PLoS ONE 4(12):e8140 (2009)
eigensoft
reduction of population bias for genetic analyses
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The EIGENSOFT package combines functionality from the group's population genetics methods (Patterson et al. 2006) and their EIGENSTRAT stratification method (Price et al. 2006). The EIGENSTRAT method uses principal components analysis to explicitly model ancestry differences between cases and controls along continuous axes of variation; the resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. The EIGENSOFT package has a built-in plotting script and supports multiple file formats and quantitative phenotypes.

Please cite: Alkes L. Price, Nick J. Patterson, Robert M. Plenge, Michael E. Weinblatt, Nancy A. Shadick and David Reich: Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38:904 - 909 (2006)
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elph
DNA/protein sequence motif finder
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ELPH (Estimated Locations of Pattern Hits) is a general-purpose Gibbs sampler for finding motifs in a set of DNA or protein sequences. The program takes as input a set containing anywhere from a few dozen to thousands of sequences, and searches through them for the most common motif, assuming that each sequence contains one copy of the motif. ELPH was used to find patterns such as ribosome binding sites (RBSs) and exon splicing enhancers (ESEs).

embassy-domainatrix
Extra EMBOSS commands to handle domain classification file
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The DOMAINATRIX programs were developed by Jon Ison and colleagues at MRC HGMP for their protein domain research. They are included as an EMBASSY package as a work in progress.

Applications in the current domainatrix release are cathparse (generates DCF file from raw CATH files), domainnr (removes redundant domains from a DCF file), domainreso (removes low resolution domains from a DCF file), domainseqs (adds sequence records to a DCF file), domainsse (adds secondary structure records to a DCF file), scopparse (generates DCF file from raw SCOP files) and ssematch (searches a DCF file for secondary structure matches).

embassy-domalign
Extra EMBOSS commands for protein domain alignment
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The DOMALIGN programs were developed by Jon Ison and colleagues at MRC HGMP for their protein domain research. They are included as an EMBASSY package as a work in progress.

Applications in the current domalign release are allversusall (sequence similarity data from all-versus-all comparison), domainalign (generates alignments (DAF file) for nodes in a DCF file), domainrep (reorders DCF file to identify representative structures) and seqalign (extend alignments (DAF file) with sequences (DHF file)).

embassy-domsearch
Extra EMBOSS commands to search for protein domains
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The DOMSEARCH programs were developed by Jon Ison and colleagues at MRC HGMP for their protein domain research. They are included as an EMBASSY package as a work in progress.

Applications in this DOMSEARCH release are seqfraggle (removes fragment sequences from DHF files), seqnr (removes redundancy from DHF files), seqsearch (generates PSI-BLAST hits (DHF file) from a DAF file), seqsort (Remove ambiguous classified sequences from DHF files) and seqwords (Generates DHF files from keyword search of UniProt).

emboss
European molecular biology open software suite
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EMBOSS is a free Open Source software analysis package specially developed for the needs of the molecular biology (e.g. EMBnet) user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole. EMBOSS breaks the historical trend towards commercial software packages.

The package is enhanced by the following packages: clustalw primer3
Please cite: Peter Rice, Ian Longden and Alan Bleasby: EMBOSS: The European Molecular Biology Open Software Suite. (PubMed) Trends in Genetics 16(6):276 - 277 (2000)
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emmax
genetic mapping considering population structure
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EMMAX is a statistical test for large scale human or model organism association mapping accounting for the sample structure. In addition to the computational efficiency obtained by EMMA algorithm, EMMAX takes advantage of the fact that each locus explains only a small fraction of complex traits, which allows one to avoid repetitive variance component estimation procedure, resulting in a significant amount of increase in computational time of association mapping using mixed model.

Please cite: Hyun Min Kang, Jae Hoon Sul, Susan K Service, Noah A Zaitlen, Sit-yee Kong, Nelson B Freimer, Chiara Sabatti and Eleazar Eskin: Variance component model to account for sample structure in genome-wide association studies. (PubMed) Nature Genetics 42(4):348-54 (2010)
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estscan
ORF-independent detector of coding DNA sequences
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ESTScan is a program that can detect coding regions in DNA sequences, even if they are of low quality. ESTScan will also detect and correct sequencing errors that lead to frameshifts. ESTScan is not a gene prediction program , nor is it an open reading frame detector. In fact, its strength lies in the fact that it does not require an open reading frame to detect a coding region. As a result, the program may miss a few translated amino acids at either the N or the C terminus, but will detect coding regions with high selectivity and sensitivity.

ESTScan takes advantages of the bias in hexanucleotide usage found in coding regions relative to non-coding regions. This bias is formalized as an inhomogeneous 3-periodic fifth-order Hidden Markov Model (HMM). Additionally, the HMM of ESTScan has been extended to allows insertions and deletions when these improve the coding region statistics.

Please cite: C. Lottaz, C. Iseli, CV. Jongeneel and Philipp Bucher: Modeling sequencing errors by combining Hidden Markov models Bioinformatics 19:103-112 (2003)
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examl
Exascale Maximum Likelihood (ExaML) code for phylogenetic inference
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Exascale Maximum Likelihood (ExaML) is a code for phylogenetic inference using MPI. This code implements the popular RAxML search algorithm for maximum likelihood based inference of phylogenetic trees.

ExaML is a strapped-down light-weight version of RAxML for phylogenetic inference on huge datasets. It can only execute some very basic functions and is intended for computer-savvy users that can write little perl-scripts and have experience using queue submission scripts for clusters. ExaML only implements the CAT and GAMMA models of rate heterogeneity for binary, DNA, and protein data.

ExaML uses a radically new MPI parallelization approach that yields improved parallel efficiency, in particular on partitioned multi-gene or whole-genome datasets. It also implements a new load balancing algorithm that yields better parallel efficiency.

It is up to 4 times faster than its predecessor RAxML-Light and scales to a larger number of processors.

Please cite: Alexey M. Kozlov, Andre J. Aberer and Alexandros Stamatakis: ExaML version 3: a tool for phylogenomic analyses on supercomputers. (PubMed,eprint) Bioinformatics 31(15):2577-2579 (2015)
exonerate
generic tool for pairwise sequence comparison
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Exonerate allows you to align sequences using a many alignment models, using either exhaustive dynamic programming, or a variety of heuristics. Much of the functionality of the Wise dynamic programming suite was reimplemented in C for better efficiency. Exonerate is an intrinsic component of the building of the Ensembl genome databases, providing similarity scores between RNA and DNA sequences and thus determining splice variants and coding sequences in general.

An In-silico PCR Experiment Simulation System (see the ipcress man page) is packaged with exonerate.

This package also comes with a selection of utilities for performing simple manipulations quickly on fasta files beyond 2Gb

Please cite: Guy C. Slater and Ewan Birney: Automated generation of heuristics for biological sequence comparison. (PubMed,eprint) BMC Bioinformatics 6(1):31 (2005)
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fasta3
tools for searching collections of biological sequences
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The FASTA programs find regions of local or global similarity between Protein or DNA sequences, either by searching Protein or DNA databases, or by identifying local duplications within a sequence. Other programs provide information on the statistical significance of an alignment. Like BLAST, FASTA can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

  • Protein
  • Protein-protein FASTA
  • Protein-protein Smith-Waterman (ssearch)
  • Global Protein-protein (Needleman-Wunsch) (ggsearch)
  • Global/Local protein-protein (glsearch)
  • Protein-protein with unordered peptides (fasts)
  • Protein-protein with mixed peptide sequences (fastf)

  • Nucleotide

  • Nucleotide-Nucleotide (DNA/RNA fasta)
  • Ordered Nucleotides vs Nucleotide (fastm)
  • Un-ordered Nucleotides vs Nucleotide (fasts)

  • Translated

  • Translated DNA (with frameshifts, e.g. ESTs) vs Proteins (fastx/fasty)
  • Protein vs Translated DNA (with frameshifts) (tfastx/tfasty)
  • Peptides vs Translated DNA (tfasts)

  • Statistical Significance

  • Protein vs Protein shuffle (prss)
  • DNA vs DNA shuffle (prss)
  • Translated DNA vs Protein shuffle (prfx)

  • Local Duplications

  • Local Protein alignments (lalign)
  • Plot Protein alignment "dot-plot" (plalign)
  • Local DNA alignments (lalign)
  • Plot DNA alignment "dot-plot" (plalign)

This software is often used via a web service at the EBI with readily indexed reference databases at http://www.ebi.ac.uk/Tools/fasta/.

Please cite: William R. Pearson and D. J. Lipman: Improved tools for biological sequence comparison. (PubMed,eprint) Proc Natl Acad Sci U S A 85(8):2444-8 (1988)
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fastahack
utility for indexing and sequence extraction from FASTA files
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fastahack is a small application for indexing and extracting sequences and subsequences from FASTA files. The included Fasta.cpp library provides a FASTA reader and indexer that can be embedded into applications which would benefit from directly reading subsequences from FASTA files. The library automatically handles index file generation and use.

Features:

  • FASTA index (.fai) generation for FASTA files
  • Sequence extraction
  • Subsequence extraction
  • Sequence statistics (currently only entropy is provided)

Sequence and subsequence extraction use fseek64 to provide fastest-possible extraction without RAM-intensive file loading operations. This makes fastahack a useful tool for bioinformaticists who need to quickly extract many subsequences from a reference FASTA sequence.

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fastani
Fast alignment-free computation of whole-genome Average Nucleotide Identity
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ANI is defined as mean nucleotide identity of orthologous gene pairs shared between two microbial genomes. FastANI supports pairwise comparison of both complete and draft genome assemblies.

fastaq
FASTA and FASTQ file manipulation tools
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Fastaq represents a diverse collection of scripts that perform useful and common FASTA/FASTQ manipulation tasks, such as filtering, merging, splitting, sorting, trimming, search/replace, etc. Input and output files can be gzipped (format is automatically detected) and individual Fastaq commands can be piped together.

Topics: Bioinformatics
fastdnaml
Tool for construction of phylogenetic trees of DNA sequences
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fastDNAml is a program derived from Joseph Felsenstein's version 3.3 DNAML (part of his PHYLIP package). Users should consult the documentation for DNAML before using this program.

fastDNAml is an attempt to solve the same problem as DNAML, but to do so faster and using less memory, so that larger trees and/or more bootstrap replicates become tractable. Much of fastDNAml is merely a recoding of the PHYLIP 3.3 DNAML program from PASCAL to C.

Note that the homepage of this program is not available any more and so this program will probably not see any further updates.

Please cite: Gary J. Olsen, Hideo Matsuda, Ray Hagstrom and Ross Overbeek: fastDNAml: a tool for construction of phylogenetic trees of DNA sequences using maximum likelihood. (PubMed,eprint) Comput Appl Biosci 10(1):41-48 (1994)
fastlink
faster version of pedigree programs of Linkage
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Genetic linkage analysis is a statistical technique used to map genes and find the approximate location of disease genes. There was a standard software package for genetic linkage called LINKAGE. FASTLINK is a significantly modified and improved version of the main programs of LINKAGE that runs much faster sequentially, can run in parallel, allows the user to recover gracefully from a computer crash, and provides abundant new documentation. FASTLINK has been used in over 1000 published genetic linkage studies.

This package contains the following programs:

 ilink:    GEMINI optimization procedure to find a locally
           optimal value of the theta vector of recombination
           fractions
 linkmap:  calculates location scores of one locus against a
           fixed map of other loci
 lodscore: compares likelihoods at locally optimal theta
 mlink:    calculates lod scores and risk with two of more loci
 unknown:  identify possible genotypes for unknowns
Please cite: R. W. Cottingham Jr., R. M. Idury and A. A. Schaffer: Faster Sequential Genetic Linkage Computations. (PubMed,eprint) American Journal of Human Genetics 53(1):252-263 (1993)
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fastml
maximum likelihood ancestral amino-acid sequence reconstruction
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FastML is a bioinformatics tool for the reconstruction of ancestral sequences based on the phylogenetic relations between homologous sequences. FastML runs several algorithms that reconstruct the ancestral sequences with emphasis on an accurate reconstruction of both indels and characters. For character reconstruction the previously described FastML algorithms are used to efficiently infer the most likely ancestral sequences for each internal node of the tree. Both joint and the marginal reconstructions are provided. For indels reconstruction the sequences are first coded according to the indel events detected within the multiple sequence alignment (MSA) and then a state-of-the-art likelihood model is used to reconstruct ancestral indels states. The results are the most probable sequences, together with posterior probabilities for each character and indel at each sequence position for each internal node of the tree. FastML is generic and is applicable for any type of molecular sequences (nucleotide, protein, or codon sequences).

Please cite: Haim Ashkenazy, Osnat Penn, Adi Doron-Faigenboim, Ofir Cohen, Gina Cannarozzi, Oren Zomer and Tal Pupko: FastML: a web server for probabilistic reconstruction of ancestral sequences. (PubMed,eprint) Nucleic Acids Research 40(Web Server issue):W580-W584 (2012)
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fastp
Ultra-fast all-in-one FASTQ preprocessor
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All-in-one FASTQ preprocessor, fastp provides functions including quality profiling, adapter trimming, read filtering and base correction. It supports both single-end and paired-end short read data and also provides basic support for long-read data.

The package is enhanced by the following packages: multiqc
Please cite: Shifu Chen, Yanqing Zhou, Yaru Chen and Jia Gu: fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884-i890 (2018)
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fastq-pair
Rewrites paired end fastq so all reads have a mate to separate out singletons
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This package rewrites the fastq files with the sequences in order, with matching files for the two files provided on the command line, and then any single reads that are not matched are place in two separate files, one for each original file.

This code is designed to be fast and memory efficient, and works with large fastq files. It does not store the whole file in memory, but rather just stores the locations of each of the indices in the first file provided in memory.

fastqc
quality control for high throughput sequence data
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FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis.

The main functions of FastQC are

  • Import of data from BAM, SAM or FastQ files (any variant)
  • Providing a quick overview to tell you in which areas there may be problems
  • Summary graphs and tables to quickly assess your data
  • Export of results to an HTML based permanent report
  • Offline operation to allow automated generation of reports without running the interactive application
The package is enhanced by the following packages: multiqc
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Topics: Sequencing
fastqtl
Quantitative Trait Loci (QTL) mapper in cis for molecular phenotypes
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The goal of FastQTL is to identify single-nucleotide polymorphisms (SNPs) which are significantly associated with various molecular phenotypes (i.e. expression of known genes, cytosine methylation levels, etc). It performs scans for all possible phenotype-variant pairs in cis (i.e. variants located within a specific window around a phenotype). FastQTL implements a new permutation scheme (Beta approximation) to accurately and rapidly correct for multiple-testing at both the genotype and phenotype levels.

The package is enhanced by the following packages: fastqtl-doc
Please cite: Halit Ongen, Alfonso Buil, Andrew Anand Brown, Emmanouil T. Dermitzakis and and Olivier Delaneau: Fast and efficient QTL mapper for thousands of molecular phenotypes. (eprint) Bioinformatics (2015)
fasttree
phylogenetic trees from alignments of nucleotide or protein sequences
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FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein sequences. It handles alignments with up to a million of sequences in a reasonable amount of time and memory. For large alignments, FastTree is 100-1,000 times faster than PhyML 3.0 or RAxML 7.

FastTree is more accurate than PhyML 3 with default settings, and much more accurate than the distance-matrix methods that are traditionally used for large alignments. FastTree uses the Jukes-Cantor or generalized time-reversible (GTR) models of nucleotide evolution and the JTT (Jones-Taylor-Thornton 1992) model of amino acid evolution. To account for the varying rates of evolution across sites, FastTree uses a single rate for each site (the "CAT" approximation). To quickly estimate the reliability of each split in the tree, FastTree computes local support values with the Shimodaira-Hasegawa test (these are the same as PhyML 3's "SH-like local supports").

This package contains a single threaded version (fasttree) and a parallel version which uses OpenMP (fasttreMP).

Please cite: Morgan N. Price, Paramvir S. Dehal and Adam P. Arkin: FastTree 2 -- Approximately Maximum-Likelihood Trees for Large Alignments.. (PubMed,eprint) PLoS ONE 5(3):e9490 (2010)
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ffindex
simple index/database for huge amounts of small files
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FFindex is a very simple index/database for huge amounts of small files. The files are stored concatenated in one big data file, separated by '\0'. A second file contains a plain text index, giving name, offset and length of the small files. The lookup is currently done with a binary search on an array made from the index file.

This package provides the executables.

figtree
graphical phylogenetic tree viewer
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FigTree is designed as a graphical viewer of phylogenetic trees and as a program for producing publication-ready figures. In particular it is designed to display summarized and annotated trees produced by BEAST.

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filtlong
quality filtering tool for long reads of genome sequences
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Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

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fitgcp
fitting genome coverage distributions with mixture models
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Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses.

fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm.

Please cite: Martin S. Lindner, Maximilian Kollock, Franziska Zickmann and Bernhard Y. Renard: Analyzing genome coverage profiles with applications to quality control in metagenomics. (PubMed,eprint) Bioinformatics 29(10):1260-1267 (2013)
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flash
Fast Length Adjustment of SHort reads
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FLASH (Fast Length Adjustment of SHort reads) is a very fast and accurate software tool to merge paired-end reads from next-generation sequencing experiments. FLASH is designed to merge pairs of reads when the original DNA fragments are shorter than twice the length of reads. The resulting longer reads can significantly improve genome assemblies. They can also improve transcriptome assembly when FLASH is used to merge RNA-seq data.

The package is enhanced by the following packages: multiqc
Please cite: Tanja Magoč and Steven L Salzberg: FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies. (PubMed,eprint) Bioinformatics 27(21):2957-2963 (2011)
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flexbar
flexible barcode and adapter removal for sequencing platforms
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Flexbar preprocesses high-throughput sequencing data efficiently. It demultiplexes barcoded runs and removes adapter sequences. Moreover, trimming and filtering features are provided. Flexbar increases mapping rates and improves genome and transcriptome assemblies. It supports next-generation sequencing data in fasta/q and csfasta/q format from Illumina, Roche 454, and the SOLiD platform.

Parameter names changed in Flexbar. Please review scripts. The recent months, default settings were optimised, several bugs were fixed and various improvements were made, e.g. revamped command-line interface, new trimming modes as well as lower time and memory requirements.

The package is enhanced by the following packages: multiqc
Please cite: Matthias Dodt, Johannes T. Roehr, Rina Ahmed and Christoph Dieterich: FLEXBAR — Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms. (eprint) Biology 1(3):895-905 (2012)
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flye
de novo assembler for single molecule sequencing reads using repeat graphs
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Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PacBio / ONT reads as input and outputs polished contigs. Flye also has a special mode for metagenome assembly.

Please cite: Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel A. Pevzner: Assembly of long, error-prone reads using repeat graphs. (PubMed) Nature Biotechnology 37(5):540–546 (2019)
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fml-asm
tool for assembling Illumina short reads in small regions
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Fml-asm is a command-line tool for assembling Illumina short reads in regions from 100bp to 10 million bp in size, based on the fermi-lite library. It is largely a light-weight in-memory version of fermikit without generating any intermediate files. It inherits the performance, the relatively small memory footprint and the features of fermikit. In particular, fermi-lite is able to retain heterozygous events and thus can be used to assemble diploid regions for the purpose of variant calling.

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freebayes
Bayesian haplotype-based polymorphism discovery and genotyping
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FreeBayes is a Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs (single-nucleotide polymorphisms), indels (insertions and deletions), MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment.

Please cite: Erik Garrison and Gabor Marth: Haplotype-based variant detection from short-read sequencing. (eprint) arXiv (2012)
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freecontact
fast protein contact predictor
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FreeContact is a protein residue contact predictor optimized for speed. Its input is a multiple sequence alignment. FreeContact can function as an accelerated drop-in for the published contact predictors EVfold-mfDCA of DS. Marks (2011) and PSICOV of D. Jones (2011).

FreeContact is accelerated by a combination of vector instructions, multiple threads, and faster implementation of key parts. Depending on the alignment, 8-fold or higher speedups are possible.

A sufficiently large alignment is required for meaningful results. As a minimum, an alignment with an effective (after-weighting) sequence count bigger than the length of the query sequence should be used. Alignments with tens of thousands of (effective) sequences are considered good input.

jackhmmer(1) from the hmmer package, or hhblits(1) from hhsuite can be used to generate the alignments, for example.

This package contains the command line tool freecontact(1).

Please cite: László Kaján, Thomas A. Hopf, Matúš Kalaš, Debora S. Marks and Burkhard Rost: FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics (2014)
Topics: Structure prediction; Sequence analysis
fsa
Fast Statistical Alignment of protein, RNA or DNA sequences
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FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz.

FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:

  • FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.
  • FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.
  • FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.
  • The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned
Please cite: Robert K. Bradley, Adam Roberts, Michael Smoot, Sudeep Juvekar, Jaeyoung Do, Colin Dewey, Ian Holmes and Lior Pachter: Fast Statistical Alignment. (PubMed,eprint) PLoS Comput Biol. 5(5):e1000392 (2009)
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Remark of Debian Med team: Precondition for T-Coffee

see http://wiki.debian.org/DebianMed/TCoffee

Upstream address bounced when contacting about segfaults so it seems to be dead upstream and no good code quality.

fsm-lite
frequency-based string mining (lite)
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A singe-core implementation of frequency-based substring mining used in bioinformatics to extract substrings that discriminate two (or more) datasets inside high-throughput sequencing data.

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gamgi
General Atomistic Modelling Graphic Interface (GAMGI)
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The General Atomistic Modelling Graphic Interface (GAMGI) provides a graphical interface to build, view and analyze atomic structures. The program is aimed at the scientific community and provides a graphical interface to study atomic structures and to prepare images for presentations, and for teaching the atomic structure of matter.

The package is enhanced by the following packages: gamgi-data gamgi-doc
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garli
phylogenetic analysis of molecular sequence data using maximum-likelihood
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GARLI, Genetic Algorithm for Rapid Likelihood Inference is a program for inferring phylogenetic trees. Using an approach similar to a classical genetic algorithm, it rapidly searches the space of evolutionary trees and model parameters to find the solution maximizing the likelihood score. It implements nucleotide, amino acid and codon-based models of sequence evolution, and runs on all platforms. The latest version adds support for partitioned models and morphology-like datatypes.

garlic
visualization program for biomolecules
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Garlic is written for the investigation of membrane proteins. It may be used to visualize other proteins, as well as some geometric objects. This version of garlic recognizes PDB format version 2.1. Garlic may also be used to analyze protein sequences.

It only depends on the X libraries, no other libraries are needed.

Features include:

  • The slab position and thickness are visible in a small window.
  • Atomic bonds as well as atoms are treated as independent drawable objects.
  • The atomic and bond colors depend on position. Five mapping modes are available (as for slab).
  • Capable to display stereo image.
  • Capable to display other geometric objects, like membrane.
  • Atomic information is available for atom covered by the mouse pointer. No click required, just move the mouse pointer over the structure!
  • Capable to load more than one structure.
  • Capable to draw Ramachandran plot, helical wheel, Venn diagram, averaged hydrophobicity and hydrophobic moment plot.
  • The command prompt is available at the bottom of the main window. It is able to display one error message and one command string.
Please cite: Damir Zucic and Davor Juretic: Precise Annotation of Transmembrane Segments with Garlic - a Free Molecular Visualization Program (eprint) Croatica Chemica Acta 77(1-2):397-401 (2004)
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gasic
genome abundance similarity correction
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One goal of sequencing based metagenomic analysis is the quantitative taxonomic assessment of microbial community compositions. However, the majority of approaches either quantify at low resolution (e.g. at phylum level) or have severe problems discerning highly similar species. Yet, accurate quantification on species level is desirable in applications such as metagenomic diagnostics or community comparison. GASiC is a method to correct read alignment results for the ambiguities imposed by similarities of genomes. It has superior performance over existing methods.

The package is enhanced by the following packages: gasic-examples
Please cite: Martin S. Lindner and Bernhard Y. Renard: Metagenomic abundance estimation and diagnostic testing on species level. (PubMed,eprint) Nucleic Acids Research 41(1):e10 (2013)
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gatb-core
Genome Analysis Toolbox with de-Bruijn graph
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The GATB-CORE project provides a set of highly efficient algorithms to analyse NGS data sets. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes). Read more about GATB at https://gatb.inria.fr/. By itself GATB-CORE is not an NGS data analysis tool. However, it can be used to create such tools. There already exist a set of ready-to-use tools relying on GATB-CORE library: see https://gatb.inria.fr/software/

Please cite: Erwan Drezen, Guillaume Rizk, Rayan Chikhi, Charles Deltel, Claire Lemaitre, Pierre Peterlongo and Dominique Lavenier: GATB: Genome Assembly & Analysis Tool Box. Bioinformatics 30(20):2959-2961 (2014)
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gbrowse
GMOD Generic Genome Browser
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Generic Genome Browser is a simple but highly configurable web-based genome browser. It is a component of the Generic Model Organism Systems Database project (GMOD). Some of its features:

  • Simultaneous bird's eye and detailed views of the genome;
  • Scroll, zoom, center;
  • Attach arbitrary URLs to any annotation;
  • Order and appearance of tracks are customizable by administrator and end-user;
  • Search by annotation ID, name, or comment;
  • Supports third party annotation using GFF formats;
  • Settings persist across sessions;
  • DNA and GFF dumps;
  • Connectivity to different databases, including BioSQL and Chado;
  • Multi-language support;
  • Third-party feature loading;
  • Customizable plug-in architecture (e.g. run BLAST, dump & import many formats, find oligonucleotides, design primers, create restriction maps, edit features).
The package is enhanced by the following packages: libbio-samtools-perl
Please cite: Maureen J. Donlin: Using the Generic Genome Browser (GBrowse). (eprint) Department of Biochemistry and Molecular Biology and Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine (2009)
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gdpc
visualiser of molecular dynamic simulations
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gpdc is a graphical program for visualising output data from molecular dynamics simulations. It reads input in the standard xyz format, as well as other custom formats, and can output pictures of each frame in JPG or PNG format.

gemma
Genome-wide Efficient Mixed Model Association
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GEMMA is the software implementing the Genome-wide Efficient Mixed Model Association algorithm for a standard linear mixed model and some of its close relatives for genome-wide association studies (GWAS):

  • It fits a univariate linear mixed model (LMM) for marker association tests with a single phenotype to account for population stratification and sample structure, and for estimating the proportion of variance in phenotypes explained (PVE) by typed genotypes (i.e. "chip heritability").
  • It fits a multivariate linear mixed model (mvLMM) for testing marker associations with multiple phenotypes simultaneously while controlling for population stratification, and for estimating genetic correlations among complex phenotypes.
  • It fits a Bayesian sparse linear mixed model (BSLMM) using Markov chain Monte Carlo (MCMC) for estimating PVE by typed genotypes, predicting phenotypes, and identifying associated markers by jointly modeling all markers while controlling for population structure.
  • It estimates variance component/chip heritability, and partitions it by different SNP functional categories. In particular, it uses HE regression or REML AI algorithm to estimate variance components when individual-level data are available. It uses MQS to estimate variance components when only summary statisics are available.

GEMMA is computationally efficient for large scale GWAS and uses freely available open-source numerical libraries.

Please cite: Xiang Zhou and Matthew Stephens: Genome-wide efficient mixed-model analysis for association studies Nature Genetics 44:821-824 (2012)
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genometester
toolkit for performing set operations on k-mer lists
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Toolkit for performing set operations - union, intersection and complement - on k-mer lists.

GenomeTester4 toolkit, which contains a novel tool GListCompare for performing union, intersection and complement (difference) set operations on k-mer lists. It contains examples of how these general operations can be combined to solve a variety of biological analysis tasks.

Please cite: Lauris Kaplinski, Maarja Lepamets and Maido Remm: GenomeTester4: a toolkit for performing basic set operations - union, intersection and complement on k-mer lists. (PubMed,eprint) GigaScience 4(1):58 (2015)
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genomethreader
software tool to compute gene structure predictions
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GenomeThreader is a software tool to compute gene structure predictions. The gene structure predictions are calculated using a similarity-based approach where additional cDNA/EST and/or protein sequences are used to predict gene structures via spliced alignments. GenomeThreader was motivated by disabling limitations in GeneSeqer, a popular gene prediction program which is widely used for plant genome annotation.

Please cite: G. Gremme, V. Brendel, M.E. Sparks and S. Kurtz: Engineering a software tool for gene structure prediction in higher organisms. Information and Software Technology 47(15):965-978 (2005)
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genometools
versatile genome analysis toolkit
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The GenomeTools contains a collection of useful tools for biological sequence analysis and -presentation combined into a single binary.

The toolkit contains binaries for sequence and annotation handling, sequence compression, index structure generation and access, annotation visualization, and much more.

Please cite: Gordon Gremme, Sascha Steinbiss and Stefan Kurtz: GenomeTools: a comprehensive software library for efficient processing of structured genome annotations.. (PubMed) IEEE/ACM Transactions on Computational Biology and Bioinformatics 10(3):645-656 (2013)
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genomicsdb-tools
sparse array storage library for genomics (tools)
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GenomicsDB is built on top of a htslib fork and an internal array storage system for importing, querying and transforming variant data. Variant data is sparse by nature (sparse relative to the whole genome) and using sparse array data stores is a perfect fit for storing such data.

The GenomicsDB stores variant data in a 2D array where:

  • Each column corresponds to a genomic position (chromosome + position);
  • Each row corresponds to a sample in a VCF (or CallSet in the GA4GH terminology);
  • Each cell contains data for a given sample/CallSet at a given position; data is stored in the form of cell attributes;
  • Cells are stored in column major order - this makes accessing cells with the same column index (i.e. data for a given genomic position over all samples) fast.
  • Variant interval/gVCF interval data is stored in a cell at the start of the interval. The END is stored as a cell attribute. For variant intervals (such as deletions and gVCF REF blocks), an additional cell is stored at the END value of the variant interval. When queried for a given genomic position, the query library performs an efficient sweep to determine all intervals that intersect with the queried position.

This package contains some tools to be run as executable files.

gentle
suite to plan genetic cloning
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GENtle is a software for DNA and amino acid editing, database management, plasmid maps, restriction and ligation, alignments, sequencer data import, calculators, gel image display, PCR, and much more.

Please cite: Magnus Manske: GENtle, a free multi-purpose molecular biology tool. (eprint) (2006)
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gff2aplot
pair-wise alignment-plots for genomic sequences in PostScript
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A program to visualize the alignment of two genomic sequences together with their annotations. From GFF-format input files it produces PostScript figures for that alignment. The following menu lists many features of gff2aplot:

  • Comprehensive alignment plots for any GFF-feature. Attributes are defined separately so you can modify only whatsoever attributes for a given file or share same customization across different data-sets.
  • All parameters are set by default within the program, but it can be also fully configured via gff2ps-like flexible customization files. Program can handle several of such files, summarizing all the settings before producing the corresponding figure. Moreover, all customization parameters can be set via command-line switches, which allows users to play with those parameters before adding any to a customization file.
  • Source order is taken from input files, if you swap file order you can visualize alignment and its annotation with the new input arrangement.
  • All alignment scores can be visualized in a PiP box below gff2aplot area, using grey-color scale, user-defined color scale or score-dependent gradients.
  • Scalable fonts, which can also be chosen among the basic PostScript default fonts. Feature and group labels can be rotated to improve readability in both annotation axes.
  • The program is still defined as a Unix filter so it can handle data from files, redirections and pipes, writing output to standard-output and warnings to standard error.
  • gff2aplot is able to manage many physical page formats (from A0 to A10, and more -see available page sizes in its manual-), including user-defined ones. This allows, for instance, the generation of poster size genomic maps, or the use of a continuous-paper supporting plotting device, either in portrait or landscape.
  • You can draw different alignments on same alignment plot and distinguish them by using different colors for each.
  • Shape dictionary has been expanded, so that further feature shapes are now available (see manual).
  • Annotation projections through alignment plots (so called ribbons) emulate transparencies via complementary color fill patterns. This feature allows one to show color pseudo-blending when horizontal and vertical ribbons overlap.
Please cite: J. F. Abril, R. Guigó and T. Wiehe: gff2aplot: Plotting sequence comparisons. (PubMed,eprint) Bioinformatics 19(18):2477-2479 (2003)
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gff2ps
produces PostScript graphical output from GFF-files
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gff2ps is a script program developed with the aim of converting gff-formatted records into high quality one-dimensional plots in PostScript. Such plots maybe useful for comparing genomic structures and to visualizing outputs from genome annotation programs. It can be used in a very simple way, because it assumes that the GFF file itself carries enough formatting information, but it also allows through a number of options and/or a configuration file, for a great degree of customization.

Please cite: J. F. Abril and R. Guigó: gff2ps: visualizing genomic annotations.. (PubMed,eprint) Bioinformatics 16(8):743-744 (2000)
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gffread
GFF/GTF format conversions, region filtering, FASTA sequence extraction
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Gffread is a GFF/GTF parsing utility providing format conversions, region filtering, FASTA sequence extraction and more.

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ggd-utils
programs for use in ggd
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Takes a genome file and (currently) a .vcf.gz or a .bed.gz and checks that:

* a .tbi is present
* the VCF has ""##fileformat=VCF" as the first
line
* the VCF has a #CHROM header
* the chromosome are in the order specified by
the genome file (and present)
* the positions are sorted
* the positions are <= the chromosome lengths
defined in the genome file.

As a result, any new genome going into GGD will have a .genome file that will dictate the sort order and presence or absence of the 'chr' prefix for chromosomes

ghemical
GNOME molecular modelling environment
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Ghemical is a computational chemistry software package written in C++. It has a graphical user interface and it supports both quantum- mechanics (semi-empirical) models and molecular mechanics models. Geometry optimization, molecular dynamics and a large set of visualization tools using OpenGL are currently available.

Ghemical relies on external code to provide the quantum-mechanical calculations. Semi-empirical methods MNDO, MINDO/3, AM1 and PM3 come from the MOPAC7 package (Public Domain), and are included in the package. The MPQC package is used to provide ab initio methods: the methods based on Hartree-Fock theory are currently supported with basis sets ranging from STO-3G to 6-31G**.

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ghmm
General Hidden-Markov-Model library - tools
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python3 bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continuous emissions, basic training, HMM clustering, HMM mixtures.

This package contains some tools using the library.

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glam2
gapped protein motifs from unaligned sequences
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GLAM2 is a software package for finding motifs in sequences, typically amino-acid or nucleotide sequences. A motif is a re-occurring sequence pattern: typical examples are the TATA box and the CAAX prenylation motif. The main innovation of GLAM2 is that it allows insertions and deletions in motifs.

This package includes programs for discovering motifs shared by a set of sequences and finding matches to these motifs in a sequence database, as well as utilities for converting glam2 motifs to standard alignment formats, masking glam2 motifs out of sequences so that weaker motifs can be found, and removing highly similar members of a set of sequences.

The package includes these programs:

 glam2:       discovering motifs shared by a set of sequences;
 glam2scan:   finding matches, in a sequence database, to a motif discovered
              by glam2;
 glam2format: converting glam2 motifs to  standard alignment formats;
 glam2mask:   masking glam2 motifs out of sequences, so that weaker motifs
              can be found;
 glam2-purge: removing highly similar members of a set of sequences.

In this binary package, the fast Fourier algorithm (FFT) was enabled for the glam2 program.

Please cite: Martin C. Frith, Neil F. W. Saunders, Bostjan Kobe and Timothy L. Bailey: Discovering Sequence Motifs with Arbitrary Insertions and Deletions. (PubMed) PLoS Computational Biology 4(5):e1000071 (2008)
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gmap
spliced and SNP-tolerant alignment for mRNA and short reads
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This package contains the programs GMAP and GSNAP as well as utilities to manage genome databases in GMAP/GSNAP format. GMAP (Genomic Mapping and Alignment Program) is a tool for aligning EST, mRNA and cDNA sequences. GSNAP (Genomic Short-read Nucleotide Alignment Program) is a tool for aligning single-end and paired-end transcriptome reads. Both tools can use a database of

  • known splice sites and identify novel splice sites.
  • known single-nucleotide polymorphisms (SNPs). GSNAP can align bisulfite-treated DNA.
Please cite: Thomas D. Wu and Serban Nacu: Fast and SNP-tolerant detection of complex variants and splicing in short reads. (PubMed,eprint) Bioinformatics 26(7):873-81 (2010)
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grabix
wee tool for random access into BGZF files
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In biomedical research it is increasing practice to study the genetic basis of disease. This now frequently comprises the sequencing of human sequences. The output of the machine however is redundant, and the real sequence is the best sequence to explain the redundancy. The exchange of data happens only with compressed files - to huge and redundant to perform otherwise. One should avoid uncompression whenever possible.

grabix leverages the fantastic BGZF library of the samtools package to provide random access into text files that have been compressed with bgzip. grabix creates it's own index (.gbi) of the bgzipped file. Once indexed, one can extract arbitrary lines from the file with the grab command. Or choose random lines with the, well, random command.

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graphlan
circular representations of taxonomic and phylogenetic trees
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GraPhlAn is a software tool for producing high-quality circular representations of taxonomic and phylogenetic trees. It focuses on concise, integrative, informative, and publication-ready representations of phylogenetically- and taxonomically-driven investigation.

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grinder
Versatile omics shotgun and amplicon sequencing read simulator
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Grinder is a versatile program to create random shotgun and amplicon sequence libraries based on DNA, RNA or proteic reference sequences provided in a FASTA file.

Grinder can produce genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, metaproteomic shotgun and amplicon datasets from current sequencing technologies such as Sanger, 454, Illumina. These simulated datasets can be used to test the accuracy of bioinformatic tools under specific hypothesis, e.g. with or without sequencing errors, or with low or high community diversity. Grinder may also be used to help decide between alternative sequencing methods for a sequence-based project, e.g. should the library be paired-end or not, how many reads should be sequenced.

Please cite: Florent E. Angly, Dana Willner, Forest Rohwer, Philip Hugenholtz and Gene W. Tyson: Grinder: a versatile amplicon and shotgun sequence simulator. (PubMed,eprint) Nucleic Acids Research Epub ahead of print (2012)
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gromacs
Molecular dynamics simulator, with building and analysis tools
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GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles.

It is primarily designed for biochemical molecules like proteins and lipids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non- biological systems, e.g. polymers.

This package contains variants both for execution on a single machine, and using the MPI interface across multiple machines.

Please cite: Berk Hess, Carsten Kutzner, David van der Spoel and Erik Lindahl: GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. (eprint) J. Chem. Theory Comput. 4(3):435-447 (2008)
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gsort
sort genomic data
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gsort is a tool to sort genomic files according to a genomefile. For example, to sort VCF to have order: X, Y, 2, 1, 3, ... and the header needs to be kept at the top.

As a more likely example, if a file nneds to be sorted to match GATK order (1 ... X, Y, MT) which is not possible with any other sorting tool. With gsort one can simply place MT as the last chrom in the ".genome" file.

It will also be useful for getting files ready for use in bedtools.

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gubbins
phylogenetic analysis of genome sequences
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Gubbins supports rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences.

Gubbins (Genealogies Unbiased By recomBinations In Nucleotide Sequences) is an algorithm that iteratively identifies loci containing elevated densities of base substitutions while concurrently constructing a phylogeny based on the putative point mutations outside of these regions. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistic models of short-term bacterial evolution, and can be run in only a few hours on alignments of hundreds of bacterial genome sequences.

Please cite: Nicholas J. Croucher, Andrew J. Page, Thomas R. Connor, Aidan J. Delaney, Jacqueline A. Keane, Stephen D. Bentley, Julian Parkhill and Simon R. Harris: Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. (PubMed,eprint) Nucleic Acids Research 43(3):e15 (2014)
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gwama
Genome-Wide Association Meta Analysis
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GWAMA (Genome-Wide Association Meta Analysis) software performs meta-analysis of the results of GWA studies of binary or quantitative phenotypes. Fixed- and random-effect meta-analyses are performed for both directly genotyped and imputed SNPs using estimates of the allelic odds ratio and 95% confidence interval for binary traits, and estimates of the allelic effect size and standard error for quantitative phenotypes. GWAMA can be used for analysing the results of all different genetic models (multiplicative, additive, dominant, recessive). The software incorporates error trapping facilities to identify strand alignment errors and allele flipping, and performs tests of heterogeneity of effects between studies.

Please cite: Reedik Mägi and Andrew P. Morris: GWAMA: software for genome-wide association meta-analysis. (eprint) BMC Bioinformatics 11(May):288 (2010)
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harvest-tools
archiving and postprocessing for reference-compressed genomic multi-alignments
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HarvestTools is a utility for creating and interfacing with Gingr files, which are efficient archives that the Harvest Suite uses to store reference-compressed multi-alignments, phylogenetic trees, filtered variants and annotations. Though designed for use with Parsnp and Gingr, HarvestTools can also be used for generic conversion between standard bioinformatics file formats.

Please cite: Todd J. Treangen, Brian D. Ondov, Sergey Koren and Adam M. Phillippy: Rapid Core-Genome Alignment and Visualization for Thousands of Intraspecific Microbial Genomes. (PubMed,eprint) bioRxiv 15(11):524 (2014)
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hhsuite
sensitive protein sequence searching based on HMM-HMM alignment
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HH-suite is an open-source software package for sensitive protein sequence searching based on the pairwise alignment of hidden Markov models (HMMs).

This package contains HHsearch and HHblits among other programs and utilities.

HHsearch takes as input a multiple sequence alignment (MSA) or profile HMM and searches a database of HMMs (e.g. PDB, Pfam, or InterPro) for homologous proteins. HHsearch is often used for protein structure prediction to detect homologous templates and to build highly accurate query-template pairwise alignments for homology modeling.

HHblits can build high-quality MSAs starting from single sequences or from MSAs. It transforms these into a query HMM and, using an iterative search strategy, adds significantly similar sequences from the previous search to the updated query HMM for the next search iteration. Compared to PSI-BLAST, HHblits is faster, up to twice as sensitive and produces more accurate alignments.

Please cite: Michael Remmert, Andreas Biegert, Andreas Hauser and Johannes Söding: HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment.. (PubMed) Nat. Methods 9(2):173-175 (2011)
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hilive
realtime alignment of Illumina reads
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HiLive is a read mapping tool that maps Illumina HiSeq (or comparable) reads to a reference genome right in the moment when they are produced. This means, read mapping is finished as soon as the sequencer is finished generating the data.

Please cite: Martin S. Lindner, Benjamin Strauch, Jakob M. Schulze, Simon H. Tausch, Piotr W. Dabrowski, Andreas Nitsche and Bernhard Y. Renard: HiLive: real-time mapping of illumina reads while sequencing. (PubMed) Bioinformatics 33(6):917-919 (2017)
hisat2
graph-based alignment of short nucleotide reads to many genomes
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HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as against a single reference genome). Based on an extension of BWT for graphs a graph FM index (GFM) was designed and implementd. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

The package is enhanced by the following packages: multiqc
Please cite: Daehwan Kim, Joseph M. Paggi, Chanhee Park, Christopher Bennett and Steven L. Salzberg: Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology 37(8):907-915 (2019)
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hmmer
profile hidden Markov models for protein sequence analysis
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HMMER is an implementation of profile hidden Markov model methods for sensitive searches of biological sequence databases using multiple sequence alignments as queries.

Given a multiple sequence alignment as input, HMMER builds a statistical model called a "hidden Markov model" which can then be used as a query into a sequence database to find (and/or align) additional homologues of the sequence family.

Please cite: S. R. Eddy: Profile hidden Markov models. (PubMed,eprint) Bioinformatics 14(9):755-763 (1998)
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hmmer2
profile hidden Markov models for protein sequence analysis
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HMMER is an implementation of profile hidden Markov model methods for sensitive searches of biological sequence databases using multiple sequence alignments as queries.

Given a multiple sequence alignment as input, HMMER builds a statistical model called a "hidden Markov model" which can then be used as a query into a sequence database to find (and/or align) additional homologues of the sequence family.

Please cite: Eddy, Sean R.: Profile hidden Markov models. (PubMed) Bioinformatics 14(9):755-763 (1998)
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Remark of Debian Med team: This older version of HMMER is used in some applications

While Debian has HMMER 3 since some time there are users of HMMER 2 interested in having this old version available and thus the package is reintroduced.

hyphy-mpi
Hypothesis testing using Phylogenies (MPI version)
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HyPhy is an open-source software package for the analysis of genetic sequences using techniques in phylogenetics, molecular evolution, and machine learning. It features a complete graphical user interface (GUI) and a rich scripting language for limitless customization of analyses. Additionally, HyPhy features support for parallel computing environments (via message passing interface) and it can be compiled as a shared library and called from other programming environments such as Python or R. Continued development of HyPhy is currently supported in part by an NIGMS R01 award 1R01GM093939.

This package provides an executable using MPI to do multiprocessing.

Please cite: Sergei L. Kosakovsky Pond, Simon D. W. Frost and Spencer V. Muse: HyPhy: hypothesis testing using phylogenies. (PubMed,eprint) Bioinformatics 21(5):676-679 (2005)
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hyphy-pt
Hypothesis testing using Phylogenies (pthreads version)
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HyPhy is an open-source software package for the analysis of genetic sequences using techniques in phylogenetics, molecular evolution, and machine learning. It features a complete graphical user interface (GUI) and a rich scripting language for limitless customization of analyses. Additionally, HyPhy features support for parallel computing environments (via message passing interface) and it can be compiled as a shared library and called from other programming environments such as Python or R. Continued development of HyPhy is currently supported in part by an NIGMS R01 award 1R01GM093939.

This package provides an executable using pthreads to do multiprocessing.

Please cite: Sergei L. Kosakovsky Pond, Simon D. W. Frost and Spencer V. Muse: HyPhy: hypothesis testing using phylogenies. (PubMed,eprint) Bioinformatics 21(5):676-679 (2005)
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idba
iterative De Bruijn Graph short read assemblers
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IDBA stands for iterative de Bruijn graph assembler. In computational sequence biology, an assembler solves the puzzle coming from large sequencing machines that feature many gigabytes of short reads from a large genome.

This package provides several flavours of the IDBA assembler, as they all share the same source tree but serve different purposes and evolved over time.

IDBA is the basic iterative de Bruijn graph assembler for second-generation sequencing reads. IDBA-UD, an extension of IDBA, is designed to utilize paired-end reads to assemble low-depth regions and use progressive depth on contigs to reduce errors in high-depth regions. It is a generic purpose assembler and especially good for single-cell and metagenomic sequencing data. IDBA-Hybrid is another update version of IDBA-UD, which can make use of a similar reference genome to improve assembly result. IDBA-Tran is an iterative de Bruijn graph assembler for RNA-Seq data.

Please cite: Yu Peng, Henry C. M. Leung, S. M. Yiu and Francis Y. L. Chin: IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. (PubMed,eprint) Bioinformatics 28(11):1420-1428 (2012)
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igblast
Immunoglobulin and T cell receptor variable domain sequence analysis
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IgBLAST allows users to view the matches to the germline V, D, and J genes, details at rearrangement junctions, the delineation of IG V domain framework regions, and complementarity determining regions. IgBLAST has the capability to analyse nucleotide and protein sequences, and can process sequences in batches. Furthermore, IgBLAST allows searches against the germline gene databases and other sequence databases simultaneously to minimize the chance of missing possibly the best matching germline V gene.

Please cite: Jian Ye, Ning Ma, Thomas L Madden and James M Ostell: IgBLAST: an immunoglobulin variable domain sequence analysis tool. (PubMed,eprint) Nucleic Acids Res. 41:W34-W40 (2013)
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igdiscover
analyzes antibody repertoires to find new V genes
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IgDiscover analyzes antibody repertoires and discovers new V genes from high-throughput sequencing reads. Heavy chains, kappa and lambda light chains are supported (to discover VH, VK and VL genes).

Please cite: Martin M. Corcoran, Ganesh E. Phad, Néstor Vázquez Bernat, Christiane Stahl-Hennig, Noriyuki Sumida, Mats A.A. Persson, Marcel Martin and Gunilla B. Karlsson Hedestam: Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity.. (eprint) Nature Communications 7:13642 (2016)
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igor
infers V(D)J recombination processes from sequencing data
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IGoR (Inference and Generation of Repertoires) is a versatile software to analyze and model immune receptors generation, selection, mutation and all other processes.

Please cite: Quentin Marcou, Thierry Mora and Aleksandra M. Walczak: High-throughput immune repertoire analysis with IGoR. (PubMed,eprint) Nature Communications 9(1):561 (2018)
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igv
Integrative Genomics Viewer
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The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems.

Please cite: James T Robinson, Helga Thorvaldsdóttir, Wendy Winckler, Mitchell Guttman, Eric S Lander, Gad Getz and Jill P Mesirov: Integrative genomics viewer. (PubMed,eprint) Nature Biotechnology 29(1):24–26 (2011)
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indelible
powerful and flexible simulator of biological evolution
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INDELible is a new, portable, and flexible application for biological sequence simulation that combines many features in the same place for the first time. Using a length-dependent model of indel formation it can simulate evolution of multi-partitioned nucleotide, amino-acid, or codon data sets through the processes of insertion, deletion, and substitution in continuous time.

Nucleotide simulations may use the general unrestricted model or the general time reversible model and its derivatives, and amino-acid simulations can be conducted using fifteen different empirical rate matrices. Substitution rate heterogeneity can be modeled via the continuous and discrete gamma distributions, with or without a proportion of invariant sites. INDELible can also simulate under non-homogeneous and non-stationary conditions where evolutionary models are permitted to change across a phylogeny.

Unique among indel simulation programs, INDELible offers the ability to simulate using codon models that exhibit nonsynonymous/synonymous rate ratio heterogeneity among sites and/or lineages.

Please cite: William Fletcher and Ziheng Yang: INDELible: A Flexible Simulator of Biological Sequence Evolution. (eprint) Molecular Biology and Evolution 26(8):1879-1888 (2009)
Topics: Sequencing
infernal
inference of RNA secondary structural alignments
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Infernal ("INFERence of RNA ALignment") searches DNA sequence databases for RNA structure and sequence similarities. It provides an implementation of a special variant of profile stochastic context-free grammars called covariance models (CMs). A CM is like a sequence profile, but it scores a combination of sequence consensus and RNA secondary structure consensus, so in many cases, it is more capable of identifying RNA homologs that conserve their secondary structure more than their primary sequence.

The tool is an integral component of the Rfam database.

Please cite: Eric P. Nawrocki, Diana L. Kolbe and Sean R. Eddy: Infernal 1.0: inference of RNA alignments. (PubMed,eprint) Bioinformatics 25(10):1335-1337 (2009)
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insilicoseq
sequencing simulator producing realistic Illumina reads
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Primarily intended for simulating metagenomic samples, it can also be used to produce sequencing data from a single genome.

InSilicoSeq is written in Python, and use kernel density estimators to model the read quality of real sequencing data.

InSilicoSeq support substitution, insertion and deletion errors. If you don't have the use for insertion and deletion error a basic error model is provided.

Please cite: Hadrien Gourlé, Oskar Karlsson-Lindsjö, Juliette Hayer and Erik Bongcam-Rudloff: Simulating Illumina data with InSilicoSeq. Bioinformatics 35(3):521–522 (2019)
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ipig
integrating PSMs into genome browser visualisations
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iPiG targets the integration of peptide spectrum matches (PSMs) from mass spectrometry (MS) peptide identifications into genomic visualisations provided by genome browser such as the UCSC genome browser (http://genome.ucsc.edu/).

iPiG takes PSMs from the MS standard format mzIdentML (*.mzid) or in text format and provides results in genome track formats (BED and GFF3 files), which can be easily imported into genome browsers.

Please cite: Mathias Kuhring and Bernhard Y. Renard: iPiG: Integrating Peptide Spectrum Matches into Genome Browser Visualizations. (PubMed,eprint) PLoS ONE 7(12):e50246 (2012)
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iqtree
efficient phylogenetic software by maximum likelihood
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IQ-TREE is a very efficient maximum likelihood phylogenetic software with following key features among others:

  • A novel fast and effective stochastic algorithm to estimate maximum likelihood trees. IQ-TREE outperforms both RAxML and PhyML in terms of likelihood while requiring similar amount of computing time (see Nguyen et al., 2015)
  • An ultrafast bootstrap approximation to assess branch supports (see Minh et al., 2013).
  • A wide range of substitution models for binary, DNA, protein, codon, and morphological alignments.
  • Ultrafast model selection for all data types, 10 to 100 times faster than jModelTest and ProtTest.
  • Finding best partition scheme like PartitionFinder.
  • Partitioned models with mixed data types for phylogenomic (multi- gene) alignments, allowing for separate, proportional, or joint branch lengths among genes.
  • Supporting the phylogenetic likelihod library (PLL) (see Flouri et al., 2014)
Please cite: Lam Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler and Bui Quang Minh: IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. (PubMed,eprint) Mol. Biol. Evol. 32(1):268-274 (2015)
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iva
iterative virus sequence assembler
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IVA is a de novo assembler designed to assemble virus genomes that have no repeat sequences, using Illumina read pairs sequenced from mixed populations at extremely high depth.

IVA's main algorithm works by iteratively extending contigs using aligned read pairs. Its input can be just read pairs, or additionally you can provide an existing set of contigs to be extended. Alternatively, it can take reads together with a reference sequence.

Please cite: M. Hunt, A. Gall, S. H. Ong, J. Brener, B. Ferns, P. Goulder, E. Nastouli, J. A. Keane, P. Kellam and T. D. Otto: IVA: accurate de novo assembly of RNA virus genomes. (PubMed) Bioinformatics 31(14):2374-2376 (2015)
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jaligner
Smith-Waterman algorithm with Gotoh's improvement
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JAligner is an open source Java implementation of the Smith-Waterman algorithm with Gotoh's improvement for biological local pairwise sequence alignment with the affine gap penalty model.

jalview
multiple alignment editor
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JalView is a Java DNA, RNA and protein sequence alignment editor that can work with sequence alignments produced by programs implementing alignment algorithms such as clustalw, kalign and t-coffee.

It has lots of features, is actively developed, and will compare advantageously to BioEdit, while being free as in free speech!

Please cite: Andrew M. Waterhouse, James B. Procter, David M. A. Martin, Michèle Clamp and Geoffrey J. Barton: Jalview Version 2-a multiple sequence alignment editor and analysis workbench. (PubMed,eprint) Bioinformatics 25:1189-1191 (2009)
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jellyfish
count k-mers in DNA sequences
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JELLYFISH is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers using an order of magnitude less memory and an order of magnitude faster than other k-mer counting packages by using an efficient encoding of a hash table and by exploiting the "compare-and-swap" CPU instruction to increase parallelism.

JELLYFISH is a command-line program that reads FASTA and multi-FASTA files containing DNA sequences. It outputs its k-mer counts in an binary format, which can be translated into a human-readable text format using the "jellyfish dump" command.

The package is enhanced by the following packages: multiqc
Please cite: Guillaume Marcais and Carl Kingsford: A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27(6):764-770 (2011)
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jellyfish1
count k-mers in DNA sequences
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JELLYFISH is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers using an order of magnitude less memory and an order of magnitude faster than other k-mer counting packages by using an efficient encoding of a hash table and by exploiting the "compare-and-swap" CPU instruction to increase parallelism.

JELLYFISH is a command-line program that reads FASTA and multi-FASTA files containing DNA sequences. It outputs its k-mer counts in an binary format, which can be translated into a human-readable text format using the "jellyfish dump" command.

This is the latest version of the 1.x series of jellyfish which is used by some other applications that are not compatible with version 2.x which is provided inside the jellyfish package.

Please cite: Guillaume Marcais and Carl Kingsford: A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27(6):764-770 (2011)
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jmodeltest
HPC selection of models of nucleotide substitution
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jModelTest is a tool to carry out statistical selection of best-fit models of nucleotide substitution. It implements five different model selection strategies: hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a decision theory method (DT). It also provides estimates of model selection uncertainty, parameter importances and model-averaged parameter estimates, including model-averaged tree topologies. jModelTest 2 includes High Performance Computing (HPC) capabilities and additional features like new strategies for tree optimization, model- averaged phylogenetic trees (both topology and branch length), heuristic filtering and automatic logging of user activity.

Please cite: Diego Darriba, Guillermo L Taboada, Ramón Doallo and David Posada: jModelTest 2: more models, new heuristics and parallel computing. (PubMed) Nature Methods 9(8):772 (2012)
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jmol
Molecular Viewer
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Jmol is a Java molecular viewer for three-dimensional chemical structures. Features include reading a variety of file types and output from quantum chemistry programs, and animation of multi-frame files and computed normal modes from quantum programs. It includes with features for chemicals, crystals, materials and biomolecules. Jmol might be useful for students, educators, and researchers in chemistry and biochemistry.

File formats read by Jmol include PDB, XYZ, CIF, CML, MDL Molfile, Gaussian, GAMESS, MOPAC, ABINIT, ACES-II, Dalton and VASP.

Please cite: A. Herráez: Biomolecules in the computer: Jmol to the rescue. (PubMed,eprint) Biochem Mol Biol Educ. 34(4):255-261 (2006)
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kalign
Global and progressive multiple sequence alignment
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Kalign is a command line tool to perform multiple alignment of biological sequences. It employs the Muth-Manber string-matching algorithm, to improve both the accuracy and speed of the alignment. It uses global, progressive alignment approach, enriched by employing an approximate string-matching algorithm to calculate sequence distances and by incorporating local matches into the otherwise global alignment.

Please cite: Lassmann, Timo.: Kalign 3: multiple sequence alignment of large datasets. (eprint) Bioinformatics 36(6):1928-1929 (2020)
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kallisto
near-optimal RNA-Seq quantification
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Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate than existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools.

The package is enhanced by the following packages: multiqc
Please cite: Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter: Near-optimal probabilistic RNA-seq quantification. (PubMed) Nature Biotechnology 34(5):525–527 (2016)
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kaptive
obtain information about K and O types for Klebsiella genome assemblies
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Kaptive reports information about K and O types for Klebsiella genome assemblies.

Given a novel genome and a database of known loci (K or O), Kaptive will help a user to decide whether their sample has a known or novel locus. It carries out the following for each input assembly:

  • BLAST for all known locus nucleotide sequences (using blastn) to identify the best match ('best' defined as having the highest coverage).
  • Extract the region(s) of the assembly which correspond to the BLAST hits (i.e. the locus sequence in the assembly) and save it to a FASTA file.
  • BLAST for all known locus genes (using tblastn) to identify which expected genes (genes in the best matching locus) are present/missing and whether any unexpected genes (genes from other loci) are present.
  • Output a summary to a table file.

In cases where your input assembly closely matches a known locus, Kaptive should make that obvious. When your assembly has a novel type, that too should be clear. However, Kaptive cannot reliably extract or annotate locus sequences for totally novel types - if it indicates a novel locus is present then extracting and annotating the sequence is up to you! Very poor assemblies can confound the results, so be sure to closely examine any case where the locus sequence in your assembly is broken into multiple pieces.

The package is enhanced by the following packages: kaptive-data kaptive-example
Please cite: Kelly L. Wyres, Ryan R. Wick, Claire Gorrie, Adam Jenney, Rainer Follador, Nicholas R. Thomson and Kathryn E. Holt: Identification of Klebsiella capsule synthesis loci from whole genome data. (PubMed) Microbial Genomics 2(12):e000102 (2016)
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khmer
in-memory DNA sequence kmer counting, filtering & graph traversal
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khmer is a library and suite of command line tools for working with DNA sequence. It is primarily aimed at short-read sequencing data such as that produced by the Illumina platform. khmer takes a k-mer-centric approach to sequence analysis, hence the name.

Please cite: Michael R. Crusoe, Hussien F. Alameldin, Sherine Awad, Elmar Bucher, Adam Caldwell, Reed Cartwright, Amanda Charbonneau, Bede Constantinides, Greg Edvenson, Scott Fay, Jacob Fenton, Thomas Fenzl, Jordan Fish, Leonor Garcia-Gutierrez, Phillip Garland, Jonathan Gluck, Iván González, Sarah Guermond, Jiarong Guo, Aditi Gupta, Joshua R. Herr, Adina Howe, Alex Hyer, Andreas Härpfer, Luiz Irber, Rhys Kidd, David Lin, Justin Lippi, Tamer Mansour, Pamela McA'Nulty, Eric McDonald, Jessica Mizzi, Kevin D. Murray, Joshua R. Nahum, Kaben Nanlohy, Alexander Johan Nederbragt, Humberto Ortiz-Zuazaga, Jeramia Ory, Jason Pell, Charles Pepe-Ranney, Zachary N Russ, Erich Schwarz, Camille Scott, Josiah Seaman, Scott Sievert, Jared Simpson, Connor T. Skennerton, James Spencer, Ramakrishnan Srinivasan, Daniel Standage, James A. Stapleton, Joe Stein, Susan R Steinman, Benjamin Taylor, Will Trimble, Heather L. Wiencko, Michael Wright, Brian Wyss, Qingpeng Zhang, en zyme and C. Titus Brown: The khmer software package: enabling efficient sequence analysis. (2015)
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kineticstools
detection of DNA modifications
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Tools for detecting DNA modifications from single molecule, real-time (SMRT®) sequencing data. This tool implements the P_ModificationDetection module in SMRT® Portal, used by the RS_Modification_Detection and RS_Modifications_and_Motif_Detection protocol. Researchers interested in understanding or extending the modification detection algorithms can use these tools as a starting point.

This package is part of the SMRTAnalysis suite.

king-probe
Evaluate and visualize protein interatomic packing
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king-probe is a program that allows one to evaluate atomic packing, either within or between molecules. It generates "contact dots" where atoms are in close contact.

The program king-probe generates "contact dots" at points on the van der Waals surface of atoms which are in close proximity to other atoms; reading atomic coordinates in protein databank (PDB) format files and writing color-coded dot lists (spikes where atoms clash) for inclusion in a kinemage.

Please cite: J. Michael Word, Simon C. Lovell, Thomas H. LaBean, Hope C. Taylor, Michael E. Zalis, Brent K. Presley, Jane S. Richardson and David C. Richardson: Visualizing and Quantifying Molecular Goodness-of-Fit: Small-probe Contact Dots with Explicit Hydrogen Atoms. (PubMed,eprint) J. Mol. Biol. 285(4):1709-1731 (1999)
kissplice
Detection of various kinds of polymorphisms in RNA-seq data
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KisSplice is a piece of software that enables the analysis of RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows one to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.

Please cite: Gustavo AT Sacomoto, Janice Kielbassa, Rayan Chikhi, Raluca Uricaru, Pavlos Antoniou, Marie-France Sagot, Pierre Peterlongo and Vincent Lacroix: KISSPLICE: de-novo calling alternative splicing events from RNA-seq data. (PubMed,eprint) BMC Bioinformatics 13((Suppl 6)):S5 (2012)
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Topics: RNA-seq; RNA splicing; Gene structure
kleborate
tool to screen Klebsiella genome assemblies
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Kleborate is a tool to screen Klebsiella genome assemblies for:

  • MLST sequence type
  • species (e.g. K. pneumoniae, K. quasipneumoniae, K. variicola, etc.)
  • ICEKp associated virulence loci: yersiniabactin (ybt), colibactin (clb)
  • virulence plasmid associated loci: salmochelin (iro), aerobactin (iuc), hypermucoidy (rmpA, rmpA2)
  • antimicrobial resistance genes, including quinolone resistance SNPs and colistin resistance truncations
  • K (capsule) and O antigen (LPS) serotype prediction, via wzi alleles and Kaptive
Please cite: Margaret M. C. Lam, Ryan R. Wick, Kelly L. Wyres, Claire L. Gorrie, Louise M. Judd, Adam W. J. Jenney, Sylvain Brisse and Kathryn E. Holt: Genetic diversity, mobilisation and spread of the yersiniabactin-encoding mobile element ICEKp in Klebsiella pneumoniae populations. (PubMed) Microbiology Society 4(9) (2018)
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kma
mapping genomic sequences to raw reads directly against redundant databases
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KMA is mapping a method designed to map raw reads directly against redundant databases, in an ultra-fast manner using seed and extend. KMA is particularly good at aligning high quality reads against highly redundant databases, where unique matches often does not exist. It works for long low quality reads as well, such as those from Nanopore. Non- unique matches are resolved using the "ConClave" sorting scheme, and a consensus sequence are outputtet in addition to other common attributes.

Please cite: Philip T. L. C. Clausen, Frank M. Aarestrup and Ole Lund: Rapid and precise alignment of raw reads against redundant databases with KMA. (PubMed,eprint) BMC Bioinformatics 19:307 (2018)
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kmc
count kmers in genomic sequences
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The kmc software is designed for counting k-mers (sequences of consecutive k symbols) in a set of reads. K-mer counting is important for many bioinformatics applications, e.g. developing de Bruijn graph assemblers.

Building de Bruijn graphs is a commonly used approach for genome assembly with data from second-generation sequencing. Unfortunately, sequencing errors (frequent in practice) result in huge memory requirements for de Bruijn graphs, as well as long build time. One of the popular approaches to handle this problem is filtering the input reads in such a way that unique k-mers (very likely obtained as a result of an error) are discarded.

Thus, KMC scans the raw reads and produces a compact representation of all non-unique reads accompanied with number of their occurrences. The algorithm implemented in KMC makes use mostly of disk space rather than RAM, which allows one to use KMC even on rather typical personal computers. When run on high-end servers (what is necessary for KMC competitors) it outperforms them in both memory requirements and speed of computation. The disk space necessary for computation is in order of the size of input data (usually it is smaller).

Please cite: S. Deorowicz, M. Kokot, Sz. Grabowski and A. Debudaj-Grabysz: KMC 2: Fast and resource-frugal k-mer counting. (PubMed) Bioinformatics 31(10):1569-1576 (2015)
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Topics: Sequence composition, complexity and repeats
kmer
suite of tools for DNA sequence analysis
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The kmer package is a suite of tools for DNA sequence analysis. It provides tools for searching (ESTs, mRNAs, sequencing reads); aligning (ESTs, mRNAs, whole genomes); and a variety of analyses based on kmers.

This is a metapackage depending on the executable components of the kmer suite.

Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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kmerresistance
correlates mapped genes with the predicted species of WGS samples
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KmerResistance correlates mapped genes with the predicted species of WGS samples, where this allows for identification of genes in samples which have been poorly sequenced or high accuracy predictions for samples with contamination. KmerResistance has one dependency, namely KMA to perform the mapping, which is also freely available.

Please cite: Philip T. L. C. Clausen, Ea Zankari, Frank M. Aarestrup and Ole Lund: Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. (PubMed,eprint) Journal of Antimicrobial Chemotherapy 71(9):2484-8 (2016)
kraken
assigning taxonomic labels to short DNA sequences
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Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. Kraken aims to achieve high sensitivity and high speed by utilizing exact alignments of k-mers and a novel classification algorithm.

In its fastest mode of operation, for a simulated metagenome of 100 bp reads, Kraken processed over 4 million reads per minute on a single core, over 900 times faster than Megablast and over 11 times faster than the abundance estimation program MetaPhlAn. Kraken's accuracy is comparable with Megablast, with slightly lower sensitivity and very high precision.

The package is enhanced by the following packages: jellyfish1 multiqc
Please cite: Derrick E Wood and Steven L Salzberg: Kraken: ultrafast metagenomic sequence classification using exact alignments. (PubMed,eprint) Genome Biol. 15(3):R46 (2014)
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kraken2
taxonomic classification system using exact k-mer matches
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Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm. [see: Kraken 1's Webpage for more details].

Kraken 2 provides significant improvements to Kraken 1, with faster database build times, smaller database sizes, and faster classification speeds. These improvements were achieved by the following updates to the Kraken classification program:

 1. Storage of Minimizers: Instead of storing/querying entire k-mers,
    Kraken 2 stores minimizers (l-mers) of each k-mer. The length of
    each l-mer must be ≤ the k-mer length. Each k-mer is treated by
    Kraken 2 as if its LCA is the same as its minimizer's LCA.
 2. Introduction of Spaced Seeds: Kraken 2 also uses spaced seeds to
    store and query minimizers to improve classification accuracy.
 3. Database Structure: While Kraken 1 saved an indexed and sorted list
    of k-mer/LCA pairs, Kraken 2 uses a compact hash table. This hash
    table is a probabilistic data structure that allows for faster
    queries and lower memory requirements. However, this data structure
    does have a <1% chance of returning the incorrect LCA or returning
    an LCA for a non-inserted minimizer. Users can compensate for this
    possibility by using Kraken's confidence scoring thresholds.
 4. Protein Databases: Kraken 2 allows for databases built from amino
    acid sequences. When queried, Kraken 2 performs a six-frame
    translated search of the query sequences against the database.
 5. 16S Databases: Kraken 2 also provides support for databases not
    based on NCBI's taxonomy. Currently, these include the 16S
    databases: Greengenes, SILVA, and RDP.
Please cite: Derrick E Wood and Steven L Salzberg: Kraken: ultrafast metagenomic sequence classification using exact alignments. (PubMed,eprint) Genome Biol. 15(3):R46 (2014)
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lagan
highly parametrizable pairwise global genome sequence aligner
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Lagan takes local alignments generated by CHAOS as anchors, and limits the search area of the Needleman-Wunsch algorithm around these anchors.

Multi-LAGAN is a generalization of the pairwise algorithm to multiple sequence alignment. M-LAGAN performs progressive pairwise alignments, guided by a user-specified phylogenetic tree. Alignments are aligned to other alignments using the sum-of-pairs metric.

Please cite: Michael Brudno, Chuong Do, Gregory Cooper, Michael F. Kim, Eugene Davydov, Eric D. Green, Arend Sidow and Serafim Batzoglou: LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA. (PubMed,eprint) Genome Research 13(4):721-31 (2003)
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lamarc
Likelihood Analysis with Metropolis Algorithm using Random Coalescence
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LAMARC is a program which estimates population-genetic parameters such as population size, population growth rate, recombination rate, and migration rates. It approximates a summation over all possible genealogies that could explain the observed sample, which may be sequence, SNP, microsatellite, or electrophoretic data. LAMARC and its sister program Migrate are successor programs to the older programs Coalesce, Fluctuate, and Recombine, which are no longer being supported. The programs are memory-intensive but can run effectively on workstations.

Please cite: Mary K. Kuhner: Coalescent genealogy samplers: windows into population history. (PubMed) Trends in Ecology & Evolution 24(2):86-93 (2009)
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lamassemble
Merges overlapping "long" DNA reads into a consensus sequences
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lamassemble merges overlapping "long" DNA reads into a consensus sequence (i.e. assembles them). It works OK when the number of reads is less than a thousand or so. The merging is not always accurate. In particular, if the reads come from huge tandem repeats, wrong parts of the reads may get merged.

lambda-align
Local Aligner for Massive Biological DatA
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Lambda is a local biosequence aligner optimized for many query sequences and searches in protein space. It is compatible to the de facto standard tool BLAST, but often outperforms the best currently available alternatives at reproducing BLAST’s results and is the fastest compared with the current state of the art at comparable levels of sensitivity.

Please cite: Hannes Hauswedell, Jochen Singer and Knut Reinert: Lambda: the local aligner for massive biological data. (PubMed,eprint) Bioinformatics 30(17):i349-i355 (2014)
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lambda-align2
Local Aligner for Massive Biological DatA - v2
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Lambda2 is a local biosequence aligner optimized for many query sequences and searches in protein space. It is compatible to the de facto standard tool BLAST, but often outperforms the best currently available alternatives at reproducing BLAST’s results and is the fastest compared with the current state of the art at comparable levels of sensitivity.

This package is for the Lambda (align) v2.x series which has an incompatible command line interface and on disk format from Lambda (align) v1.x.

Please cite: Hannes Hauswedell, Jochen Singer and Knut Reinert: Lambda: the local aligner for massive biological data. (PubMed,eprint) Bioinformatics 30(17):i349-i355 (2014)
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last-align
genome-scale comparison of biological sequences
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LAST is software for comparing and aligning sequences, typically DNA or protein sequences. LAST is similar to BLAST, but it copes better with very large amounts of sequence data. Here are two things LAST is good at:

  • Comparing large (e.g. mammalian) genomes.
  • Mapping lots of sequence tags onto a genome.

The main technical innovation is that LAST finds initial matches based on their multiplicity, instead of using a fixed size (e.g. BLAST uses 10-mers). This allows one to map tags to genomes without repeat-masking, without becoming overwhelmed by repetitive hits. To find these variable-sized matches, it uses a suffix array (inspired by Vmatch). To achieve high sensitivity, it uses a discontiguous suffix array, analogous to spaced seeds.

Please cite: Martin C. Frith, Raymond Wan and Paul Horton: Incorporating sequence quality data into alignment improves DNA read mapping. (PubMed,eprint) Nucl. Acids Res. 38(7):e100 (2010)
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lastz
pairwise aligning DNA sequences
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LASTZ is a drop-in replacement for BLASTZ, and is backward compatible with BLASTZ’s command-line syntax. That is, it supports all of BLASTZ’s options but also has additional ones, and may produce slightly different alignment results.

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leaff
biological sequence library utilities and applications
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LEAFF (Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.

This package is part of the Kmer suite.

Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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lefse
determine features of organisms, clades, taxonomic units, genes
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LEfSe (Linear discriminant analysis Effect Size) determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.

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libpwiz-tools
ProteoWizard command line tools
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The libpwiz library from the ProteoWizard project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard chemistry and LCMS dataset computations.

The primary goal of ProteoWizard is to eliminate the existing barriers to proteomic software development so that researchers can focus on the development of new analytic approaches, rather than having to dedicate significant resources to mundane (if important) tasks, like reading data files.

This package ships command line tools that include idconvert (conversion of MS identifications) and msconvert (conversion of MS raw data files from/to any supported format).

librg-utils-perl
parsers and format conversion utilities used by (e.g.) profphd
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This package contributes to the PredictProtein server for the automated structural annotation of protein sequences. It features as series of conversion tools like:

  • blast2saf.pl
  • blastpgp_to_saf.pl
  • conv_hssp2saf.pl
  • copf.pl
  • hssp_filter.pl
  • safFilterRed.pl

which are supported by the modules:

  • RG:Utils::Conv_hssp2saf
  • RG:Utils::Copf
  • RG:Utils::Hssp_filter
libvcflib-tools
C++ library for parsing and manipulating VCF files (tools)
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The Variant Call Format (VCF) is a flat-file, tab-delimited textual format intended to concisely describe reference-indexed variations between individuals. VCF provides a common interchange format for the description of variation in individuals and populations of samples, and has become the defacto standard reporting format for a wide array of genomic variant detectors.

vcflib provides methods to manipulate and interpret sequence variation as it can be described by VCF. It is both:

  • an API for parsing and operating on records of genomic variation as it can be described by the VCF format,
  • and a collection of command-line utilities for executing complex manipulations on VCF files.

This package contains several tools using the library.

lighter
fast and memory-efficient sequencing error corrector
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Lighter is a fast, memory-efficient tool for correcting sequencing errors. Lighter avoids counting k-mers. Instead, it uses a pair of Bloom filters, one holding a sample of the input k-mers and the other holding k-mers likely to be correct. As long as the sampling fraction is adjusted in inverse proportion to the depth of sequencing, Bloom filter size can be held constant while maintaining near-constant accuracy. Lighter is parallelized, uses no secondary storage, and is both faster and more memory-efficient than competing approaches while achieving comparable accuracy.

logol
??? missing short description for package logol :-(
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loki
MCMC linkage analysis on general pedigrees
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Performs Markov chain Monte Carlo multipoint linkage analysis on large, complex pedigrees. The current package supports analyses on quantitative traits only, although this restriction will be lifted in later versions. Joint estimation of QTL number, position and effects uses Reversible Jump MCMC. It is also possible to perform affected only IBD sharing analyses.

The package is enhanced by the following packages: loki-doc
ltrsift
postprocessing and classification of LTR retrotransposons
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LTRsift is a graphical desktop tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations, such as the ones generated by LTRharvest and LTRdigest. Its user-friendly interface displays LTR retrotransposon candidates, their putative families and their internal structure in a hierarchical fashion, allowing the user to "sift" through the sometimes large results of de novo prediction software. It also offers customizable filtering and classification functionality.

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lucy
DNA sequence quality and vector trimming tool
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Lucy is a utility that prepares raw DNA sequence fragments for sequence assembly, possibly using the TIGR Assembler. The cleanup process includes quality assessment, confidence reassurance, vector trimming and vector removal. The primary advantage of Lucy over other similar utilities is that it is a fully integrated, stand alone program.

Lucy was designed and written at The Institute for Genomic Research (TIGR, now the J. Craig Venter Institute), and it has been used here for several years to clean sequence data from automated DNA sequencers prior to sequence assembly and other downstream uses. The quality trimming portion of lucy makes use of phred quality scores, such as those produced by many automated sequencers based on the Sanger sequencing method. As such, lucy’s quality trimming may not be appropriate for sequence data produced by some of the new “next-generation” sequencers.

lumpy-sv
general probabilistic framework for structural variant discovery
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LUMPY, a novel SV discovery framework that naturally integrates multiple SV signals jointly across multiple samples. LUMPY yields improved sensitivity, especially when SV signal is reduced owing to either low coverage data or low intra-sample variant allele frequency.

The package is enhanced by the following packages: lumpy-sv-examples
macs
Model-based Analysis of ChIP-Seq on short reads sequencers
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MACS empirically models the length of the sequenced ChIP fragments, which tends to be shorter than sonication or library construction size estimates, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome sequence, allowing for more sensitive and robust prediction. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, is publicly available open source, and can be used for ChIP-Seq with or without control samples.

Please cite: Yong Zhang, Tao Liu, Clifford A Meyer, Jérôme Eeckhoute, David S. Johnson, Bradley E. Bernstein, Chad Nussbaum, Richard M. Myers, Myles Brown, Wei Li and X Shirley Liu: Model-based Analysis of ChIP-Seq (MACS). (PubMed,eprint) Genome Biol. 9(9):R137 (2008)
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macsyfinder
detection of macromolecular systems in protein datasets
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MacSyFinder is a program to model and detect macromolecular systems, genetic pathways... in protein datasets. In prokaryotes, these systems have often evolutionarily conserved properties: they are made of conserved components, and are encoded in compact loci (conserved genetic architecture). The user models these systems with MacSyFinder to reflect these conserved features, and to allow their efficient detection

This package presents the Open Source Java API to biological databases and a series of mostly sequence-based algorithms.

Please cite: Sophie S. Abby, Bertrand Néron, Hervé Ménager, Marie Touchon and Eduardo P. C. Rocha: MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas System. (PubMed,eprint) PLOS ONE 9(10):e110726 (2014)
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maffilter
process genome alignment in the Multiple Alignment Format
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MafFilter applies a series of "filters" to a MAF file, in order to clean it, extract data and computer statistics while keeping track of the associated meta-data such as genome coordinates and quality scores.

  • It can process the alignment to remove low-quality / ambiguous / masked regions.
  • It can export data into a single or multiple alignment file in format such as Fasta or Clustal.
  • It can read annotation data in GFF or GTF format, and extract the corresponding alignment.
  • It can perform sliding windows calculations.
  • It can reconstruct phylogeny/genealogy along the genome alignment.
  • It can compute population genetics statistics, such as site frequency spectrum, number of fixed/polymorphic sites, etc.
The package is enhanced by the following packages: maffilter-examples
Please cite: Julien Y Dutheil, Sylvain Gaillard and Eva H Stukenbrock: MafFilter: a highly flexible and extensible multiple genome alignment files processor. (PubMed,eprint) BMC Genomics 15:53 (2014)
mafft
Multiple alignment program for amino acid or nucleotide sequences
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MAFFT is a multiple sequence alignment program which offers three accuracy-oriented methods:

  • L-INS-i (probably most accurate; recommended for <200 sequences; iterative refinement method incorporating local pairwise alignment information),
  • G-INS-i (suitable for sequences of similar lengths; recommended for <200 sequences; iterative refinement method incorporating global pairwise alignment information),
  • E-INS-i (suitable for sequences containing large unalignable regions; recommended for <200 sequences), and five speed-oriented methods:

  • FFT-NS-i (iterative refinement method; two cycles only),

  • FFT-NS-i (iterative refinement method; max. 1000 iterations),
  • FFT-NS-2 (fast; progressive method),
  • FFT-NS-1 (very fast; recommended for >2000 sequences; progressive method with a rough guide tree),
  • NW-NS-PartTree-1 (recommended for ∼50,000 sequences; progressive method with the PartTree algorithm).
Please cite: Kazutaka Katoh and Hiroyuki Toh: Recent developments in the MAFFT multiple sequence alignment program. (PubMed) Brief Bioinform 9(4):286-298 (2008)
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malt
sequence alignment and analysis tool to process sequencing data
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MALT, an acronym for MEGAN alignment tool, is a sequence alignment and analysis tool designed for processing high-throughput sequencing data, especially in the context of metagenomics. It is an extension of MEGAN6, the MEGenome Analyzer and is designed to provide the input for MEGAN6, but can also be used independently of MEGAN6.

The core of the program is a sequence alignment engine that aligns DNA or protein sequences to a DNA or protein reference database in either BLASTN (DNA queries and DNA references), BLASTX (DNA queries and protein references) or BLASTP (protein queries and protein references) mode. The engine uses a banded-alignment algorithm with ane gap scores and BLOSUM substitution matrices (in the case of protein alignments). The program can compute both local alignments (Smith-Waterman) or semi-global alignments (in which reads are aligned end-to-end into reference sequences), the latter being more appropriate for aligning metagenomic reads to references.

The package is enhanced by the following packages: multiqc
Please cite: Alexander Herbig, Frank Maixner, Kirsten I. Bos, Albert Zink, Johannes Krause and Daniel H. Huson: MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. (eprint) bioRxiv (2016)
mapdamage
tracking and quantifying damage patterns in ancient DNA sequences
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MapDamage is a computational framework written in Python and R, which tracks and quantifies DNA damage patterns among ancient DNA sequencing reads generated by Next-Generation Sequencing platforms.

MapDamage is developed at the Centre for GeoGenetics by the Orlando Group.

Please cite: Hákon Jónsson, Aurélien Ginolhac, Mikkel Schubert and Philip Johnson and Ludovic Orlando: mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. (PubMed,eprint) Bioinformatics 29(13):1682-4 (2013)
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mapsembler2
bioinformatics targeted assembly software
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Mapsembler2 is a targeted assembly software. It takes as input a set of NGS raw reads (fasta or fastq, gzipped or not) and a set of input sequences (starters).

It first determines if each starter is read-coherent, e.g. whether reads confirm the presence of each starter in the original sequence. Then for each read-coherent starter, Mapsembler2 outputs its sequence neighborhood as a linear sequence or as a graph, depending on the user choice.

Mapsembler2 may be used for (not limited to):

  • Validate an assembled sequence (input as starter), e.g. from a de Bruijn graph assembly where read-coherence was not enforced.
  • Checks if a gene (input as starter) has an homolog in a set of reads
  • Checks if a known enzyme is present in a metagenomic NGS read set.
  • Enrich unmappable reads by extending them, possibly making them mappable
  • Checks what happens at the extremities of a contig
  • Remove contaminants or symbiont reads from a read set
Please cite: Pierre Peterlongo and Rayan Chikhi: Mapsembler, targeted and micro assembly of large NGS datasets on a desktop computer. (PubMed) BMC Bioinformatics 13:48 (2012)
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maq
maps short fixed-length polymorphic DNA sequence reads to reference sequences
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Maq (short for Mapping and Assembly with Quality) builds mapping assemblies from short reads generated by the next-generation sequencing machines. It was particularly designed for Illumina-Solexa 1G Genetic Analyzer, and has a preliminary functionality to handle ABI SOLiD data. Maq is previously known as mapass2.

Developmemt of Maq stopped in 2008. Its successors are BWA and SAMtools.

Please cite: Heng Li, Jue Ruan and Richard Durbin: Mapping short DNA sequencing reads and calling variants using mapping quality scores. (PubMed,eprint) Genome Research 18(11):1851-1858 (2008)
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maqview
graphical read alignment viewer for short gene sequences
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Maqview is graphical read alignment viewer. It is specifically designed for the Maq alignment file and allows you to see the mismatches, base qualities and mapping qualities. Maqview is nothing fancy as Consed or GAP, but just a simple viewer for you to see what happens in a particular region.

In comparison to tgap-maq, the text-based read alignment viewer written by James Bonfield, Maqview is faster and takes up much less memory and disk space in indexing. This is possibly because tgap aims to be a general-purpose viewer but Maqview fully makes use of the fact that a Maq alignment file has already been sorted. Maqview is also efficient in viewing and provides a command-line tool to quickly retrieve any region in an Maq alignment file.

Please cite: Heng Li, Jue Ruan and Richard Durbin: Mapping short DNA sequencing reads and calling variants using mapping quality scores. (PubMed,eprint) Genome Research 18(11):1851-1858 (2008)
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mash
fast genome and metagenome distance estimation using MinHash
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Mash uses MinHash locality-sensitive hashing to reduce large biosequences to a representative sketch and rapidly estimate pairwise distances between genomes or metagenomes. Mash sketch databases effectively delineate known species boundaries, allow construction of approximate phylogenies, and can be searched in seconds using assembled genomes or raw sequencing runs from Illumina, Pacific Biosciences, and Oxford Nanopore. For metagenomics, Mash scales to thousands of samples and can replicate Human Microbiome Project and Global Ocean Survey results in a fraction of the time.

Please cite: Brian D. Ondovi, Todd J. Treangen, Páll Melsted, Adam B. Mallonee, Nicholas H. Bergman, Sergey Koren and Adam M. Phillippy: Mash: fast genome and metagenome distance estimation using MinHash. (PubMed,eprint) Genome Biology 17:132 (2016)
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massxpert
transitional package for massxpert -> massxpert2
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This is a transitional package. It can safely be removed. Runtime package.

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mauve-aligner
multiple genome alignment
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Mauve is a system for efficiently constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignment provides a basis for research into comparative genomics and the study of evolutionary dynamics. Aligning whole genomes is a fundamentally different problem than aligning short sequences.

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the amount of sequence being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours.

Mauve computes and interactively visualizes genome sequence comparisons. Using FastA or GenBank sequence data, Mauve constructs multiple genome alignments that identify large-scale rearrangement, gene gain, gene loss, indels, and nucleotide substutition.

Mauve is developed at the University of Wisconsin.

The package is enhanced by the following packages: progressivemauve
Please cite: Aaron C. E. Darling, Bob Mau, Frederick R. Blattner and Nicole T. Perna: Mauve: multiple alignment of conserved genomic sequence with rearrangements. (PubMed,eprint) Genome research 14(7):1394-1403 (2004)
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mcaller
find methylation in nanopore reads
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   H
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 H-C-aller
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This program is designed to call m6A from nanopore data using the differences between measured and expected currents.

mecat2
ultra-fast and accurate de novo assembly tools for SMRT reads
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An improved version of MECAT. It is an ultra-fast and accurate mapping and error correcting de novo assembly tools for single molecula sequencing (SMRT) reads. MECAT2 consists of the following three modules:

 1. mecat2map: a fast and accurate alignment tool for SMRT reads.
 2. mecat2cns: correct noisy reads based on their pairwise overlaps.
 3. fsa: a string graph based assembly tool.
Please cite: Chuan-Le Xiao, Ying Chen, Shang-Qian Xie, Kai-Ning Chen, Yan Wang, Yue Han, Feng Luo and Zhi Xie: MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nature Methods 14(11):1078 (2017)
megadepth
computes coverage from BigWig and BAM sequencing files
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A major concern for the interpretation of DNA and RNA (!) sequencing is the number of reads that cover a particular area. This package has interesting statistics for the distinction of coding and non-coding parts of the genome and knows how to interpret transcripts that span multiple exons.

This package is a successor of the program 'bamcount'.

Please cite: Christopher Wilks, Omar Ahmed, Daniel N Baker, David Zhang, Leonardo Collado-Torres and Ben Langmead: Megadepth: efficient coverage quantification for BigWigs and BAMs. (PubMed) Bioinformatics 37(18):3014–3016 (2021)
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megahit
ultra-fast and memory-efficient meta-genome assembler
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Megahit is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

The software was praised in a Briefings in Bioinformatics 5/2020 review (DOI: 10.1093/bib/bbaa085).

Please cite: Dinghua Li, Chi-Man Liu, Ruibang Luo, Kunihiko Sadakane and Tak-Wah Lam: MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. (PubMed) 31:1674-1676 (2015)
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megan-ce
interactive tool to explore and analyse microbiome sequencing data
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MEGAN Community Edition is a shotgun sequencer to analyze microbiome samples. It is a rewrite and extension of the widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. By integrating MEGAN CE with the high-throughput DNA-to-protein alignment tool DIAMOND a powerful and complete pipeline for the analysis of metagenome shotgun sequences can be provided.

Please cite: Daniel H. Huson, Sina Beier, Isabell Flade, Anna Górska, Mohamed El-Hadidi, Suparna Mitra, Hans-Joachim Ruscheweyh and Rewati Tappu: MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. (PubMed,eprint) PLoS Comput Biol. 12(6):e1004957 (2016)
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melting
compute the melting temperature of nucleic acid duplex
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This program computes, for a nucleic acid duplex, the enthalpy, the entropy and the melting temperature of the helix-coil transitions. Three types of hybridisation are possible: DNA/DNA, DNA/RNA, and RNA/RNA. The program first computes the hybridisation enthalpy and entropy from the elementary parameters of each Crick's pair by the nearest-neighbor method. Then the melting temperature is computed. The set of thermodynamic parameters can be easily changed, for instance following an experimental breakthrough.

Please cite: Le Novère, Nicolas: MELTING, computing the melting temperature of nucleic acid duplex. (PubMed,eprint) Bioinformatics 17(12):1226-1227 (2001)
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meryl
in- and out-of-core kmer counting and utilities
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meryl computes the kmer content of genomic sequences. Kmer content is represented as a list of kmers and the number of times each occurs in the input sequences. The kmer can be restricted to only the forward kmer, only the reverse kmer, or the canonical kmer (lexicographically smaller of the forward and reverse kmer at each location). Meryl can report the histogram of counts, the list of kmers and their counts, or can perform mathematical and set operations on the processed data files.

This package is part of the Kmer suite.

Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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metabat
robust statistical framework for reconstructing genomes from metagenomic data
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MetaBAT integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node.

Please cite: Dongwan D. Kang, Jeff Froula, Rob Egan and Zhong Wang: MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. (PubMed) PeerJ 3:e1165 (2015)
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metaeuk
sensitive, high-throughput gene discovery and annotation for metagenomics
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MetaEuk is a modular toolkit designed for large-scale gene discovery and annotation in eukaryotic metagenomic contigs. MetaEuk combines the fast and sensitive homology search capabilities of MMseqs2 with a dynamic programming procedure to recover optimal exons sets. It reduces redundancies in multiple discoveries of the same gene and resolves conflicting gene predictions on the same strand.

metaphlan
Metagenomic Phylogenetic Analysis
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MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. With the newly added StrainPhlAn module, it is now possible to perform accurate strain-level microbial profiling.

MetaPhlAn relies on ~1.1M unique clade-specific marker genes (the latest marker information file mpa_v31_CHOCOPhlAn_201901_marker_info.txt.bz2 can be found here) identified from ~100,000 reference genomes (~99,500 bacterial and archaeal and ~500 eukaryotic), allowing:

  • unambiguous taxonomic assignments;
  • accurate estimation of organismal relative abundance;
  • species-level resolution for bacteria, archaea, eukaryotes and viruses;
  • strain identification and tracking
  • orders of magnitude speedups compared to existing methods.
  • metagenomic strain-level population genomics
Please cite: Duy Tin Truong, Eric A Franzosa, Timothy L Tickle, Matthias Scholz, George Weingart, Edoardo Pasolli, Adrian Tett, Curtis Huttenhower and Nicola Segata: MetaPhlAn2 for enhanced metagenomic taxonomic profiling. (PubMed) Nature Methods 12(10):902–903 (2015)
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metastudent
predictor of Gene Ontology terms from protein sequence
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Often, only the sequence of a protein is known, but not its functions. Metastudent will try to predict missing functional annotations through homology searches (BLAST).

All predicted functions correspond to Gene Ontology (GO) terms from the Molecular Function (MFO), the Biological Process (BPO) and the Cellular Component Ontology (CCO) and are associated with a reliability score.

Please cite: Tobias Hamp, Rebecca Kassner, Stefan Seemayer, Esmeralda Vicedo, Christian Schaefer, Dominik Achten, Florian Auer, Ariane Boehm, Tatjana Braun, Maximilian Hecht, Mark Heron, Peter Hönigschmid, Thomas A. Hopf, Stefanie Kaufmann, Michael Kiening, Denis Krompass, Cedric Landerer, Yannick Mahlich, Manfred Roos and Burkhard Rost: Homology-based inference sets the bar high for protein function prediction.. (PubMed) BMC Bioinformatics 14(Suppl 3):S7 (2013)
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mhap
locality-sensitive hashing to detect long-read overlaps
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The MinHash Alignment Process (MHAP--pronounced MAP) is a reference implementation of a probabilistic sequence overlapping algorithm. Designed to efficiently detect all overlaps between noisy long-read sequence data. It efficiently estimates Jaccard similarity by compressing sequences to their representative fingerprints composed on min-mers (minimum k-mer).

Please cite: Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James P Drake, Jane M Landolin and Adam M Phillippy: Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. (PubMed) Nature Biotechnology 33(6):623–630 (2015)
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microbegps
explorative taxonomic profiling tool for metagenomic data
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MicrobeGPS is a bioinformatics tool for the analysis of metagenomic sequencing data. The goal is to profile the composition of metagenomic communities as accurately as possible and present the results to the user in a convenient manner. One main focus is reliability: the tool calculates quality metrics for the estimated candidates and allows the user to identify false candidates easily.

Please cite: Martin S. Lindner and Bernhard Y. Renard: Metagenomic Profiling of Known and Unknown Microbes with MicrobeGPS. (PubMed,eprint) PLoS One 10(2):e0117711 (2015)
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microbiomeutil
Microbiome Analysis Utilities
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The microbiomeutil package comes with the following utilities:

  • ChimeraSlayer: ChimeraSlayer for chimera detection.
  • NAST-iEr: NAST-based alignment tool.
  • WigeoN: A reimplementation of the Pintail 16S anomaly detection utility
  • RESOURCES: Reference 16S sequences and NAST-alignments that the tools above leverage.
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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mindthegap
performs detection and assembly of DNA insertion variants in NGS read datasets
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Designed to call insertions of any size, whether they are novel or duplicated, homozygous or heterozygous in the donor genome. It takes as input a set of reads and a reference genome. It outputs two sets of FASTA sequences: one is the set of breakpoints of detection insertion sites, the other is the set of assembled insertions for each breakpoint. MindTheGap can also be used as a genome assembly finishing tool. It can fill the gaps between a set of input contigs without any a priori on their relative order and orientation. It outputs the results in gfa file.

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minexpert2
MS^n mass spectrometric data visualization and mining (runtime)
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mineXpert2 allows the user to perform the following tasks:

  • Open mass spectrometry data files (mzML, mzXML, asc, xy, ...);
  • Display in a table view the full data set, for easy filtering;
  • Calculate and display the TIC chromatogram;
  • Calculate and display a mz=f(rt) color map;
  • For mobility data, calculate and display a mz=f(dt) color map;
  • Integrate the mass spectrometric data from any kind of data representation (mass | drift spectra, TIC | XIC chromatogram, 2D color maps) to any other kind of data representation;
  • For any mass data feature (mass peak, TIC | XIC peak, color map) integrate to a XIC single intensity value;
  • Powerful isotopic cluster calculation starting from a chemical formula, optionally with user-defined isotopic abundance ratios;
  • Gaussian fit over any isotopic cluster to estimate the average mass of a given ion;
  • Mouse-driven deconvolution of m/z data (charge envelope-based or isotopic cluster-based);
  • Export the data to text files;
  • Export the graphical representation of mass spectrometric data to graphics files in a number of formats (jpg, png, pdf).

This package ships the mineXpert2 program.

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minia
short-read biological sequence assembler
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What was referred to as "next-generation" DNA sequencing up to the year 2020 delivered only "short" reads up to ~600 base pairs in length that would then have to be puzzled by random overlaps in their sequence towards a complete genome. This is the genome assembly. And there are many biological pitfalls on long stretches of low complexity regions and copy number variations and other sorts of redundancies that render this difficult.

This package provides a short-read DNA sequence assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day.

The output of Minia is a set of contigs, i.e. stretches of gap-free linear overlaps of short reads. In the best possible case this is a whole chromosome.

Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).

Please cite: Rayan Chikhi and Guillaume Rizk: Space-Efficient and Exact de Bruijn Graph Representation Based on a Bloom Filter.. (PubMed,eprint) Algorithms for Molecular Biology 8(1):22 (2013)
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Topics: Sequence assembly
miniasm
ultrafast de novo assembler for long noisy DNA sequencing reads
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Miniasm is an experimental very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.

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Topics: Sequence assembly
minimac4
Fast Imputation Based on State Space Reduction HMM
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Minimac4 is a lower memory and more computationally efficient implementation of "minimac2/3". It is an algorithm for genotypic imputation that works on phased genotypes (say from MaCH).

Minimac4 is designed to handle very large reference panels in a more computationally efficient way with no loss of accuracy. This algorithm analyzes only the unique sets of haplotypes in small genomic segments, thereby saving on time-complexity, computational memory but no loss in degree of accuracy.

Please cite: Sayantan Das, Lukas Forer, Sebastian Schönherr, Carlo Sidore, Adam E Locke, Alan Kwong, Scott I Vrieze, Emily Y Chew, Shawn Levy, Matt McGue, David Schlessinger, Dwight Stambolian, Po-Ru Loh, William G Iacono, Anand Swaroop, Laura J Scott, Francesco Cucca, Florian Kronenberg, Michael Boehnke, Gonçalo R Abecasis and Christian Fuchsberger: Next-generation genotype imputation service and methods. Nature Genetics 48(10):1284-1287 (2016)
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minimap
tool for approximate mapping of long biosequences such as DNA reads
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Minimap is an experimental tool to efficiently find multiple approximate mapping positions between two sets of long biological sequences, such as between DNA reads and reference genomes, between genomes and between long noisy reads. Minimap does not generate alignments as of now and because of this, it is usually tens of times faster than mainstream aligners. It does not replace mainstream aligners, but it can be useful when you want to quickly identify long approximate matches at moderate divergence among a huge collection of sequences. For this task, it is much faster than most existing tools.

Please cite: Heng Li: Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. (eprint) Bioinformatics :2103-2110 (2016)
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Topics: Mapping
minimap2
versatile pairwise aligner for genomic and spliced nucleotide sequences
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Minimap2 is a versatile sequence alignment program that aligns DNA or mRNA sequences against a large reference database. Typical use cases include: (1) mapping PacBio or Oxford Nanopore genomic reads to the human genome; (2) finding overlaps between long reads with error rate up to ~15%; (3) splice-aware alignment of PacBio Iso-Seq or Nanopore cDNA or Direct RNA reads against a reference genome; (4) aligning Illumina single- or paired-end reads; (5) assembly-to-assembly alignment; (6) full- genome alignment between two closely related species with divergence below ~15%.

For ~10kb noisy reads sequences, minimap2 is tens of times faster than mainstream long-read mappers such as BLASR, BWA-MEM, NGMLR and GMAP. It is more accurate on simulated long reads and produces biologically meaningful alignment ready for downstream analyses. For >100bp Illumina short reads, minimap2 is three times as fast as BWA-MEM and Bowtie2, and as accurate on simulated data. Detailed evaluations are available from the minimap2 paper or the preprint.

Please cite: Heng Li: Minimap2: pairwise alignment for nucleotide sequences. (PubMed,eprint) Bioinformatics :2103-2110 (2018)
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mipe
Tools to store PCR-derived data
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MIPE provides a standard format to exchange and/or storage of all information associated with PCR experiments using a flat text file. This will:

  • allow for exchange of PCR data between researchers/laboratories
  • enable traceability of the data
  • prevent problems when submitting data to dbSTS or dbSNP
  • enable the writing of standard scripts to extract data (e.g. a list of PCR primers, SNP positions or haplotypes for different animals)

Although this tool can be used for data storage, it's primary focus should be data exchange. For larger repositories, relational databases are more appropriate for storage of these data. The MIPE format could then be used as a standard format to import into and/or export from these databases.

Please cite: Jan Aerts and T. Veenendaal: MIPE - a XML-format to facilitate the storage and exchange of PCR-related data. Online Journal of Bioinformatics 6(2):114-120 (2005)
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mira-assembler
Whole Genome Shotgun and EST Sequence Assembler
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The mira genome fragment assembler is a specialised assembler for sequencing projects classified as 'hard' due to high number of similar repeats. For expressed sequence tags (ESTs) transcripts, miraEST is specialised on reconstructing pristine mRNA transcripts while detecting and classifying single nucleotide polymorphisms (SNP) occurring in different variations thereof.

The assembler is routinely used for such various tasks as mutation detection in different cell types, similarity analysis of transcripts between organisms, and pristine assembly of sequences from various sources for oligo design in clinical microarray experiments.

The package provides the following executables: Binaries provided:

  • mira: for assembly of genome sequences
  • miramem: estimating memory needed to assemble projects.
  • mirabait: a "grep" like tool to select reads with kmers up to 256 bases.
  • miraconvert: is a tool to convert, extract and sometimes recalculate all kinds of data related to sequence assembly files.
Please cite: Bastien Chevreux, Thomas Pfisterer, Bernd Drescher, Albert J. Driesel, Werner E. G. Müller, Thomas Wetter and Sándor Suhai: Using the miraEST Assembler for Reliable and Automated mRNA Transcript Assembly and SNP Detection in Sequenced ESTs. (PubMed,eprint) Genome Research 14(6):1147-1159 (2004)
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mirtop
annotate miRNAs with a standard mirna/isomir naming
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The main goal of this project is to create a reflection group on metazoan microRNAs (miRNAs), open to all interested researchers, to identify blockages and develop standards and guidelines to improve miRNA research, resources and communication. This can go through the use of standardized file formats, gene and variants nomenclature guidelines, and advancements in miRNA biology understanding. The group will eventually also aim at expanding its breadth to the development of novel tools, data resources, and best-practices guidelines to benefit the scientific community by providing high confidence validated research and analysis strategies, regardless the expertise in this field. This package provides the command line interface to mirtop.

The package is enhanced by the following packages: multiqc
Please cite: Thomas Desvignes, Karen Eilbeck, Ioannis S. Vlachos, Bastian Fromm, Yin Lu, Marc K. Halushka, Michael Hackenberg, Gianvito Urgese, Elisa Ficarra, Shruthi Bandyadka, Jason Sydes, Peter Batzel, John H. Postlethwait, Phillipe Loher, Eric Londin, Aristeidis G. Telonis, Isidore Rigoutsos and Lorena Pantano Rubino: miRTOP: An open source community project for the development of a unified format file for miRNA data [version 1; not peer reviewed]. (eprint) F1000Research 7(ISCB Comm. J.):953 (Slides) (2018)
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mlv-smile
Find statistically significant patterns in sequences
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Smile determines sequence motifs on the basis of a set of DNA, RNA or protein sequences.

  • No hard limit on the number of combinations of motifs to describe subsets of sequences.
  • The sequence alphabet may be specified.
  • The use of wildcards is supported.
  • Better determination of significance of motifs by simulation.
  • Introduction of a set of sequences with negative controls that should not match automatically determined motifs.
mmb
model the structure and dynamics of macromolecules
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MacroMoleculeBuilder, previously known as RNABuilder, can be used for morphing, homology modeling, folding (e.g. using base pairing contacts), redesigning complexes, fitting to low-resolution density maps, predicting local rearrangements upon mutation, and many other applications.

mmseqs2
ultra fast and sensitive protein search and clustering
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MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge proteins/nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Please cite: Martin Steinegger and Johannes Söding: Clustering huge protein sequence sets in linear time. Nature Communications 9(1) (2018)
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mosdepth
BAM/CRAM depth calculation biological sequencing
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Many small reads are produced by high-throughput "next generation" sequencing technologies. The final sequence is derived from how these reads are overlapping towards a consensus. The more reads are covering/confirming parts of a nucleotide seq, the higher the confidence is. Too many reads would be indicative of e.g. repeats in the genome.

mosdepth can output:

  • per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.
  • mean per-window depth given a window size--as would be used for CNV calling.
  • the mean per-region given a BED file of regions.
  • a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.
  • quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)
  • threshold output to indicate how many bases in each region are covered at the given thresholds. when appropriate, the output files are bgzipped and indexed for ease of use.
The package is enhanced by the following packages: multiqc
Please cite: Brent S Pedersen and Aaron R. Quinlan: Mosdepth: quick coverage calculation for genomes and exomes. (PubMed,eprint) Bioinformatics 34(5):867-868 (2018)
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mothur
sequence analysis suite for research on microbiota
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Mothur seeks to develop a single piece of open-source, expandable software to fill the bioinformatics needs of the microbial ecology community. It has incorporated the functionality of dotur, sons, treeclimber, s-libshuff, unifrac, and much more. In addition to improving the flexibility of these algorithms, a number of other features including calculators and visualization tools were added.

Please cite: Patrick D Schloss, Sarah L Westcott, Thomas Ryabin, Justine R Hall, Martin Hartmann, Emily B Hollister, Ryan A Lesniewski, Brian B Oakley, Donovan H Parks, Courtney J Robinson, Jason W Sahl, Blaz Stres, Gerhard G Thallinger, David J Van Horn and Carolyn F Weber: Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. (PubMed) Appl Environ Microbiol 75(23):7537-7541 (2009)
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Topics: Microbial ecology
mptp
single-locus species delimitation
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Implementation of a fast species delimitation method, based on PTP (Zhang et al. 2013) with a 64-bit multi-threaded design that handles very large datasets.

The tool mPTP can handle very large biodiversity datasets. It implements a fast method to compute the ML delimitation from an inferred phylogenetic tree of the samples. Using MCMC, it also computes the support values for each clade, which can be used to assess the confidence of the ML delimitation.

ML delimitation mPTP implements two flavours of the point-estimate solution. First, it implements the original method from (Zhang et al. 2013) where all within-species processes are modelled with a single exponential distribution. mPTP uses a dynamic programming implementation which estimates the ML delimitation faster and more accurately than the original PTP. The dynamic programming implementation has similar properties as (Gulek et al. 2010). See the wiki for more information. The second method assumes a distinct exponential distribution for the branching events of each of the delimited species allowing it to fit to a wider range of empirical datasets.

MCMC method mPTP generates support values for each clades. They represent the ratio of the number of samples for which a particular node was in the between-species process, to the total number of samples.

Please cite: Paschalia Kapli, Sarah Lutteropp, Jiajie Zhang, Kassian Kobert, Pavlos Pavlidis, Alexandros Stamatakis and Tomas Flouri: Multi-rate Poisson Tree Processes for single-locus species delimitation under Maximum Likelihood and Markov Chain Monte Carlo. (PubMed,eprint) bioRxiv (2016)
mrbayes
Bayesian Inference of Phylogeny
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Bayesian inference of phylogeny is based upon a quantity called the posterior probability distribution of trees, which is the probability of a tree conditioned on the observations. The conditioning is accomplished using Bayes's theorem. The posterior probability distribution of trees is impossible to calculate analytically; instead, MrBayes uses a simulation technique called Markov chain Monte Carlo (or MCMC) to approximate the posterior probabilities of trees.

The package is enhanced by the following packages: mrbayes-doc
Please cite: Fredrik Ronquist, Maxim Teslenko, Paul van der Mark, Daniel L. Ayres, Aaron Darling, Sebastian Höhna, Bret Larget, Liang Liu, Marc A. Suchard and John P. Huelsenbeck: MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space. (PubMed,eprint) Systematic Biology (2012)
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multiqc
output integration for RNA sequencing across tools and samples
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The sequencing of DNA or RNA with current high-throughput technologies involves an array of tools and these are applied over a range of samples. It is easy to loose oversight. And gathering the data and forwarding them in a readable manner to the individuals who took the samples is a challenge for a tool in itself. Well. Here it is. MultiQC aggregates the output of multiple tools into a single report.

Reports are generated by scanning given directories for recognised log files. These are parsed and a single HTML report is generated summarising the statistics for all logs found. MultiQC reports can describe multiple analysis steps and large numbers of samples within a single plot, and multiple analysis tools making it ideal for routine fast quality control.

Please cite: Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller: MultiQC: summarize analysis results for multiple tools and samples in a single report. (PubMed,eprint) Bioinformatics 31(19):3047-8 (2016)
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mummer
Efficient sequence alignment of full genomes
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MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form. For example, MUMmer 3.0 can find all 20-basepair or longer exact matches between a pair of 5-megabase genomes in 13.7 seconds, using 78 MB of memory, on a 2.4 GHz Linux desktop computer. MUMmer can also align incomplete genomes; it handles the 100s or 1000s of contigs from a shotgun sequencing project with ease, and will align them to another set of contigs or a genome using the NUCmer program included with the system. If the species are too divergent for DNA sequence alignment to detect similarity, then the PROmer program can generate alignments based upon the six-frame translations of both input sequences.

The package is enhanced by the following packages: e-mem
Please cite: Stefan Kurtz, Adam Phillippy, Arthur L. Delcher, Michael Smoot, Martin Shumway, Corina Antonescu and Steven L. Salzberg: Versatile and open software for comparing large genomes. (PubMed) Genome Biology 5(2):R12 (2004)
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Topics: Sequence analysis
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murasaki
homology detection tool across multiple large genomes
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Murasaki is a scalable and fast, language theory-based homology detection tool across multiple large genomes. It enable whole-genome scale multiple genome global alignments. Supports unlimited length gapped-seed patterns and unique TF-IDF based filtering.

Murasaki is an anchor alignment software, which is

  • exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 52 wall minutes))
  • scalable (Arbitrarily parallelizable across multiple nodes using MPI. Even a single node with 16GB of ram can handle over 1Gbp of sequence.)
  • unlimited pattern length
  • repeat tolerant
  • intelligent noise reduction
Please cite: Kris Popendorf, Hachiya Tsuyoshi, Yasunori Osana and Yasubumi Sakakibara: Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes. (PubMed,eprint) PLOS one 5(9):e12651 (2010)
murasaki-mpi
homology detection tool across multiple large genomes (MPI-version)
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Murasaki is a scalable and fast, language theory-based homology detection tool across multiple large genomes. It enable whole-genome scale multiple genome global alignments. Supports unlimited length gapped-seed patterns and unique TF-IDF based filtering.

Murasaki is an anchor alignment software, which is

  • exteremely fast (17 CPU hours for whole Human x Mouse genome (with 40 nodes: 52 wall minutes))
  • scalable (Arbitrarily parallelizable across multiple nodes using MPI. Even a single node with 16GB of ram can handle over 1Gbp of sequence.)
  • unlimited pattern length
  • repeat tolerant
  • intelligent noise reduction

This package provides the MPI-enabled binary for murasaki. While this will speed up operation on multi-processor machines it will slow down on a single processor.

Please cite: Kris Popendorf, Hachiya Tsuyoshi, Yasunori Osana and Yasubumi Sakakibara: Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes. (PubMed,eprint) PLOS one 5(9):e12651 (2010)
muscle
Multiple alignment program of protein sequences
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MUSCLE is a multiple alignment program for protein sequences. MUSCLE stands for multiple sequence comparison by log-expectation. In the authors tests, MUSCLE achieved the highest scores of all tested programs on several alignment accuracy benchmarks, and is also one of the fastest programs out there.

Muscle v5 is a major re-write of MUSCLE based on new algorithms.

Users should be aware that command line arguments compared to version 3.x of MUSCLE have changed!

Highest accuracy, scalable to thousands of sequences

Compared to previous versions, Muscle v5 is much more accurate, is often faster, and scales to much larger datasets. At the time of writing (late 2021), Muscle v5 has the highest scores on multiple alignment benchmarks including Balibase, Bralibase, Prefab and Balifam. It can align tens of thousands of sequences with high accuracy on a low-cost commodity computer (say, an 8-core Intel CPU with 32 Gb RAM). On large datasets, Muscle v5 is 20-30% more accurate than MAFFT and Clustal-Omega.

Alignment ensembles

Muscle v5 can generate ensembles of high-accuracy alternative alignments. All replicates have equal average accuracy on benchmark test, including the MSA made with default parameters. By comparing results of downstream analysis (trees, structure prediction...) on different replicates, you can assess the effects of alignment errors on your study.

Please cite: Robert C. Edgar: MUSCLE: multiple sequence alignment with high accuracy and high throughput. (PubMed,eprint) Nucleic Acids Research 32(5):1792-1797 (2004)
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Topics: Sequence analysis
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muscle3
multiple alignment program of protein sequences
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MUSCLE is a multiple alignment program for protein sequences. MUSCLE stands for multiple sequence comparison by log-expectation. In the authors tests, MUSCLE achieved the highest scores of all tested programs on several alignment accuracy benchmarks, and is also one of the fastest programs out there.

This is version 3 of the muscle program. It is a different program than muscle version 5 which is packaged as muscle in Debian.

Please cite: Robert C. Edgar: MUSCLE: multiple sequence alignment with high accuracy and high throughput. (PubMed,eprint) Nucleic Acids Research 32(5):1792-1797 (2004)
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Topics: Sequence analysis
mustang
multiple structural alignment of proteins
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Mustang is an algorithm to align multiple protein structures. Given a set of PDB files, the program uses the spatial information in the Calpha atoms of the set to produce a sequence alignment. Based on a progressive pairwise heuristic the algorithm then proceeds through a number of refinement passes. Mustang reports the multiple sequence alignment and the corresponding superposition of structures.

The package is enhanced by the following packages: mustang-testdata
Please cite: Arun S. Konagurthu, James C. Whisstock, Peter J. Stuckey and Arthur M. Lesk: MUSTANG: A multiple structural alignment algorithm. (PubMed) Proteins: Structure, Function, and Bioinformatics 64(3):559-574 (2006)
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nanofilt
filtering and trimming of long read sequencing data
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Filtering and trimming of long read sequencing data. Filtering on quality and/or read length, and optional trimming after passing filters. Reads from stdin, writes to stdout. Optionally reads directly from an uncompressed file specified on the command line.

Intended to be used:

 1. directly after fastq extraction.
 2. prior to mapping.
 3. in a stream between extraction and mapping.
Please cite: Wouter De Coster, Svenn D'Hert, Darrin T. Schultz and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34 (2018)
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nanolyse
remove lambda phage reads from a fastq file
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NanoLyse is a tool for rapid removal of contaminant DNA, using the Minimap2 aligner through the mappy Python binding. A typical application would be the removal of the lambda phage control DNA fragment supplied by ONT, for which the reference sequence is included in the package. However, this approach may lead to unwanted loss of reads from regions highly homologous to the lambda phage genome.

Please cite: Wouter De Coster, Svenn D’Hert, Darrin T Schultz, Marc Cruts and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. (PubMed,eprint) Bioinformatics 34(15):2666-2669 (2018)
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nanook
pre- and post-alignment analysis of nanopore sequencing data
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NanoOK is a flexible, multi-reference software for pre- and post- alignment analysis of nanopore sequencing data, quality and error profiles.

NanoOK (pronounced na-nook) is a tool for extraction, alignment and analysis of Nanopore reads. NanoOK will extract reads as FASTA or FASTQ files, align them (with a choice of alignment tools), then generate a comprehensive multi-page PDF report containing yield, accuracy and quality analysis. Along the way, it generates plain text files which can be used for further analysis, as well as graphs suitable for inclusion in presentations and papers.

The package is enhanced by the following packages: nanook-examples
Please cite: Richard M. Leggett, Darren Heavens, Mario Caccamo, Matthew D. Clark and Robert P. Davey: NanoOK: multi-reference alignment analysis of nanopore sequencing data, quality and error profiles. (PubMed,eprint) Bioinformatics 32(1):142-144 (2016)
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nanopolish
consensus caller for nanopore sequencing data
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Nanopolish uses a signal-level hidden Markov model for consensus calling of nanopore genome sequencing data. It can perform signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more.

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nanostat
statistics on long biological sequences
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NanoStat calculates various statistics from a long read sequencing dataset in fastq, bam or albacore sequencing summary format. It is meant to augment pipelines for the interpretation of long DNA sequences as generated with the Nanopore.

This package provides the executable NanoStat.

nanosv
structural variant caller for nanopore data
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NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers. NanoSV has been extensively tested using Oxford Nanopore MinION sequencing data.

Please cite: Mircea Cretu Stancu, Markus J. van Roosmalen, Ivo Renkens, Marleen M. Nieboer, Sjors Middelkamp, Joep de Ligt, Giulia Pregno, Daniela Giachino, Giorgia Mandrile, Jose Espejo Valle-Inclan, Jerome Korzelius, Ewart de Bruijn, Edwin Cuppen, Michael E. Talkowski, Tobias Marschall, Jeroen de Ridder and Wigard P. Kloosterman: Mapping and phasing of structural variation in patient genomes using nanopore sequencing.. (eprint) Nature Communications 8:1326 (2017)
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nast-ier
NAST-based DNA alignment tool
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The NAST-iEr alignment utility aligns a single raw nucleotide sequence against one or more NAST formatted sequences.

The alignment algorithm involves global dynamic programming profile alignment to fixed (NAST-formatted) multiply aligned template sequences without any end-gap penalty.

NAST-iEr is part of the microbiomeutil suite.

The package is enhanced by the following packages: microbiomeutil-data
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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ncbi-acc-download
download genome files from NCBI by accession
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This package provides a script to download sequences from GenBank/RefSeq by accession through the NCBI ENTREZ API.

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ncbi-blast+
next generation suite of BLAST sequence search tools
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The Basic Local Alignment Search Tool (BLAST) is the most widely used sequence similarity tool. There are versions of BLAST that compare protein queries to protein databases, nucleotide queries to nucleotide databases, as well as versions that translate nucleotide queries or databases in all six frames and compare to protein databases or queries. PSI-BLAST produces a position-specific-scoring-matrix (PSSM) starting with a protein query, and then uses that PSSM to perform further searches. It is also possible to compare a protein or nucleotide query to a database of PSSM’s. The NCBI supports a BLAST web page at blast.ncbi.nlm.nih.gov as well as a network service.

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ncbi-blast+-legacy
NCBI Blast legacy call script
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This package adds some fake scripts to call NCBI+ programs with the NCBI blast command line. It makes use of the legacy_blast script in ncbi-blast+ package.

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ncbi-entrez-direct
NCBI Entrez utilities on the command line
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Entrez Direct (EDirect) is an advanced method for accessing NCBI's set of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a terminal window or script. Functions take search terms from command-line arguments. Individual operations are combined to build multi-step queries. Record retrieval and formatting normally complete the process.

EDirect also provides an argument-driven function that simplifies the extraction of data from document summaries or other results that are returned in structured XML format. This can eliminate the need for writing custom software to answer ad hoc questions. Queries can move seamlessly between EDirect commands and UNIX utilities or scripts to perform actions that cannot be accomplished entirely within Entrez.

ncbi-epcr
Tool to test a DNA sequence for the presence of sequence tagged sites
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Electronic PCR (e-PCR) is computational procedure that is used to identify sequence tagged sites(STSs), within DNA sequences. e-PCR looks for potential STSs in DNA sequences by searching for subsequences that closely match the PCR primers and have the correct order, orientation, and spacing that could represent the PCR primers used to generate known STSs.

The new version of e-PCR implements a fuzzy matching strategy. To reduce likelihood that a true STS will be missed due to mismatches, multiple discontiguous words may be used instead of a single exact word. Each of this word has groups of significant positions separated by 'wildcard' positions that are not required to match. In addition, it is also possible to allow gaps in the primer alignments.

The main motivation for implementing reverse searching (called Reverse e-PCR) was to make it feasible to search the human genome sequence and other large genomes. The new version of e-PCR provides a search mode using a query sequence against a sequence database.

This program is retired upstream and it is suggested to use Primer-Blast

 https://www.ncbi.nlm.nih.gov/tools/primer-blast/
instead.
Please cite: Gregory D. Schuler: Sequence Mapping by Electronic PCR. (PubMed,eprint) Genome Research 7(5):541-550 (1997)
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ncbi-seg
tool to mask segments of low compositional complexity in amino acid sequences
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ncbi-seg (a.k.a. SEG) is a program for identifying and masking segments of low compositional complexity in amino acid sequences.

ncbi-seg divides sequences into contrasting segments of low-complexity and high-complexity. Low-complexity segments defined by the algorithm represent "simple sequences" or "compositionally-biased regions".

This program is inappropriate for masking nucleotide sequences and, in fact, may strip some nucleotide ambiguity codes from nt. sequences as they are being read.

Please cite: John C. Wootton and Scott Federhen: Statistics of local complexity in amino acid sequences and sequence databases.. Computers & Chemistry 17:149-163 (1993)
ncbi-tools-bin
NCBI libraries for biology applications (text-based utilities)
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This package includes various utilities distributed with the NCBI C SDK, including the development tools asntool and errhdr (formerly of libncbi6-dev). None of the programs in this package require X; you can find the X-based utilities in the ncbi-tools-x11 package. BLAST and related tools now come from a separate source base, corresponding to the ncbi-blast+ and ncbi-blast+-legacy packages.

The package is enhanced by the following packages: mcl
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ncbi-tools-x11
NCBI libraries for biology applications (X-based utilities)
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This package includes some X-based utilities distributed with the NCBI C SDK: Network Entrez, Sequin, ddv, and udv. These programs are not part of ncbi-tools-bin because they depend on several additional library packages.

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ncl-tools
tools to deal with NEXUS files
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The NEXUS Class Library is a C++ library for parsing NEXUS files.

The NEXUS file format is widely used in bioinformatics. Several popular phylogenetic programs such as Paup, MrBayes, Mesquite, and MacClade use this format.

Please cite: Paul O. Lewis: NCL: a C++ class library for interpreting data files in NEXUS format. (PubMed,eprint) Bioinformatics 19(17):2330-2331 (2003)
ncoils
coiled coil secondary structure prediction
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The program predicts the coiled coil secondary structure predictions from protein sequences. The algorithm was published in Lupas, van Dyke & Stock, Predicting coiled coils from protein sequences Science, 252, 1162-1164, 1991.

Please cite: Andrei Lupas, Marc Van Dyke and Jeff Stock: Predicting coiled coils from protein sequences. (PubMed) Science 252:1162-1164 (1991)
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neobio
computes alignments of amino acid and nucleotide sequences
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Library and graphical user interface for pairwise sequence alignments. Implementation of the dynamic programming methods of Needleman & Wunsch (global alignment) and Smith & Waterman (local alignment).

Please cite: Maxime Crochemore, Gad M. Landau and Michal Ziv-Ukelson: A sub-quadratic sequence alignment algorithm for unrestricted cost matrices. :679-688 (2002)
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ngmlr
CoNvex Gap-cost alignMents for Long Reads
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Ngmlr is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith- Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments.

Please cite: Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler and Michael C. Schatz: Accurate detection of complex structural variations using single-molecule sequencing. Nature Methods 15:461–468 (2018)
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njplot
phylogenetic tree drawing program
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NJplot is able to draw any dendrogram expressed in the Newick standard phylogenetic tree format (e.g., the format used by the Phylip package). NJplot is especially convenient for rooting the unrooted trees obtained from parsimony, distance or maximum likelihood tree-building methods.

Please cite: G. Perrière and M. Gouy: WWW-query: An on-line retrieval system for biological sequence banks. (PubMed) Biochimie 78(5):364–369 (1996)
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norsnet
tool to identify unstructured loops in proteins
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NORSnet can distinguish between very long contiguous segments with non-regular secondary structure (NORS regions) and well-folded proteins.

NORSnet was trained on predicted information rather than on experimental data. This allows NORSnet to reach into regions in sequence space that are not covered by specialized disorder predictors. One disadvantage of this approach is that it is not optimal for the identification of the "average" disordered region.

NORSnet takes the following input, further described on norsnet(1):

  • a protein sequence in a FASTA file
  • secondary structure and solvent accessibility prediction by prof(1)
  • an HSSP file
  • flexible/rigid residues prediction by profbval(1)
Please cite: Avner Schlessinger, Jinfeng Liu and Burkhard Rost: Natively unstructured loops differ from other loops.. (PubMed,eprint) PLoS Comput Biol. 3:e140 (2007)
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norsp
predictor of non-regular secondary structure
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NORSp is a publicly available predictor for disordered regions in proteins. Specifically, it predicts long regions with no regular secondary structure. Upon submission of a protein sequence, NORSp analyses the protein about its secondary structure, the presence of transmembrane helices and coiled-coils. It then returns the presence and position of disordered regions.

NORSp can be useful for biologists in several ways. For example, crystallographers can check whether their proteins contain NORS regions and make the decision about whether to proceed with the experiments since NORS proteins may be difficult to crystallise, as demonstrated by the their low occurrence in PDB. Biologists interested in protein structure-function relationship may also find it interesting to verify whether the protein-protein interaction sites coincide with NORS regions.

Please cite: Jinfeng Liu and Burkhard Rost: NORSp: Predictions of long regions without regular secondary structure.. (PubMed,eprint) Nucleic Acids Res 31(13):3833-3835 (2003)
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ntcard
Streaming algorithm to estimate cardinality in genomics datasets
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As input it takes file(s) in fasta, fastq, sam, or bam formats and computes the total number of distinct k-mers, F0, and also the k-mer coverage frequency histogram, fi, i>=1.

Please cite: Hamid Mohamadi, Hamza Khan and Inanc Birol: ntCard: a streaming algorithm for cardinality estimation in genomics data. (PubMed,eprint) Bioinformatics 33(9):1324-1330 (2017)
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nxtrim
Optimized trimming of Illumina mate pair reads
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This package helps rmove Nextera Mate Pair junction adapters and categorise reads according to the orientation implied by the adapter location.

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obitools
programs to analyze NGS data in a DNA metabarcoding context
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The OBITools programs aims to help you to manipulate various data and sequence files in a convenient way using the Unix command line interface. They follow the standard Unix interface for command line program, allowing to chain a set of commands using the pipe mechanism.

Please cite: Frédéric Boyer, Céline Mercier, Aurélie Bonin, Yvan Le Bras, Pierre Taberlet and Eric Coissac: obitools: a unix-inspired software package for DNA metabarcoding.. (PubMed,eprint) Mol. Ecol. Resour. 16(1):176-182 (2016)
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openms
package for LC/MS data management and analysis
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OpenMS is a package for LC/MS data management and analysis. OpenMS offers an infrastructure for the development of mass spectrometry-related software and powerful 2D and 3D visualization solutions.

TOPP (the OpenMS proteomic pipeline) is a pipeline for the analysis of HPLC/MS data. It consists of a set of numerous small applications that can be chained together to create analysis pipelines tailored for a specific problem.

This package is a metapackage that depends on both the libopenms library package (libOpenMS and libOpenMS_GUI) and the OpenMS Proteomic Pipeline (topp) package.

Please cite: Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert and Oliver Kohlbacher: OpenMS – an Open-Source Software Framework for Mass Spectrometry. (PubMed,eprint) BMC Bioinformatics 9(163) (2008)
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optimir
Integrating genetic variations in miRNA alignment
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OptimiR is a miRSeq data alignment workflow. It integrates genetic information to assess the impact of variants on miRNA expression.

OptimiR: A bioinformatics pipeline designed to detect and quantify miRNAs, isomiRs and polymiRs from miRSeq data, & study the impact of genetic variations on polymiRs' expression.

Please cite: Florian Thibord, Claire Perret, Maguelonne Roux, Pierre Suchon, Marine Germain, Jean-François Deleuze, Pierre-Emmanuel Morange and David-Alexandre Trégouët: OPTIMIR, a novel algorithm for integrating available genome-wide genotype data into miRNA sequence alignment analysis. RNA (2019)
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pal2nal
converts proteins to genomic DNA alignment
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PAL2NAL is a program that converts a multiple sequence alignment of proteins and the corresponding DNA (or mRNA) sequences into a codon-based DNA alignment. The program automatically assigns the corresponding codon sequence even if the input DNA sequence has mismatches with the input protein sequence, or contains UTRs, polyA tails. It can also deal with frame shifts in the input alignment, which is suitable for the analysis of pseudogenes. The resulting codon-based DNA alignment can further be subjected to the calculation of synonymous (Ks) and non-synonymous (Ka) substitution rates.

Please cite: Mikita Suyama, David Torrents and Peer Bork: PAL2NAL: robust conversion of protein sequence alignment into the corresponding codon alignments. (PubMed,eprint) Nucleic Acids Research 34:W609-W612 (2006)
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paleomix
pipelines and tools for the processing of ancient and modern HTS data
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The PALEOMIX pipelines are a set of pipelines and tools designed to aid the rapid processing of High-Throughput Sequencing (HTS) data: The BAM pipeline processes de-multiplexed reads from one or more samples, through sequence processing and alignment, to generate BAM alignment files useful in downstream analyses; the Phylogenetic pipeline carries out genotyping and phylogenetic inference on BAM alignment files, either produced using the BAM pipeline or generated elsewhere; and the Zonkey pipeline carries out a suite of analyses on low coverage equine alignments, in order to detect the presence of F1-hybrids in archaeological assemblages. In addition, PALEOMIX aids in metagenomic analysis of the extracts.

The pipelines have been designed with ancient DNA (aDNA) in mind, and includes several features especially useful for the analyses of ancient samples, but can all be for the processing of modern samples, in order to ensure consistent data processing.

Please cite: Mikkel Schubert, Luca Ermini, Clio Der Sarkissian, Hákon Jónsson, Aurélien Ginolhac, Robert Schaefer, Michael D Martin, Ruth Fernández, Martin Kircher, Molly McCue, Eske Willerslev and Ludovic Orlando: Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. (PubMed) Nature Protocols 9(5):1056-82 (2014)
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paml
Phylogenetic Analysis by Maximum Likelihood (PAML)
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PAML is a package of programs for phylogenetic analyses of DNA or protein sequences using maximum likelihood. PAML is not good for tree making. It may be used to estimate parameters and test hypotheses to study the evolutionary process, when you have reconstructed trees using other programs such as PAUP*, PHYLIP, MOLPHY, PhyML, RaxML, etc.

Please cite: Ziheng Yang: PAML 4: phylogenetic analysis by maximum likelihood. (PubMed,eprint) Molecular Biology and Evolution 24(8):1586-91 (2007)
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paraclu
Parametric clustering of genomic and transcriptomic features
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Paraclu finds clusters in data attached to sequences. It was first applied to transcription start counts in genome sequences, but it could be applied to other things too.

Paraclu is intended to explore the data, imposing minimal prior assumptions, and letting the data speak for itself.

One consequence of this is that paraclu can find clusters within clusters. Real data sometimes exhibits clustering at multiple scales: there may be large, rarefied clusters; and within each large cluster there may be several small, dense clusters.

Please cite: Martin C. Frith, Eivind Valen, Anders Krogh, Yoshihide Hayashizaki, Piero Carninci and Albin Sandelin: A code for transcription initiation in mammalian genomes. (eprint) Genome Research 18(1):1-12 (2008)
parasail
Aligner based on libparasail
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This package contains a command-line aligner based on libparasail. Parasail is a SIMD C library containing implementations of the Smith-Waterman, Needleman-Wunsch, and various semi-global pairwise sequence alignment algorithm.

parsinsert
Parsimonious Insertion of unclassified sequences into phylogenetic trees
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ParsInsert efficiently produces both a phylogenetic tree and taxonomic classification for sequences for microbial community sequence analysis. This is a C++ implementation of the Parsimonious Insertion algorithm.

The package is enhanced by the following packages: parsinsert-testdata
parsnp
rapid core genome multi-alignment
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Parsnp was designed to align the core genome of hundreds to thousands of bacterial genomes within a few minutes to few hours. Input can be both draft assemblies and finished genomes, and output includes variant (SNP) calls, core genome phylogeny and multi-alignments. Parsnp leverages contextual information provided by multi-alignments surrounding SNP sites for filtration/cleaning, in addition to existing tools for recombination detection/filtration and phylogenetic reconstruction.

Please cite: Todd J. Treangen, Brian D. Ondov, Sergey Koren and Adam M. Phillippy: The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. (PubMed,eprint) Genome Biology 15(11):524 (2014)
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patman
rapid alignment of short sequences to large databases
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Patman searches for short patterns in large DNA databases, allowing for approximate matches. It is optimized for searching for many small pattern at the same time, for example microarray probes.

Please cite: Kay Prüfer, Udo Stenzel, Michael Dannemann, Richard E Green, Michael Lachmann and Janet Kelso: PatMaN: rapid alignment of short sequences to large databases. (PubMed,eprint) Bioinformatics 24(13):1530-1 (2008)
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pbdagcon
sequence consensus using directed acyclic graphs
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pbdagcon is a tool that implements DAGCon (Directed Acyclic Graph Consensus) which is a sequence consensus algorithm based on using directed acyclic graphs to encode multiple sequence alignment.

It uses the alignment information from blasr to align sequence reads to a "backbone" sequence. Based on the underlying alignment directed acyclic graph (DAG), it will be able to use the new information from the reads to find the discrepancies between the reads and the "backbone" sequences. A dynamic programming process is then applied to the DAG to find the optimum sequence of bases as the consensus. The new consensus can be used as a new backbone sequence to iteratively improve the consensus quality.

While the code is developed for processing PacBio(TM) raw sequence data, the algorithm can be used for general consensus purpose. Currently, it only takes FASTA input. For shorter read sequences, one might need to adjust the blasr alignment parameters to get the alignment string properly.

The code and the underlying graphical data structure have been used for some algorithm development prototyping including phasing reads and pre-assembly.

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pbhoney
genomic structural variation discovery
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PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

PBHoney is part of the PBSuite.

pbjelly
genome assembly upgrading tool
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PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes.

PBJelly is part of the PBSuite.

pbsim
simulator for PacBio sequencing reads
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PacBio DNA sequencers produce two types of characteristic reads: CCS (short and low error rate) and CLR (long and high error rate), both of which could be useful for de novo assembly of genomes. PBSIM simulates those PacBio reads from a reference sequence by using either a model-based or sampling-based simulation. Simulated reads are useful, for example, when developing or evaluating sequence assemblers targeted at PacBio data.

Please cite: Yukiteru Ono, Kiyoshi Asai and Michiaki Hamada: PBSIM: PacBio reads simulator - toward accurate genome assembly. (PubMed,eprint) Bioinformatics 29(1):119-121 (2013)
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Topics: Sequence analysis
pbsuite
software for Pacific Biosciences sequencing data
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The PBSuite contains two projects created for analysis of Pacific Biosciences long-read sequencing data.

  • PBJelly - genome upgrading tool
  • PBHoney - structural variation discovery
pdb2pqr
Preparation of protein structures for electrostatics calculations
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PDB2PQR is a Python software package that automates many of the common tasks of preparing structures for continuum electrostatics calculations. It thus provides a platform-independent utility for converting protein files in PDB format to PQR format. These tasks include:

  • Adding a limited number of missing heavy atoms to biomolecular structures
  • Determining side-chain pKas
  • Placing missing hydrogens
  • Optimizing the protein for favorable hydrogen bonding
  • Assigning charge and radius parameters from a variety of force fields
Please cite: Todd J Dolinsky, Paul Czodrowski, Hui Li, Jens E Nielsen, Jan H Jensen, Gerhard Klebe and Nathan A Baker: PDB2PQR: Expanding and upgrading automated preparation of biomolecular structures for molecular simulations. (PubMed,eprint) Nucleic Acids Research 35:W522-5 (2007)
perlprimer
Graphical design of primers for PCR
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PerlPrimer is a free, open-source GUI application written in Perl that designs primers for standard Polymerase Chain Reaction (PCR), bisulphite PCR, real-time PCR (QPCR) and sequencing. It aims to automate and simplify the process of primer design.

If operated online, the tool nicely communicates with the Ensembl project for further insights into the gene structure, i.e., allowing for taking the location of exons and introns into account for the design of the primers. The sequences themselves can be retrieved, too.

Please cite: Owen J. Marshall: PerlPrimer: cross-platform, graphical primer design for standard, bisulphite and real-time PCR. (PubMed,eprint) Bioinformatics 20(15):2471-2472 (2004)
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perm
efficient mapping of short reads with periodic spaced seeds
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PerM is a software package which was designed to perform highly efficient genome scale alignments for hundreds of millions of short reads produced by the ABI SOLiD and Illumina sequencing platforms. Today PerM is capable of providing full sensitivity for alignments within 4 mismatches for 50bp SOLID reads and 9 mismatches for 100bp Illumina reads.

Please cite: Yangho Chen, Tade Souaiaia and Ting Chen: PerM: efficient mapping of short sequencing reads with periodic full sensitive spaced seeds. (PubMed,eprint) Bioinformatics 25(19):2514-21 (2009)
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pftools
build and search protein and DNA generalized profiles
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The pftools package contains all the software necessary to build protein and DNA generalized profiles and use them to scan and align sequences, and search databases.

File formats used by the pftools

  • Generalized profiles format and syntax.
  • The multiple sequence alignment format (PSA).
  • The extended header multiple sequence alignment format (XPSA).

Programs to build generalized profiles

 pfmake
   Build a profile from a multiple sequence alignment.
 pfscale
   Fit parameters of an extreme-value distribution to a profile score list.
 pfw
   Weight sequences of a multiple sequence alignment to correct for
   sampling bias.

Programs to search with generalized profiles

 pfsearch / pfsearchV3
   Search a protein or DNA sequence library for sequence segments matching
   a profile (V3 is the new version of this tool).
 pfscan
   Scan a protein or DNA sequence with a profile library

Conversion programs

 psa2msa
   Reformat PSA file to Pearson/Fasta multiple sequence alignment file.
 ptof
   Convert a protein profile into a frame-search profile to search DNA
   sequences. To be used with 2ft.
 2ft
   Converts both strands of DNA into so-called interleaved
   frame-translated DNA sequences to search with protein profiles. To be
   used with ptof.
 6ft
   Translates all six reading frames of a double-stranded DNA sequence
   into individual protein sequences.
 pfgtop
   Convert a profile in GCG format into PROSITE format.
 pfhtop
   Convert a HMMER1 ASCII-formatted HMM into an equivalent PROSITE profile.
 ptoh
   Converts a generalized profile into an approximately equivalent HMM
   profile in HMMER1 format (can be read by the hmmconvert program from
   the HMMER2 package).
Please cite: Christian J. A. Sigrist, Lorenzo Cerutti, Nicolas Hulo, Alexandre Gattiker, Laurent Falquet, Marco Pagni, Amos Bairoch and Philipp Bucher: PROSITE: a documented database using patterns and profiles as motif descriptors. (PubMed,eprint) Briefings in Bioinformatics 3(3):265-74 (2002)
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phast
phylogenetic analysis with space/time models
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PHAST is a software package for comparative and evolutionary genomics. It consists of about half a dozen major programs, plus more than a dozen utilities for manipulating sequence alignments, phylogenetic trees, and genomic annotations. For the most part, PHAST focuses on two kinds of applications: the identification of novel functional elements, including protein-coding exons and evolutionarily conserved sequences; and statistical phylogenetic modeling, including estimation of model parameters, detection of signatures of selection, and reconstruction of ancestral sequences.

PHAST does not support phylogeny reconstruction or sequence alignment, and it is designed for use with DNA sequences only (see Comparison).

Please cite: Melissa J. Hubisz, Katherine S. Pollard and Adam Siepel: PHAST and RPHAST: phylogenetic analysis with space/time models. (PubMed,eprint) Bioinformatics 12(1):41-51 (2011)
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phipack
PHI test and other tests of recombination
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The PhiPack software package implements a few tests for recombination and can produce refined incompatibility matrices as well. Specifically, PHIPack implements the 'Pairwise Homoplasy Index', Maximum Chi2 and the 'Neighbour Similarity Score'. The program Phi can be run to produce a p-value of recombination within a data set and the program profile can be run to determine regions exhibiting strongest evidence mosaicism.

Please cite: Trevor C. Bruen, Hervé Philippe and David Bryant: A Simple and Robust Statistical Test for Detecting the Presence of Recombination. (PubMed,eprint) Genetics 172(4):2665-2681 (2006)
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phybin
binning/clustering newick trees by topology
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PhyBin is a simple command line tool that classifies a set of Newick tree files by their topology. The purpose of it is to take a large set of tree files and browse through the most common tree topologies.

It can do simple binning of identical trees or more complex clustering based on an all-to-all Robinson-Foulds distance matrix.

phybin produces output files that characterize the size and contents of each bin or cluster (including generating GraphViz-based visual representations of the tree topologies).

Please cite: Ryan R. Newton and Irene L.G. Newton: PhyBin: binning trees by topology. (PubMed,eprint) PeerJ 1:e187 (2013)
phylip
package of programs for inferring phylogenies
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The PHYLogeny Inference Package is a package of programs for inferring phylogenies (evolutionary trees) from sequences. Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites, distance matrices, and 0/1 discrete characters.

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phylonium
Fast and Accurate Estimation of Evolutionary Distances
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This is the phylonium program for estimating the evolutionary distances between closely related genomes. It is much faster than alignment based approaches for phylogeny reconstruction and usually more accurate than competing alignment-free methods.

Topics: Phylogenetics
phyml
Phylogenetic estimation using Maximum Likelihood
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PhyML is a software that estimates maximum likelihood phylogenies from alignments of nucleotide or amino acid sequences. It provides a wide range of options that were designed to facilitate standard phylogenetic analyses. The main strengths of PhyML lies in the large number of substitution models coupled to various options to search the space of phylogenetic tree topologies, going from very fast and efficient methods to slower but generally more accurate approaches. It also implements two methods to evaluate branch supports in a sound statistical framework (the non-parametric bootstrap and the approximate likelihood ratio test).

PhyML was designed to process moderate to large data sets. In theory, alignments with up to 4,000 sequences 2,000,000 character-long can be analyzed. In practice however, the amount of memory required to process a data set is proportional of the product of the number of sequences by their length. Hence, a large number of sequences can only be processed provided that they are short. Also, PhyML can handle long sequences provided that they are not numerous. With most standard personal computers, the “comfort zone” for PhyML generally lies around 3 to 500 sequences less than 2,000 character long.

This package also includes PhyTime.

Please cite: Stéphane Guindon: Bayesian estimation of divergence times from large sequence alignments. (PubMed,eprint) Molecular Biology and Evolution 27(8):1768-81 (2010)
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physamp
sample sequence alignment corresponding to phylogeny
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The PhySamp package currently contains two programs: bppphysamp, which samples sequences according to their similarity, and bppalnoptim, which samples a sequence alignment by removing sequences in order to maximize the number of sites suitable for a given analysis. The bppalnoptim program has three running modes:

  • Interactive: the user will be iteratively proposed a set of choices for sequence removal, with their corresponding site gains. The procedure stops when the user does not want to remove more sequences, and the resulting filtered alignment is written.
  • Automatic: the user enters an a priori criterion for stopping the filtering procedure (for instance a minimum number of sequences to keep).
  • Diagnostic: this mode allows one to plot the trade-off curve, by showing the site gain as a function of the number of removed sequences.
Please cite: Julien Y. Dutheil and Emeric Figuet: Optimization of sequence alignments according to the number of sequences vs. number of sites trade-off. (PubMed,eprint) BMC Bioinformatics 16:160 (2015)
phyutility
simple analyses or modifications on both phylogenetic trees and data matrices
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Phyutility (fyoo-til-i-te) is a command line program that performs simple analyses or modifications on both trees and data matrices.

Currently it performs the following functions (to suggest another feature, submit an Issue and use the label Type-Enhancement) :

Trees

  • rerooting
  • pruning
  • type conversion
  • consensus
  • leaf stability
  • lineage movement
  • tree support

Data Matrices

  • concatenate alignments
  • genbank parsing
  • trimming alignments
  • search NCBI
  • fetch NCBI
Please cite: Stephen A. Smith and Casey W. Dunn: Phyutility: a phyloinformatics utility for trees, alignments, and molecular data. (PubMed,eprint) Bioinformatics 24(5):715-716 (2008)
phyx
UNIX-style phylogenetic analyses on trees and sequences
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phyx provides a convenient, lightweight and inclusive toolkit consisting of programs spanning the wide breadth of programs utilized by researchers performing phylogenomic analyses. Modeled after Unix/GNU/Linux command line tools, individual programs perform a single task and operate on standard I/O streams. A result of this stream-centric approach is that, for most programs, only a single sequence or tree is in memory at any moment. Thus, large datasets can be processed with minimal memory requirements. phyx’s ever-growing complement of programs consists of over 35 programs focused on exploring, manipulating, analyzing and simulating phylogenetic objects (alignments, trees and MCMC logs). As with standard Unix command line tools, these programs can be piped (together with non-phyx tools), allowing the easy construction of efficient analytical pipelines.

picard-tools
Command line tools to manipulate SAM and BAM files
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SAM (Sequence Alignment/Map) format is a generic format for storing large nucleotide sequence alignments. Picard Tools includes these utilities to manipulate SAM and BAM files:

 AddCommentsToBam                  FifoBuffer
 AddOrReplaceReadGroups            FilterSamReads
 BaitDesigner                      FilterVcf
 BamIndexStats                     FixMateInformation
                                   GatherBamFiles
 BedToIntervalList                 GatherVcfs
 BuildBamIndex                     GenotypeConcordance
 CalculateHsMetrics                IlluminaBasecallsToFastq
 CalculateReadGroupChecksum        IlluminaBasecallsToSam
 CheckIlluminaDirectory            LiftOverIntervalList
 CheckTerminatorBlock              LiftoverVcf
 CleanSam                          MakeSitesOnlyVcf
 CollectAlignmentSummaryMetrics    MarkDuplicates
 CollectBaseDistributionByCycle    MarkDuplicatesWithMateCigar
 CollectGcBiasMetrics              MarkIlluminaAdapters
 CollectHiSeqXPfFailMetrics        MeanQualityByCycle
 CollectIlluminaBasecallingMetrics MergeBamAlignment
 CollectIlluminaLaneMetrics        MergeSamFiles
 CollectInsertSizeMetrics          MergeVcfs
 CollectJumpingLibraryMetrics      NormalizeFasta
 CollectMultipleMetrics            PositionBasedDownsampleSam
 CollectOxoGMetrics                QualityScoreDistribution
 CollectQualityYieldMetrics        RenameSampleInVcf
 CollectRawWgsMetrics              ReorderSam
 CollectRnaSeqMetrics              ReplaceSamHeader
 CollectRrbsMetrics                RevertOriginalBaseQualitiesAndAddMateCigar
 CollectSequencingArtifactMetrics  RevertSam
 CollectTargetedPcrMetrics         SamFormatConverter
 CollectVariantCallingMetrics      SamToFastq
 CollectWgsMetrics                 ScatterIntervalsByNs
 CompareMetrics                    SortSam
 CompareSAMs                       SortVcf
 ConvertSequencingArtifactToOxoG   SplitSamByLibrary
 CreateSequenceDictionary          SplitVcfs
 DownsampleSam                     UpdateVcfSequenceDictionary
 EstimateLibraryComplexity         ValidateSamFile
 ExtractIlluminaBarcodes           VcfFormatConverter
 ExtractSequences                  VcfToIntervalList
 FastqToSam                        ViewSam
The package is enhanced by the following packages: multiqc
Please cite: Broad Institute: Picard toolkit. Broad Institute, GitHub repository (2019)
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Topics: Sequencing; Document, record and content management
picopore
lossless compression of Nanopore files
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The Nanopore is a device to determine the sequences of single moleculres of DNA. No amplification. The output is gigantic and tools like this one help to reduce it.

Over time, other means have substitute the need for this one. Upstream has halted development. Some tutorials and pipelines of the Nanopore still refer to it, though.

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pigx-rnaseq
pipeline for checkpointed and distributed RNA-seq analyses
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This package provides a automated workflow for the automated analysis of RNA-seq experiments. A series of well-accecpted tools are connected in Python scripts and controlled via snakemake. This supports the parallel execution of these workflows and provides checkpointing, such that interrupted workflows can take up their work again.

Please cite: Ricardo Wurmus, Bora Uyar, Brendan Osberg, Vedran Franke, Alexander Gosdschan, Katarzyna Wreczycka, Jonathan Ronen and and Altuna Akalin: PiGx: Reproducible Genomics Analysis Pipelines with GNU Guix. (PubMed,eprint) GigaScience 7(12):giy123 (2018)
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piler
genomic repeat analysis
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PILER (Parsimonious Inference of a Library of Elementary Repeats) searches a genome sequence for repetitive elements. It implements search algorithms that identify characteristic patterns of local alignments induced by certain classes of repeats.

Please cite: Robert C. Edgar and Eugene W. Myers: PILER: identification and classification of genomic repeats. (PubMed,eprint) Bioinformatics 21(suppl 1):i152-i158 (2005)
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pilercr
software for finding CRISPR repeats
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CRISPR elements are short, highly conserved repeats in prokaryotic genomes separated by unique sequences of similar length. PILERCR is designed for the identification and analysis of CRISPR repeats.

Please cite: R. C. Edgar: PILER-CR: fast and accurate identification of CRISPR repeats. (PubMed,eprint) BMC Bioinformatics 8:18 (2007)
pilon
automated genome assembly improvement and variant detection tool
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Pilon is a software tool which can be used to:

  • Automatically improve draft assemblies
  • Find variation among strains, including large event detection Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

  • Single base differences

  • Small indels
  • Larger indel or block substitution events
  • Gap filling
  • Identification of local misassemblies, including optional opening of new gaps
Please cite: Bruce J. Walker, Thomas Abeel, Terrance Shea, Margaret Priest, Amr Abouelliel, Sharadha Sakthikumar, Christina A. Cuomo, Qiandong Zeng, Jennifer Wortman, Sarah K. Young and Ashlee M. Earl: Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement". (PubMed,eprint) PLOSone 9(11):e11296 (2014)
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pinfish
Collection of tools to annotate genomes using long read transcriptomics data
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The toolchain is composed of the following tools: 1. spliced_bam2gff - a tool for converting sorted BAM files containing spliced alignments into GFF2 format. Each read will be represented as a distinct transcript. This tool comes handy when visualizing spliced reads at particular loci and to provide input to the rest of the toolchain.

  1. cluster_gff - this tool takes a sorted GFF2 file as input and clusters together reads having similar exon/intron structure and creates a rough consensus of the clusters by taking the median of exon boundaries from all transcripts in the cluster.

  2. polish_clusters - this tool takes the cluster definitions generated by cluster_gff and for each cluster creates an error corrected read by mapping all reads on the read with the median length and polishing it using racon. The polished reads can be mapped to the genome using minimap2 or GMAP.

  3. collapse_partials - this tool takes GFFs generated by either cluster_gff or polish_clusters and filters out transcripts which are likely to be based on RNA degradation products from the 5' end. The tool clusters the input transcripts into "loci" by the 3' ends and discards transcripts which have a compatible transcripts in the loci with more exons.

pique
software pipeline for performing genome wide association studies
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PIQUE is a software pipeline for performing genome wide association studies (GWAS). The main function of PIQUE is to provide ‘convenience’ wrappers that allow users to perform GWAS using the popular program EMMAX (Kang et al., 2010) without the need to be familiar with all of the software tools used to generate the required EMMAX input files. PIQUE will also perform a number of quality control steps prior to running EMMAX, ensuring that the various input data files are in the correct format. PIQUE proceeds in two main stages although there are multiple entry and exit points from which the pipeline can be run. The first stage consists of running the “pique-input” program, which can read genotype and phenotype information in several different formats and generates all the necessary input files required to run EMMAX. The second step in the pipeline uses the “pique-run” program to actually run EMMAX using the files generated by “pique-input” (or pre-existing user-supplied input files) to perform the GWAS and output the analysis summary files.

The package is enhanced by the following packages: pique-doc
pirs
Profile based Illumina pair-end Reads Simulator
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The program pIRS can be used for simulating Illumina PE reads, with a series of characters generated by Illumina sequencing platform, such as insert size distribution, sequencing error(substitution, insertion, deletion), quality score and GC content-coverage bias.

The insert size follows a normal distribution, so users should set the mean value and standard deviation. Usually the standard deviation is set as 1/20 of the mean value. The normal distribution by Box-Muller method is simulated.

The program simulates sequencing error, quality score and GC content- coverage bias according to the empirical distribution profile. Some default profiles counted from lots of real sequencing data are provided.

To simulate reads from diploid genome, users should simulate the diploid genome sequence firstly by setting the ratio of heterozygosis SNP, heterozygosis InDel and structure variation.

Please cite: Xuesong Hu, Jianying Yuan, Yujian Shi, Jianliang Lu, Binghang Liu, Zhenyu Li, Yanxiang Chen, Desheng Mu, Hao Zhang, Nan Li, Zhen Yue, Fan Bai, Heng Li and Wei Fan: pIRS: Profile-based Illumina pair-end reads simulator. (PubMed,eprint) Bioinformatics 28(11):1533-5 (2012)
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pizzly
Identifies gene fusions in RNA sequencing data
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For the interpretation of the transcriptome (the abundance and sequence of RNA) of tomour cells one is particularly interested in transcripts that cannot be mapped to single genes but that are seen to be fused as parts from two genes. Likely eplanations are chromosomal translocations.

Pizzly can identify novel such peculiarities, building on interpretations on variable splicing by the tool kallisto. Both tools are elements of the bcbio workflow.

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placnet
Plasmid Constellation Network project
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Placnet is a new tool for plasmid analysis in NGS projects. Placnet is optimized to work with Illumina sequences but it also works with 454, Iontorrent or any of the actual sequence technologies.

The input of placnet is a set of contigs and one or more SAM files with the mapping of the reads against the contigs. Placnet obtains a set of files, easily opened on Cytoscape software or other network tools.

Please cite: Val F. Lanza, María de Toro, M. Pilar Garcillán-Barcia, Azucena Mora, Jorge Blanco, Teresa M. Coque and Fernando de la Cruz: Plasmid Flux in Escherichia coli ST131 Sublineages, Analyzed by Plasmid Constellation Network (PLACNET), a New Method for Plasmid Reconstruction from Whole Genome Sequences. (PubMed,eprint) PLOS 10(12):e1004766 (2014)
plasmidid
mapping-based, assembly-assisted plasmid identification tool
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PlasmidID is a mapping-based, assembly-assisted plasmid identification tool that analyzes and gives graphic solution for plasmid identification.

PlasmidID is a computational pipeline that maps Illumina reads over plasmid database sequences. The k-mer filtered, most covered sequences are clustered by identity to avoid redundancy and the longest are used as scaffold for plasmid reconstruction. Reads are assembled and annotated by automatic and specific annotation. All information generated from mapping, assembly, annotation and local alignment analyses is gathered and accurately represented in a circular image which allow user to determine plasmidic composition in any bacterial sample.

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plasmidomics
draw plasmids and vector maps with PostScript graphics export
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Plasmidomics is written for easy drawing of plasmids and vector maps to use them in theses, presentations or other forms of publications. It natively supports PostScript as output format.

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plasmidseeker
identification of known plasmids from whole-genome sequencing reads
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PlasmidSeeker is a k-mer based program for the identification of known plasmids from bacterial whole genome sequencing reads.

PlasmidSeeker that enables the detection of plasmids from bacterial WGS data without read assembly. The PlasmidSeeker algorithm is based on k-mers and uses k-mer abundance to distinguish between plasmid and bacterial sequences. The performance of PlasmidSeeker was tested on a set of simulated and real bacterial WGS samples, resulting in 100% sensitivity and 99.98% specificity.

Please cite: Märt Roosaare, Mikk Puustusmaa, Märt Möls, Mihkel Vaher and Maido Remm: PlasmidSeeker: identification of known plasmids from bacterial whole genome sequencing reads. (PubMed,eprint) PeerJ - Life & Environment 6:e4588 (2018)
plast
Parallel Local Sequence Alignment Search Tool
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PLAST is a fast, accurate and NGS scalable bank-to-bank sequence similarity search tool providing significant accelerations of seeds- based heuristic comparison methods, such as the Blast suite of algorithms.

Relying on unique software architecture, PLAST takes full advantage of recent multi-core personal computers without requiring any additional hardware devices.

Please cite: Van Hoa Nguyen and Dominique Lavenier: PLAST: parallel local alignment search tool for database comparison. (PubMed,eprint) BMC Bioinformatics 10:329 (2009)
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plink
whole-genome association analysis toolset
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plink expects as input the data from SNP (single nucleotide polymorphism) chips of many individuals and their phenotypical description of a disease. It finds associations of single or pairs of DNA variations with a phenotype and can retrieve SNP annotation from an online source.

SNPs can evaluated individually or as pairs for their association with the disease phenotypes. The joint investigation of copy number variations is supported. A variety of statistical tests have been implemented.

Please note: The executable was renamed to plink1 because of a name clash. Please read more about this in /usr/share/doc/plink/README.Debian.

Please cite: Shaun Purcell, Benjamin Neale, Kathe Todd-Brown, Lori Thomas, Manuel A. R. Ferreira, David Bender, Julian Maller, Pamela Sklar, Paul I. W. de Bakker, Mark J. Daly and Pak C. Sham: PLINK: a toolset for whole-genome association and population-based linkage analysis. (PubMed) American Journal of Human Genetics 81(3):559-75 (2007)
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plink1.9
whole-genome association analysis toolset
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plink expects as input the data from SNP (single nucleotide polymorphism) chips of many individuals and their phenotypical description of a disease. It finds associations of single or pairs of DNA variations with a phenotype and can retrieve SNP annotation from an online source.

SNPs can evaluated individually or as pairs for their association with the disease phenotypes. The joint investigation of copy number variations is supported. A variety of statistical tests have been implemented.

plink1.9 is a comprehensive update of plink with new algorithms and new methods, faster and less memory consumer than the first plink.

Please note: The executable was renamed to plink1.9 because of a name clash. Please read more about this in /usr/share/doc/plink1.9/README.Debian.

Please cite: Christopher C. Chang, Carson C. Chow, Laurent C.A.M. Tellier, Shashaank Vattikuti, Shaun M. Purcell and James J. Lee: Second-generation PLINK: rising to the challenge of larger and richer datasets. (eprint) GigaScience 4(1):7 (2015)
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plink2
whole-genome association analysis toolset
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plink expects as input the data from SNP (single nucleotide polymorphism) chips of many individuals and their phenotypical description of a disease. It finds associations of single or pairs of DNA variations with a phenotype and can retrieve SNP annotation from an online source.

SNPs can evaluated individually or as pairs for their association with the disease phenotypes. The joint investigation of copy number variations is supported. A variety of statistical tests have been implemented.

plink2 is a comprehensive update of plink and plink1.9 with new algorithms and new methods, faster and less memory consumer than the first plink.

Please cite: Christopher C. Chang, Carson C. Chow, Laurent C.A.M. Tellier, Shashaank Vattikuti, Shaun M. Purcell and James J. Lee: Second-generation PLINK: rising to the challenge of larger and richer datasets. (eprint) GigaScience 4(1):7 (2015)
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plip
fully automated protein-ligand interaction profiler
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The Protein-Ligand Interaction Profiler (PLIP) is a tool to analyze and visualize protein-ligand interactions in PDB files.

Features include:

  • Detection of eight different types of noncovalent interactions
  • Automatic detection of relevant ligands in a PDB file
  • Direct download of PDB structures from wwPDB server if valid PDB ID is given
  • Processing of custom PDB files containing protein-ligand complexes (e.g. from docking)
  • No need for special preparation of a PDB file, works out of the box
  • Atom-level interaction reports in rST and XML formats for easy parsing
  • Generation of PyMOL session files (.pse) for each pairing, enabling easy preparation of images for publications and talks
  • Rendering of preview image for each ligand and its interactions with the protein
Please cite: Sebastian Salentin, Sven Schreiber, V. Joachim Haupt, Melissa F. Adasme and Michael Schroeder: PLIP: fully automated protein–ligand interaction profiler. (eprint) Nucleic Acids Research (W1) (2015)
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poa
Partial Order Alignment for multiple sequence alignment
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POA is Partial Order Alignment, a fast program for multiple sequence alignment (MSA) in bioinformatics. Its advantages are speed, scalability, sensitivity, and the superior ability to handle branching / indels in the alignment. Partial order alignment is an approach to MSA, which can be combined with existing methods such as progressive alignment. POA optimally aligns a pair of MSAs and which therefore can be applied directly to progressive alignment methods such as CLUSTAL. For large alignments, Progressive POA is 10-30 times faster than CLUSTALW.

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populations
population genetic software
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Populations is a population genetic software. It computes genetic distances between populations or individuals. It builds phylogenetic trees (NJ or UPGMA) with bootstrap values.

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porechop
adapter trimmer for Oxford Nanopore reads
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Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity. Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

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poretools
toolkit for nanopore nucleotide sequencing data
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poretools is a flexible toolkit for exploring datasets generated by nanopore sequencing devices from MinION for the purposes of quality control and downstream analysis. Poretools operates directly on the native FAST5 (a variant of the HDF5 standard) file format produced by ONT and provides a wealth of format conversion utilities and data exploration and visualization tools.

Please cite: Nicholas Loman and Aaron Quinlan: Poretools: a toolkit for analyzing nanopore sequence data. (PubMed,eprint) Bioinformatics 30(23):3399-3401 (2014)
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pplacer
phylogenetic placement and downstream analysis
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Pplacer places reads on a phylogenetic tree. guppy (Grand Unified Phylogenetic Placement Yanalyzer) yanalyzes them. rppr is a helpful tool for working with reference packages.

Pplacer places query sequences on a fixed reference phylogenetic tree to maximize phylogenetic likelihood or posterior probability according to a reference alignment. Pplacer is designed to be fast, to give useful information about uncertainty, and to offer advanced visualization and downstream analysis.

Please cite: Frederick A Matsen, Robin B Kodner and E Virginia Armbrust: pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. (PubMed,eprint) BMC Bioinformatics 11:538 (2010)
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prank
Probabilistic Alignment Kit for DNA, codon and amino-acid sequences
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PRANK is a probabilistic multiple alignment program for DNA, codon and amino-acid sequences. It's based on a novel algorithm that treats insertions correctly and avoids over-estimation of the number of deletion events. In addition, PRANK borrows ideas from maximum likelihood methods used in phylogenetics and correctly takes into account the evolutionary distances between sequences. Lastly, PRANK allows for defining a potential structure for sequences to be aligned and then, simultaneously with the alignment, predicts the locations of structural units in the sequences.

PRANK is a command-line program for UNIX-style environments but the same sequence alignment engine is implemented in the graphical program PRANKSTER. In addition to providing a user-friendly interface to those not familiar with Unix systems, PRANKSTER is an alignment browser for alignments saved in the HSAML format. The novel format allows for storing all the information generated by the aligner and the alignment browser is a convenient way to analyse and manipulate the data.

PRANK aims at an evolutionarily correct sequence alignment and often the result looks different from ones generated with other alignment methods. There are, however, cases where the different look is caused by violations of the method's assumptions. To understand why things may go wrong and how to avoid that, read this explanation of differences between PRANK and traditional progressive alignment methods.

Please cite: Ari Löztznoja: Phylogeny-aware alignment with PRANK. (PubMed) Methods Mol. Biol. 1079:155-170 (2014)
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predictnls
prediction and analysis of protein nuclear localization signals
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predictnls is a method for the prediction and analysis of protein nuclear localization signals (NLS). In addition to reporting the positions of NLSs found, predictnls also gives short statistics.

Please cite: Murat Cokol, Rajesh Nair and Burkhard Rost: Finding nuclear localization signals.. (PubMed,eprint) EMBO reports 1(5):411-415 (2000)
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presto
toolkit for processing B and T cell sequences
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pRESTO is a toolkit for processing raw reads from high-throughput sequencing of B cell and T cell repertoires.

Dramatic improvements in high-throughput sequencing technologies now enable large-scale characterization of lymphocyte repertoires, defined as the collection of trans-membrane antigen-receptor proteins located on the surface of B cells and T cells. The REpertoire Sequencing TOolkit (pRESTO) is composed of a suite of utilities to handle all stages of sequence processing prior to germline segment assignment. pRESTO is designed to handle either single reads or paired-end reads. It includes features for quality control, primer masking, annotation of reads with sequence embedded barcodes, generation of unique molecular identifier (UMI) consensus sequences, assembly of paired-end reads and identification of duplicate sequences. Numerous options for sequence sorting, sampling and conversion operations are also included.

Please cite: Jason A. Vander Heiden, Gur Yaari, Mohamed Uduman, Joel N.H. Stern, Kevin C. O’Connor, David A. Hafler, Francois Vigneault and Steven H. Kleinstein: pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. (PubMed,eprint) Bioinformatics 30(13):1930-1932 (2014)
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prime-phylo
bayesian estimation of gene trees taking the species tree into account
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PrIME (Probabilistic Integrated Models of Evolution) is a package supporting inference of evolutionary parameters in a Bayesian framework using Markov chain Monte Carlo simulation. A distinguishing feature of PrIME is that the species tree is taken into account when analyzing gene trees.

The input data to PrIME is a multiple sequence alignment in FASTA format and the output data contains trees in Newick format.

Please cite: Ö. Åkerborg, B. Sennblad, L. Arvestad and J. Lagergren: Simultaneous Bayesian gene tree reconstruction and reconciliation analysis. (PubMed,eprint) Proceedings of the National Academy of Sciences 106(14):5714-5719 (2009)
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primer3
tool to design flanking oligo nucleotides for DNA amplification
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Primer3 picks primers for Polymerase Chain Reactions (PCRs), considering as criteria oligonucleotide melting temperature, size, GC content and primer-dimer possibilities, PCR product size, positional constraints within the source sequence, and miscellaneous other constraints. All of these criteria are user-specifiable as constraints, and some are specifiable as terms in an objective function that characterizes an optimal primer pair.

Please cite: Steve Rozen and Helen J. Skaletsky: Primer3 on the WWW for general users and for biologist programmers. (PubMed,eprint) Methods Mol Biol. 132(3):365-86 (2000)
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prinseq-lite
PReprocessing and INformation of SEQuence data (lite version)
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PRINSEQ will help you to preprocess your genomic or metagenomic sequence data in FASTA or FASTQ format. It is a tool that generates summary statistics of sequence and quality data and that is used to filter, reformat and trim next-generation sequence data. It is particular designed for 454/Roche data, but can also be used for other types of sequence data. The standalone version is primarily designed for data preprocessing and does not generate summary statistics in graphical form.

Please cite: Schmieder R and Edwards R: Quality control and preprocessing of metagenomic datasets. (PubMed,eprint) Bioinformatics 27(6):863-864 (2011)
proalign
Probabilistic multiple alignment program
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ProAlign performs probabilistic sequence alignments using hidden Markov models (HMM). It includes a graphical interface (GUI) allowing to (i) perform alignments of nucleotide or amino-acid sequences, (ii) view the quality of solutions, (iii) filter the unreliable alignment regions and (iv) export alignments to other software.

ProAlign uses a progressive method, such that multiple alignment is created stepwise by performing pairwise alignments in the nodes of a guide tree. Sequences are described with vectors of character probabilities, and each pairwise alignment reconstructs the ancestral (parent) sequence by computing the probabilities of different characters according to an evolutionary model.

Please cite: Ari Löytynoja and Michel C Milinkovitch: A hidden Markov model for progressive multiple alignment. (PubMed,eprint) Bioinformatics 19(12):1505-13 (2003)
probabel
Toolset for Genome-Wide Association Analysis
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The ProbABEL package is part of the GenABEL project for analysis of genome-wide data. ProbABEL is used to run GWAS. Using files in filevector/DatABEL format even allows for running GWAS on computers with only a few GB of RAM.

The package is enhanced by the following packages: probabel-examples
Please cite: Yurii S Aulchenko, Maksim V Struchalin and Cornelia M van Duijn: ProbABEL package for genome-wide association analysis of imputed data.. (PubMed,eprint) BMC Bioinformatics 11:134 (2010)
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probalign
multiple sequence alignment using partition function posterior probabilities
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Probalign uses partition function posterior probability estimates to compute maximum expected accuracy multiple sequence alignments. It performs statistically significantly better than the leading alignment programs Probcons v1.1, MAFFT v5.851, and MUSCLE v3.6 on BAliBASE 3.0, HOMSTRAD, and OXBENCH benchmarks. Probalign improvements are largest on datasets containing N/C terminal extensions and on datasets with long and heterogeneous length sequences. On heteregeneous length datasets containing repeats Probalign alignment accuracy is 10% and 15% higher than the other three methods when standard deviation of length is at least 300 and 400.

Please cite: Usman Roshan and Dennis R. Livesay: Probalign: multiple sequence alignment using partition function posterior probabilities. (PubMed,eprint) Bioinformatics 22(22):2715-21 (2006)
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probcons
PROBabilistic CONSistency-based multiple sequence alignment
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Tool for generating multiple alignments of protein sequences. Using a combination of probabilistic modeling and consistency-based alignment techniques, PROBCONS has achieved the highest accuracies of all alignment methods to date. On the BAliBASE benchmark alignment database, alignments produced by PROBCONS show statistically significant improvement over current programs, containing an average of 7% more correctly aligned columns than those of T-Coffee, 11% more correctly aligned columns than those of CLUSTAL W, and 14% more correctly aligned columns than those of DIALIGN.

Please cite: Chuong B. Do, Mahathi S.P. Mahabhashyam, Michael Brudno and Serafim Batzoglou: ProbCons: Probabilistic consistency-based multiple sequence alignment. (PubMed,eprint) Genome Research 15(2):330-340 (2005)
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proda
multiple alignment of protein sequences
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ProDA is a system for automated detection and alignment of homologous regions in collections of proteins with arbitrary domain architectures. Given an input set of unaligned sequences, ProDA identifies all homologous regions appearing in one or more sequences, and returns a collection of local multiple alignments for these regions.

Please cite: Tu Minh Phuong, Chuong B. Do, Robert C. Edgar and Serafim Batzoglou: Multiple alignment of protein sequences with repeats and rearrangements. (PubMed,eprint) Nucl. Acids Res. 34(20):5932-5942 (2006)
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prodigal
Microbial (bacterial and archaeal) gene finding program
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Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:

Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.

Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).

Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.

GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.

Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.

Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.

Please cite: Doug Hyatt, Gwo-Liang Chen, Philip F. Locascio, Miriam L. Land, Frank W. Larimer and Loren J. Hauser: Prodigal: prokaryotic gene recognition and translation initiation site identification. (PubMed,eprint) BMC Bioinformatics 11:119 (2010)
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profbval
predictor of flexible/rigid protein residues from sequence
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PROFbval can be useful for both protein structure and function predictions. For instance, a biologist can locate potentially antigenic determinants by identifying the most flexible residues on the protein surface. Additionally, a crystallographer can locate residues that potentially have high experimental B-values.

PROFbval takes the following input, further described on profbval(1):

  • a protein sequence in a FASTA file
  • secondary structure and solvent accessibility prediction by prof(1)
  • an HSSP file

Background: the mobility of a given residue on the protein surface is related to its functional role. Therefore, identification of extremely rigid or flexible residues on the protein surface is helpful for identifying functionally important residues in proteins. A common measure of atom mobility in proteins is B-value data from x-ray crystallography structures. PROFbval is the first tool to predict normalized backbone B-values from amino-acid sequence.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: PROFbval: predict flexible and rigid residues in proteins.. (PubMed) Bioinformatics 22(7):891-3 (2006)
profisis
prediction of protein-protein interaction sites from sequence
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Profisis (ISIS) identifies interacting protein residues in protein-protein interfaces from sequence alone.

The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment.

Please cite: Yanay Ofran and Burkhard Rost: ISIS: interaction sites identified from sequence. (PubMed,eprint) Bioinformatics 23(2):e13-e16 (2007)
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profnet-bval
neural network architecture for profbval
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profbval.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-chop
neural network architecture for profchop
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profchop.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-con
neural network architecture for profcon
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profcon.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-isis
neural network architecture for profisis
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profisis.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-md
neural network architecture for metadisorder
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for metadisorder.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-norsnet
neural network architecture for norsnet
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for norsnet.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-prof
neural network architecture for profacc
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profsec and profacc.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profnet-snapfun
neural network architecture for snapfun
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for snapfun.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profphd
secondary structure and solvent accessibility predictor
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This package provides prof(1), the protein secondary structure, accessibility and transmembrane helix predictor from Burkhard Rost. Prediction is either done from protein sequence alone or from an alignment - the latter should be used for optimal performance.

How well does prof(1) perform?

  • Secondary structure is predicted at an expected average accuracy > 72% for the three states helix, strand and loop.

  • Solvent accessibility is predicted at a correlation coefficient (correlation between experimentally observed and predicted relative solvent accessibility) of 0.54

  • Transmembrane helix prediction has an expected per-residue accuracy of about 95%. The number of false positives, i.e., transmembrane helices predicted in globular proteins, is about 2%.

Please cite: Burkhard Rost and Chris Sander: Combining evolutionary information and neural networks to predict protein secondary structure. (PubMed) Proteins 19(1):55-72 (1994)
profphd-net
neural network architecture for profphd
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Profnet is a component of the prediction methods that make up the Predict Protein service by the lab of Burkhard Rost. It provides the neural network component to a variety of predictors that perform protein feature prediction directly from sequence. This neural network implementation has to be compiled for every different network architecture.

This package contains the neural network architecture for profphd.

Please cite: Avner Schlessinger, Guy Yachdav and Burkhard Rost: OPRFbval: predict flexible and rigid residues in proteins. (PubMed,eprint) Bioinformatics 22(7):891-893 (2006 Apr 1)
profphd-utils
profphd helper utilities convert_seq and filter_hssp
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The package provides the following binary utilities: convert_seq, filter_hssp. These are used by prof from the profphd package: a secondary structure, accessibility and transmembrane helix predictor from Burkhard Rost.

proftmb
per-residue prediction of bacterial transmembrane beta barrels
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proftmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria.

For each query protein, proftmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops.

The package is enhanced by the following packages: proftmb-dbg
Please cite: H. Bigelow and B. Rost: PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins.. (PubMed) Nucleic Acids Res 34(Web Server issue):W186-8 (2006)
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progressivemauve
multiple genome alignment algorithms
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The mauveAligner and progressiveMauve alignment algorithms have been implemented as command-line programs included with the downloadable Mauve software. When run from the command-line, these programs provide options not yet available in the graphical interface.

Mauve is a system for efficiently constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignment provides a basis for research into comparative genomics and the study of evolutionary dynamics. Aligning whole genomes is a fundamentally different problem than aligning short sequences.

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the amount of sequence being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours.

Mauve computes and interactively visualizes genome sequence comparisons. Using FastA or GenBank sequence data, Mauve constructs multiple genome alignments that identify large-scale rearrangement, gene gain, gene loss, indels, and nucleotide substutition.

Mauve is developed at the University of Wisconsin.

Please cite: Aaron E. Darling, Bob Mau and Nicole T. Perna: progressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. (PubMed,eprint) PloS one 5(6):e11147 (2010)
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prokka
rapid annotation of prokaryotic genomes
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A typical 4 Mbp genome can be fully annotated in less than 10 minutes on a quad-core computer, and scales well to 32 core SMP systems. It produces GFF3, GBK and SQN files that are ready for editing in Sequin and ultimately submitted to Genbank/DDJB/ENA.

The package is enhanced by the following packages: multiqc
Please cite: Torsten Seemann: Prokka: rapid prokaryotic genome annotation. (PubMed,eprint) Bioinformatics 30(14):2068-2069 (2014)
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proteinortho
Detection of (Co-)orthologs in large-scale protein analysis
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Proteinortho is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. Proteinortho was applied to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. Authors succeeded identifying thirty proteins present in 99% of all bacterial proteomes.

Please cite: Marcus Lechner, Sven Findeiß, Lydia Steiner, Manja Marz, Peter F Stadler and Sonja J Prohaska: Proteinortho: Detection of (Co-)orthologs in large-scale analysis. (PubMed,eprint) BMC Bioinformatics 12:124 (2011)
prottest
selection of best-fit models of protein evolution
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PROTTEST (ModelTest's relative) is a program for selecting the model of protein evolution that best fits a given set of sequences (alignment). This java program is based on the Phyml program (for maximum likelihood calculations and optimization of parameters) and uses the PAL library as well. Models included are empirical substitution matrices (such as WAG, LG, mtREV, Dayhoff, DCMut, JTT, VT, Blosum62, CpREV, RtREV, MtMam, MtArt, HIVb, and HIVw) that indicate relative rates of amino acid replacement, and specific improvements (+I:invariable sites, +G: rate heterogeneity among sites, +F: observed amino acid frequencies) to account for the evolutionary constraints impossed by conservation of protein structure and function. ProtTest uses the Akaike Information Criterion (AIC) and other statistics (AICc and BIC) to find which of the candidate models best fits the data at hand.

Please cite: Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada: ProtTest 3: fast selection of best-fit models of protein evolution. (PubMed,eprint) Bioinformatics 27(8):1164-5 (2011)
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provean
Protein Variation Effect Analyzer
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PROVEAN (Protein Variation Effect Analyzer) is a software tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein.

PROVEAN is useful for filtering sequence variants to identify nonsynonymous or indel variants that are predicted to be functionally important.

The performance of PROVEAN is comparable to popular tools such as SIFT or PolyPhen-2.

A fast computation approach to obtain pairwise sequence alignment scores enabled the generation of precomputed PROVEAN predictions for 20 single AA substitutions and a single AA deletion at every amino acid position of all protein sequences in human and mouse.

pscan-chip
ChIP-based identifcation of TF binding sites
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Regulation of transcription is one of the main check points of gene expression regulation and plays a key role in fundamental processes like cellular differentiation and dynamic molecular responses to stimuli The transcriptional activity of genes is finely regulated by the interaction of sequence elements on the DNA (transcription factor binding sites or TFBSs) and particular proteins called Transcription Factors (TFs). , TFBSs are usually clustered in specific regulatory genomic regions called promoters and enhancers. TFs usually recognize TFBSs in a loose sequence specific fashion but there is no computational way to determine if any given sequence motif on the DNA is actually bound in-vivo by a TF, even when the motif is an istance of the sequences typically bound by the TF itself.

Tools like Pscan and PscanChIP analyse a set of regulatory sequences to detect motif enrichment. The rationale is that if a given TFBS is present in a "surpisingly high" number of istances then there is a good chance that the TF that recognize that motif is a common regulator of the input sequences, thus they use redundancy as an information source.

While Pscan (of the pscan-tfbs package) is tailored to work on promoters, that is the regulatory regions upstream of transcription start sites, PscanChIP is suited to work on more general regulatory genomic regions like the ones identified through ChIP-Seq experiments.

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pscan-tfbs
search for transcription factor binding sites
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Pscan finds Over-represented Transcription Factor Binding Site Motifs in Sequences from Co-Regulated or Co-Expressed Genes.

Pscan is a software tool that scans a set of sequences (e.g. promoters) from co-regulated or co-expressed genes with motifs describing the binding specificity of known transcription factors and assesses which motifs are significantly over- or under-represented, providing thus hints on which transcription factors could be common regulators of the genes studied, together with the location of their candidate binding sites in the sequences. Pscan does not resort to comparisons with orthologous sequences and experimental results show that it compares favorably to other tools for the same task in terms of false positive predictions and computation time. The website is free and open to all users and there is no login requirement.

Please cite: Federico Zambelli, Graziano Pesole and Giulio Pavesi: Pscan: Finding Over-represented Transcription Factor Binding Site Motifs in Sequences from Co-Regulated or Co-Expressed Genes. (PubMed,eprint) Nucleic Acids Research 37(Web Server Issue):W247-W252 (2009)
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psortb
bacterial localization prediction tool
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PSORTb enables prediction of bacterial protein subcellular localization (SCL) and provides a quick and inexpensive means for gaining insight into protein function, verifying experimental results, annotating newly sequenced bacterial genomes, detecting potential cell surface/secreted drug targets, as well as identifying biomarkers for microbes.

Please cite: Nancy Y. Yu, James R. Wagner, Matthew R. Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, S. Cenk Sahinalp, Martin Ester, Leonard J. Foster and F. S. Brinkman: PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. (PubMed,eprint) Bioinformatics 26(13):1608-1615 (2010)
pullseq
Extract sequence from a fasta or fastq
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This is a utility to extract sequence from a fasta or fastq. Also helps filter sequences by a minimum length or maximum length. Fast, written in C, using kseq.h library.

pycoqc
computes metrics and generates Interactive QC plots
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PycoQC computes metrics and generates interactive QC plots for Oxford Nanopore technologies sequencing data

PycoQC relies on the sequencing_summary.txt file generated by Albacore and Guppy, but if needed it can also generates a summary file from basecalled fast5 files. The package supports 1D and 1D2 runs generated with Minion, Gridion and Promethion devices and basecalled with Albacore 1.2.1+ or Guppy 2.1.3+

The package is enhanced by the following packages: multiqc
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pycorrfit
tool for fitting correlation curves on a logarithmic plot
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PyCorrFit is a general-purpose FCS evaluation software that, amongst other formats, supports the established Zeiss ConfoCor3 ~.fcs file format. PyCorrFit comes with several built-in model functions, covering a wide range of applications in standard confocal FCS. In addition, it contains equations dealing with different excitation geometries like total internal reflection (TIR).

Please cite: Paul Müller, Petra Schwille and Thomas Weidemann: PyCorrFit—generic data evaluation for fluorescence correlation spectroscopy. (PubMed) Bioinformatics 30(17):2532–2533 (2014)
pyensembl
installs data from the Ensembl genome database
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The Ensembl genome database is an established reference for genomic sequences and their automated annotation. To have this data local has advantages for bulk analyses, e.g. for the mapping of reads from RNA-seq against the latest golden path - or a previous one to compare analyses.

This package provides a reproducible way to insatll this data and thus simplify the automation of respective workflows.

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pyfastx
fast random access to sequences from FASTA/Q file - command
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The pyfastx is a lightweight Python C extension that enables users to randomly access to sequences from plain and gzipped FASTA/Q files. This module aims to provide simple APIs for users to extract sequence from FASTA and reads from FASTQ by identifier and index number. The pyfastx will build indexes stored in a sqlite3 database file for random access to avoid consuming excessive amount of memory. In addition, the pyfastx can parse standard (sequence is spread into multiple lines with same length) and nonstandard (sequence is spread into one or more lines with different length) FASTA format.

It features:

  • a single file for the Python extension;
  • lightweight, memory efficient FASTA/Q file parsing;
  • fast random access to sequences from gzipped FASTA/Q file;
  • sequences reading from FASTA file line by line;
  • N50 and L50 calculation of sequences in FASTA file;
  • GC content and nucleotides composition calculation;
  • reverse, complement and antisense sequences extraction;
  • excellent compatibility: support for parsing nonstandard FASTA file;
  • support for FASTQ quality score conversion;
  • a command line interface for splitting FASTA/Q file.

This package provides the command line interface.

Please cite: Lianming Du, Qin Liu, Zhenxin Fan, Jie Tang, Xiuyue Zhang, Megan Price, Bisong Yue and Kelei Zhao: Pyfastx: a robust Python package for fast random access to sequences from plain and gzipped FASTA/Q files. (PubMed) Briefings in Bioinformatics 22(4) (2021)
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pymol
Molecular Graphics System
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PyMOL is a molecular graphics system targeted at medium to large biomolecules like proteins. It can generate high-quality publication-ready molecular graphics images and animations.

Features include:

  • Visualization of molecules, molecular trajectories and surfaces of crystallography data or orbitals
  • Molecular builder and sculptor
  • Internal raytracer and movie generator
  • Fully extensible and scriptable via a Python interface

File formats PyMOL can read include PDB, XYZ, CIF, MDL Molfile, ChemDraw, CCP4 maps, XPLOR maps and Gaussian cube maps.

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pyscanfcs
scientific tool for perpendicular line scanning FCS
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When a membrane is scanned perpendicularly to its surface, the fluorescence signal originating from the membrane itself must be separated from the signal of the surrounding medium for an FCS analysis. PyScanFCS interactively extracts the fluctuating fluorescence signal from such measurements and applies a multiple-tau algorithm. The obtained correlation curves can be evaluated using PyCorrFit.

Package provides the Python module pyscanfcs and its graphical user interface. The graphical user interface is written in wxPython.

python3-biomaj3-daemon
BioMAJ daemon library
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BioMAJ downloads remote data banks, checks their status and applies transformation workflows, with consistent state, to provide ready-to-use data for biologists and bioinformaticians. For example, it can transform original FASTA files into BLAST indexes. It is very flexible and its post-processing facilities can be extended very easily.

BioMAJ3 is a rewrite of BioMAJ v1.x, see online documentation for migration.

This package contains the library and microservice to manage daemon and CLI in BioMAJ3

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python3-bioxtasraw
process biological small angle scattering data
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BioXTAS RAW is a GUI based, Python program for reduction and analysis of small-angle X-ray solution scattering (SAXS) data. The package is designed for biological SAXS data.

BioXTAS RAW provides an alternative to closed source programs such as Primus and Scatter for primary data analysis. Because it can calibrate, mask, and integrate images it also provides an alternative to synchrotron beamline pipelines that scientists can install on their own computers and use both at home and at the beamline.

python3-cogent3
framework for genomic biology
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PyCogent is a software library for genomic biology. It is a fully integrated and thoroughly tested framework for:

  • controlling third-party applications,
  • devising workflows; querying databases,
  • conducting novel probabilistic analyses of biological sequence evolution, and
  • generating publication quality graphics. It is distinguished by many unique built-in capabilities (such as true codon alignment) and the frequent addition of entirely new methods for the analysis of genomic data.
Please cite: Rob Knight, Peter Maxwell, Amanda Birmingham, Jason Carnes, J Gregory Caporaso, Brett C Easton, Michael Eaton, Micah Hamady, Helen Lindsay, Zongzhi Liu, Catherine Lozupone, Daniel McDonald, Michael Robeson, Raymond Sammut, Sandra Smit, Matthew J Wakefield, Jeremy Widmann, Shandy Wikman, Stephanie Wilson, Hua Ying and Gavin A Huttley: PyCogent: a toolkit for making sense from sequence. (PubMed,eprint) Genome Biology 8(8):R171 (2007)
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python3-emperor
visualizing high-throughput microbial community data
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Emperor is an interactive next generation tool for the analysis, visualization and understanding of high throughput microbial ecology datasets.

Due to its tailor-made graphical user interface, delving into a new dataset to elucidate the patterns hidden in the data, has never been easier. Emperor brings a rich set of customizations and modifications that can be integrated into any QIIME or scikit-bio compliant dataset; with lightweight data files and hardware accelerated graphics, constitutes itself as the state of the art for analyzing N-dimensional data using principal coordinates analysis.

Please cite: Yoshiki Vázquez-Baeza, Meg Pirrung, Antonio Gonzalez and Rob Knight: EMPeror: a tool for visualizing high-throughput microbial community data. (PubMed) Gigascience 2(1):16 (2013)
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python3-geneimpacts
wraps command line tools to assess variants in gene sequences
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Interpersonal differences in DNA is responsible for variations in response to external stimuli, the efficiency of metabolism or may even cause what is referenced as a genetic disorder.

A range of tools have been created to predict the importance of differences (polymorphisms) in genetic sequences at single nucleotides, SNPs. This Python class wraps and represents findings provided by any of the tools snpEff, VEP and BCFT.

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python3-gffutils
Work with GFF and GTF files in a flexible database framework
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A Python package for working with and manipulating the GFF and GTF format files typically used for genomic annotations. Files are loaded into a sqlite3 database, allowing much more complex manipulation of hierarchical features (e.g., genes, transcripts, and exons) than is possible with plain-text methods alone.

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python3-pairtools
Framework to process sequencing data from a Hi-C experiment
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Simple and fast command-line framework to process sequencing data from a Hi-C experiment.

Process pair-end sequence alignments and perform the following operations:

  • Detect ligation junctions (a.k.a. Hi-C pairs) in aligned paired-end sequences of Hi-C DNA molecules
  • Sort .pairs files for downstream analyses
  • Detect, tag and remove PCR/optical duplicates
  • Generate extensive statistics of Hi-C datasets
  • Select Hi-C pairs given flexibly defined criteria
  • Restore .sam alignments from Hi-C pairs
The package is enhanced by the following packages: python3-pairtools-examples
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python3-pybedtools
Python 3 wrapper around BEDTools for bioinformatics work
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The BEDTools suite of programs is widely used for genomic interval manipulation or “genome algebra”. pybedtools wraps and extends BEDTools and offers feature-level manipulations from within Python.

This is the Python 3 version.

Please cite: R. K. Dale, B. S. Pedersen and A. R. Quinlan: Pybedtools: a flexible Python library for manipulating genomic datasets and annotations". Bioinformatics 27(24):3423-3424 (2011)
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python3-sqt
SeQuencing Tools for biological DNA/RNA high-throughput data
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sqt is a collection of command-line tools for working with high-throughput sequencing data. Conceptionally not fixed to use any particular language, many sqt subcommands are currently implemented in Python. For them, a Python package is available with functions for reading and writing FASTA/FASTQ files, computing alignments, quality trimming, etc.

The following tools are offered:

  • sqt-coverage -- Compute per-reference statistics such as coverage and GC content
  • sqt-fastqmod -- FASTQ modifications: shorten, subset, reverse complement, quality trimming.
  • sqt-fastastats -- Compute N50, min/max length, GC content etc. of a FASTA file
  • sqt-qualityguess -- Guess quality encoding of one or more FASTA files.
  • sqt-globalalign -- Compute a global or semiglobal alignment of two strings.
  • sqt-chars -- Count length of the first word given on the command line.
  • sqt-sam-cscq -- Add the CS and CQ tags to a SAM file with colorspace reads.
  • sqt-fastamutate -- Add substitutions and indels to sequences in a FASTA file.
  • sqt-fastaextract -- Efficiently extract one or more regions from an indexed FASTA file.
  • sqt-translate -- Replace characters in FASTA files (like the 'tr' command).
  • sqt-sam-fixn -- Replace all non-ACGT characters within reads in a SAM file.
  • sqt-sam-insertsize -- Mean and standard deviation of paired-end insert sizes.
  • sqt-sam-set-op -- Set operations (union, intersection, ...) on SAM/BAM files.
  • sqt-bam-eof -- Check for the End-Of-File marker in compressed BAM files.
  • sqt-checkfastqpe -- Check whether two FASTQ files contain correctly paired paired-end data.
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python3-treetime
inference of time stamped phylogenies and ancestral reconstruction (Python 3)
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TreeTime provides routines for ancestral sequence reconstruction and the maximum likelihoo inference of molecular-clock phylogenies, i.e., a tree where all branches are scaled such that the locations of terminal nodes correspond to their sampling times and internal nodes are placed at the most likely time of divergence.

TreeTime aims at striking a compromise between sophisticated probabilistic models of evolution and fast heuristics. It implements GTR models of ancestral inference and branch length optimization, but takes the tree topology as given. To optimize the likelihood of time-scaled phylogenies, treetime uses an iterative approach that first infers ancestral sequences given the branch length of the tree, then optimizes the positions of unconstraine d nodes on the time axis, and then repeats this cycle. The only topology optimization are (optional) resolution of polytomies in a way that is most (approximately) consistent with the sampling time constraints on the tree. The package is designed to be used as a stand-alone tool or as a library used in larger phylogenetic analysis workflows.

Features

  • ancestral sequence reconstruction (marginal and joint maximum likelihood)
  • molecular clock tree inference (marginal and joint maximum likelihood)
  • inference of GTR models
  • rerooting to obtain best root-to-tip regression
  • auto-correlated relaxed molecular clock (with normal prior)

This package provides the Python 3 module.

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pyvcf
helper scripts for Variant Call Format (VCF) parser
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The Variant Call Format (VCF) specifies the format of a text file used in bioinformatics for storing gene sequence variations. The format has been developed with the advent of large-scale genotyping and DNA sequencing projects, such as the 1000 Genomes Project.

The intent of this module is to mimic the csv module in the Python stdlib, as opposed to more flexible serialization formats like JSON or YAML. vcf will attempt to parse the content of each record based on the data types specified in the meta-information lines -- specifically the ##INFO and ##FORMAT lines. If these lines are missing or incomplete, it will check against the reserved types mentioned in the spec. Failing that, it will just return strings.

This package provides helper scripts using python3-pyvcf.

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qcat
demultiplexing Oxford Nanopore reads from FASTQ files
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Qcat is a command-line tool for demultiplexing Oxford Nanopore reads from FASTQ files. It accepts basecalled FASTQ files and splits the reads into separate FASTQ files based on their barcode. Qcat makes the demultiplexing algorithms used in albacore/guppy and EPI2ME available to be used locally with FASTQ files. Currently qcat implements the EPI2ME algorithm.

The package is enhanced by the following packages: qcat-examples
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qcumber
quality control of genomic sequences
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QCPipeline is a tool for quality control. The workflow is as follows:

 1. Quality control with FastQC
 2. Trim Reads with Trimmomatic
 3. Quality control of trimmed reads with FastQC
 4. Map reads against reference using bowtie2
 5. Classify reads with Kraken
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qiime
Quantitative Insights Into Microbial Ecology
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Microbes are surrounding us, animals, plants and all their parasites with strong effect on these and the environment these live in. Soil quality comes to mind but also the effect that bacteria have on each other. Humans are influencing the absolute and relative abundance of bacteria by antibiotics, food, fertilizers - you name it - and these changes affect us.

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (PubMed,eprint) Nature Biotechnology 37:852 - 857 (2019)
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Topics: Microbial ecology
qtltools
Tool set for molecular QTL discovery and analysis
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QTLtools is a tool set for molecular Quantitative Trait Loci (QTL) discovery and analysis. It allows user to go from the raw sequence data to collection of molecular QTL in few easy-to-perform steps. QTLtools contains multiple methods to prepare the data, to discover proximal and distal molecular QTL and to finally integrate them with GWAS variants and functional annotations of the genome.

The package is enhanced by the following packages: qtltools-example
Please cite: Olivier Delaneau, Halit Ongen, Andrew A. Brown, Alexandre Fort, Nikolaos I. Panousis and Emmanouil T. Dermitzakis: A complete tool set for molecular QTL discovery and analysis. (eprint) Nature Communications (2017)
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quicktree
Neighbor-Joining algorithm for phylogenies
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QuickTree is an efficient implementation of the Neighbor-Joining algorithm (PMID: 3447015), capable of reconstructing phylogenies from huge alignments in time less than the age of the universe.

QuickTree accepts both distance matrix and multiple-sequence-aligment inputs. The former should be in PHYLIP format. The latter should be in Stockholm format, which is the native alignment format for the Pfam database. Alignments in various formats can be converted to Stockholm format with the sreformat program, which is part of the HMMer package (hmmer.org).

The tress are written to stdout, in the Newick/New-Hampshire format use by PHYLIP and many other programs

quorum
QUality Optimized Reads of genomic sequences
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QuorUM enables to obtain trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. QuorUM provides best performance compared to other published error correctors in several metrics. QuorUM is efficiently implemented making use of current multi- core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core). The third-party assembler (SOAPdenovo) benefits significantly from using QuorUM error- corrected reads. QuorUM error corrected reads result in a factor of 1.1 to 4 improvement in N50 contig size compared to using the original reads with SOAPdenovo for the data sets investigated.

Please cite: Guillaume Marçais, James A. Yorke and Aleksey Zimin: QuorUM: An Error Corrector for Illumina Reads. (PubMed,eprint) PLoS One 10(6):e0130821 (2015)
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qutemol
interactive visualization of macromolecules
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QuteMol is an interactive, high quality molecular visualization system. It exploits the current GPU capabilities through OpenGL shaders to offer an array of innovative visual effects. QuteMol visualization techniques are aimed at improving clarity and an easier understanding of the 3D shape and structure of large molecules or complex proteins.

Qutemol uses advanced OpenGL techniques and might not work correctly with all video cards and drivers.

Features QuteMol offers include:

  • Real time ambient occlusion
  • Depth aware silhouette enhancement
  • Ball-and-stick, space-filling and liquorice visualization modes
  • High resolution antialiased snapshots for creating publication quality renderings
  • Automatic generation of animated gifs of rotating molecules for web page animations
  • Interactive rendering of macromolecules (>100k atoms)

QuteMol reads PDB files as input.

Please cite: Marco Tarini, Paolo Cignoni and Claudio Montani: Ambient Occlusion and Edge Cueing for Enhancing Real Time Molecular Visualization. (eprint) IEEE Transactions on Visualization and Computer Graphics 12(5):1237-1244 (2006)
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r-bioc-annotate
BioConductor annotation for microarrays
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This BioConductor module provides methods for annotation for microarrays.

In its current state the basic purpose of annotate is to supply interface routines that support user actions that rely on the different meta-data packages provided through the Bioconductor Project.

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r-bioc-biostrings
GNU R string objects representing biological sequences
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Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or set of sequences.

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r-bioc-bitseq
transcript expression inference and analysis for RNA-seq data
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The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression.

Please cite: Peter Glaus, Antti Honkela and Magnus Rattray: Identifying differentially expressed transcripts from RNA-seq data with biological variation. (PubMed,eprint) Bioinformatics 28(13):1721–1728 (2012)
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r-bioc-cner
CNE Detection and Visualization
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Large-scale identification and advanced visualization of sets of conserved noncoding elements.

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r-bioc-cummerbund
tool for analysis of Cufflinks RNA-Seq output
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Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.

Please cite: L. Goff and C. Trapnell: cummeRbund: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data (2012)
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r-bioc-deseq2
R package for RNA-Seq Differential Expression Analysis
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Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Please cite: Michael I Love, Wolfgang Huber and Simon Anders: Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq}2. (eprint) Genome Biol 15(12) (2014)
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r-bioc-ebseq
R package for RNA-Seq Differential Expression Analysis
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r-bioc-ebseq is an R package for identifying genes and isoforms differentially expressed (DE) across two or more biological conditions in an RNA-seq experiment.

Please cite: Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M. G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart and Christina Kendziorski: EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. (eprint) Bioinformatics 29(8):1035-1043 (2013)
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r-bioc-edger
Empirical analysis of digital gene expression data in R
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Bioconductor package for differential expression analysis of whole transcriptome sequencing (RNA-seq) and digital gene expression profiles with biological replication. It uses empirical Bayes estimation and exact tests based on the negative binomial distribution. It is also useful for differential signal analysis with other types of genome-scale count data.

Please cite: Mark D. Robinson, Davis J. McCarthy and Gordon K. Smyth: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. (PubMed,eprint) Bioinformatics 26,:139-140 (2010)
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r-bioc-genefilter
methods for filtering genes from microarray experiments
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This BioConductor module provides methods for filtering genes from microarray experiments. It contains some basic functions for filtering genes.

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r-bioc-geoquery
Get data from NCBI Gene Expression Omnibus (GEO)
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The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

Please cite: Sean Davis and Paul Meltzer: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor Bioinformatics 14,:1846-1847, (2007,)
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r-bioc-hilbertvis
GNU R package to visualise long vector data
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This tool allows one to display very long data vectors in a space-efficient manner, by organising it along a 2D Hilbert curve. The user can then visually judge the large scale structure and distribution of features simultaenously with the rough shape and intensity of individual features.

In bioinformatics, a typical use case is ChIP-Chip and ChIP-Seq, or basically all the kinds of genomic data, that are conventionally displayed as quantitative track ("wiggle data") in genome browsers such as those provided by Ensembl or UCSC.

Please cite: Simon Anders: Visualization of genomic data with the Hilbert curve. (PubMed,eprint) Bioinformatics 25(10):1231-1235 (2009)
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Remark of Debian Med team: It would be interesting to package HilbertVisGUI as well.
r-bioc-htsfilter
GNU R filter replicated high-throughput transcriptome sequencing data
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This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.

r-bioc-impute
Imputation for microarray data
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R package which provide a function to perform imputation for microarray data (currently KNN only).

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r-bioc-limma
linear models for microarray data
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Microarrays are microscopic plates with carefully arranged short DNA strands and/or chemically prepared surfaces to which other DNA preferably binds. The amount of DNA binding at different locations of these chips, typically determined by a fluorescent dye, is to be interpreted. The technology is typically used with DNA that is derived from RNA, i.e to determine the activity of a gene and/or its splice variants. But the technology is also used to determine sequence variations in genomic DNA.

This Bioconductor package supports the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The package includes pre-processing capabilities for two-colour spotted arrays. The differential expression methods apply to all array platforms and treat Affymetrix, single channel and two channel experiments in a unified way.

Please cite: Gordon K. Smyth: Limma: linear models for microarray data. (eprint) :397-420 (2005)
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r-bioc-megadepth
BioCOnductor BigWig and BAM related utilities
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This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3.

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r-bioc-mergeomics
Integrative network analysis of omics data
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The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts: 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).

Please cite: Le Shu, Yuqi Zhao, Zeyneb Kurt, Sean Geoffrey Byars, Taru Tukiainen, Johannes Kettunen, Luz D. Orozco, Matteo Pellegrini, Aldons J. Lusis, Samuli Ripatti, Bin Zhang, Michael Inouye, Ville-Petteri Mäkinen and Xia Yang: Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. (eprint) BMC Genomics (2016)
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r-bioc-metagenomeseq
GNU R statistical analysis for sparse high-throughput sequencing
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MetagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

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r-bioc-mofa
Multi-Omics Factor Analysis (MOFA)
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Multi-Omics Factor Analysis: an unsupervised framework for the integration of multi-omics data sets.

Upstream no longer supports this package. This package only still ships to help with rerunning/comparing/transitioning existing projects. For new projects please upgrade to MOFA2 (MOFA+). Actually, also when adding new data to old projects, MOFA2 has further improved the handling of multiple factors, and to compensate for a batch effect that is likely introduced with additional data, may be an immediate use case for that new version.

Please cite: Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber and Oliver Stegle: Link to publication Mol Syst Biol 14:e8124 (2018)
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r-bioc-multiassayexperiment
Software for integrating multi-omics experiments in BioConductor
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MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames.

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r-bioc-mutationalpatterns
GNU R comprehensive genome-wide analysis of mutational processes
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This BioConductor package provides an extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution catalogs.

r-bioc-phyloseq
GNU R handling and analysis of high-throughput microbiome census data
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The Bioconductor module phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

Please cite: Paul J. McMurdie and Susan Holmes: phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4):e61217 (2013)
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r-bioc-rtracklayer
GNU R interface to genome browsers and their annotation tracks
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Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

Please cite: Michael Lawrence, Robert Gentleman and "Vincent Carey: rtracklayer: an R package for interfacing with genome browsers. (PubMed,eprint) Bioinformatics 25(14):1841-1842 (2009)
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r-bioc-scater
Single-Cell Analysis Toolkit for Gene Expression Data in R
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A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

Please cite: McCarthy DJ, Campbell KR, Lun ATL and Willis QF: Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R. Bioinformatics 33:1179-1186 (2017)
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r-bioc-tfbstools
GNU R Transcription Factor Binding Site (TFBS) Analysis
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TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software.

Please cite: Ge Tan and Boris Lenhard: TFBSTools: an R/bioconductor package for transcription factor binding site analysis. (PubMed,eprint) Bioinformatics 32(10):1555–1556 (2016)
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r-cran-adegenet
GNU R exploratory analysis of genetic and genomic data
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Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.

Please cite: Thibaut Jombart: adegenet: a R package for the multivariate analysis of genetic markers. (PubMed,eprint) Bioinformatics 24(11):1403-5 (2008)
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r-cran-adephylo
GNU R exploratory analyses for the phylogenetic comparative method
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This GNU R package provides multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa.

Please cite: Thibaut Jombart, François Balloux and Stéphane Dray: adephylo: new tools for investigating the phylogenetic signal in biological traits. (PubMed,eprint) Bioinformatics 26(15):1907-1909 (2010)
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r-cran-alakazam
Immunoglobulin Clonal Lineage and Diversity Analysis
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Alakazam is part of the Immcantation analysis framework for Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) and provides a set of tools to investigate lymphocyte receptor clonal lineages, diversity, gene usage, and other repertoire level properties, with a focus on high-throughput immunoglobulin (Ig) sequencing.

Alakazam serves five main purposes:

  • Providing core functionality for other R packages in the Immcantation framework. This includes common tasks such as file I/O, basic DNA sequence manipulation, and interacting with V(D)J segment and gene annotations.
  • Providing an R interface for interacting with the output of the pRESTO and Change-O tool suites.
  • Performing lineage reconstruction on clonal populations of Ig sequences and analyzing the topology of the resultant lineage trees.
  • Performing clonal abundance and diversity analysis on lymphocyte repertoires.
  • Performing physicochemical property analyses of lymphocyte receptor sequences.
Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. (eprint) 31(20):3356–3358 (2017)
r-cran-ape
GNU R package for Analyses of Phylogenetics and Evolution
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This package provides functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ, BIONJ, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.

Please cite: Emmanuel Paradis and Klaus Schliep: ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics (2018)
r-cran-bio3d
GNU R package for biological structure analysis
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The bio3d package contains utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform atom summaries, atom selection, re-orientation, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, and principal component analysis (PCA). In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data.

Please cite: Barry J. Grant, Ana P. C. Rodrigues, Karim M. ElSawy, J. Andrew McCammon and Leo S. D. Caves: Bio3d: an R package for the comparative analysis of protein structures. (PubMed,eprint) Bioinformatics 22(21):2695-2696 (2006)
r-cran-distory
GNU R distance between phylogenetic histories
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This GNU R package enables calculation of geodesic distance between phylogenetic trees and associated functions.

r-cran-genabel
GNU R package for genome-wide SNP association analysis
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The package offers the R library GenABEL for the hunt of genetic contributions to a disease (or any other pheonypical trait) by so called genome-wide association analysis. Additional input commonly comes from DNA mircoarray experiments, performed on every individual, that determine differences (polymorphisms) in the population. GenABEL finds associations between quantitative or binary traits and single-nucleiotide polymorphisms (SNPs).

Package 'GenABEL’ was removed from the CRAN repository. The code was obtained from the archive.

Please cite: Yurii S. Aulchenko, Stephan Ripke, Aaron Isaacs and Cornelia M. van Duijn: GenABEL: an R library for genome-wide association analysis. (PubMed,eprint) Bioinformatics 23(10):1294-1296 (2007)
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r-cran-kaos
Encoding of Sequences Based on Frequency Matrix Chaos
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Sequences encoding by using the chaos game representation. Löchel et al. (2019) .

Please cite: Hannah F. Löchel, Dominic Eger, Theodor Sperlea and Dominik Heider: Deep learning on chaos game representation for proteins. Bioinformatics (2019)
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r-cran-metamix
GNU R bayesian mixture analysis for metagenomic community profiling
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Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture.

Please cite: Sofia Morfopoulou and Vincent Plagnol: Bayesian mixture analysis for metagenomic community profiling. (eprint) Bioinformatics 31(18):2930-2938 (2015)
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r-cran-phangorn
GNU R package for phylogenetic analysis
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phangorn is a tool for reconstructing phylogenies, using distance-based methods, maximum parsimony or maximum likelihood, and performing Hadamard conjugation. It also offers functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analysis.

Please cite: K.P. Schliep: phangorn: phylogenetic analysis in R. (PubMed) Bioinformatics 27(4):592-593 (2011)
r-cran-phytools
GNU R phylogenetic tools for comparative biology
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A wide range of functions for phylogenetic analysis. Functionality is concentrated in phylogenetic comparative biology, but also includes a diverse array of methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic trees and data. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, simulation of phylogenies and data, and multivariate analysis. There are a broad range of plotting methods for phylogenies and comparative data which include, but are not restricted to, methods for mapping trait evolution on trees, for projecting trees into phenotypic space or a geographic map, and for visualizing correlated speciation between trees. Finally, there are a number of functions for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data not covered by other packages. For instance, there are functions for randomly or non-randomly attaching species or clades to a phylogeny, for estimating supertrees or consensus phylogenies from a set, for simulating trees and phylogenetic data under a range of models, and for a wide variety of other manipulations and analyses that phylogenetic biologists might find useful in their research.

Please cite: Liam J. Revell: phytools: an R package for phylogenetic comparative biology (and other things). (eprint) Methods in Ecology and Evolution 3(2):217-223 (2012)
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r-cran-pscbs
R package: Analysis of Parent-Specific DNA Copy Numbers
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Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumoronly analyses are supported.

Please cite: Adam B. Olshen, Henrik Bengtsson, Pierre Neuvial, Paul T. Spellman, Richard A. Olshen and Venkatraman E. Seshan: Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. (PubMed,eprint) Bioinformatics 27(15):2038-2046 (2011)
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r-cran-qtl
GNU R package for genetic marker linkage analysis
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R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. It is implemented as an add-on-package for the freely available and widely used statistical language/software R (see http://www.r-project.org).

The development of this software as an add-on to R allows one to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. The goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing.

A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. The main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses were implemented.

The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation.

Please cite: Karl W. Broman, Hao Wu, Saunak Sen and Gary A. Churchill: R/qtl: QTL mapping in experimental crosses. (PubMed,eprint) Bioinformatics 19:889-890 (2003)
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r-cran-rotl
GNU R interface to the 'Open Tree of Life' API
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An interface to the 'Open Tree of Life' API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The 'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all named species.

r-cran-samr
GNU R significance analysis of microarrays
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This GNU R package provides significance analysis of microarrays. A microarray is a multiplex lab-on-a-chip. It is a 2D array on a solid substrate (usually a glass slide or silicon thin-film cell) that assays large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods.

This package helps analysing this kind of microarrays.

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r-cran-sdmtools
Species Distribution Modelling Tools
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This package provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.

This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.

r-cran-seqinr
GNU R biological sequences retrieval and analysis
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Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Includes also utilities for sequence data management under the ACNUC system.

r-cran-seurat
Tools for Single Cell Genomics
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A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Butler A and Satija R (2017) for more details.

Please cite: Rahul Satija, Jeffrey A. Farrell, David Gennert, Alexander F. Schier and Aviv Regev: Spatial reconstruction of single-cell gene expression data. (PubMed) Nature Biotechnology 33:495–502 (2015)
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r-cran-shazam
Immunoglobulin Somatic Hypermutation Analysis
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Provides a computational framework for Bayesian estimation of antigen-driven selection in immunoglobulin (Ig) sequences, providing an intuitive means of analyzing selection by quantifying the degree of selective pressure. Also provides tools to profile mutations in Ig sequences, build models of somatic hypermutation (SHM) in Ig sequences, and make model-dependent distance comparisons of Ig repertoires.

SHazaM is part of the Immcantation analysis framework for Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) and provides tools for advanced analysis of somatic hypermutation (SHM) in immunoglobulin (Ig) sequences. Shazam focuses on the following analysis topics:

  • Quantification of mutational load SHazaM includes methods for determine the rate of observed and expected mutations under various criteria. Mutational profiling criteria include rates under SHM targeting models, mutations specific to CDR and FWR regions, and physicochemical property dependent substitution rates.
  • Statistical models of SHM targeting patterns Models of SHM may be divided into two independent components: 1) a mutability model that defines where mutations occur and 2) a nucleotide substitution model that defines the resulting mutation. Collectively these two components define an SHM targeting model. SHazaM provides empirically derived SHM 5-mer context mutation models for both humans and mice, as well tools to build SHM targeting models from data.
  • Analysis of selection pressure using BASELINe The Bayesian Estimation of Antigen-driven Selection in Ig Sequences (BASELINe) method is a novel method for quantifying antigen-driven selection in high-throughput Ig sequence data. BASELINe uses SHM targeting models can be used to estimate the null distribution of expected mutation frequencies, and provide measures of selection pressure informed by known AID targeting biases.
  • Model-dependent distance calculations SHazaM provides methods to compute evolutionary distances between sequences or set of sequences based on SHM targeting models. This information is particularly useful in understanding and defining clonal relationships.
Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.. (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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r-cran-spp
GNU R ChIP-seq processing pipeline
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R package for anlaysis of ChIP-seq and other functional sequencing data

  • Assess overall DNA-binding signals in the data and select appropriate quality of tag alignment.
  • Discard or restrict positions with abnormally high number of tags.
  • Calculate genome-wide profiles of smoothed tag density and save them in WIG files for viewing in other browsers.
  • Calculate genome-wide profiles providing conservative statistical estimates of fold enrichment ratios along the genome. These can be exported for browser viewing, or thresholded to determine regions of significant enrichment/depletion.
  • Determine statistically significant point binding positions
  • Assess whether the set of point binding positions detected at a current sequencing depth meets saturation criteria, and if does not, estimate what sequencing depth would be required to do so.
Please cite: Peter V Kharchenko, Michael Y Tolstorukov and Peter J Park: Design and analysis of ChIP-seq experiments for DNA-binding proteins. (PubMed) Nature biotechnology 26(12):1351–1359 (2008)
r-cran-tcr
Advanced Data Analysis of Immune Receptor Repertoires
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Cells of the immune system are the grand exception to the rule that all cells of an individuum have (mostly exact) copies of the same DNA. B cells (which produce antibodies) and T cells (which communicate with cells) however have a section of their DNA with genes of the groups V, D and J that are reorganised within the genomic DNA to provide the flexibility to deal with yet unknown pathogens.

This package provides a platform for the advanced analysis of T cell receptor repertoire data and its visualisations.

Caveat: This package is soon to be replaced by http://github.com/immunomind/immunarch which is not yet available as a Debian package.

Please cite: Vadim I. Nazarov, Mikhail V. Pogorelyy, Ekaterina A. Komech, Ivan V. Zvyagin, Dmitry A. Bolotin, Mikhail Shugay, Dmitry M. Chudakov, Yury B. Lebedev and Ilgar Z. Mamedov: tcR: an R package for T cell receptor repertoire advanced data analysis. (eprint) BMC Bioinformatics 16:175 (2015)
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r-cran-tigger
Infers new Immunoglobulin alleles from Rep-Seq Data
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Summary: Infers the V genotype of an individual from immunoglobulin (Ig) repertoire-sequencing (Rep-Seq) data, including detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences.

High-throughput sequencing of B cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline V, D and J gene segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that utilize previously undetected alleles, whose frequency in the population is unclear.

TIgGER is a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual (including novel alleles) from V(D)J-rearrange sequences. TIgGER can then infer a subject’s genotype from these sequences, and use this genotype to correct the initial V(D)J allele assignments.

The application of TIgGER continues to identify a surprisingly high frequency of novel alleles in humans, highlighting the critical need for this approach. TIgGER, however, can and has been used with data from other species.

Core Abilities:

  • Detecting novel alleles
  • Inferring a subject’s genotype
  • Correcting preliminary allele calls

Required Input

  • A table of sequences from a single individual, with columns containing the following:
  • V(D)J-rearranged nucleotide sequence (in IMGT-gapped format)
  • Preliminary V allele calls
  • Preliminary J allele calls
  • Length of the junction region
  • Germline Ig sequences in IMGT-gapped fasta format (e.g., as those downloaded from IMGT/GENE-DB)

The former can be created through the use of IMGT/HighV-QUEST and Change-O.

Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. (eprint) 31(20):3356–3358 (2017)
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r-cran-treespace
Statistical Exploration of Landscapes of Phylogenetic Trees
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Tools for the exploration of distributions of phylogenetic trees. This package includes a shiny interface which can be started from R using 'treespaceServer()'.

r-cran-tsne
t-distributed stochastic neighbor embedding for R (t-SNE)
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A "pure R" implementation of the t-SNE algorithm.

r-cran-vegan
Community Ecology Package for R
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R package for community ecologists. It contains most multivariate analysis needed in analysing ecological communities, and tools for diversity analysis. Most diversity methods assume that data are counts of individuals.

These tools are sometimes used outside the field of ecology, for instance to study populations of white blood cells or RNA molecules.

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r-cran-webgestaltr
find over-represented properties in gene lists
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The web version WebGestalt http://www.webgestalt.org supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.

r-cran-wgcna
Weighted Correlation Network Analysis
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Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Please cite: Peter Langfelder and Steve Horvath: WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559 (2012)
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r-other-ascat
Allele-Specific Copy Number Analysis of Tumours
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ASCAT (allele-specific copy number analysis of tumors) is a allele- specific copy number analysis of the in vivo breast cancer genome. It can be used to accurately dissect the allele-specific copy number of solid tumors, simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture.

Please cite: Peter Van Loo, Silje H Nordgard, Ole Christian Lingjærde, Hege G Russnes, Inga H Rye, Wei Sun, Victor J Weigman, Peter Marynen, Anders Zetterberg, Bjørn Naume, Charles M Perou, Anne-Lise Børresen-Dale and Vessela N Kristensen: Allele-specific Copy Number Analysis of Tumors. (PubMed) PNAS 107(39):16910-5 (2010)
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r-other-mott-happy.hbrem
GNU R package for fine-mapping complex diseases
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Happy is an R interface into the HAPPY C package for fine-mapping Quantitative Trait Loci (QTL) in Heterogenous Stocks (HS). An HS is an advanced intercross between (usually eight) founder inbred strains of mice. HS are suitable for fine-mapping QTL. It uses a multipoint analysis which offers significant improvements in statistical power to detect QTLs over that achieved by single-marker association.

The happy package is an extension of the original C program happy; it uses the C code to compute the probability of descent from each of the founders, at each locus position, but the happy packager allows a much richer range of models to be fit to the data.

Read /usr/share/doc/r-other-mott-happy/README.Debian for a more detailed explanation.

Please cite: Richard Mott, Christopher J. Talbot, Maria G. Turri, Allan C. Collins and Jonathan Flint: A method for fine mapping quantitative trait loci in outbred animal stocks. (PubMed,eprint) Proc. Natl. Acad. Sci. USA 97(23):12649-12654 (2000)
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r-other-rajewsky-dropbead
Basic Exploration and Analysis of Drop-seq Data
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Dropbead offers a quick and straightforward way to explore and perform basic analysis of single cell sequencing data coming from droplet sequencing. It has been particularly tailored for Drop-seq.

Please cite: J. Alles, N. Karaiskos, S. Praktiknjo, S. Grosswendt, P. Wahle, P.-L. Ruffault, S. Ayoub, L. Schreyer, A. Boltengagen, C. Birchmeier, R. Zinzen an, C. Kocks and N. Rajewsky: Cell fixation and preservation for droplet-based single-cell transcriptomics. (PubMed,eprint) BMC Biology 15(44) (2017)
racon
consensus module for raw de novo DNA assembly of long uncorrected reads
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Racon is intended as a standalone consensus module to correct raw contigs generated by rapid assembly methods which do not include a consensus step. The goal of Racon is to generate genomic consensus which is of similar or better quality compared to the output generated by assembly methods which employ both error correction and consensus steps, while providing a speedup of several times compared to those methods. It supports data produced by both Pacific Biosciences and Oxford Nanopore Technologies.

Racon can be used as a polishing tool after the assembly with either Illumina data or data produced by third generation of sequencing. The type of data inputed is automatically detected.

Racon takes as input only three files: contigs in FASTA/FASTQ format, reads in FASTA/FASTQ format and overlaps/alignments between the reads and the contigs in MHAP/PAF/SAM format. Output is a set of polished contigs in FASTA format printed to stdout. All input files can be compressed with gzip.

Racon can also be used as a read error-correction tool. In this scenario, the MHAP/PAF/SAM file needs to contain pairwise overlaps between reads including dual overlaps.

A wrapper script is also available to enable easier usage to the end- user for large datasets. It has the same interface as racon but adds two additional features from the outside. Sequences can be subsampled to decrease the total execution time (accuracy might be lower) while target sequences can be split into smaller chunks and run sequentially to decrease memory consumption. Both features can be run at the same time as well.

Please cite: Robert Vaser, Ivan Sovic, Niranjan Nagarajan and Mile Sikic: Fast and accurate de novo genome assembly from long uncorrected reads. (PubMed,eprint) Genome Research 27(5):737-746 (2017)
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radiant
explore hierarchical metagenomic data with zoomable pie charts
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Krona allows hierarchical data to be explored with zoomable pie charts. Krona charts include support for several bioinformatics tools and raw data formats. The charts can be viewed with a recent version of any major web browser.

Please cite: Brian D Ondov, Nicholas H Bergman and Adam M Phillippy: Interactive metagenomic visualization in a Web browser. (PubMed,eprint) BMC Bioinformatics 12:385 (2011)
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ragout
Reference-Assisted Genome Ordering UTility
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Ragout (Reference-Assisted Genome Ordering UTility) is a tool for chromosome-level scaffolding using multiple references. Given initial assembly fragments (contigs/scaffolds) and one or multiple related references (complete or draft), it produces a chromosome-scale assembly (as a set of scaffolds).

The approach is based on the analysis of genome rearrangements (like inversions or chromosomal translocations) between the input genomes and reconstructing the most parsimonious structure of the target genome.

Ragout now supports both small and large genomes (of mammalian scale and complexity). The assembly of highly polymorphic genomes is currently limited.

Please cite: Mikhail Kolmogorov, Joel Armstrong, Brian J. Raney, Ian Streeter, Matthew Dunn, Fengtang Yang, Duncan Odom, Paul Flicek, Thomas M. Keane, David Thybert, Benedict Paten and Son Pham: Chromosome assembly of large and complex genomes using multiple references. (PubMed,eprint) Genome Research 28(11):1720-1732 (2018)
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rambo-k
Read Assignment Method Based On K-mers
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RAMBO-K is a tool for rapid and sensitive removal of background sequences from Next Generation Sequencing data.

RAMBO-K is a reference-based tool for rapid and sensitive extraction of one organisms reads from a mixed dataset. It is based on a Markov chain implementation, which uses genomic characteristics of each reference to assign reads to the associated set.

Please cite: Simon H. Tausch, Bernhard Y. Renard, Andreas Nitsche and Piotr Wojciech Dabrowski: RAMBO-K: Rapid and Sensitive Removal of Background Sequences from Next Generation Sequencing Data. (PubMed,eprint) PLOS one 10(9):e0137896 (20015)
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rampler
module for sampling genomic sequences
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Standalone module for sampling genomic sequences. It supports two modes, random subsampling of sequencer data to a desired depth (given the reference length) and file splitting to desired size in bytes.

Rampler takes as first input argument a file in FASTA/FASTQ format which can be compressed with gzip. The rest of input parameters depend on the mode of operation. The output is stored into a file(s) which is in the same format as the input file but uncompressed.

rapmap
rapid sensitive and accurate DNA read mapping via quasi-mapping
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RapMap is a testing ground for ideas in quasi-mapping / (lightweight / pseudo) transcriptome alignment. That means that, at this point, it is somewhat experimental. The develop branch will have the latest improvements and additions, but is not guaranteed to be stable between commits. Breaking changes to the master branch will be accompanied by a tag to the version before the breaking change. Currently, RapMap is a stand-alone quasi-mapper that can be used with other tools. It is also being used as part of Sailfish and Salmon. Eventually, the hope is to create and stabilize an API so that it can be used as a library from other tools.

Quasi-mapping / (lightweight / pseudo)-alignment is the term that is used here for the type of information required for certain tasks (e.g. transcript quantification) that is less "heavyweight" than what is provided by traditional alignment. For example, one may only need to know the transcripts / contigs to which a read aligns and, perhaps, the position within those transcripts rather than the optimal alignment and base-for-base CIGAR string that aligns the read and substring of the transcript. For details on RapMap (quasi-mapping in particular), please check out the associated paper. Note: RapMap implements both quasi- mapping and pseudo-alignment (originally introduced in Bray et al. 2016), these two are not the same thing. They are distinct concepts, and RapMap simply happens to implement algorithms for computing both.

Please cite: Avi Srivastava, Hirak Sarkar, Nitish Gupta and Rob Patro: RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. (PubMed,eprint) Bioinformatics 32(12):i192-i200 (2016)
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rasmol
visualization of biological macromolecules
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RasMol is a molecular graphics program intended for the visualisation of proteins, nucleic acids and small molecules. The program is aimed at display, teaching and generation of publication quality images.

The program reads in a molecule coordinate file and interactively displays the molecule on the screen in a variety of colour schemes and molecule representations. Currently available representations include depth-cued wireframes, 'Dreiding' sticks, spacefilling (CPK) spheres, ball and stick, solid and strand biomolecular ribbons, atom labels and dot surfaces.

Supported input file formats include Protein Data Bank (PDB), Tripos Associates' Alchemy and Sybyl Mol2 formats, Molecular Design Limited's (MDL) Mol file format, Minnesota Supercomputer Center's (MSC) XYZ (XMol) format, CHARMm format, CIF format and mmCIF format files.

This package installs two versions of RasMol, rasmol-gtk has a modern GTK-based user interface and rasmol-classic is the version with the old Xlib GUI.

The package is enhanced by the following packages: rasmol-doc
Please cite: Roger A. Sayle and E. James Milner-White: RasMol: Biomolecular graphics for all. (PubMed) Trends in Biochemical Sciences (TIBS) 20(9):374 (1995)
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raster3d
tools for generating images of proteins or other molecules
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Raster3D is a set of tools for generating high quality raster images of proteins or other molecules. The core program renders spheres, triangles, cylinders, and quadric surfaces with specular highlighting, Phong shading, and shadowing. It uses an efficient software Z-buffer algorithm which is independent of any graphics hardware. Ancillary programs process atomic coordinates from PDB files into rendering descriptions for pictures composed of ribbons, space-filling atoms, bonds, ball+stick, etc. Raster3D can also be used to render pictures composed in other programs such as Molscript in glorious 3D with highlights, shadowing, etc. Output is to pixel image files with 24 bits of color information per pixel.

Please cite: E.A. Merritt and D.J. Bacon: Raster3D Photorealistic Molecular Graphics. (PubMed) Methods in Enzymology 277:505-524 (1997)
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rate4site
detector of conserved amino-acid sites
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Rate4Site calculates the relative evolutionary rate at each site using a probabilistic-based evolutionary model. This allows taking into account the stochastic process underlying sequence evolution within protein families and the phylogenetic tree of the proteins in the family. The conservation score at a site corresponds to the site's evolutionary rate.

Please cite: I. Mayrose, D. Graur, N. Ben-Tal and T. and Pupko: Comparison of site-specific rate-inference methods: Bayesian methods are superior. (PubMed,eprint) Mol Biol Evol 21(2):1781-1791 (2004)
raxml
Randomized Axelerated Maximum Likelihood of phylogenetic trees
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RAxML is a program for sequential and parallel Maximum Likelihood-based inference of large phylogenetic trees. It has originally been derived from fastDNAml.

Please cite: Alexandros Stamatakis: RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. (PubMed,eprint) Bioinformatics Epub ahead of print (2014)
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ray
de novo genome assemblies of next-gen sequencing data
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Ray is a parallel software that computes de novo genome assemblies with next-generation sequencing data. Ray is written in C++ and can run in parallel on numerous interconnected computers using the message-passing interface (MPI) standard. Included:

  • Ray de novo assembly of single genomes
  • Ray Méta de novo assembly of metagenomes
  • Ray Communities microbe abundance + taxonomic profiling
  • Ray Ontologies gene ontology profiling
Please cite: Sébastien Boisvert, François Laviolette and Jacques Corbeil: Ray: Simultaneous Assembly of Reads from a Mix of High-Throughput Sequencing Technologies. (PubMed,eprint) Journal of Computational Biology 17(11):1519-1533 (2010)
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rdp-alignment
Ribosomal Database Project (RDP) alignment tools package
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The Alignment tools package contains commands to do defined community analysis, pairwise alignment and hidden markov model (HMMER3 models, no training).

The package also contains the AlignmentTools Java library which is used by other RDP tools.

Please cite: Jordan A. Fish, Benli Chai, Qiong Wang, Yanni Sun, C. Titus Brown, James M. Tiedje and James R Cole: FunGene: the functional gene pipeline and repository. (PubMed,eprint) Front Microbiology 4:291 (2013)
rdp-classifier
extensible sequence classifier for fungal lsu, bacterial and archaeal 16s
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The RDP Classifier is a naive Bayesian classifier which was developed to provide rapid taxonomic placement based on rRNA sequence data. The RDP Classifier can rapidly and accurately classify bacterial and archaeal 16s rRNA sequences, and Fungal LSU sequences. It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The RDP Classifier likely can be adapted to additional phylogenetically coherent bacterial taxonomies.

Please cite: Qiong Wang, George M. Garrity, James M. Tiedje and James R. Cole: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. (PubMed,eprint) Appl Environ Microbiol. 73(16):5261-7 (2007)
rdp-readseq
Ribosomal Database Project (RDP) sequence reading and writing
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Rdp-readseq is a simple user interface to the sequence reading library developed by the Ribosomal Database Project. It can handle genbank, embl, fasta, fastq, sff and sto files. It can read from files or streams, and can handle indexing files.

The package also contains the ReadSeq Java library which is used by other RDP tools.

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readseq
Conversion between sequence formats
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Reads and writes nucleic/protein sequences in various formats. Data files may have multiple sequences. Readseq is particularly useful as it automatically detects many sequence formats, and converts between them.

Please cite: Don Gilbert: Sequence file format conversion with command-line readseq. (PubMed,eprint) Current Protocols in Bioinformatics Appendix 1:E (2003)
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readucks
Nanopore read de-multiplexer (read demux -> readux -> readucks, innit)
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This package is inspired by the demultiplexing options in porechop but without the adapter trimming options - it just demuxes. It uses the parasail library with its Python bindings to do pairwise alignment which provides a considerable speed up over the seqan library used by porechop due to its low-level use of vector processor instructions.

reapr
universal tool for genome assembly evaluation
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REAPR is a tool that evaluates the accuracy of a genome assembly using mapped paired end reads, without the use of a reference genome for comparison. It can be used in any stage of an assembly pipeline to automatically break incorrect scaffolds and flag other errors in an assembly for manual inspection. It reports mis-assemblies and other warnings, and produces a new broken assembly based on the error calls.

The software requires as input an assembly in FASTA format and paired reads mapped to the assembly in a BAM file. Mapping information such as the fragment coverage and insert size distribution is analysed to locate mis-assemblies. REAPR works best using mapped read pairs from a large insert library (at least 1000bp). Additionally, if a short insert Illumina library is also available, REAPR can combine this with the large insert library in order to score each base of the assembly.

Please cite: Martin Hunt, Taisei Kikuchi, Mandy Sanders, Chris Newbold, Matthew Berriman and Thomas D Otto: REAPR: a universal tool for genome assembly evaluation. (PubMed,eprint) Genome Biology 14(5):R47 (2013)
recan
genetic distance plotting for recombination events analysis
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recan is a Python package which allows one to construct genetic distance plots to explore and discover recombination events in viral genomes.

This method has been previously implemented in desktop software tools: RAT, Simplot and RDP4.

relion
toolkit for 3D reconstructions in cryo-electron microscopy
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RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy.

RELION provides a GUI, several command-line tools in parallel (MPI) and serial versions, optionally with CUDA/GPU support.

relion provides the serial and parallel (MPI) command-line tools without CUDA/GPU support.

Please cite: Sjors H. W. Scheres: RELION: implementation of a Bayesian approach to cryo-EM structure determination. (PubMed) J. Struct. Biol. 180(3):519-30 (2012)
relion-gui
toolkit for 3D reconstructions in cryo-electron microscopy (gui apps)
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RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy.

RELION provides a GUI, several command-line tools in parallel (MPI) and serial versions, optionally with CUDA/GPU support.

relion-gui provides the graphical user interface without CUDA/GPU support.

Please cite: Sjors H. W. Scheres: RELION: implementation of a Bayesian approach to cryo-EM structure determination. (PubMed) J. Struct. Biol. 180(3):519-30 (2012)
repeatmasker-recon
finds repeat families from biological sequences
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The RECON package implements a de novo algorithm for the identification of repeat families from biological sequences.

The program implements an approach for the de novo identification and classification of repeat sequence families that is based on extensions to the usual approach of single linkage clustering of local pairwise alignments between genomic sequences. The extensions use multiple alignment information to define the boundaries of individual copies of the repeats and to distinguish homologous but distinct repeat element families. When tested on the human genome, this approach was able to properly identify and group known transposable elements. The program, should be useful for first-pass automatic classification of repeats in newly sequenced genomes.

Please cite: Zhirong Bao and Sean R. Eddy: Automated De Novo Identification of Repeat Sequence Families in Sequenced Genomes. (PubMed,eprint) Genome Research 12(8):1269-76 (2002)
reprof
protein secondary structure and accessibility predictor
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'reprof' is an improved implementation of 'prof', a popular protein secondary structure and accessibility predictor. Prediction is either done from protein sequence alone or from an alignment - the latter should be used for optimal performance.

This package provides the 'reprof' command. It is only a command line interface to the functionality provided by the modules in librg-reprof-bundle-perl.

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resfinder
identify acquired antimicrobial resistance genes
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ResFinder identifies acquired antimicrobial resistance genes in total or partial sequenced isolates of bacteria.

ResFinder that uses BLAST for identification of acquired antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete or partial genomes, and short sequence reads from four different sequencing platforms. The method was evaluated on 1862 GenBank files containing 1411 different resistance genes, as well as on 23 de-novo-sequenced isolates.

Please cite: Ea Zankari, Henrik Hasman, Salvatore Cosentino, Martin Vestergaard, Simon Rasmussen, Ole Lund, Frank M. Aarestrup and Mette Voldby Larsen: Identification of acquired antimicrobial resistance genes. (PubMed,eprint) Journal of Antimicrobial Chemotherapy 67(11):2640-4 (2012)
rna-star
ultrafast universal RNA-seq aligner
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Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, the authors experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy.

The package is enhanced by the following packages: multiqc
Please cite: Alexander Dobin, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson and Thomas R. Gingeras: STAR: ultrafast universal RNA-seq aligner. (PubMed,eprint) Bioinformatics 29(1):15-21 (2012)
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Topics: Sequence analysis
rnahybrid
Fast and effective prediction of microRNA/target duplexes
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RNAhybrid is a tool for finding the minimum free energy hybridisation of a long and a short RNA. The hybridisation is performed in a kind of domain mode, ie. The short sequence is hybridised to the best fitting part of the long one. The tool is primarily meant as a means for microRNA target prediction.

Please cite: Marc Rehmsmeier, Peter Steffen, Matthias Höchsmann and Robert Giegerich: Fast and effective prediction of microRNA/target duplexes. (PubMed,eprint) RNA 10(10):1507-1517 (2004)
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roary
high speed stand alone pan genome pipeline
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Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (as produced, for instance, by Prokka) and calculates the pan genome. Using a standard desktop PC, it can analyse datasets with thousands of samples, something which is computationally infeasible with existing methods, without compromising the quality of the results. 128 samples can be analysed in under 1 hour using 1 GB of RAM and a single processor. To perform this analysis using existing methods would take weeks and hundreds of GB of RAM. Roary is not intended for meta-genomics or for comparing extremely diverse sets of genomes.

Please cite: Andrew J. Page, Carla A. Cummins, Martin Hunt, Vanessa K. Wong, Sandra Reuter, Matthew T. G. Holden, Maria Fookes, Daniel Falush, Jacqueline A. Keane and Julian Parkhill: Roary: Rapid large-scale prokaryote pan genome analysis. (PubMed,eprint) Bioinformatics 31(22):3691-3693 (2015)
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rockhopper
system for analyzing bacterial RNA-seq data
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Rockhopper is a comprehensive and user-friendly system for computational analysis of bacterial RNA-seq data. As input, Rockhopper takes RNA sequencing reads output by high-throughput sequencing technology (FASTQ, QSEQ, FASTA, SAM, or BAM files). Rockhopper supports the following tasks:

  • Reference based transcript assembly (when one or more reference genomes are available)
  • Aligning reads to genomes
  • Assembling transcripts
  • Identifying transcript boundaries and novel transcripts such as small RNAs
  • De novo transcript assembly (when reference genomes are unavailable)
  • Normalizing data from different experiments
  • Quantifying transcript abundance
  • Testing for differential gene expression
  • Characterizing operon structures
  • Visualizing results in a genome browser
The package is enhanced by the following packages: multiqc
Please cite: Brian Tjaden: De novo assembly of bacterial transcriptomes from RNA-seq data. (PubMed,eprint) Genome Biology (2015)
roguenarok
versatile and scalable algorithm for rogue taxon identification
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RogueNaRok is a versatile and scalable algorithm for rogue taxon identification. It also includes implementations of the maximum agreement subtree, leaf stability index and taxonomic instability index.

Please cite: Andre J. Aberer, Denis Krompass and Alexandros Stamatakis: Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice. (PubMed,eprint) Systematic Biology 62(1):162-166 (2013)
rsem
RNA-Seq by Expectation-Maximization
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RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. For visualization, It can generate BAM and Wiggle files in both transcript-coordinate and genomic-coordinate. Genomic-coordinate files can be visualized by both UCSC Genome browser and Broad Institute’s Integrative Genomics Viewer (IGV). Transcript-coordinate files can be visualized by IGV. RSEM also has its own scripts to generate transcript read depth plots in pdf format. The unique feature of RSEM is, the read depth plots can be stacked, with read depth contributed to unique reads shown in black and contributed to multi-reads shown in red. In addition, models learned from data can also be visualized. Last but not least, RSEM contains a simulator.

The package is enhanced by the following packages: multiqc
Please cite: Bo Li and Colin Dewey: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. (PubMed,eprint) BMC Bioinformatics 12(1):323 (2011)
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rtax
Classification of sequence reads of 16S ribosomal RNA gene
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Short-read technologies for microbial community profiling are increasingly popular, yet previous techniques for assigning taxonomy to paired-end reads perform poorly. RTAX provides rapid taxonomic assignments of paired-end reads using a consensus algorithm.

Please cite: David A. W. Soergel, Neelendu Dey, Rob Knight and Steven E. Brenner: Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. (PubMed,eprint) The ISME Journal 6:1440–1444 (2012)
runcircos-gui
GUI tool to run circos
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runCircos-gui is a simple yet comprehensive crossplatform software to execute Circos from a graphical user interface. The software eliminates the need to use command line for running Circos without compremising the power of complete commandline parameters and options.

runCircos-gui optimize running parameters (both toggle options & options with arguments) and install Perl packages without using command line.

saint
Significance Analysis of INTeractome
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SAINT implements the scoring algorithm for protein-protein interaction data using label free quantitative proteomics data in AP-MS experiments. It was used for spectral count data in the yeast kinase interactome work not incorporating control purification, as well as a generalized implementation for spectral count data with and without control purification.

Alternatively, you can also run SAINT in combination with ProHits.

The package was written for either doing analysis without or with control IPs and

Please cite: A. Breitkreutz, H. Choi, J.R. Sharom, L. Boucher, V. Neduva, B. Larsen, Z.Y. Lin, B.J. Breitkreutz, C. Stark, G. Liu, J. Ahn, D. Dewar-Darch, T. Reguly, X. Tang, R. Almeida, Z.S. Qin, T. Pawson, A.-C. Gingras, A.I. Nesvizhskii and M. Tyers: A global protein kinase and phosphatase interaction network in yeast. (PubMed) Science 328(5981):1043-6 (2010)
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salmid
rapid Kmer based Salmonella identifier from sequence data
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SalmID enables rapid confirmation of Salmonella spp. and subspp. from sequence data. This is done by checking taxonomic ID of single isolate samples. Currently only IDs Salmonella species and subspecies, and some common contaminants (Listeria, Escherichia).

salmon
wicked-fast transcript quantification from RNA-seq data
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Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. Salmon achieves is accuracy and speed via a number of different innovations, including the use of lightweight alignments (accurate but fast-to-compute proxies for traditional read alignments) and massively-parallel stochastic collapsed variational inference. The result is a versatile tool that fits nicely into many different pipelines. For example, you can choose to make use of the lightweight alignments by providing Salmon with raw sequencing reads, or, if it is more convenient, you can provide Salmon with regular alignments (e.g. computed with your favorite aligner), and it will use the same wicked-fast, state-of-the-art inference algorithm to estimate transcript-level abundances for your experiment.

The package is enhanced by the following packages: multiqc
Please cite: Rob Patro, Geet Duggal, Michael I Love, Rafael A Irizarry and Carl Kingsford: Salmon provides fast and bias-aware quantification of transcript expression. (eprint) Nature Methods 14(4):417-419 (2017)
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sambamba
tools for working with SAM/BAM data
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Sambamba positions itself as a performant alternative to samtools and provides tools for

  • Powerful filtering with sambamba view --filter
  • Picard-like SAM header merging in the merge tool
  • Optional for operations on whole BAMs
  • Fast copying of a region to a new file with the slice tool
  • Duplicate marking/removal, using the Picard criteria
Please cite: Artem Tarasov, Albert J. Vilella, Edwin Cuppen, Isaac J. Nijman and Pjotr Prins: Sambamba: fast processing of NGS alignment formats. (PubMed,eprint) Bioinformatics 31(12):2032-2034 (2015)
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samblaster
marks duplicates, extracts discordant/split reads
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Current "next-generation" sequencing technologies cannot tell what exact sequence they will be reading. They take what is available. And if some sequences are read very often, then this needs some extra biomedical thinking. The genome could for instance be duplicated.

samblaster is a fast and flexible program for marking duplicates in read-id grouped paired-end SAM files. It can also optionally output discordant read pairs and/or split read mappings to separate SAM files, and/or unmapped/clipped reads to a separate FASTQ file. When marking duplicates, samblaster will require approximately 20MB of memory per 1M read pairs.

The package is enhanced by the following packages: multiqc
Please cite: Gregory G. Faust and Ira M. Hall: SAMBLASTER: fast duplicate marking and structural variant read extraction. (PubMed,eprint) Bioinformatics 30(17):2503-2505 (2014)
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samclip
filter SAM file for soft and hard clipped alignments
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Most short read aligners perform local alignment of reads to the reference genome. Examples includes bwa mem, minimap2, and bowtie2 (unless in --end-to-end mode). This means the ends of the read may not be part of the best alignment.

This can be caused by:

  • adapter sequences (aren't in the reference)
  • poor quality bases (mismatches only make the alignment score worse)
  • structural variation in your sample compared to the reference
  • reads overlapping the start and end of contigs (including circular genomes)

Read aligners output a SAM file. Column 6 in this format stores the CIGAR string. which describes which parts of the read aligned and which didn't. The unaligned ends of the read can be "soft" or "hard" clipped, denoted with S and H at each end of the CIGAR string. It is possible for both types to be present, but that is not common. Soft and hard don't mean anything biologically, they just refer to whether the full read sequence is in the SAM file or not.

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samtools
processing sequence alignments in SAM, BAM and CRAM formats
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Samtools is a set of utilities that manipulate nucleotide sequence alignments in the binary BAM format. It imports from and exports to the ascii SAM (Sequence Alignment/Map) and CRAM formats, does sorting, merging and indexing, and allows one to retrieve reads in any regions swiftly. It is designed to work on a stream, and is able to open a BAM or CRAM (not SAM) file on a remote FTP or HTTP server.

The package is enhanced by the following packages: libbio-samtools-perl multiqc
Please cite: Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin and 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map (SAM) Format and SAMtools. (PubMed,eprint) Bioinformatics 25(16):2078-2079 (2009)
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savvy-util
conversion tool for SAV file format
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Savvy is the official C++ interface for the SAV file format and offers seamless support for BCF and VCF files.

The binary sav can be used to handle SAV filed.

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scoary
pangenome-wide association studies
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Scoary is designed to take the gene_presence_absence.csv file from Roary as well as a traits file created by the user and calculate the associations between all genes in the accessory genome and the traits. It reports a list of genes sorted by strength of association per trait.

Please cite: Ola Brynildsrud, Jon Bohlin, Lonneke Scheffer and Vegard Eldholm: Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. (PubMed,eprint) Genome Biology 17(238) (2016)
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scrappie
basecaller for Nanopore sequencer
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The Nanopore is a device for DNA/RNA sequencing that does not require an amplification of the material. The polynucleotides are threaded through a pore and while these pass through, the change in the electrostatic potential allows one to identify ("call") the actual base that resides in the pore. Scrappie goes a step further and also attempts to describe modifications to the nucleic acid.

Please cite: Ryan R. Wick, Louise M. Judd and Kathryn E. Holt: Performance of neural network basecalling tools for Oxford Nanopore sequencing.. (eprint) Genome Biol. 20:129 (2019)
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scrm
simulator of evolution of genetic sequences
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scrm simulates the evolution of genetic sequences.

It takes a neutral evolutionary model as input, and generates random sequences that evolved under the model. As coalescent simulator, it traces the ancestry of the sampled sequences backwards in time and is therefore extremely efficient. Compared to other coalescent simulators, it can simulate chromosome-scale sequences without a measureable reduction of genetic linkage between different sites.

Please cite: Paul R. Staab, Sha Zhu, Dirk Metzler and Gerton Lunter: scrm: efficiently simulating long sequences using the approximated coalescent with recombination.. (2015)
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scythe
Bayesian adaptor trimmer for sequencing reads
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Scythe uses a Naive Bayesian approach to classify contaminant substrings in sequence reads. It considers quality information, which can make it robust in picking out 3'-end adapters, which often include poor quality bases.

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seaview
Multiplatform interface for sequence alignment and phylogeny
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SeaView is a viewer and editor of multiple sequence alignments, i.e. DNA or protein sequences are positioned each in their own separate line, such that the nucleotide/amino acid at a particular position (column) is presumed to have the same biochemical property.

SeaView reads and writes various file formats (NEXUS, MSF, CLUSTAL, FASTA, PHYLIP, MASE, Newick) of DNA and protein sequences and of phylogenetic trees. Alignments can be manually edited. It drives the programs Muscle or Clustal Omega for multiple sequence alignment, and also allows one to use any external alignment algorithm able to read and write FASTA-formatted files. It computes phylogenetic trees by parsimony using PHYLIP's dnapars/protpars algorithm, by distance with NJ or BioNJ algorithms on a variety of evolutionary distances, or by maximum likelihood using the program PhyML 3.0.

SeaView draws phylogenetic trees on screen or PostScript files, and allows one to download sequences from EMBL/GenBank/UniProt using the Internet.

The package is enhanced by the following packages: muscle muscle3
Please cite: Manolo Gouy, Stephane Guindon and Olivier Gascuel: SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. (PubMed,eprint) Mol Biol Evol 27(2):221-224 (2010)
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seer
genomic sequence element (kmer) enrichment analysis
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Bacterial genomes vary extensively in terms of both gene content and gene sequence - this plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. SEER provides a computationally scalable and widely applicable statistical method for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to even tens of thousands of genomes by counting variable- length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogen Streptococcus pneumoniae, SEER identifies relevant previously characterised resistance determinants for several antibiotics.

Please cite: John A Lees, Minna Vehkala, Niko Välimäki, Simon R Harris, Claire Chewapreecha, Nicholas J Croucher, Pekka Marttinen, Mark R Davies, Andrew C Steer, Stephen Y C Tong, Antti Honkela, Julian Parkhill, Stephen D Bentley and Jukka Corander: Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. (eprint) bioRxiv (2016)
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segemehl
short read mapping with gaps
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Segemehl is a software to map short sequencer reads to reference genomes. Segemehl implements a matching strategy based on enhanced suffix arrays (ESA). Segemehl accepts fasta and fastq queries (gzip'ed and bgzip'ed). In addition to the alignment of reads from standard DNA- and RNA-seq protocols, it also allows the mapping of bisulfite converted reads (Lister and Cokus) and implements a split read mapping strategy. The output of segemehl is a SAM or BAM formatted alignment file. In the case of split-read mapping, additional BED files are written to the disc. These BED files may be summarized with the postprocessing tool haarz. In the case of the alignment of bisulfite converted reads, raw methylation rates may also be called with haarz.

In brief, for each suffix of a read, segemehl aims to find the best-scoring seed. Seeds might contain insertions, deletions, and mismatches (differences). The number of differences allowed within a single seed is user-controlled and is crucial for the runtime of the program. Subsequently, seeds that undercut the user-defined E-value are passed on to an exact semi-global alignment procedure. Finally, reads with a minimum accuracy of percent are reported to the user.

Please cite: Steve Hoffmann, Christian Otto, Stefan Kurtz, Cynthia M. Sharma, Philipp Khaitovich, Jörg Vogel, Peter F. Stadler and Jörg Hackermüller: Fast Mapping of Short Sequences with Mismatches, Insertions and Deletions Using Index Structures. (PubMed,eprint) PLoS Computational Biology 5(9):e1000502 (2009)
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sepp
phylogeny with ensembles of Hidden Markov Models
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The tool SEPP implementing these methods uses ensembles of Hidden Markov Models (HMMs) in different ways, each focusing on a different problem.

SEPP stands for "SATe-enabled Phylogenetic Placement", and addresses the problem of phylogenetic placement of short reads into reference alignments and trees.

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seqan-apps
C++ library for the analysis of biological sequences
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SeqAn is a C++ template library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. This library applies a unique generic design that guarantees high performance, generality, extensibility, and integration with other libraries. SeqAn is easy to use and simplifies the development of new software tools with a minimal loss of performance. This package contains the applications dfi, pair_align, micro_razers, seqan_tcoffee, seqcons, razers and tree_recon.

Please cite: Andreas Doring, David Weese, Tobias Rausch and Knut Reinert: SeqAn An efficient, generic C++ library for sequence analysis. (PubMed,eprint) BMC Bioinformatics 9(1):11 (2008)
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seqan-needle
pre-filter for the counting of very large collections of nucleotide sequences
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Needle is a tool for semi-quantitative analysis of very large collections of nucleotide sequences.

Needle stores its data in multiple interleaved Bloom filter, a fast and space efficient probabilistic data structure and uses a windowing scheme (also called minimisers) to reduce the amount of data to store. How many interleaved Bloom filter are used is defined by the user. Each interleaved Bloom filter has a so called expression threshold and stores minimisers with an occurrence greater than or equal to its own expression threshold and smaller than the next biggest expression threshold (if there is no bigger expression threshold, all greater than or equal to the threshold are stored). These expression thresholds are then used during the query (called estimate) to approximate the expression values of given transcripts.

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seqan-raptor
pre-filter for querying very large collections of nucleotide sequences
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Raptor is a system for approximately searching many queries such as next-generation sequencing reads or transcripts in large collections of nucleotide sequences. Raptor uses winnowing minimizers to define a set of representative k-mers, an extension of the interleaved Bloom filters (IBFs) as a set membership data structure and probabilistic thresholding for minimizers. This approach allows compression and partitioning of the IBF to enable the effective use of secondary memory.

Please cite: Enrico Seiler, Svenja Mehringer, Mitra Darvish, Etienne Turc and Knut Reinert: Raptor: A fast and space-efficient pre-filter for querying very large collections of nucleotide sequences. iScience 24(7) (2021)
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seqkit
cross-platform and ultrafast toolkit for FASTA/Q file manipulation
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SeqKit describes a cross-platform ultrafast comprehensive toolkit for FASTA/Q processing. SeqKit provides executable binary files for all major operating systems, including Windows, Linux, and Mac OS X, and can be directly used without any dependencies or pre-configurations. SeqKit demonstrates competitive performance in execution time and memory usage compared to similar tools. The efficiency and usability of SeqKit enable researchers to rapidly accomplish common FASTA/Q file manipulations.

Please cite: Wei Shen, Shuai Le, Yan Li and Fuquan Hu: SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. (PubMed,eprint) PlosOne 11(10):e0163962 (2016)
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seqmagick
imagemagick-like frontend to Biopython SeqIO
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Seqmagick is a little utility to expose the file format conversion in BioPython in a convenient way.

Features include:

  • Modifying sequences:
  • Remove gaps
  • Reverse & reverse complement
  • Trim to a range of residues
  • Change case
  • Sort by length or ID
  • Displaying information about sequence files
  • Subsetting sequence files by:
  • Position
  • ID
  • Deduplication
  • Filtering sequences by quality score
  • Trimming alignments to a region of interest defined by the forward and reverse primers
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seqprep
stripping adaptors and/or merging paired reads of DNA sequences with overlap
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SeqPrep is a program to merge paired end Illumina reads that are overlapping into a single longer read. It may also just be used for its adapter trimming feature without doing any paired end overlap. When an adapter sequence is present, that means that the two reads must overlap (in most cases) so they are forcefully merged. When reads do not have adapter sequence they must be treated with care when doing the merging, so a much more specific approach is taken. The default parameters were chosen with specificity in mind, so that they could be ran on libraries where very few reads are expected to overlap. It is always safest though to save the overlapping procedure for libraries where you have some prior knowledge that a significant portion of the reads will have some overlap.

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seqsero
Salmonella serotyping from genome sequencing data
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SeqSero is a pipeline for Salmonella serotype determination from raw sequencing reads or genome assemblies.

SeqSero is a novel tool for determining Salmonella serotypes using high- throughput genome sequencing data. SeqSero is based on curated databases of Salmonella serotype determinants (rfb gene cluster, fliC and fljB alleles) and is predicted to determine serotype rapidly and accurately for nearly the full spectrum of Salmonella serotypes (more than 2,300 serotypes), from both raw sequencing reads and genome assemblies. The performance of SeqSero was evaluated by testing

 1. raw reads from genomes of 308 Salmonella isolates of known serotype
 2. raw reads from genomes of 3,306 Salmonella isolates sequenced and
    made publicly available by GenomeTrakr, a U.S. national monitoring
    network operated by the Food and Drug Administration; and
 3. 354 other publicly available draft or complete Salmonella genomes.
SeqSero can help to maintain the well-established utility of Salmonella

serotyping when integrated into a platform of WGS-based pathogen subtyping and characterization.

Please cite: Shaokang Zhang, Yanlong Yin, Marcus B. Jones, Zhenzhen Zhang, Brooke L. Deatherage Kaiser, Blake A. Dinsmore, Collette Fitzgerald, Patricia I. Fields and Xiangyu Deng: Salmonella Serotype Determination Utilizing High-throughput Genome Sequencing Data. (PubMed,eprint) Journal of Clinical Microbiology 53(5):1685-92 (2015)
seqtk
Fast and lightweight tool for processing sequences in the FASTA or FASTQ format
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Currently, seqtk supports quality based trimming with the phred algorithm, converting fastq to fasta, reverse complementing sequences, extracting or masking subsequences in regions given in a BED/name list file, and more. It contains a subsampling module to sample exactly n sequences or a fraction of sequences.

Seqtk supports both fasta and fastq input files, which can be optionally gzip compressed.

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sga
de novo genome assembler that uses string graphs
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The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

SGA is a de novo assembler for DNA sequence reads. It is based on Gene Myers' string graph formulation of assembly and uses the FM-index/Burrows-Wheeler transform to efficiently find overlaps between sequence reads.

Please cite: Jared T. Simpson and Richard Durbin: Efficient de novo assembly of large genomes using compressed data structures.. (PubMed,eprint) Genome Res 22(3):549-555 (2012)
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shasta
nanopore whole genome assembly (binaries and scripts)
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De novo assembly from Oxford Nanopore reads. The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

  • Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.

  • Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

Shasta assembly quality is comparable or better than assembly quality achieved by other long read assemblers.

This package contains the executable binaries (tools) and accommodating scripts.

Please cite: K. Shafin, T. Pesout and R. Lorig-Roach et al.: Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nature Biotechnology (2020)
shovill
Assemble bacterial isolate genomes from Illumina paired-end reads
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Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.

sibelia
comparative genomics tool
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Sibelia (Synteny Block ExpLoration tool) is a comparative genomics tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.

Sibelia is useful in finding:

 1) shared regions,
 2) regions that present in one group of genomes but not in others,
 3) rearrangements that transform one genome to other genomes.
Please cite: Ilya Minkin, Anand Patel, Mikhail Kolmogorov, Nikolay Vyahhi and Son Pham: Sibelia: A Scalable and Comprehensive Synteny Block Generation Tool for Closely Related Microbial Genomes. Lecture Notes in Computer Science (2013)
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sibsim4
align expressed RNA sequences on a DNA template
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The SIBsim4 project is based on sim4, which is a program designed to align an expressed DNA sequence with a genomic sequence, allowing for introns. SIBsim4 is a fairly extensive rewrite of the original code with the following goals:

  • speed improvement;
  • allow large, chromosome scale, DNA sequences to be used;
  • provide more detailed output about splice types;
  • provide more detailed output about polyA sites;
  • misc code cleanups and fixes.
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sickle
windowed adaptive trimming tool for FASTQ files using quality
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Most modern sequencing technologies produce reads that have deteriorating quality towards the 3'-end. Incorrectly called bases here negatively impact assembles, mapping, and downstream bioinformatics analyses.

Sickle is a tool that uses sliding windows along with quality and length thresholds to determine when quality is sufficiently low to trim the 3'-end of reads. It will also discard reads based upon the length threshold. It takes the quality values and slides a window across them whose length is 0.1 times the length of the read. If this length is less than 1, then the window is set to be equal to the length of the read. Otherwise, the window slides along the quality values until the average quality in the window drops below the threshold. At that point the algorithm determines where in the window the drop occurs and cuts both the read and quality strings there. However, if the cut point is less than the minimum length threshold, then the read is discarded entirely.

Sickle supports four types of quality values: Illumina, Solexa, Phred, and Sanger. Note that the Solexa quality setting is an approximation (the actual conversion is a non-linear transformation). The end approximation is close.

Sickle also supports gzipped file inputs.

The package is enhanced by the following packages: multiqc
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sigma-align
Simple greedy multiple alignment of non-coding DNA sequences
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Sigma (“Simple greedy multiple alignment”) is an alignment program. It's algorithm and scoring scheme are designed specifically for non-coding DNA sequence.

It uses a strategy of seeking the best possible gapless local alignments. This happens at each step making the best possible alignment consistent with existing alignments. It scores the significance of the alignment based on the lengths of the aligned fragments and a background model. These may be supplied or estimated from an auxiliary file of intergenic DNA.

Please cite: Siddharthan, Rahul: Sigma: multiple alignment of weakly-conserved non-coding DNA sequence. (PubMed) BMC Bioinformatics 7(1):143 (2006)
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sim4
tool for aligning cDNA and genomic DNA
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sim4 is a similarity-based tool for aligning an expressed DNA sequence (EST, cDNA, mRNA) with a genomic sequence for the gene. It also detects end matches when the two input sequences overlap at one end (i.e., the start of one sequence overlaps the end of the other).

sim4 employs a blast-based technique to first determine the basic matching blocks representing the "exon cores". In this first stage, it detects all possible exact matches of W-mers (i.e., DNA words of size W) between the two sequences and extends them to maximal scoring gap-free segments. In the second stage, the exon cores are extended into the adjacent as-yet-unmatched fragments using greedy alignment algorithms, and heuristics are used to favor configurations that conform to the splice-site recognition signals (GT-AG, CT-AC). If necessary, the process is repeated with less stringent parameters on the unmatched fragments.

Please cite: Liliana Florea, George Hartzell, Zheng Zhang, Gerald M. Rubin and Webb Miller: A Computer Program for Aligning a cDNA Sequence with a Genomic DNA Sequence. (PubMed,eprint) Genome Research 8:967-974 (1998)
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sim4db
batch spliced alignment of cDNA sequences to a target genome
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Sim4db performs fast batch alignment of large cDNA (EST, mRNA) sequence sets to a set of eukaryotic genomic regions. It uses the sim4 and sim4cc algorithms to determine the alignments, but incorporates a fast sequence indexing and retrieval mechanism, implemented in the sister package 'leaff', to speedily process large volumes of sequences.

While sim4db produces alignments in the same way as sim4 or sim4cc, it has additional features to make it more amenable for use with whole-genome annotation pipelines. A script file can be used to group pairings between cDNAs and their corresponding genomic regions, to be aligned as one run and using the same set of parameters. Sim4db also optionally reports more than one alignment for the same cDNA within a genomic region, as long as they meet user-defined criteria such as minimum length, percentage sequence identity or coverage. This feature is instrumental in finding all alignments of a gene family at one locus. Lastly, the output is presented either as custom sim4db alignments or as GFF3 gene features.

This package is part of the Kmer suite.

The package is enhanced by the following packages: kmer-examples
Please cite: B. Walenz and L. Florea: Sim4db and leaff: Utilities for fast batched spliced alignment and sequence indexing. (PubMed) Bioinformatics 27(13):1869-1870 (2011)
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simka
comparative metagenomics method dedicated to NGS datasets
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Simka is a de novo comparative metagenomics tool. Simka represents each dataset as a k-mer spectrum and compute several classical ecological distances between them.

Please cite: Gaëtan Benoit, Pierre Peterlongo, Mahendra Mariadassou, Erwan Drezen, Sophie Schbath, Domonique Lavenier and Claire Lemaitre: Multiple comparative metagenomics using multiset k-mer counting. :25 (2016)
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simkamin
approximate comparative metagenomics method dedicated to NGS datasets
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Simka is a de novo comparative metagenomics tool. Simka represents each dataset as a k-mer spectrum and compute several classical ecological distances between them.

The difference with Simka stands in the fact that SimkaMin outputs approximate (but very similar) results by subsampling the kmer space. With this strategy, and with default parameters, SimkaMin is an order of magnitude faster, uses 10 times less memory and 70 times less disk than Simka.

Please cite: Gaëtan Benoit, Pierre Peterlongo, Mahendra Mariadassou, Erwan Drezen, Sophie Schbath, Domonique Lavenier and Claire Lemaitre: Multiple comparative metagenomics using multiset k-mer counting. :25 (2016)
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ska
Split Kmer Analysis
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SKA (Split Kmer Analysis) is a toolkit for prokaryotic (and any other small, haploid) DNA sequence analysis using split kmers. A split kmer is a pair of kmers in a DNA sequence that are separated by a single base. Split kmers allow rapid comparison and alignment of small genomes, and is particulalry suited for surveillance or outbreak investigation. SKA can produce split kmer files from fasta format assemblies or directly from fastq format read sequences, cluster them, align them with or without a reference sequence and provide various comparison and summary statistics. Currently all testing has been carried out on high-quality Illumina read data, so results for other platforms may vary.

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skesa
strategic Kmer extension for scrupulous assemblies
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SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity, handles low-level contamination in reads, is fast, and produces an identical assembly for the same input when assembled multiple times with the same or different compute resources. SKESA has been used for assembling over 272,000 read sets in the Sequence Read Archive at NCBI and for real-time pathogen detection.

Please cite: Alexandre Souvorov, Richa Agarwala and David J. Lipman: SKESA: strategic k-mer extension for scrupulous assemblies. (PubMed,eprint) Genome Biology 19(1):153 (2018)
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skewer
post-processing of high-throughput DNA sequence reads
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skewer implements the bit-masked k-difference matching algorithm dedicated to the task of adapter trimming and it is specially designed for processing next-generation sequencing (NGS) paired-end sequences.

Features

  • Detection and removal of adapter sequences
  • Insertion and deletion allowed in pattern matching
  • Targeted at Single End, Paired End (PE), and Long Mate Pair (LMP) reads
  • Demultiplexing of barcoded sequencing runs
  • Multi-threading support
  • Trimming based on phred quality scores
  • IUPAC characters for barcodes and adapters
  • Compressed input and output support
The package is enhanced by the following packages: multiqc
Please cite: H. Jiang, R. Lei, S. W. Ding and S. Zhu: Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads.. (eprint) BMC Bioinformatics 15:182 (2014)
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smalt
Sequence Mapping and Alignment Tool
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SMALT efficiently aligns DNA sequencing reads with a reference genome. Reads from a wide range of sequencing platforms, for example Illumina, Roche-454, Ion Torrent, PacBio or ABI-Sanger, can be processed including paired reads.

The software employs a perfect hash index of short words (< 20 nucleotides long), sampled at equidistant steps along the genomic reference sequences.

For each read, potentially matching segments in the reference are identified from seed matches in the index and subsequently aligned with the read using a banded Smith-Waterman algorithm.

The best gapped alignments of each read is reported including a score for the reliability of the best mapping. The user can adjust the trade-off between sensitivity and speed by tuning the length and spacing of the hashed words.

A mode for the detection of split (chimeric) reads is provided. Multi-threaded program execution is supported.

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Remark of Debian Med team: This can be regarded as successor of ssaha2

This program is from the same author as ssaha2 and according to its author faster and more precise than ssaha2 (except for sequences > 2000bp).

smithwaterman
determine similar regions between two strings or genomic sequences
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The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.

smrtanalysis
software suite for single molecule, real-time sequencing
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SMRT® Analysis is a powerful, open-source bioinformatics software suite available for analysis of DNA sequencing data from Pacific Biosciences’ SMRT technology. Users can choose from a variety of analysis protocols that utilize PacBio® and third-party tools. Analysis protocols include de novo genome assembly, cDNA mapping, DNA base-modification detection, and long-amplicon analysis to determine phased consensus sequences.

This is a metapackage that depends on the components of SMRT Analysis.

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snap
location of genes from DNA sequence with hidden markov model
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SNAP is a general purpose gene finding program suitable for both eukaryotic and prokaryotic genomes. SNAP is an acroynm for Semi-HMM-based Nucleic Acid Parser.

Please cite: Ian Korf: Gene finding in novel Genomes. (PubMed,eprint) BMC Bioinformatics 5:59 (2004)
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snap-aligner
Scalable Nucleotide Alignment Program
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SNAP is a new sequence aligner that is 3-20x faster and just as accurate as existing tools like BWA-mem, Bowtie2 and Novoalign. It runs on commodity x86 processors, and supports a rich error model that lets it cheaply match reads with more differences from the reference than other tools. This gives SNAP up to 2x lower error rates than existing tools (in some cases) and lets it match larger mutations that they may miss. SNAP also natively reads BAM, FASTQ, or gzipped FASTQ, and natively writes SAM or BAM, with built-in sorting, duplicate marking, and BAM indexing.

Please cite: Matei Zaharia, William J. Bolosky, Kristal Curtis, Armando Fox, David Patterson, Scott Shenker, Ion Stoica, Richard M. Karp and Taylor Sittler: Faster and More Accurate Sequence Alignment with SNAP. (eprint) arXiv preprint arXiv:1111.5572 (2011)
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sniffles
structural variation caller using third-generation sequencing
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Sniffles is a structural variation (SV) caller using third-generation sequencing data such as those from Pacific Biosciences or Oxford Nanopore platforms. It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis.

Please cite: Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler and Michael Schatz: Accurate detection of complex structural variations using single molecule sequencing. (eprint) bioRxiv (2017)
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snippy
rapid haploid variant calling and core genome alignment
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Snippy finds SNPs between a haploid reference genome and your NGS sequence reads. It will find both substitutions (snps) and insertions/deletions (indels). It will use as many CPUs as you can give it on a single computer (tested to 64 cores). It is designed with speed in mind, and produces a consistent set of output files in a single folder. It can then take a set of Snippy results using the same reference and generate a core SNP alignment (and ultimately a phylogenomic tree).

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snp-sites
Binary code for the package snp-sites
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This program finds single nucleotide polymorphism (SNP) sites from multi-fasta alignment input files (which might be compressed). Its output can be in various widely used formats (Multi Fasta Alignment, Vcf, phylip).

The software has been developed at the Wellcome Trust Sanger Institute.

A Single Nucleotide - polymorphism (SNP, pronounced snip; plural snips) is a DNA sequence variation occurring when a Single Nucleotide — A, T, C or G — in the genome (or other shared sequence) differs between members of a biological species or paired chromosomes. For example, two sequenced DNA fragments from different individuals, AAGCCTA to AAGCTTA, contain a difference in a single nucleotide. In this case there are two alleles. Almost all common SNPs have only two alleles.

Please cite: Andrew J. Page, Ben Taylor, Aidan J. Delaney, Jorge Soares, Torsten Seemann, Jacqueline A. Keane and Simon R. Harris: SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. (eprint) Microbial Genomics 2(4) (2016)
Topics: Genetic variation
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snpeff
genetic variant annotation and effect prediction toolbox - tool
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"We are all different!" Geneticists agree to this. Even twins, who are said to be identical are on a molecular level only "mostly" identical. And even within the exact same individual, healthy cells acquire mutations such that we are all genetic mosaics. Changes to individual cells may be induced by environmental factors, e.g. like UV light, or happen sporadically as mishaps during cellular divisions.

Because there are so many genetic differences, and most have just no particular meaning for the development of a phenotype, i.e. most have no effect, it would be nice to have heuristics implemented that direct the researcher towards single-nucleotide polymorphisms (SNPs) that are most likely to be relevant. This identifies the gene that causes or contributes to, e.g, an illness, and possibly also genes that are affected by that change. Such mechanistic understanding of a disease, particularly when multiple genes and multiple genetic variants are contributing to the then "polygenic" phenotype, is at the onset of drug development and increasingly also for selecting individualized therapies in the clinic.

SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of variants on genes (such as amino acid changes). The inputs are predicted variants (SNPs, insertions, deletions and MNPs). The input file is usually obtained as a result of a sequencing experiment, and it is usually in variant call format (VCF).

SnpEff analyzes the input variants. It annotates the variants and calculates the effects they produce on known genes (e.g. amino acid changes).

This package contains the command line tool.

The package is enhanced by the following packages: multiqc
Please cite: Pablo Cingolani, Adrian Platts, Le Lily Wang, Melissa Coon, Tung Nguyen, Luan Wang, Susan J. Land, Douglas M. Ruden and Xiangyi Lu: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w^1118; iso-2; iso-3. (PubMed,eprint) Fly 6(2):80-92 (2012)
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snpomatic
fast, stringent short-read mapping software
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High throughput sequencing technologies generate large amounts of short reads. Mapping these to a reference sequence consumes large amounts of processing time and memory, and read mapping errors can lead to noisy or incorrect alignments.

SNP-o-matic is a fast, stringent short-read mapping software. It supports a multitude of output types and formats, for uses in filtering reads, alignments, sequence-based genotyping calls, assisted reassembly of contigs etc.

Please cite: Heinrich Magnus Manske and Dominic P. Kwiatkowski: SNP-o-matic. (PubMed,eprint) Bioinformatics 25(18):2434-2435 (2009)
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Topics: Genetic variation; Mapping
snpsift
tool to annotate and manipulate genome variants - tool
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SnpSift is a toolbox that allows one to filter and manipulate annotated files. Once the genomic variants have been annotated, one needs to filter them out in order to find the "interesting / relevant variants". Given the large data files, this is not a trivial task (e.g. one cannot load all the variants into XLS spreadsheet). SnpSift helps to perform this VCF file manipulation and filtering required at this stage in data processing pipelines.

This package contains the command line tool.

soapaligner
aligner of short reads of next generation sequencers
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This package addresses a common problem in bioinformatics that has become routine now also in clinical research: the assembly and comparison of the very long genomic DNA sequences from many short reads that the machines provide.

SOAPaligner/soap2 is a member of the Short Oligonucleotide Analysis Package (SOAP) and an updated version of SOAP software for short oligonucleotide alignment (soap v1). The new program features in super fast and accurate alignment for huge amounts of short reads generated by Illumina/Solexa Genome Analyzer. Compared to soap v1, it is one order of magnitude faster. It require only 2 minutes aligning one million single-end reads onto the human reference genome. Another remarkable improvement of SOAPaligner is that it now supports a wide range of the read length.

SOAPaligner/soap2 benefitted in time and space efficiency by a revolution in the basic data structures and algorithms used. The core algorithms and the indexing data structures (2way-BWT) are developed by the algorithms research group of the Department of Computer Science, the University of Hong Kong (T.W. Lam, Alan Tam, Simon Wong, Edward Wu and S.M. Yiu).

Please cite: Ruiqiang Li, Yingrui Li, Karsten Kristiansen and Jun Wang: SOAP: short oligonucleotide alignment program. (PubMed,eprint) Genome Res. 24(5):713-714 (2008)
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soapdenovo
short-read assembly method to build de novo draft assembly
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SOAPdenovo is a novel short-read assembly method that can build a de novo draft assembly for the human-sized genomes. The program is specially designed to assemble Illumina GA short reads.

It creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost effective way.

This version is not maintained anymore, consider using soapdenovo2.

Please cite: Ruiqiang Li, Hongmei Zhu, Jue Ruan, Wubin Qian, Xiaodong Fang, Zhongbin Shi, Yingrui Li, Shengting Li, Gao Shan, Karsten Kristiansen, Songgang Li, Huanming Yang, Jian Wang and Jun Wang: De novo assembly of human genomes with massively parallel short read sequencing. (PubMed,eprint) Genome Research 20(2):265-72 (2009)
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soapdenovo2
short-read assembly method to build de novo draft assembly
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SOAPdenovo is a novel short-read assembly method that can build a de novo draft assembly for the human-sized genomes. The program is specially designed to assemble Illumina GA short reads.

It creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost effective way.

Please cite: Ruibang Luo, Binghang Liu, Yinlong Xie, Zhenyu Li, Weihua Huang, Jianying Yuan, Guangzhu He, Yanxiang Chen, Qi Pan, Yunjie Liu, Jingbo Tang, Gengxiong Wu, Hao Zhang, Yujian Shi, Yong Liu, Chang Yu, Bo Wang, Yao Lu, Changlei Han, David W Cheung, Siu-Ming Yiu, Shaoliang Peng, Zhu Xiaoqian, Guangming Liu, Xiangke Liao, Yingrui Li, Huanming Yang, Jian Wang, Tak-Wah Lam and Jun Wang: SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Giga Science 1(1):18 (2012)
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soapsnp
resequencing utility that can assemble consensus sequence of genomes
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For getting ideas on the cause of diseases or their response to therapy, and for understanding either for a particular patient, doctors around the globe are starting to look at the genes or the whole genome and how that sequence is different from a healthy / well responding individual.

SOAPsnp is a member of the SOAP (Short Oligonucleotide Analysis Package). The program is a resequencing utility. It assembles the consensus sequence for the genome of a newly sequenced individual based on the alignment of the raw sequencing reads on a known reference. SNPs can then be identified on the consensus sequence through the comparison with the reference.

SOAPsnp uses a method based on Bayes' theorem (the reverse probability model) to call consensus genotype by carefully considering the data quality, alignment, and recurring experimental errors. All these kinds of information was integrated into a single quality score for each base in PHRED scale to measure the accuracy of consensus calling. Currently, it supports the alignment format of SOAPaligner (soap2).

Please cite: Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K and Wang J.: SNP detection for massively parallel whole-genome resequencing. (PubMed,eprint) Genome Res. 19(6):1124-1132 (2009)
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sortmerna
tool for filtering, mapping and OTU-picking NGS reads
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SortMeRNA is a biological sequence analysis tool for filtering, mapping and OTU-picking NGS reads. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data. Additional applications include OTU-picking and taxonomy assignation available through QIIME v1.9+ (http://qiime.org - v1.9.0-rc1). SortMeRNA takes as input a file of reads (fasta or fastq format) and one or multiple rRNA database file(s), and sorts apart rRNA and rejected reads into two files specified by the user. Optionally, it can provide high quality local alignments of rRNA reads against the rRNA database. SortMeRNA works with Illumina, 454, Ion Torrent and PacBio data, and can produce SAM and BLAST-like alignments.

The package is enhanced by the following packages: multiqc
Please cite: Evguenia Kopylova, Laurent Noé and Hélène Touzet: SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data". (PubMed,eprint) Bioinformatics 28(24):3211-3217 (2012)
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spaced
alignment-free sequence comparison using spaced words
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Spaced (Words) is a new approach to alignment-free sequence comparison. While most alignment-free algorithms compare the word-composition of sequences, spaced uses a pattern of care and don't care positions. The occurrence of a spaced word in a sequence is then defined by the characters at the match positions only, while the characters at the don't care positions are ignored. Instead of comparing the frequencies of contiguous words in the input sequences, this new approach compares the frequencies of the spaced words according to the pre-defined pattern. An information-theoretic distance measure is then used to define pairwise distances on the set of input sequences based on their spaced-word frequencies. Systematic test runs on real and simulated sequence sets have shown that, for phylogeny reconstruction, this multiple-spaced-words approach is far superior to the classical alignment-free approach based on contiguous word frequencies.

Please cite: Burkhard Morgenstern, Bingyao Zhu, Sebastian Horwege and Chris-Andre Leimeister: Estimating evolutionary distances between genomic sequences from spaced-words matches. (PubMed,eprint) Algorithms for Molecular Biology 10(1):1-12 (2015)
spades
genome assembler for single-cell and isolates data sets
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The SPAdes – St. Petersburg genome assembler is intended for both standard isolates and single-cell MDA bacteria assemblies. It works with Illumina or IonTorrent reads and is capable of providing hybrid assemblies using PacBio and Sanger reads. You can also provide additional contigs that will be used as long reads.

This package provides the following additional pipelines:

  • metaSPAdes – a pipeline for metagenomic data sets
  • plasmidSPAdes – a pipeline for extracting and assembling plasmids from WGS data sets
  • metaplasmidSPAdes – a pipeline for extracting and assembling plasmids from metagenomic data sets
  • rnaSPAdes – a de novo transcriptome assembler from RNA-Seq data
  • truSPAdes – a module for TruSeq barcode assembly
  • biosyntheticSPAdes – a module for biosynthetic gene cluster assembly with paired-end reads

SPAdes provides several stand-alone binaries with relatively simple command-line interface: k-mer counting (spades-kmercounter), assembly graph construction (spades-gbuilder) and long read to graph aligner (spades-gmapper).

Please cite: Anton Bankevich, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, Sergey I. Nikolenko, Son Pham, Andrey D. Prjibelski, Alexey V. Pyshkin, Alexander V. Sirotkin, Nikolay Vyahhi, Glenn Tesler, Max A. Alekseyev and Pavel A. Pevzner: SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. (PubMed,eprint) Journal of Computational Biology 19(5):455-477 (2012)
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spaln
splicing-aware transcript-alignment to genomic DNA
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Spaln (space-efficient spliced alignment) is a stand-alone program that maps and aligns a set of cDNA or protein sequences onto a whole genomic sequence in a single job. It also performs spliced or ordinary alignment after rapid similarity search against a protein sequence database, if a genomic segment or an amino acid sequence is given as a query.

spaln supports a combination of protein sequence database and a given genomic segment and performs rapid similarity searches and (semi-)global alignments of a set of protein sequence queries against a protein sequence database. Spaln adopts multi-phase heuristics that makes it possible to perform the job on a conventional personal computer.

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spoa
SIMD partial order alignment tool
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Spoa (SIMD POA) is a c++ implementation of the partial order alignment (POA) algorithm (as described in 10.1093/bioinformatics/18.3.452) which is used to generate consensus sequences (as described in 10.1093/bioinformatics/btg109). It supports three alignment modes: local (Smith-Waterman), global (Needleman-Wunsch) and semi-global alignment (overlap).

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sprai
single-pass sequencing read accuracy improver
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Sprai is a tool to correct sequencing errors in single-pass reads for de novo assembly. It is originally designed for correcting sequencing errors in single-molecule DNA sequencing reads, especially in Continuous Long Reads (CLRs) generated by PacBio RS sequencers. The goal of Sprai is not maximizing the accuracy of error-corrected reads. Instead, Sprai aims at maximizing the continuity (i.e., N50 contig length) of assembled contigs after error correction.

spread-phy
analyze and visualize phylogeographic reconstructions
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SPREAD is a user-friendly application to analyze and visualize phylogeographic reconstructions resulting from Bayesian inference of spatio-temporal diffusion.

There is a tutorial for SPREAD online at http://www.kuleuven.be/aidslab/phylogeography/tutorial/spread_tutorial.html

Originally this program is named "spread". However, there is just such a package inside Debian and thus a 'phy' for phylogeny was prepended.

Please cite: Filip Bielejec, Andrew Rambaut, Marc A. Suchard and Philippe Lemey: SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics. (PubMed,eprint) Bioinformatics 27(20):2910-2912 (2011)
squizz
Converter for genetic sequences and alignments
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Squizz is a sequence/alignment format checker, but it has some conversion capabilities too. Most common sequence and alignment formats are supported :

  • EMBL, FASTA, GCG, GDE, GENBANK, IG, NBRF, PIR (codata), RAW, and SWISSPROT.
  • CLUSTAL, FASTA, MEGA, MSF, NEXUS, PHYLIP (interleaved and sequential) and STOCKHOLM.
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sra-toolkit
utilities for the NCBI Sequence Read Archive
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Tools for reading the SRA archive, generally by converting individual runs into some commonly used format such as fastq.

The textual dumpers "sra-dump" and "vdb-dump" are provided in this release as an aid in visual inspection. It is likely that their actual output formatting will be changed in the near future to a stricter, more formalized representation[s]. PLEASE DO NOT RELY UPON THE OUTPUT FORMAT SEEN IN THIS RELEASE.

Other tools distributed in this package are:

 abi-dump, abi-load
 align-info
 bam-load
 cache-mgr
 cg-load
 copycat
 fasterq-dump
 fastq-dump, fastq-load
 helicos-load
 illumina-dump, illumina-load
 kar
 kdbmeta
 latf-load
 pacbio-load
 prefetch
 rcexplain
 remote-fuser
 sff-dump, sff-load
 sra-pileup, sra-sort, sra-stat, srapath
 srf-load
 test-sra
 vdb-config, vdb-copy, vdb-decrypt, vdb-encrypt, vdb-get, vdb-lock,
 vdb-passwd, vdb-unlock, vdb-validate

The "help" information will be improved in near future releases, and the tool options will become standardized across the set. More documentation will also be provided documentation on the NCBI web site.

Tool options may change in the next release. Version 1 tool options will remain supported wherever possible in order to preserve operation of any existing scripts.

Please cite: Rasko Leinonen, Ruth Akhtar, Ewan Birney, James Bonfield, Lawrence Bower, Matt Corbett, Ying Cheng, Fehmi Demiralp, Nadeem Faruque, Neil Goodgame, Richard Gibson, Gemma Hoad, Christopher Hunter, Mikyung Jang, Steven Leonard, Quan Lin, Rodrigo Lopez, Michael Maguire, Hamish McWilliam, Sheila Plaister, Rajesh Radhakrishnan, Siamak Sobhany, Guy Slater, Petra Ten Hoopen, Franck Valentin, Robert Vaughan, Vadim Zalunin, Daniel Zerbino and Guy Cochrane: Improvements to services at the European Nucleotide Archive. (PubMed,eprint) Nucleic Acids Research 38(Database issue):D39-45 (2010)
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srst2
Short Read Sequence Typing for Bacterial Pathogens
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This program is designed to take Illumina sequence data, a MLST database and/or a database of gene sequences (e.g. resistance genes, virulence genes, etc) and report the presence of STs and/or reference genes.

Please cite: Michael Inouye, Harriet Dashnow, Lesley-Ann Raven, Mark B Schultz, Bernard J Pope, Takehiro Tomita, Justin Zobel and Kathryn E Holt: SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. (PubMed,eprint) Genome Medicine 6(11):90 (2014)
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ssake
genomics application for assembling millions of very short DNA sequences
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The Short Sequence Assembly by K-mer search and 3′ read Extension (SSAKE) is a genomics application for aggressively assembling millions of short nucleotide sequences by progressively searching for perfect 3′-most k-mers using a DNA prefix tree. SSAKE is designed to help leverage the information from short sequences reads by stringently clustering them into contigs that can be used to characterize novel sequencing targets.

Please cite: Rene L. Warren, Granger G. Sutton, Steven J. M. Jones and Robert A. Holt: Assembling millions of short DNA sequences using SSAKE. (PubMed,eprint) Bioinformatics 23(4):500-501 (2007)
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Topics: Sequence assembly
sspace
scaffolding pre-assembled contigs after extension
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SSAKE-based Scaffolding of Pre-Assembled Contigs after Extension (SSPACE) is a script able to extend and scaffold pre-assembled contigs using one or more mate pairs or paired-end libraries, or even a combination.

SSPACE is built based on SSAKE. Code of SSAKE is changed to be able to extend and scaffold pre-assembled contigs for multiple paired reads libraries.

This is the free 'basic' version of SSPACE. The non-free 'standard' version is available directly from Baseclear.

Please cite: Marten Boetzer, Christiaan V. Henkel, Hans J. Jansen, Derek Butler and Walter Pirovano: Scaffolding pre-assembled contigs using SSPACE. (PubMed,eprint) Bioinformatics 27(4):578-579 (2010)
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ssw-align
Smith-Waterman aligner based on libssw
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This package provides a command-line aligner based on the libssw library, a fast SIMD accelerated implementation of the Smith-Waterman algorithm. The input files can be in FASTA or FASTQ format. Both target and query files can contain multiple sequences. Each sequence in the query file will be aligned with all sequences in the target file. Output is provided in SAM or BLAST-like text format.

stacks
pipeline for building loci from short-read DNA sequences
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Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.

Note that this package installs Stacks such that all commands must be run as: $ stacks

The package is enhanced by the following packages: multiqc
Please cite: Julian Catchen, Paul A. Hohenlohe, Susan Bassham, Angel Amores and William A. Cresko: Stacks: an analysis tool set for population genomics. (PubMed) Molecular Ecology 22(11):3124-40 (2013)
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staden
DNA sequence assembly (Gap4/Gap5), editing and analysis tools
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Staden is a fully developed set of DNA sequence assembly (Gap4 and Gap5), editing and analysis tools (Spin).

Gap4 performs sequence assembly, contig ordering based on read pair data, contig joining based on sequence comparisons, assembly checking, repeat searching, experiment suggestion, read pair analysis and contig editing. It has graphical views of contigs, templates, readings and traces which all scroll in register. Contig editor searches and experiment suggestion routines use confidence values to calculate the confidence of the consensus sequence and hence identify only places requiring visual trace inspection or extra data. The result is extremely rapid finishing and a consensus of known accuracy.

Pregap4 provides a graphical user interface to set up the processing required to prepare trace data for assembly or analysis, and automates these processes.

Trev is a rapid and flexible viewer and editor for ABI, ALF, SCF and ZTR trace files.

Prefinish analyses partially completed sequence assemblies and suggests the most efficient set of experiments to help finish the project.

Tracediff and hetscan automatically locate mutations by comparing trace data against reference traces. They annotate the mutations found ready for viewing in gap4.

Spin analyses nucleotide sequences to find genes, restriction sites, motifs, etc. It can perform translations, find open reading frames, count codons, etc. Many results are presented graphically and a sliding sequence window is linked to the graphics cursor. Spin also compares pairs of sequences in many ways. It has very rapid dot matrix analysis, global and local alignment algorithms, plus a sliding sequence window linked to the graphical plots. It can compare nucleic acid against nucleic acid, protein against protein, and protein against nucleic acid.

Please cite: James K. Bonfield and Andrew Whitwham: Gap5--editing the billion fragment sequence assembly. (PubMed,eprint) Bioinformatics 26(14):1699-1703 (2010)
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staden-io-lib-utils
programs for manipulating DNA sequencing files
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The io_lib from the Staden package is a library of file reading and writing code to provide a general purpose trace file (and Experiment File) reading interface. It has been compiled and tested on a variety of unix systems, MacOS X and MS Windows.

This package contains the programs that are distributed with the Staden io_lib for manipulating and converting sequencing data files, and in particular files to manipulate short reads generated by second and third generation sequencers and stored in SRF format.

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stringtie
assemble short RNAseq reads to transcripts
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The abundance of transcripts in a human tissue sample can be determined by RNA sequencing. The exact sequence sampled may be random, depending on the technology used. And it may be short, i.e. shorter than the transcript. At some point, many shorter reads need to be assembled to the model the complete transcripts.

StringTie knows how to assemble of RNA-Seq into potential transcripts without the need of a reference genome and provides a quantification also of the splice variants.

Please cite: Mihaela Pertea, Geo M. Pertea, Corina .M. Antonescu, Tsung-Cheng Chang, Joshua T. Mendell and Steven L. Salzberg: StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology 33:290–295 (2015)
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subread
toolkit for processing next-gen sequencing data
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Subread aligner can be used to align both gDNA-seq and RNA-seq reads. Subjunc aligner was specified designed for the detection of exon-exon junction. For the mapping of RNA-seq reads, Subread performs local alignments and Subjunc performs global alignments.

Please cite: Yang Lian, Gordon K. Smyth and Wei Shi: The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. (PubMed) Nucleic Acids Research 47(8):e47-e47 (2019)
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suitename
categorize each suite in an RNA backbone
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Suitename is a program that supports the ROC RNA Ontology Consortium consensus RNA backbone nomenclature and conformer-list development.

From dihedral-angle input for a specific RNA structure (usually from Dangle), Suitename categorizes the RNA backbone geometry of each suite (the sugar-to-sugar version of a residue) either as an outlier or as belonging to one of the 53 defined conformer bins. The output is either a one-line-per-suite report, or a linear conformer string (as shown below the image here) in one of several variant formats. Suitename is built into MolProbity, producing entries in the multi-criterion chart for an RNA model and also a suitestring file.

Please cite: Jane S. Richardson, Bohdan Schneider, Laura W. Murray, Gary J. Kapral, Robert M. Immormino, Jeffrey J. Headd, David C. Richardson, Daniela Ham, Eli Hershkovits, Loren Dean Williams, Kevin S. Keating, Anna Marie Pyle, David Micallef, John Westbrook and Helen M. Berman: RNA backbone: Consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution). (PubMed,eprint) RNA 14(3):465-481 (2008)
sumaclust
fast and exact clustering of genomic sequences
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With the development of next-generation sequencing, efficient tools are needed to handle millions of sequences in reasonable amounts of time. Sumaclust is a program developed by the LECA. Sumaclust aims to cluster sequences in a way that is fast and exact at the same time. This tool has been developed to be adapted to the type of data generated by DNA metabarcoding, i.e. entirely sequenced, short markers. Sumaclust clusters sequences using the same clustering algorithm as UCLUST and CD- HIT. This algorithm is mainly useful to detect the 'erroneous' sequences created during amplification and sequencing protocols, deriving from 'true' sequences.

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sumatra
fast and exact comparison and clustering of sequences
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With the development of next-generation sequencing, efficient tools are needed to handle millions of sequences in reasonable amounts of time. Sumatra is a program developed by the LECA. Sumatra aims to compare sequences in a way that is fast and exact at the same time. This tool has been developed to be adapted to the type of data generated by DNA metabarcoding, i.e. entirely sequenced, short markers. Sumatra computes the pairwise alignment scores from one dataset or between two datasets, with the possibility to specify a similarity threshold under which pairs of sequences that have a lower similarity are not reported. The output can then go through a classification process with programs such as MCL or MOTHUR.

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sumtrees
Phylogenetic Tree Summarization and Annotation
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SumTrees is a program to summarize non-parameteric bootstrap or Bayesian posterior probability support for splits or clades on phylogenetic trees.

The basis of the support assessment is typically given by a set of non-parametric bootstrap replicate tree samples produced by programs such as GARLI or RAxML, or by a set of MCMC tree samples produced by programs such as Mr. Bayes or BEAST. The proportion of trees out of the samples in which a particular split is found is taken to be the degree of support for that split as indicated by the samples. The samples that are the basis of the support can be distributed across multiple files, and a burn-in option allows for an initial number of trees in each file to be excluded from the analysis if they are not considered to be drawn from the true support distribution.

Please cite: Jeet Sukumaran and Mark T. Holder: DendroPy: a Python library for phylogenetic computing. (PubMed,eprint) Bioinformatics 26(12):1569-1571 (2010)
surankco
Supervised Ranking of Contigs in de novo Assemblies
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SuRankCo is a machine learning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet.

Please cite: Mathias Kuhring, Piotr Wojtek Dabrowski, Vitor C. Piro, Andreas Nitsche and Bernhard Y. Renard: SuRankCo: supervised ranking of contigs in de novo assemblies. (PubMed,eprint) BMC Bioinformatics 16(1):240 (2015)
surpyvor
modification of VCF files with SURVIVOR
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SURVIVOR is a tool set for simulating/evaluating structural variations, merging and comparing SVs within and among samples, and includes various methods to reformat or summarize structural variations.

This package provides a Python wrapper to help with its integration in various Python-based workflows.

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survivor
tool set for simulating/evaluating SVs
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SURVIVOR is a tool set for simulating/evaluating structural variantions, merging and comparing SVs within and among samples, and includes various methods to reformat or summarize structural variantions.

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svim
Structural variant caller for long sequencing reads
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SVIM is a structural variant caller for long sequencing reads. It is able to detect, classify and genotype five different classes of structural variants. Unlike existing methods, SVIM integrates information from across the genome to precisely distinguish similar events, such as tandem and interspersed duplications and simple insertions.

Please cite: David Heller and Martin Vingron: Link to publication (PubMed,eprint) Bioinformatics 35(17):2907-2915 (2019)
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swarm
robust and fast clustering method for amplicon-based studies
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The purpose of swarm is to provide a novel clustering algorithm to handle large sets of amplicons. Traditional clustering algorithms results are strongly input-order dependent, and rely on an arbitrary global clustering threshold. swarm results are resilient to input-order changes and rely on a small local linking threshold d, the maximum number of differences between two amplicons. swarm forms stable high-resolution clusters, with a high yield of biological information.

Please cite: Frédéric Mahé, Torbjørn Rognes, Christopher Quince, Colomban de Vargas and Micah Dunthorn: Swarm v2: highly-scalable and high-resolution amplicon clustering. (PubMed,eprint) PeerJ 3(e1420) (2015)
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sweed
assessment of SNPs for their evolutionary advantage
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Biological sequences are available in ever increasing abundance across ever larger populations for ever increasing fractions of the genome. This tool sorts the SNPs for their active or passive contribution to a genetic drift, i.e. to see particular sequences at a higher fraction over time.

Please cite: Pavlos Pavlidis, Daniel Živković, Alexandros Stamatakis and Nikolaos Alachiotis: SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes. (eprint) Molecular Biology and Evolution 30(9):2224–2234 (2013)
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t-coffee
Multiple Sequence Alignment
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T-Coffee is a multiple sequence alignment package. Given a set of sequences (Proteins or DNA), T-Coffee generates a multiple sequence alignment. Version 2.00 and higher can mix sequences and structures.

T-Coffee allows the combination of a collection of multiple/pairwise, global or local alignments into a single model. It can also estimate the level of consistency of each position within the new alignment with the rest of the alignments. See the pre-print for more information

T-Coffee has a special called M-Coffee that makes it possible to combine the output of many multiple sequence alignment packages. In its published version, it uses MUSCLE, PROBCONS, POA, DiAlign-TS, MAFFT, Clustal W, PCMA and T-Coffee. A special version has been made for Debian, DM-Coffee, that uses only free software by replacing Clustal W by Kalign. Using the 8 Methods of M-Coffee can sometimes be a bit heavy. You can use a subset of your favorite methods if you prefer.

The package is enhanced by the following packages: clustalw dialign-tx kalign mafft muscle muscle3 ncbi-blast+ poa prank probcons tm-align
Please cite: Cédric Notredame, Desmond G. Higgins and Jaap Heringa: T-coffee: a novel method for fast and accurate multiple sequence alignment. (PubMed) Journal of Molecular Biology 302(1):205-217 (2000)
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tabix
generic indexer for TAB-delimited genome position files
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Tabix indexes files where some columns indicate sequence coordinates: name (usually a chromosome), start and stop. The input data file must be position sorted and compressed by bgzip (provided in this package), which has a gzip like interface. After indexing, tabix is able to quickly retrieve data lines by chromosomal coordinates. Fast data retrieval also works over network if an URI is given as a file name.

This package is built from the HTSlib source, and provides the bgzip, htsfile, and tabix tools.

Please cite: Heng Li: Tabix: fast retrieval of sequence features from generic TAB-delimited files. (PubMed,eprint) Bioinformatics 27(5):718-719 (2011)
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tantan
low complexity and tandem repeat masker for biosequences
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tantan is a tool to mask simple regions (low complexity and short-period tandem repeats) in DNA, RNA, and protein sequences. The aim of tantan is to prevent false predictions when searching for homologous regions between two sequences. Simple repeats often align strongly to each other, causing false homology predictions.

Please cite: Frith, Martin C.: A new repeat-masking method enables specific detection of homologous sequences. (PubMed) Nucleic Acids Research 39(4):e23 (2011)
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Topics: Sequence composition, complexity and repeats
terraphast
enumerate terraces in phylogenetic tree space
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Terraphast takes a .nkw file in Newick format and a genes/sites file, which denotes whether (1) or not (0) gene i is present in species j.

Program output states some imput data properties, the species whose leaf edge is used as a new tree root, and the resulting supertree in compressed newick format.

Please cite: Michael J. Sanderson, Michelle M. McMahon and Mike Steel: Terraces in phylogenetic tree space. (PubMed) Science 333(6041):448-450 (2011)
theseus
superimpose macromolecules using maximum likelihood
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Theseus is a program that simultaneously superimposes multiple macromolecular structures. Theseus finds the optimal solution to the superposition problem using the method of maximum likelihood. By down-weighting variable regions of the superposition and by correcting for correlations among atoms, the ML superposition method produces very accurate structural alignments.

When macromolecules with different residue sequences are superimposed, other programs and algorithms discard residues that are aligned with gaps. Theseus, however, uses a novel superimposition algorithm that includes all of the data.

The package is enhanced by the following packages: theseus-examples
Please cite: Douglas L. Theobald and Deborah S. Wuttke: THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures. (eprint) Bioinformatics 22(17):2171-2172 (2006)
thesias
Testing Haplotype Effects In Association Studies
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The objectif of the THESIAS program is to performed haplotype-based association analysis in unrelated individuals. This program is based on the maximum likelihood model described in Tregouet et al. 2002 (Hum Mol Genet 2002,11: 2015-2023) and is linked to the SEM algorithm (Tregouet et al. Ann Hum Genet 2004,68: 165-177). THESIAS allows one to simultaneous estimate haplotype frequencies and their associate effects on the phenotype of interest. In this new THESIAS release, quantitative, qualitative (logistic and matched-pair analysis), categorical and survival outcomes can be studied. X-linked haplotype analysis is also feasible. Covariate-adjusted haplotype effects as well as haplotype x covariate interactions can also be investigated.

Please cite: David-Alexandre Trégouët and Valérie Garelle: "A new JAVA interface implementation of THESIAS: testing haplotype effects in association studies". (eprint) Bioinformatics 23(8):1038-1039 (2007)
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tiddit
structural variant calling
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TIDDIT is a tool to used to identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. TIDDIT identifies intra and inter- chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.

TIDDIT has two analysis modules. The sv mode, which is used to search for structural variants. And the cov mode that analyse the read depth of a bam file and generates a coverage report.

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tigr-glimmer
Gene detection in archea and bacteria
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Developed by the TIGR institute this software detects coding sequences in bacteria and archea.

Glimmer is a system for finding genes in microbial DNA, especially the genomes of bacteria and archaea. Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA.

Please cite: Steven L. Salzberg, Arthur L. Delcher, S. Kasif and O. White: Microbial gene identification using interpolated Markov models. (PubMed,eprint) Nucleic Acids Research 26(2):544-8 (1998)
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tipp
tool for Taxonomic Identification and Phylogenetic Profiling
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TIPP is a modification of SEPP for classifying query sequences (i.e. reads) using phylogenetic placement.

TIPP inserts each read into a taxonomic tree and uses the insertion location to identify the taxonomic lineage of the read. The novel idea behind TIPP is that rather than using the single best alignment and placement for taxonomic identification, it uses a collection of alignments and placements and considers statistical support for each alignment and placement.

TIPP can also be used for abundance estimation by computing an abundance profile on the reads binned to marker genes in a reference dataset.

tm-align
structural alignment of proteins
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TM-align is a computer algorithm for protein structure alignment using dynamic programming. The scoring is performed by the TM-score rotation matrix. This is similar to the RMSD in that unaligned portions of the structure influence the scoring less than the more structurally conserved regions.

Please cite: Yang Zhang and Jeffrey Skolnick: TM-align: A protein structure alignment algorithm based on TM-score. (PubMed,eprint) Nucleic Acids Research 33(7):2302-2309 (2005)
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tnseq-transit
statistical calculations of essentiality of genes or genomic regions
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This is a software that can be used to analyze Tn-Seq datasets. It includes various statistical calculations of essentiality of genes or genomic regions (including conditional essentiality between 2 conditions). These methods were developed and tested as a collaboration between the Sassetti lab (UMass) and the Ioerger lab (Texas A&M)

TRANSIT is capable of analyzing TnSeq libraries constructed with Himar1 or Tn5 datasets.

TRANSIT assumes you have already done pre-processing of raw sequencing files (.fastq) and extracted read counts into a .wig formatted file. The .wig file should contain the counts at all sites where an insertion could take place (including sites with no reads). For Himar1 datasets this is all TA sites in the genome. For Tn5 datasets this would be all nucleotides in the genome.

Please cite: Michael A. DeJesus, Chaitra Ambadipudi, Richard Baker, Christopher Sassetti and Thomas R. Ioerger: TRANSIT - A Software Tool for Himar1 TnSeq Analysis. (PubMed,eprint) PLOS 11(10):e1004401 (2015)
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toil
cross-platform workflow engine
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Toil is a scalable, efficient, cross-platform and easy-to-use workflow engine in pure Python. It works with several well established load balancers like Slurm or the Sun Grid Engine. Toil is also compatible with the Common Workflow Language (CWL) via the "toil-cwl-runner" interface, which this package make available via the Debian alternativess system under the alias "cwl-runner".

Please cite: John Vivian, Arjun Arkal Rao, Frank Austin Nothaft, Christopher Ketchum, Joel Armstrong, Adam Novak, Jacob Pfeil, Jake Narkizian Alden D. Deran, Audrey Musselman-Brown, Hannes Schmidt, Peter Amstutz, Brian Craft, Mary Goldman, Kate Rosenbloom, Melissa Cline, Brian O'Connor, Megan Hanna, Chet Birger, W. James Kent David A. Patterson, Anthony D. Joseph, Jingchun Zhu, Sasha Zaranek, Gad Getz, David Haussler and Benedict Paten: Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology 35(4):314–316 (2017)
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tombo
identification of modified nucleotides from raw nanopore sequencing data
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Tombo is a suite of tools primarily for the identification of modified nucleotides from nanopore sequencing data. Tombo also provides tools for the analysis and visualization of raw nanopore signal.

Please cite: Marcus Stoiber, Joshua Quick, Rob Egan, Ji Eun Lee, Susan Celniker, Robert K. Neely, Nicholas Loman, Len A Pennacchio and James Brown: De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing. (eprint) bioRxiv (2016)
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tophat
fast splice junction mapper for RNA-Seq reads
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TopHat aligns RNA-Seq reads to mammalian-sized genomes using the ultra high-throughput short read aligner Bowtie, and then analyzes the mapping results to identify splice junctions between exons. TopHat is a collaborative effort between the University of Maryland Center for Bioinformatics and Computational Biology and the University of California, Berkeley Departments of Mathematics and Molecular and Cell Biology.

The package is enhanced by the following packages: cufflinks multiqc
Please cite: Cole Trapnell, Lior Pachter and Steven L. Salzberg: TopHat: discovering splice junctions with RNA-Seq. (PubMed,eprint) Bioinformatics 25(9):1105-1111 (2009)
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tophat-recondition
post-processor for TopHat unmapped reads
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tophat-recondition is a post-processor for TopHat unmapped reads (contained in unmapped.bam), making them compatible with downstream tools (e.g., the Picard suite, samtools, GATK) (TopHat issue #17). It also works around bugs in TopHat:

  • the "mate is unmapped" SAM flag is not set on any reads in the unmapped.bam file (TopHat issue #3)
  • the mapped mate of an unmapped read can be absent from accepted_hits.bam, creating a mismatch between the file and the unmapped read's flags (TopHat issue #16)
Please cite: Christian Brueffer and Lao H. Saal: A post-processor for TopHat unmapped reads. Bioinformatics 17(1):199 (2016)
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topp
set of programs implementing The OpenMS Proteomic Pipeline
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TOPP (the OpenMS proteomic pipeline) is a pipeline for the analysis of HPLC/MS data. It consists of a set of numerous small applications that can be chained together to create analysis pipelines tailored for a specific problem. The applications make use of the libopenms library. Some examples of these applications are :

  • TOPPView: A viewer for mass spectrometry data.
  • TOPPAS: An assistant for GUI-driven TOPP workflow design.
  • DTAExtractor: Extracts spectra of an MS run file to several files in DTA format.
  • FileConverter: Converts between different MS file formats.
  • FileFilter: Extracts or manipulates portions of data from peak, feature or consensus feature files.
  • SpectraMerger: Merges spectra from an LC/MS map, either by precursor or by RT blocks.
  • BaselineFilter: Removes the baseline from profile spectra using a top-hat filter.
  • InternalCalibration: Applies an internal calibration.
  • PTModel: Trains a model for the prediction of proteotypic peptides from a training set.
  • RTPredict: Predicts retention times for peptides using a model trained by RTModel.
  • ExecutePipeline: Executes workflows created by TOPPAS.
Please cite: Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert and Oliver Kohlbacher: OpenMS – an Open-Source Software Framework for Mass Spectrometry. (PubMed,eprint) BMC Bioinformatics 9(163) (2008)
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toppred
transmembrane topology prediction
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Toppred is a program to determine the topology of a transmembrane protein based on G. von Heijne algorithm.

Each sequence from seq data in fasta format is processed, and toppred generate the Hydrophobycity profile of the sequence, and the corresponding hydrophobycities values in the file sequence-ID.hydro.

Furthermore, the predicted topologies are represented as png images. Each topology is stored in file sequence-ID-number.png

The hydrophobicity profile is computed using a window formed by a core rectangular window of size n, flanked by 2 triangular windows of size q. NB rectangular and triangular mean that the ponderation values inside those windows are respectively constant and variable.

This program is a new implementation of the original toppred program, based on G. von Heijne algorithm

Please cite: Gunnar von Heijne: Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. (PubMed) Journal of Molecular Biology 225(2):487-94 (1992)
tortoize
Application to calculate ramachandran z-scores
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Tortoize validates protein structure models by checking the Ramachandran plot and side-chain rotamer distributions. Quality Z-scores are given at the residue level and at the model level (ramachandran-z and torsions-z). Higher scores are better. To compare models or to describe the reliability of the model Z-scores jackknife- based standard deviations are also reported (ramachandran-jackknife-sd and torsion-jackknife-sd).

Please cite: Oleg V. Sobolev, Pavel V. Afonine, Nigel W. Moriarty and Robbie P. Joosten: A Global Ramachandran Score Identifies Protein Structures with Unlikely Stereochemistry, Structure. (2020)
trace2dbest
bulk submission of chromatogram data to dbEST
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ESTs are short sequences derived from reverse-transcribed RNA. Their abundances yield insights in the expression of genes across tissues, support the discovery of new genes and allow one to assess the coverage of whole genome sequencing projects. Public databases like dbEST at the NCBI collect this data.

trace2dbEST process raw sequenceing chromatograph trace files from EST projects into quality-checked sequences, ready for submission to dbEST. trace2dbEST guides you through the creation of all the necessary files for submission of ESTs to dbEST. trace2dbest makes use of other software (available free under academic licence) that you will need to have installed, namely phred, cross_match and (optionally) BLAST.

Please cite: John Parkinson, Alasdair Anthony, James Wasmuth, Ralf Schmid, Ann Hedley and Mark Blaxter: PartiGene—constructing partial genomes. (eprint) Bioinformatics 20(9):1398–1404 (2004)
tracetuner
interpretation of DNA Sanger sequencing data
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Tracetuner is a tool for base and quality calling of trace files from DNA sequencing instruments. Traditional DNA sequencing yields curves from four different channels or light frequencies that human or (preferably) machines are interpreting to determine the actual base (A,C,G or T) and the confidence with which this is determined.

TraceTuner is a DNA sequencing quality value, base calling and trace processing software application originally developed by Paracel, Inc. While providing a flexible interface and capability to adopt the "pure" base calls produced by Phred, KB or any other "original" caller, it offers competitive features not currently available in other tools, such as customized calibration of quality values, advanced heterozygote and mixed base calling and deconvolving the "mixed" electropherograms resulting from the presence of indels into a couple of "pure" electropherograms.

Later versions Previous versions of TraceTuner were used by Celera Genomics to process over 27 million reads from both Drosophila and human genome projects. In 2000, Applied Biosystems bundled TraceTuner with ABI3700 Genome Analyzers and shipped it to the customers of these capillary electrophoresis sequencers. its SNP detection and genotyping software product SeqScape.

TraceTuner implements an advanced peak processing technology for resolving overlapping peaks of the same dye color into individual, or "intrinsic" peaks. TraceTuner, for its support of mixed base calling, have been used by the research community, the private biotech sector, and the U.S. government as components of different variant detection, genotyping and forensic software applications (e.g. Applied Biosystems SeqScape, Paracel Genome Assembler, MTexpert, etc.).

This technology was protected by US Patent #6,681,186. Currently, TraceTuner is an open source software, which has been used by J. Craig Venter Institute's DNA Sequencing and Resequencing pipelines.

This package prepares an important piece of human history to be used with new data on new machines or to revisit older observations..

Please cite: G.A.Denisov, A.B.Arehart and M.D.Curtin: A system and method for improving the accuracy of DNA sequencing and error probability estimation through application of a mathematical model to the analysis of electropherograms. US Patent 6681186. (2004)
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transdecoder
find coding regions within RNA transcript sequences
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TransDecoder identifies candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to the genome using Tophat and Cufflinks.

TransDecoder identifies likely coding sequences based on the following criteria:

  • a minimum length open reading frame (ORF) is found in a transcript sequence
  • a log-likelihood score similar to what is computed by the GeneID software is > 0.
  • the above coding score is greatest when the ORF is scored in the 1st reading frame as compared to scores in the other 5 reading frames.
  • if a candidate ORF is found fully encapsulated by the coordinates of another candidate ORF, the longer one is reported. However, a single transcript can report multiple ORFs (allowing for operons, chimeras, etc).
  • optional the putative peptide has a match to a Pfam domain above the noise cutoff score.
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transrate-tools
helper for transrate
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Transrate is a library and command-line tool for quality assessment of de-novo transcriptome assemblies.

This package provides command line tools used by transrate to process BAM files.

Please cite: Richard Smith-Unna, Chris Boursnell, Rob Patro, Julian M. Hibberd and Steven Kelly: TransRate: reference-free quality assessment of de novo transcriptome assemblies.. (PubMed,eprint) Genome Research 26(8):1134-1144 (2016)
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transtermhp
find rho-independent transcription terminators in bacterial genomes
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TransTermHP finds rho-independent transcription terminators in bacterial genomes. Each terminator found by the program is assigned a confidence value that estimates its probability of being a true terminator. TransTermHP is the successor of TransTerm which was using very different search and scoring algorithms.

Please cite: Carleton L Kingsford, Kunmi Ayanbule and Steven L Salzberg: Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake. (PubMed,eprint) Genome Biology 8(2):R22 (2007)
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tree-ppuzzle
Parallelized reconstruction of phylogenetic trees by maximum likelihood
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TREE-PUZZLE (the new name for PUZZLE) is an interactive console program that implements a fast tree search algorithm, quartet puzzling, that allows analysis of large data sets and automatically assigns estimations of support to each internal branch. TREE-PUZZLE also computes pairwise maximum likelihood distances as well as branch lengths for user specified trees. Branch lengths can also be calculated under the clock-assumption. In addition, TREE-PUZZLE offers a novel method, likelihood mapping, to investigate the support of a hypothesized internal branch without computing an overall tree and to visualize the phylogenetic content of a sequence alignment.

This is the parallelized version of tree-puzzle.

Please cite: Heiko A. Schmidt, Korbinian Strimmer, Martin Vingron and Arndt von Haeseler: TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. (PubMed,eprint) Bioinformatics 18(3):502-504 (2002)
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tree-puzzle
Reconstruction of phylogenetic trees by maximum likelihood
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TREE-PUZZLE (the new name for PUZZLE) is an interactive console program that implements a fast tree search algorithm, quartet puzzling, that allows analysis of large data sets and automatically assigns estimations of support to each internal branch. TREE-PUZZLE also computes pairwise maximum likelihood distances as well as branch lengths for user specified trees. Branch lengths can also be calculated under the clock-assumption. In addition, TREE-PUZZLE offers a novel method, likelihood mapping, to investigate the support of a hypothesized internal branch without computing an overall tree and to visualize the phylogenetic content of a sequence alignment.

Please cite: Heiko A. Schmidt, Korbinian Strimmer, Martin Vingron and Arndt von Haeseler: TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. (PubMed,eprint) Bioinformatics 18(3):502-504 (2002)
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treeview
Java re-implementation of Michael Eisen's TreeView
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TreeView creates a matrix-like display of expression data, known as Eisen clustering. The original implementation was a Windows program named TreeView by Michael Eisen. This TreeView package, sometimes also referred to as jTreeView, was rewritten in Java under a free license.

Java TreeView is an extensible viewer for microarray data in PCL or CDT format.

Please cite: Alok J. Saldanha: Java Treeview -- extensible visualization of microarray data. (PubMed,eprint) Bioinformatics 20(17):3246-3248 (2004)
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treeviewx
Displays and prints phylogenetic trees
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TreeView X is an open source and multi-platform program to display phylogenetic trees. It can read and display NEXUS and Newick format tree files (such as those output by PAUP*, ClustalX, TREE-PUZZLE, and other programs). It allows one to order the branches of the trees, and to export the trees in SVG format.

Please cite: Page, Roderic D. M.: TreeView: an application to display phylogenetic trees on personal computers. (PubMed) Comput. Appl. Biosci. 12(4):357-8 (1996)
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trf
locate and display tandem repeats in DNA sequences
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A tandem repeat in DNA is two or more adjacent, approximate copies of a pattern of nucleotides. Tandem Repeats Finder is a program to locate and display tandem repeats in DNA sequences. In order to use the program, the user submits a sequence in FASTA format. There is no need to specify the pattern, the size of the pattern or any other parameter. The output consists of two files: a repeat table file and an alignment file. The repeat table, viewable in a web browser, contains information about each repeat, including its location, size, number of copies and nucleotide content. Clicking on the location indices for one of the table entries opens a second browser page that shows an alignment of the copies against a consensus pattern. The program is very fast, analyzing sequences on the order of .5Mb in just a few seconds. Submitted sequences may be of arbitrary length. Repeats with pattern size in the range from 1 to 2000 bases are detected.

The package is enhanced by the following packages: trf-examples
Please cite: Gary Benson: Tandem repeats finder: a program to analyze DNA sequences. (PubMed,eprint) Nucleic Acids Research 27(2):573–580 (1999)
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trim-galore
automate quality and adapter trimming for DNA sequencing
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Trim Galore! is a wrapper script to automate quality and adapter trimming as well as quality control, with some added functionality to remove biased methylation positions for RRBS sequence files (for directional, non-directional (or paired-end) sequencing). It's main features are:

  • For adapter trimming, Trim Galore! uses the first 13 bp of Illumina standard adapters ('AGATCGGAAGAGC') by default (suitable for both ends of paired-end libraries), but accepts other adapter sequence, too
  • For MspI-digested RRBS libraries, Trim Galore! performs quality and adapter trimming in two subsequent steps. This allows it to remove 2 additional bases that contain a cytosine which was artificially introduced in the end-repair step during the library preparation
  • For any kind of FastQ file other than MspI-digested RRBS, Trim Galore! can perform single-pass adapter- and quality trimming
  • The Phred quality of basecalls and the stringency for adapter removal can be specified individually
  • Trim Galore! can remove sequences if they become too short during the trimming process. For paired-end files Trim Galore! removes entire sequence pairs if one (or both) of the two reads became shorter than the set length cutoff. Reads of a read-pair that are longer than a given threshold but for which the partner read has become too short can optionally be written out to single-end files. This ensures that the information of a read pair is not lost entirely if only one read is of good quality
  • Trim Galore! can trim paired-end files by 1 additional bp from the 3' end of all reads to avoid problems with invalid alignments with Bowtie 1
  • Trim Galore! accepts and produces standard or gzip compressed FastQ files
  • FastQC can optionally be run on the resulting output files once trimming has completed
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trimmomatic
flexible read trimming tool for Illumina NGS data
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Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data.The selection of trimming steps and their associated parameters are supplied on the command line.

The current trimming steps are:

  • ILLUMINACLIP: Cut adapter and other illumina-specific sequences from the read.
  • SLIDINGWINDOW: Perform a sliding window trimming, cutting once thes average quality within the window falls below a threshold.
  • LEADING: Cut bases off the start of a read, if below a threshold quality
  • TRAILING: Cut bases off the end of a read, if below a threshold quality
  • CROP: Cut the read to a specified length
  • HEADCROP: Cut the specified number of bases from the start of the read
  • MINLENGTH: Drop the read if it is below a specified length
  • TOPHRED33: Convert quality scores to Phred-33
  • TOPHRED64: Convert quality scores to Phred-64 It works with FASTQ (using phred + 33 or phred + 64 quality scores, depending on the Illumina pipeline used), either uncompressed or gzipp'ed FASTQ. Use of gzip format is determined based on the .gz extension.
The package is enhanced by the following packages: multiqc
Please cite: A.M. Bolger, M. Lohse and B. Usadel: Trimmomatic: a flexible trimmer for Illumina sequence data. (PubMed,eprint) Bioinformatics 30(15):2114-2120 (2014)
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trinityrnaseq
RNA-Seq De novo Assembly
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Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes.

Please cite: Manfred G Grabherr, Brian J Haas, Moran Yassour, Joshua Z Levin, Dawn A Thompson, Ido Amit, Xian Adiconis, Lin Fan, Raktima Raychowdhury, Qiandong Zeng, Zehua Chen, Evan Mauceli, Nir Hacohen, Andreas Gnirke, Nicholas Rhind, Federica di Palma, Bruce W Birren, Chad Nusbaum, Kerstin Lindblad-Toh, Nir Friedman and Aviv Regev: Full-length transcriptome assembly from RNA-Seq data without a reference genome.. (PubMed) Nature Biotechnology 29(7):644-652 (2011)
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tvc
genetic variant caller for Ion Torrent sequencing platforms
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Torrent Variant Caller (TVC) is a genetic variant caller for Ion Torrent sequencing platforms, and is specially optimized to exploit the underlying flow signal information in the statistical model to evaluate variants. Torrent Variant Caller is designed to call single-nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs), insertions, deletions, and block substitutions.

twopaco
build the compacted de Bruijn graph from many complete genomes
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TwoPaCo is an implementation of the algorithm described in the paper "TwoPaCo: An efficient algorithm to build the compacted de Bruijn graph from many complete genomes".

This package contains two programs:

 twopaco: tool for direct construction of the compressed graph from
          multiple complete genomes
 graphdump: utility that turns output of twopaco into a text format
Please cite: Ilia Minkin, Son Pham and Paul Medvedev: TwoPaCo: an efficient algorithm to build the compacted de Bruijn graph from many complete genomes. (PubMed,eprint) Bioinformatics 33(24):4024-4032 (2017)
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uc-echo
error correction algorithm designed for short-reads from NGS
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ECHO is an error correction algorithm designed for short-reads from next-generation sequencing platforms such as Illumina's Genome Analyzer II. The algorithm uses a Bayesian framework to improve the quality of the reads in a given data set by employing maximum a posteriori estimation.

Please cite: W.-C. Kao, A.H. Chan and Y.S. Song: ECHO: A reference-free short-read error correction algorithm. (PubMed,eprint) Genome Research 21:1181-1192 (2011)
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Topics: Data management; Sequencing
ugene
integrated bioinformatics toolkit
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Unipro UGENE is a cross-platform visual environment for DNA and protein sequence analysis. UGENE integrates the most important bioinformatics computational algorithms and provides an easy-to-use GUI for performing complex analysis of the genomic data. One of the main features of UGENE is a designer for custom bioinformatics workflows.

Please cite: Konstantin Okonechnikov, Olga Golosova, Mikhail Fursov and the UGENE team: Unipro UGENE: a unified bioinformatics toolkit. (PubMed,eprint) Bioinformatics 28(8):1166-1167 (2012)
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umap-learn
Uniform Manifold Approximation and Projection
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Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t- SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data:

 1. The data is uniformly distributed on a Riemannian manifold;
 2. The Riemannian metric is locally constant (or can be
    approximated as such);
 3. The manifold is locally connected.

From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

Please cite: Leland McInnes, John Healy and James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. (eprint) arXiv (2018)
umis
tools for processing UMI RNA-tag data
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Umis provides tools for estimating expression in RNA-Seq data which performs sequencing of end tags of transcript, and incorporate molecular tags to correct for amplification bias.

There are four steps in this process.

 1. Formatting reads
 2. Filtering noisy cellular barcodes
 3. Pseudo-mapping to cDNAs
 4. Counting molecular identifiers
Please cite: Valentine Svensson, Kedar Nath Natarajan, Lam-Ha Ly, Ricardo J Miragaia, Charlotte Labalette, Iain C Macaulay, Ana Cvejic and Sarah A Teichmann: Power analysis of single-cell RNA-sequencing experiments. (PubMed) Nature methods 14:381–387 (2017)
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uncalled
Utility for Nanopore Current Alignment to Large Expanses of DNA
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Streaming algorithm for mapping raw nanopore signal to DNA references

Enables real-time enrichment or depletion on Oxford Nanopore Technologies (ONT) MinION runs via ReadUntil.

Also supports standalone signal mapping of fast5 reads

Please cite: Sam Kovaka, Yunfan Fan, Bohan Ni, Winston Timp and Michael C. Schatz: Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. (eprint) Nature Biotechnology (2020)
unicycler
hybrid assembly pipeline for bacterial genomes
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Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Please cite: Ryan R. Wick, Louise M. Judd, Claire L. Gorrie and Kathryn E. Holt: Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. (PubMed,eprint) PLOS Computational Biology 13(6):e1005595 (2017)
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unikmer
Toolkit for nucleic acid k-mer analysis
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unikmer is a golang package and a toolkit for nucleic acid k-mer analysis, providing functions including set operation k-mers optional with TaxIDs but without count information.

K-mers are either encoded (k<=32) or hashed (arbitrary k) into uint64, and serialized in binary file with extension .unik.

TaxIDs can be assigned when counting k-mers from genome sequences, and LCA (Lowest Common Ancestor) is computed during set opertions including computing union, intersecton, set difference, unique and repeated k-mers.

varna
Visualization Applet for RNA
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VARNA is Java lightweight Applet dedicated to drawing the secondary structure of RNA. It is also a Swing component that can be very easily included in an existing Java code working with RNA secondary structure to provide a fast and interactive visualization.

Being free of fancy external library dependency and/or network access, the VARNA Applet can be used as a base for a standalone applet. It looks reasonably good and scales up or down nicely to adapt to the space available on a web page, thanks to the anti-aliasing drawing primitives of Swing.

Please cite: Kévin Darty, Alain Denise and Yann Ponty: VARNA: Interactive drawing and editing of the RNA secondary structure. (eprint) Bioinformatics 25(15):1974-1975 (2009)
vcfanno
annotate a VCF with other VCFs/BEDs/tabixed files
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Vcfanno allows you to quickly annotate your VCF with any number of INFO fields from any number of VCFs or BED files. It uses a simple conf file to allow the user to specify the source annotation files and fields and how they will be added to the info of the query VCF.

  • For VCF, values are pulled by name from the INFO field with special cases of ID and FILTER to pull from those VCF columns.
  • For BED, values are pulled from (1-based) column number.
  • For BAM, depth (count), "mapq" and "seq" are currently supported.

Vcfanno is written in go and it supports custom user-scripts written in Lua. It can annotate more than 8000 variants per second with 34 annotations from 9 files on a modest laptop and over 30K variants per second using 12 processes on a server.

Please cite: Brent S. Pedersen, Ryan M. Layer and Aaron R. Quinlan: Vcfanno: fast, flexible annotation of genetic variants. (PubMed,eprint) Genome Biology 17(1):118 (2016)
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vcftools
Collection of tools to work with VCF files
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VCFtools is a program package designed for working with VCF files, such as those generated by the 1000 Genomes Project. The aim of VCFtools is to provide methods for working with VCF files: validating, merging, comparing and calculate some basic population genetic statistics.

The package is enhanced by the following packages: multiqc
Please cite: Petr Danecek, Adam Auton, Goncalo Abecasis, Cornelis A. Albers, Eric Banks, Mark A. DePristo, Robert E. Handsaker, Gerton Lunter, Gabor T. Marth, Stephen T. Sherry, Gilean McVean and Richard Durbin: The variant call format and VCFtools. (PubMed,eprint) Bioinformatics 27(15):2156-8 (2011)
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velvet
Nucleic acid sequence assembler for very short reads
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Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.

Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired read information, if available, to retrieve the repeated areas between contigs.

Please cite: Daniel R. Zerbino and Ewan Birney: Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. (PubMed,eprint) Genome Research 18(5):821-829 (2008)
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velvet-long
Nucleic acid sequence assembler for very short reads, long version
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Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.

Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired read information, if available, to retrieve the repeated areas between contigs.

This package installs special long-mode versions of Velvet, as recommended in the Velvet tutorials.

Please cite: Daniel R. Zerbino and Ewan Birney: Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. (PubMed,eprint) Genome Research 18(5):821-829 (2008)
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velvetoptimiser
automatically optimise Velvet do novo assembly parameters
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VelvetOptimiser is a multi-threaded Perl script for automatically optimising the three primary parameter options (K, -exp_cov, -cov_cutoff) for the Velvet de novo sequence assembler.

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veryfasttree
Speeding up the estimation of phylogenetic trees from sequences
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VeryFastTree is a highly efficient implementation inspired by the FastTree-2 tool, designed to expedite the inference of approximately-maximum-likelihood phylogenetic trees from nucleotide or protein sequence alignments. It is an optimized implementation designed to accelerate the estimation of phylogenies for large alignments. By leveraging parallelization and vectorization strategies, VeryFastTree significantly improves the performance and scalability of phylogenetic analysis, allowing it to construct phylogenetic trees in a fraction of the time previously required.

Maintaining the integrity of FastTree-2, VeryFastTree retains the same phases, methods, and heuristics used for estimating phylogenetic trees. This ensures that the topological accuracy of the trees produced by VeryFastTree remains equivalent to that of FastTree-2. Moreover, unlike the parallel version of FastTree-2, VeryFastTree guarantees deterministic results, eliminating any potential variations in the output.

To facilitate a seamless transition for users, VeryFastTree adopts the exact same command line arguments as FastTree-2. This means that by simply substituting FastTree-2 with VeryFastTree, and using the same set of options, users can significantly enhance the overall performance of their phylogenetic analyses.

vg
tools for working with genome variation graphs
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variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

  • nodes, which are labeled by sequences and ids
  • edges, which connect two nodes via either of their respective ends
  • paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

This model is similar to a number of sequence graphs that have been used in assembly and multiple sequence alignment. Paths provide coordinate systems relative to genomes encoded in the graph, allowing stable mappings to be produced even if the structure of the graph is changed.

Please cite: Erik Garrison, Jouni Sirén, Adam M Novak, Glenn Hickey, Jordan M Eizenga, Eric T Dawson, William Jones, Shilpa Garg, Charles Markello, Michael F Lin, Benedict Paten and Richard Durbin: Variation graph toolkit improves read mapping by representing genetic variation in the reference. (PubMed) Nature Biotechnology 36(9):875–879 (2018)
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viewmol
graphical front end for computational chemistry programs
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Viewmol is able to graphically aid in the generation of molecular structures for computations and to visualize their results.

At present Viewmol includes input filters for Discover, DMol3, Gamess, Gaussian 9x/03, Gulp, Mopac, PQS, Turbomole, and Vamp outputs as well as for PDB files. Structures can be saved as Accelrys' car-files, MDL files, and Turbomole coordinate files. Viewmol can generate input files for Gaussian 9x/03. Viewmol's file format has been added to OpenBabel so that OpenBabel can serve as an input as well as an output filter for coordinates.

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virulencefinder
identify virulence genes in total or partial sequenced isolates of bacteria
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The VirulenceFinder service contains one Python script virulencefinder.py which is the script of the latest version of the VirulenceFinder service. VirulenceFinder identifies viruelnce genes in total or partial sequenced isolates of bacteria - at the moment only E. coli, Enterococcus, S. aureus and Listeria are available.

Please cite: Katrine Grimstrup Joensen, Flemming Scheutz, Ole Lund, Henrik Hasman, Rolf S. Kaas, Eva M. Nielsen and Frank M. Aarestrup: Real-Time Whole-Genome Sequencing for Routine Typing, Surveillance, and Outbreak Detection of Verotoxigenic Escherichia coli. (PubMed,eprint) Journal of Clinical Microbiology 52(5):1501-10 (2014)
vmatch
large scale sequence analysis software
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Vmatch is a versatile software tool for efficiently solving large scale sequence matching tasks. It subsumes the software tool REPuter, but is much more general, with a very flexible user interface, and improved space and time requirements.

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vsearch
tool for processing metagenomic sequences
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Versatile 64-bit multithreaded tool for processing metagenomic sequences, including searching, clustering, chimera detection, dereplication, sorting, masking and shuffling

The aim of this project is to create an alternative to the USEARCH tool developed by Robert C. Edgar (2010). The new tool should:

  • have a 64-bit design that handles very large databases and much more than 4GB of memory
  • be as accurate or more accurate than usearch
  • be as fast or faster than usearch
The package is enhanced by the following packages: vsearch-examples
Please cite: Torbjørn Rognes, Tomáš Flouri, Ben Nichols, Christopher Quince and Frédéric Mahé: VSEARCH: a versatile open source tool for metagenomics. (eprint) PeerJ 4:e2584
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vt
toolset for short variant discovery in genetic sequence data
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vt is a variant tool set that discovers short variants from Next Generation Sequencing data.

Vt-normalize is a tool to normalize representation of genetic variants in the VCF. Variant normalization is formally defined as the consistent representation of genetic variants in an unambiguous and concise way. In vt a simple general algorithm to enforce this is implemented.

The package is enhanced by the following packages: vt-examples
Please cite: Adrian Tan, Gonçalo R. Abecasis and Hyun Min Kang: Unified representation of genetic variants. (PubMed,eprint) Bioinformatics 31(13):2202–2204 (2015)
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wham-align
Wisconsin's High-Throughput Alignment Method
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This package provides functionality analogous to BWA or bowtie in aligning reads from next-generation DNA sequencing machines against a reference genome.

Please cite: Yinan Li, Allie Terrell and Jignesh M. Patel: WHAM: A High-throughput Sequence Alignment Method (eprint) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece (2011)
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wigeon
reimplementation of the Pintail 16S DNA anomaly detection utility
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WigeoN examines the sequence conservation between a query and a trusted reference sequence, both in NAST alignment format. Based on the sequence identity between the query and the reference sequence, there is an expected amount of variation among the alignment. If the observed variation is greater than the 95% quantile of the distribution of variation observed between non-anomalous sequences, then it is flagged as an anomaly.

WigeoN is a flexible command-line based reimplementation of the Pintail algorithm Appl Environ Microbiol. 2005 Dec;7112:7724-36.

WigeoN is useful for flagging chimeras and anomalies only in near full-length 16S rRNA sequences. WigeoN lacks sensitivity with sequences less than 1000 bp.

To run WigeoN, you need NAST-formatted sequences generated by the nast-ier utility.

WigeoN is part of the microbiomeutil suite.

The package is enhanced by the following packages: microbiomeutil-data
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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wise
comparison of biopolymers, like DNA and protein sequences
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Wise2 is a package focused on comparisons of biopolymers, commonly DNA and protein sequences. There are many other packages which do this, probably the best known being BLAST package (from NCBI) and the Fasta package (from Bill Pearson). There are other packages, such as the HMMER package (Sean Eddy) or SAM package (UC Santa Cruz) focused on hidden Markov models (HMMs) of biopolymers.

Wise2's particular forte is the comparison of DNA sequence at the level of its protein translation. This comparison allows the simultaneous prediction of say gene structure with homology based alignment.

Wise2 also contains other algorithms, such as the venerable Smith-Waterman algorithm, or more modern ones such as Stephen Altschul's generalised gap penalties, or even experimental ones developed in house, such as dba. The development of these algorithms is due to the ease of developing such algorithms in the environment used by Wise2.

Wise2 has also been written with an eye for reuse and maintainability. Although it is a pure C package you can access its functionality directly in Perl. Parts of the package (or the entire package) can be used by other C or C++ programs without namespace clashes as all externally linked variables have the unique identifier Wise2 prepended.

Please cite: Ewan Birney, Michele Clamp and Richard Durbin: GeneWise and Genomewise. (PubMed,eprint) Genome Research 14(5):988-95 (2004)
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xpore
Nanopore analysis of differential RNA modifications
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RNA is transcribed from DNA, possibly spliced and exported to the cytoplasm - fine - but its bases can also be modified, edited, and not all such modifications are visible by Sanger sequencing methods and its derivatives.

The Nanopore measures potentials, and if the bases have a different shape then this is measured - not necessarily in an interpretable manner, something must be left for mass spectrometry, but one may be pointed to a difference. And maybe one can even statistically associate such differences with a molecular or clinical phenotype.

yaha
find split-read mappings on single-end queries
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yaha is an open source, flexible, sensitive and accurate DNA aligner designed for single-end reads. It supports three major modes of operation:

  • The default “Optimal Query Coverage” (-OQC) mode reports the best set of alignments that cover the length of each query.
  • Using “Filter By Similarity” (-FBS), along with the best set of alignments, yaha will also output alignments that are highly similar to an alignment in the best set.
  • Finally, yaha can output all the alignments found for each query. The -OQC and -FBS modes are specifically tuned to form split read mappings that can be used to accurately identify structural variation events (deletions, duplications, insertions or inversions) between the subject query and the reference genome.
Please cite: Gregory G. Faust and Ira M. Hall: YAHA: fast and flexible long-read alignment with optimal breakpoint detection. (PubMed,eprint) Bioinformatics 28(19):2417–2424 (2012)
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yanagiba
filter low quality Oxford Nanopore reads basecalled with Albacore
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Yanagiba is used to filter short or low quality Oxford Nanopore reads which have been basecalled with Albacore. It takes fastq.gz and an Albacore summary file as input. If no Albacore summary file is provided attempt to calculate mean qscore from directly from fastq file using NanoMath. Note: Calculated quality scores appear to be lower for reads called with Metrichor, you may need to lower your minqual setting in this case.

yanosim
read simulator nanopore DRS datasets
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Yanosim has three options:

  1. yanosim model:

    Creates an model of mismatches, insertions and deletions based on an alignment of nanopore DRS reads to a reference. Reads should be aligned to a transcriptome i.e. without spliced alignment, using minimap2. They should have the cs tag. 2. yanosim quantify:

    Quantify the number of reads mapping to each transcript in a reference, so that the right number of reads can be simulated. 3. yanosim simulate:

    Given a model created using yanosim model, and per-transcript read counts created using yanosim simulate, simulate error-prone long-reads from the given fasta file.

Official Debian packages with lower relevance

adun.app
Molecular Simulator for GNUstep (GUI)
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Adun is a biomolecular simulator that also includes data management and analysis capabilities. It was developed at the Computational Biophysics and Biochemistry Laboratory, a part of the Research Unit on Biomedical Informatics of the UPF.

This package contains UL, the Adun GUI frontend.

Please cite: Michael A. Johnston, Ignacio Fdez. Galván and Jordi Villà-Freixa: Framework-based design of a new all-purpose molecular simulation application: The Adun simulator. (PubMed) J. Comp. Chem. 26(15):1647-1659 (2005)
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catfishq
concatenates fastq files
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FASTQ is the most common format to store the reads from high-throughput biological sequencing. This tool takes paths to an arbritary number of zipped and unzipped FASTQ files and/or folders containing zipped or unzipped FASTQ files, concatenates them and prints them to standard out (default) or an unzipped output file.

Supported file extensions are: '.fastq', '.fastq.gz', '.fasta', '.fasta.gz', '.fa', '.fa.gz', '.fq', '.fq.gz'

conda-package-handling
create and extract conda packages of various formats
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Cph is an abstraction of conda package handling and a tool for extracting, creating, and converting between formats.

At the time of writing, the standard conda package format is a .tar.bz2 file. That will need to be maintained for quite a long time, thanks to the long tail of people using old conda versions. There is a new conda format, described at https://docs.google.com/document/d/1HGKsbg_j69rKXP- ihhpCb1kNQSE8Iy3yOsUU2x68x8uw/edit?usp=sharing. This new format is designed to have much faster metadata access and utilize more modern compression algorithms, while also facilitating package signing without adding sidecar files.

Remark of Debian Med team: Dead upstream

The homepage of this project vanished as well as the Download area. An old unmaintained version remained at code.google.com. Please drop the maintainer a note if you have any news of this project.

dascrubber
alignment-based scrubbing pipeline for DNA sequencing reads
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The Dazzler Scrubbing Suite produces a set of edited reads that are guaranteed to

  • be continuous stretches of the underlying genome (i.e. no unremoved adapters and not chimers)
  • have no very low quality stretches (i.e. the error rate never exceeds some reasonable maximum, 20% or so in the case of Pacbio data). Its secondary goal is to do so with the minimum removal of data and splitting of reads.
dnapi
adapter prediction for small RNA sequencing - utils
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This package provides de novo adapter prediction (iterative) algorithm for small RNA sequencing data.

DNApi can predict most 3' adapters correctly with the default parameters. You can tweak the parameters and can run different prediction modes.

Please cite: Junko Tsuji and Zhiping Weng: DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data. (PubMed,eprint) PLoS One 11(10):e0164228 (2016)
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emboss-explorer
web-based GUI to EMBOSS
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EMBOSS explorer is a web-based graphical user interface to the EMBOSS suite of bioinformatics tools. It is written in Perl.

If you use the Apache HTTP server, you will at most have to restart it before using EMBOSS explorer. For other web servers, you will have to do the configuration by yourself.

epigrass
scientific tool for simulations and scenario analysis in network epidemiology
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Epigrass is a software for visualizing, analyzing and simulating of epidemic processes on geo-referenced networks.

EpiGrass can interact with the GRASS GIS from which it can obtain maps and other geo-referenced information. However, EpiGrass does not require an installation of the GRASS GIS for most of its features.

The package is enhanced by the following packages: epigrass-doc
Please cite: Flavio Coelho, Oswaldo Cruz and Claudia Codeco: Epigrass: a tool to study disease spread in complex networks. (PubMed) Source Code for Biology and Medicine 3(1):3 (2008)
getdata
management of external databases
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Many scientific communities share the problem of regularly updating external databases. With every update, also various tasks need to be performed for the update of indices that need to be recreated. This work depends on the tools that are available locally and is not always completely simple.

This package provides the getData Perl script, which in some not so complicated manner performs the invocation to wget to download data and then knows how to perform the indexing. There is only a hash table to be filled with the commands to be executed. Maintainers of scientific packages that are strongly coupled to public datasets are invited to add a runtime dependency to this package and add instructions for getData to follow.

hts-nim-tools
tools biological sequences: bam-filter, count-reads, vcf-check
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This package provides several tools that (at least at the time of their creation) provide functionalities beyond the routine provided by samtools and other reverse dependencies of the htslib.

These new tools are

 • bam-filter : filter BAM/CRAM/SAM files with a simple expression language
 • count-reads: count BAM/CRAM reads in regions given in a BED file
 • vcf-check  : check regions of a VCF against a background for missing chunks

and yes, as the name suggests, these tools are all implemented in nim, using the nim-hts (upstream: hts-nim) wrapper for the htslib.

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idseq-bench
Benchmark generator for the IDseq Portal
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IDseq (Infectious Disease Sequencing Platform) is an unbiased global software platform that helps scientists identify pathogens in metagenomic sequencing data.

  • Discover - Identify the pathogen landscape
  • Detect - Monitor and review potential outbreaks
  • Decipher - Find potential infecting organisms in large datasets

This package provides the benchmark generator for the IDseq Portal.

illustrate
cartoonish representations of large biological molecules
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This package provides a binary to transform PDF-formatted proteins into simplified but instructive graphics. The software has been used for the Protein-of-the-month's biomolecular illustrations for the past 20 years.

Please cite: D.S. Goodsell and A.J. Olson: Molecular Illustration in Black and White. (PubMed) J. Mol. Graphics 10(4):235-240 (1992)
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libhdf5-dev
HDF5 - development files - serial version
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Hierarchical Data Format 5 (HDF5) is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O.

This package contains development files for serial platforms.

libhnswlib-dev
fast approximate nearest neighbor search
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Header-only C++ HNSW implementation with Python bindings.

A common task in data analysis but also in scientific computations is to find data that is very close (multi-dimensional space) or similar (same thing) to a given data point. Also as heuristics for physics engines, it is the objects closest to you that you are most likely to collide with. This library knows how to do this fast.

Please cite: Yu. A. Malkov and D. A. Yashunin: Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. (eprint) arXiv.org arXiv(1603.0932) (2016)
maude
high-performance logical framework
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Maude is a high-performance reflective language and system supporting both equational and rewriting logic specification and programming for a wide range of applications. Maude has been influenced in important ways by the OBJ3 language, which can be regarded as an equational logic sublanguage. Besides supporting equational specification and programming, Maude also supports rewriting logic computation.

Rewriting logic is a logic of concurrent change that can naturally deal with state and with concurrent computations. It has good properties as a general semantic framework for giving executable semantics to a wide range of languages and models of concurrency. In particular, it supports very well concurrent object-oriented computation. The same reasons making rewriting logic a good semantic framework make it also a good logical framework, that is, a metalogic in which many other logics can be naturally represented and executed.

Maude supports in a systematic and efficient way logical reflection. This makes Maude remarkably extensible and powerful, supports an extensible algebra of module composition operations, and allows many advanced metaprogramming and metalanguage applications. Indeed, some of the most interesting applications of Maude are metalanguage applications, in which Maude is used to create executable environments for different logics, theorem provers, languages, and models of computation.

Maude is of interest to the biomedical community for modeling and analysis of biological systems.

Please cite: M. Matsumoto and T. Nishimura: Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation 8(1):3-30 (1998)
metastudent-data
predictor of Gene Ontology terms from protein sequence - data files
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Often, only the sequence of a protein is known, but not its functions. Metastudent will try to predict missing functional annotations through homology searches (BLAST).

All predicted functions correspond to Gene Ontology (GO) terms from the Molecular Function Ontology (MFO) and the Biological Process Ontology (BPO) and are associated with a reliability score.

This package contains data files for metastudent.

Please cite: Tobias Hamp, Rebecca Kassner, Stefan Seemayer, Esmeralda Vicedo, Christian Schaefer, Dominik Achten, Florian Auer, Ariane Boehm, Tatjana Braun, Maximilian Hecht, Mark Heron, Peter Hönigschmid, Thomas A. Hopf, Stefanie Kaufmann, Michael Kiening, Denis Krompass, Cedric Landerer, Yannick Mahlich, Manfred Roos and Burkhard Rost: Homology-based inference sets the bar high for protein function prediction. (PubMed) BMC Bioinformatics 14(Suppl 3):S7 (2013)
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metastudent-data-2
predictor of Gene Ontology terms from protein sequence - data #2
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Often, only the sequence of a protein is known, but not its functions. Metastudent will try to predict missing functional annotations through homology searches (BLAST).

All predicted functions correspond to Gene Ontology (GO) terms from the Molecular Function Ontology (MFO) and the Biological Process Ontology (BPO) and are associated with a reliability score.

This package contains additional data files for metastudent.

Please cite: Tobias Hamp, Rebecca Kassner, Stefan Seemayer, Esmeralda Vicedo, Christian Schaefer, Dominik Achten, Florian Auer, Ariane Boehm, Tatjana Braun, Maximilian Hecht, Mark Heron, Peter Hönigschmid, Thomas A. Hopf, Stefanie Kaufmann, Michael Kiening, Denis Krompass, Cedric Landerer, Yannick Mahlich, Manfred Roos and Burkhard Rost: Homology-based inference sets the bar high for protein function prediction. (PubMed) BMC Bioinformatics 14(Suppl 3):S7 (2013)
mrs
Information Retrieval System for Biomedical databanks
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MRS is a complete system to retrieve, index and query biological and medical databanks. It comes with all the code required to fetch data using FTP or rsync, then creates a local databank with indices using a databank specific parser written in Perl. It can then serve this data using a built-in web application and webservices.

Searching can be done on words and Boolean queries. As a bonus you can search protein sequences using a custom Blast algorithm.

Please cite: Maarten L. Hekkelman and Gert Vriend: MRS: a fast and compact retrieval system for biological data. (PubMed,eprint) Nucleic Acids Res. 33:W766-W769 (2005)
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Remark of Debian Med team: mrs might occupy a lot of space on users disk - so you want to avoid this package from the metapackage recommends
python3-alignlib
edit and Hamming distances for biological sequences
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A small Python module providing edit distance and Hamming distance computation. It is a dependency for the IgDiscover package and likely future others.

python3-anndata
annotated gene by sample numpy matrix
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AnnData provides a scalable way of keeping track of data together with learned annotations. It is used within Scanpy, for which it was initially developed. Both packages have been introduced in Genome Biology (2018).

Please cite: F. Alexander Wolf, Philipp Angerer and Fabian J. Theis: SCANPY: large-scale single-cell gene expression data analysis.. (PubMed) Genome Biol. 19:15 (2018)
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python3-cgecore
Python3 module for the Center for Genomic Epidemiology
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This Python3 module contains classes and functions needed to run the service wrappers and pipeline scripts developed by the Center for Genomic Epidemiology.

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python3-cyvcf2
VCF parser based on htslib (Python 3)
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This modules allows fast parsing of VCF and BCF including region-queries with Python. This is essential for efficient analyses of nucleotide variation with Python on high-throughput sequencing data.

cyvcf2 is a cython wrapper around htslib. Attributes like variant.gt_ref_depths return a numpy array directly so they are immediately ready for downstream use.

This package installs the library for Python 3.

Please cite: Brent S. Pedersen and Aaron R. Quinlan: cyvcf2: fast, flexible variant analysis with Python. (eprint) Bioinformatics 33(12):1867–1869 (2017)
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python3-deeptools
platform for exploring biological deep-sequencing data
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Aiming for compatibility with the Galaxy worklfow environment, but also independently contributing to a series of workflows in genomics, this package provides a series of tools to address common tasks for the processing of high-throughput DNA/RNA sequencing.

Please cite: Fidel Ramirez, Devon P. Ryan, Björn Grüning, Sarah Diehl, Vivek Bhardwaj, Fabian Kilpert, Andreas S Richter, Steffen Heyne, Friederike Dündar and Thomas Manke: deepTools2: a next generation web server for deep-sequencing data analysis. (eprint) Nucleic Acids Research :W160–W165 (2016)
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python3-deeptoolsintervals
handlig GTF-like sequence-associated interal-annotation
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Regions in biological sequences are described (annotated) as genes, transcription factor binding sites, low complexity, ... whatever biological research brings.

This package supports the efficienct operation with this information.

python3-htseq
Python3 high-throughput genome sequencing read analysis utilities
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HTSeq can be used to performing a number of common analysis tasks when working with high-throughput genome sequencing reads:

  • Getting statistical summaries about the base-call quality scores to study the data quality.
  • Calculating a coverage vector and exporting it for visualization in a genome browser.
  • Reading in annotation data from a GFF file.
  • Assigning aligned reads from an RNA-Seq experiments to exons and genes.

This package contains the Python 3 module.

Please cite: Simon Anders, Paul Theodor Pyl and Wolfgang Huber: HTSeq—a Python framework to work with high-throughput sequencing data. (PubMed,eprint) Bioinformatics 31(2):166-169 (2015)
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python3-intake
lightweight package for finding and investigating data
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Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps you:

 1. Load data from a variety of formats into containers you already know,
    like Pandas dataframes, Python lists, NumPy arrays and more.
 2. Convert boilerplate data loading code into reusable intake plugins.
 3. Describe data sets in catalog files for easy reuse and sharing
    between projects and with others.
 4. Share catalog information (and data sets) over the network with the
    Intake server.
python3-loompy
access loom formatted files for bioinformatics
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Loom is an efficient file format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations. Loom also supports sparse graphs. Loom files are used to store single-cell gene expression data: the main matrix contains the actual expression values (one column per cell, one row per gene); row and column annotations contain metadata for genes and cells, such as Name, Chromosome, Position (for genes), and Strain, Sex, Age (for cells).

Loom files (.loom) are created in the HDF5 file format, which supports an internal collection of numerical multidimensional datasets. HDF5 is supported by many computer languages, including Java, MATLAB, Mathematica, Python, R, and Julia. .loom files are accessible from any language that supports HDF5.

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python3-nanoget
extract information from Oxford Nanopore sequencing data and alignments
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The Python3 module nanoget provides functions to extract useful metrics from Oxford Nanopore sequencing reads and alignments.

Data can be presented in the following formats, using the following functions:

  • sorted bam file process_bam(bamfile, threads)
  • standard fastq file process_fastq_plain(fastqfile, 'threads')
  • fastq file with metadata from MinKNOW or Albacore process_fastq_rich(fastqfile)
  • sequencing_summary file generated by Albacore process_summary(sequencing_summary.txt, 'readtype')

Fastq files can be compressed using gzip, bzip2 or bgzip. The data is returned as a pandas DataFrame with standardized headernames for convenient extraction. The functions perform logging while being called and extracting data.

The package is enhanced by the following packages: python3-nanoget-examples
python3-nanomath
simple math function for other Oxford Nanopore processing scripts
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This Python3 module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data.

  • Calculate read N50 from a set of lengths get_N50(readlenghts)
  • Remove extreme length outliers from a dataset remove_length_outliers(dataframe, columname)
  • Calculate the average Phred quality of a read ave_qual(qualscores)
  • Write out the statistics report after calling readstats function write_stats(dataframe, outputname)
  • Compute a number of statistics, return a dictionary calc_read_stats(dataframe)
python3-ncls
datastructure for interval overlap queries
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The Nested Containment List is a datastructure for interval overlap queries, like the interval tree. It is usually an order of magnitude faster than the interval tree both for building and query lookups.

The implementation here is a revived version of the one used in the now defunct PyGr library, which died of bitrot. It was now made less memory-consuming and wrapper functions allow batch-querying the NCLS for further speed gains.

This package was implemented to be the cornerstone of the PyRanges project, but was made available to the Python community as a stand-alone library.

Please cite: Endre Bakken Stovner and Pål Sætrom: PyRanges: efficient comparison of genomic intervals in Python. Bioinformatics 36(3):918–919 (2020)
python3-py2bit
access to 2bit files
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From https://genome.ucsc.edu/FAQ/FAQformat.html#format7: A .2bit file stores multiple DNA sequences (up to 4 Gb total) in a compact randomly-accessible format. The file contains masking information as well as the DNA itself.

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python3-pybel
Biological Expression Language
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PyBEL is a pure Python package for parsing and handling biological networks encoded in the Biological Expression Language (BEL) version 2. It also facilitates data interchange between common formats and databases such as NetworkX, JSON, CSV, SIF, Cytoscape, CX, NDEx, SQL, and Neo4J.

This package installs the library for Python 3.

Please cite: Charles Tapley Hoyt, Andrej Konotopez and Christian Ebeling: PyBEL: a computational framework for Biological Expression Language. (eprint) Bioinformatics 34(4):703–704 (2018)
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python3-pychopper
identify, orient and trim full-length Nanopore cDNA reads
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Pychopper v2 is a Python module to identify, orient and trim full-length Nanopore cDNA reads. It is also able to rescue fused reads and provides the script 'pychopper.py'. The general approach of Pychopper v2 is the following:

  • Pychopper first identifies alignment hits of the primers across the length of the sequence. The default method for doing this is using nhmmscan with the pre-trained strand specific profile HMMs, included with the package. Alternatively, one can use the edlib backend, which uses a combination of global and local alignment to identify the primers within the read.
  • After identifying the primer hits by either of the backends, the reads are divided into segments defined by two consecutive primer hits. The score of a segment is its length if the configuration of the flanking primer hits is valid (such as SPP,-VNP for forward reads) or zero otherwise.
  • The segments are assigned to rescued reads using a dynamic programming algorithm maximizing the sum of used segment scores (hence the amount of rescued bases). A crucial observation about the algorithm is that if a segment is included as a rescued read, then the next segment must be excluded as one of the primer hits defining it was "used up" by the previous segment. This put constraints on the dynamic programming graph. The arrows in read define the optimal path for rescuing two fused reads with the a total score of l1 + l3.

A crucial parameter of Pychopper v2 is -q, which determines the stringency of primer alignment (E-value in the case of the pHMM backend). This can be explicitly specified by the user, however by default it is optimized on a random sample of input reads to produce the maximum number of classified reads.

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python3-pyfaidx
efficient random access to fasta subsequences for Python 3
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Samtools provides a function "faidx" (FAsta InDeX), which creates a small flat index file ".fai" allowing for fast random access to any subsequence in the indexed FASTA file, while loading a minimal amount of the file in to memory. This Python module implements pure Python classes for indexing, retrieval, and in-place modification of FASTA files using a samtools compatible index. The pyfaidx module is API compatible with the pygr seqdb module. A command-line script "faidx" is installed alongside the pyfaidx module, and facilitates complex manipulation of FASTA files without any programming knowledge.

This package provides the Python 3 modules to access fasta files.

Please cite: Matthew D. Shirley, Zhaorong Ma, Brent S. Pedersen and Sarah J. Wheelan: Efficient "pythonic" access to FASTA files using pyfaidx. PeerJ PrePrints 3:e1196 (2015)
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python3-pyflow
lightweight parallel task engine for Python
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pyFlow is a tool to manage tasks in the context of a task dependency graph. It has some similarities to make. pyFlow is not a program – it is a Python module, and workflows are defined using pyFlow by writing regular Python code with the pyFlow API.

python3-pyranges
2D representation of genomic intervals and their annotations
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A PyRanges object must have the columns Chromosome, Start and End. These describe the genomic position and function as implicit row labels. A Strand column is optional and adds strand information to the intervals. Any other columns are allowed and are considered metadata.

The structure can be filled from .bed, .bam or .gff files, also from tabular or textual representations.

Please cite: Endre Bakken Stovner and Pål Sætrom: PyRanges: efficient comparison of genomic intervals in Python. Bioinformatics 36(3):918–919 (2020)
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python3-pyrle
run length arithmetic in Python
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As opposed to S4Vectors, pyrle does not rotate the shortest vector, but rather extends the shorter Rle with zeroes. This is likely the desired behavior in almost all cases.

python3-pysam
interface for the SAM/BAM sequence alignment and mapping format (Python 3)
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Pysam is a Python module for reading and manipulating Samfiles. It's a lightweight wrapper of the samtools C-API. Pysam also includes an interface for tabix.

This package installs the module for Python 3.

The package is enhanced by the following packages: python-pysam-tests
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python3-tinyalign
numerical representation of differences between strings
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A small Python module providing edit distance (aka Levenshtein distance, that is, counting insertions, deletions and substitutions) and Hamming distance computation.

Its main purpose is to speed up computation of edit distance by allowing to specify a maximum number of differences maxdiff (banding). If that parameter is provided, the returned edit distance is anly accurate up to maxdiff. That is, if the actual edit distance is higher than maxdiff, a value larger than maxdiff is returned, but not necessarily the actual edit distance.

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q2-alignment
QIIME 2 plugin for generating and manipulating alignments
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-cutadapt
QIIME 2 plugin to work with adapters in sequence data
Versions of package q2-cutadapt
ReleaseVersionArchitectures
bullseye2020.11.1-1amd64,arm64,mips64el,ppc64el
bookworm2022.11.1-2amd64,arm64,mips64el,ppc64el
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Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-dada2
QIIME 2 plugin to work with adapters in sequence data
Versions of package q2-dada2
ReleaseVersionArchitectures
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License: DFSG free
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

This package wraps the dada2 R package in BioConductor for modeling and correcting Illumina-sequenced amplicon errors. This was shown to improve the sensitivity of diversity analyses.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-demux
QIIME 2 plugin for demultiplexing of sequence reads
Versions of package q2-demux
ReleaseVersionArchitectures
bullseye2020.11.1-1all
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bookworm2022.11.1+dfsg-2all
Popcon: 31 users (4 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-emperor
QIIME2 plugin for display of ordination plots
Versions of package q2-emperor
ReleaseVersionArchitectures
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Popcon: 28 users (1 upd.)*
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License: DFSG free
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-feature-classifier
QIIME 2 plugin supporting taxonomic classification
Versions of package q2-feature-classifier
ReleaseVersionArchitectures
bookworm2022.11.1-2all
bullseye2020.11.1-2all
sid2024.2.0-1all
Popcon: 33 users (4 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
Registry entries: Bio.tools 
q2-feature-table
QIIME 2 plugin supporting operations on feature tables
Versions of package q2-feature-table
ReleaseVersionArchitectures
bookworm2022.11.1+dfsg-2all
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upstream2024.2.2
Popcon: 30 users (6 upd.)*
Newer upstream!
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-fragment-insertion
QIIME 2 plugin for fragment insertion
Versions of package q2-fragment-insertion
ReleaseVersionArchitectures
bookworm2022.11.1-3amd64,arm64,mips64el,ppc64el
sid2024.2.0-1amd64,arm64,mips64el,ppc64el,riscv64
trixie2023.9.0-1amd64,arm64,mips64el,ppc64el
Popcon: 29 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-metadata
QIIME 2 plugin for working with and visualizing Metadata
Versions of package q2-metadata
ReleaseVersionArchitectures
trixie2023.9.0+dfsg-1amd64,arm64,mips64el,ppc64el
bookworm2022.8.0-1amd64,arm64,mips64el,ppc64el
sid2024.2.0+dfsg-1amd64,arm64,mips64el,ppc64el,riscv64
bullseye2020.11.1+dfsg-1amd64,arm64,mips64el,ppc64el
Popcon: 30 users (5 upd.)*
Versions and Archs
License: DFSG free
Git

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-phylogeny
QIIME 2 plugin for phylogeny
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QIIME 2 plugin for phylogenetic reconstruction, and operations on phylogenetic trees.

q2-quality-control
QIIME 2 plugin for quality assurance of feature and sequence data
Versions of package q2-quality-control
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-quality-filter
QIIME2 plugin for PHRED-based filtering and trimming
Versions of package q2-quality-filter
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-sample-classifier
QIIME 2 plugin for machine learning prediction of sample data
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. The q2-sample-classifier plugin makes these methods more accessible, reproducible, and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition or other "omics" data

Registry entries: Bio.tools 
q2-taxa
QIIME 2 plugin for working with feature taxonomy annotations
Versions of package q2-taxa
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-types
QIIME 2 plugin defining types for microbiome analysis
Versions of package q2-types
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2cli
Click-based command line interface for QIIME 2
Versions of package q2cli
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2templates
Design template package for QIIME 2 Plugins
Versions of package q2templates
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

This package provides templates for QIIME 2 plugins.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
r-bioc-annotationhub
GNU R client to access AnnotationHub resources
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access.

Registry entries: Bio.tools 
r-bioc-aroma.light
BioConductor methods normalization and visualization of microarray data
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This BioConductor module provides light-weight methods for normalization and visualization of microarray data using only basic R data types.

Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Please cite: Henrik Bengtsson, Pierre Neuvial and Terence P. Speed: TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays. (PubMed,eprint) BMC Bioinformatics 11:245 (2010)
Registry entries: Bio.tools  SciCrunch  Bioconda 
r-bioc-beachmat
I/O for several formats storing matrix data
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Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing.

r-bioc-biocneighbors
Nearest Neighbor Detection for Bioconductor Packages
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Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance.

r-bioc-biocsingular
Singular Value Decomposition for Bioconductor Packages
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Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

r-bioc-ctc
Cluster and Tree Conversion
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Tools for export and import classification trees and clusters to other programs.

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r-bioc-dnacopy
R package: DNA copy number data analysis
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Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

This package is for analyzing array DNA copy number data, which is usually (but not always) called array Comparative Genomic Hybridization (array CGH) data It implements a methodology for finding change-points in these data which are points after which the (log) test over reference ratios have changed location. This model is that the change-points correspond to positions where the underlying DNA copy number has changed. Therefore, change-points can be used to identify regions of gained and lost copy number. Also provided is a function for making relevant plots of these data.

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r-bioc-ensembldb
GNU R utilities to create and use an Ensembl based annotation database
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The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

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r-bioc-experimenthub
BioConductor client to access ExperimentHub resources
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This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access.

r-bioc-geneplotter
R package of functions for plotting genomic data
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Geneplotter contains plotting functions for microarrays.

The functions cPlot and cColor allow the user to associate microarray expression data with chromosomal location. The plots can include any subset (by default all chromosomes are shown) of chromosomes for the organism being considered.

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r-bioc-genomicalignments
BioConductor representation and manipulation of short genomic alignments
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This BioConductor package provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.

Please cite: Michael Lawrence, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan and Vincent J. Carey: Software for Computing and Annotating Genomic Ranges. (PubMed,eprint) PLoS Computational Biology 9(8):e1003118 (2013)
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r-bioc-genomicfiles
Distributed computing by file or by range
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This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation.

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r-bioc-genomicranges
BioConductor representation and manipulation of genomic intervals
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The ability to efficiently store genomic annotations and alignments is playing a central role when it comes to analyze high-throughput sequencing data (a.k.a. NGS data). The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome.

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r-bioc-go.db
annotation maps describing the entire Gene Ontology
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This package is part of BioConductor and provides a set of annotation maps describing the entire Gene Ontology assembled using data from GO.

The package helps running the test suites of some BioConductor packages.

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r-bioc-grohmm
GRO-seq Analysis Pipeline
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This BioConductor package provides a pipeline for the analysis of GRO- seq data. Among the more advanced features, r-bioc-grohmm predicts the boundaries of transcriptional activity across the genome de novo using a two-state hidden Markov model (HMM).

The used model essentially divides the genome into transcribed and non- transcribed regions in a strand specific manner. HMMs are used to identify the leading edge of Pol II at genes activated by a stimulus in GRO-seq time course data. This approach allows the genome-wide interrogation of transcription rates in cells.

Please cite: Minho Chae, Charles G. Danko and and W. Lee Kraus: groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data. (PubMed) BMC Bioinformatics 16:222.0 (2015)
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r-bioc-gviz
Plotting data and annotation information along genomic coordinates
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Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.

Please cite: Michael Lawrence, Robert Gentleman and "Vincent Carey: rtracklayer: an R package for interfacing with genome browsers. (PubMed,eprint) Bioinformatics 25(14):1841-1842 (2009)
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r-bioc-isoformswitchanalyzer
Identify, Annotate and Visualize Alternative Splicing and
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Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc.

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r-bioc-org.hs.eg.db
genome-wide annotation for Human
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This package provides descriptions of parts of the human genome that have been identified to be coding for RNA, and likely also for proteins. It also offers links to entries of equivalent (orthologous) genes in other species.

This package is prepared from the BioConductor community and contributes to many workflows and routine analyses in computational biology.

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r-bioc-qusage
qusage: Quantitative Set Analysis for Gene Expression
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This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

Please cite: Gur Yaari, Christopher R. Bolen, Juilee Thakar and Steven H. Kleinstein: Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. (PubMed,eprint) Nucleic Acids Res. 41(18):e170 (2013)
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r-bioc-savr
GNU R parse and analyze Illumina SAV files
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This BioConductor module enables to parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots.

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r-bioc-singlecellexperiment
S4 Classes for Single Cell Data
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Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.

r-bioc-structuralvariantannotation
Variant annotations for structural variants
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StructuralVariantAnnotation contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects.

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r-bioc-tximport
transcript-level estimates for biological sequencing
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Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

Please cite: Charlotte Soneson, Michael I. Love and Mark D. Robinson: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521 (2015)
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r-cran-amap
Another Multidimensional Analysis Package
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Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).

r-cran-biwt
biweight mean vector and covariance and correlation
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Compute multivariate location, scale, and correlation estimates based on Tukey's biweight M-estimator.

r-cran-boolnet
assembling, analyzing and visualizing Boolean networks
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BoolNet is an R package that provides tools for assembling, analyzing and visualizing synchronous and asynchronous Boolean networks as well as probabilistic Boolean networks.

Please cite: Christoph Muessel, Martin Hopfensitz and Hans A. Kestler: BoolNet -- an R package for generation, reconstruction and analysis of Boolean networks. (eprint) Bioinformatics 26(6):1378-1380 (2010)
r-cran-corrplot
Visualization of a Correlation Matrix
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A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.

r-cran-dynamictreecut
Methods for Detection of Clusters in Hierarchical Clustering
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Dendrograms Contains methods for detection of clusters in hierarchical clustering dendrograms.

r-cran-epir
GNU R Functions for analysing epidemiological data
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A package for analysing epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.

r-cran-fitdistrplus
support fit of parametric distribution
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Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.

Please cite: Marie Laure Delignette-Muller Christophe Dutang: fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software 64(4):1-34 (2015)
r-cran-forecast
GNU R forecasting functions for time series and linear models
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Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

r-cran-gprofiler2
Interface to the 'g:Profiler' Toolset
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A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (https://biit.cs.ut.ee/gprofiler). The main tools are: (1) 'g:GOSt' - functional enrichment analysis and visualization of

    gene lists;
(2) 'g:Convert' - gene/protein/transcript identifier conversion across
    various namespaces;
(3) 'g:Orth' - orthology search across species;

(4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions,

    genes and variant effects This package is an R interface
    corresponding to the 2019 update of 'g:Profiler' and provides access
    to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the
    package 'gProfileR' for accessing older versions from the
    'g:Profiler' toolset.
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r-cran-minerva
Maximal Information-Based Nonparametric Exploration
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Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at http://minepy.readthedocs.io/en/latest.

r-cran-optimalcutpoints
Computing Optimal Cutpoints in Diagnostic Tests
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Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

Please cite: Mónica López-Ratón María Xosé Rodríguez-Álvarez, Carmen Cadarso Suárez and Francisco Gude Sampedro: OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. Journal of Statistical Software 61(8):1-36 (2014)
r-cran-parmigene
Parallel Mutual Information to establish Gene Networks
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The package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks.

r-cran-pheatmap
GNU R package to create pretty heatmaps
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GNU R implementation of heatmaps that offers more control over dimensions and appearance.

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r-cran-qqman
R package for visualizing GWAS results using Q-Q and manhattan plots
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qqman is an add-on package for the R statistical environment. This package provides functions for visualizing Genome-Wide Association Studies (GWAS) results using Manhattan plots and Quantile-Quantile plots.

r-cran-rcpphnsw
R bindings for a Library for Approximate Nearest Neighbors
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'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See https://github.com/nmslib/hnswlib for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.

r-cran-rentrez
GNU R interface to the NCBI's EUtils API
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Provides an R interface to the NCBI's EUtils API allowing users to search databases like GenBank and PubMed, process the results of those searches and pull data into their R sessions.

r-cran-sctransform
Variance Stabilizing Transformations for Single Cell UMI Data
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A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019 for more details.

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resfinder-db
ResFinder database is a curated database of acquired resistance genes
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ResFinder identifies acquired antimicrobial resistance genes in total or partial sequenced isolates of bacteria.

ResFinder that uses BLAST for identification of acquired antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete or partial genomes, and short sequence reads from four different sequencing platforms. The method was evaluated on 1862 GenBank files containing 1411 different resistance genes, as well as on 23 de-novo-sequenced isolates.

This package provides the database needed for resfinder.

Please cite: Ea Zankari, Henrik Hasman, Salvatore Cosentino, Martin Vestergaard, Simon Rasmussen, Ole Lund, Frank M. Aarestrup and Mette Voldby Larsen: Identification of acquired antimicrobial resistance genes. (PubMed,eprint) Journal of Antimicrobial Chemotherapy 67(11):2640-4 (2012)
science-workflow
workflow management systems useful for scientific research
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This task lists some packages providing workflow management systems useful for scientific research.

Debian packages in contrib or non-free

arb
phylogenetic sequence analysis suite - main program
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ARB is a graphical suite of tools for sequence database handling and data analysis. A central database of processed (aligned) sequences and any type of additional data linked to the sequence entries is structured according to phylogeny or other user-defined criteria.

The ARB project (from the Latin "arbor", a tree) is a joint initiative of the Lehrstuhl für Mikrobiologie and the LRR of the Technische Universität München in 1992. Since 2014 the ARB project is continued by the Department of Molecular Ecology http://www.mpi-bremen.de/en/Department_of_Molecular_Ecology.html at the Max Planck Institute for Marine Microbiology in Bremen in cooperation with Ribocon GmbH http://www.ribocon.com .

bcbio
toolkit for analysing high-throughput sequencing data
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This package installs the command line tools of the bcbio-nextgen toolkit implementing best-practice pipelines for fully automated high throughput sequencing analysis.

A high-level configuration file specifies inputs and analysis parameters to drive a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The project contributes a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.

This package builds and having it in Debian unstable helps the Debian developers to synchronize their efforts. But unless a series of external dependencies are not installed manually, the functionality of bcbio in Debian is only a shadow of itself. Please use the official distribution of bcbio for the time being, which means "use conda". The TODO file in the Debian directory should give an overview on progress for Debian packaging.

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blimps-utils
blocks database improved searcher
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BLIMPS (BLocks IMProved Searcher) is a searching tool that scores a protein sequence against blocks or a block against sequences.

This package contains the binaries.

The package is enhanced by the following packages: blimps-examples
Please cite: J. G. Henikoff, S. Pietrokovski, C. M. McCallum and S. Henikoff: Blocks-based methods for detecting protein homology. (PubMed,eprint) Electrophoresis 21(9):1700-6 (2000)
caftools
maintenance of DNA sequence assemblies
Maintainer: Steffen Moeller
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This package contains code from different authors that allow sequence assemblies to be converted into formats such as CAF (Common Assembly Format) or GAP4.

CAF is a text format for describing sequence assemblies. It is acedb-compliant and is an extension of the ace-file format used earlier, but with support for base quality measures and a more extensive description of the Sequence data. CAF was designed during the Sanger sequencing era. Its modern-day successor is the SAM format, or its binary equivalents BAM and CRAM.

Please cite: Simon Dear, Richard Durbin, LaDeana Hillier, Gabor Marth, Jean Thierry-Mieg and and Richard Mott: Sequence assembly with CAFTOOLS. (PubMed,eprint) Genome research 8(3):260-267 (1998)
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cluster3
Reimplementation of the Eisen-clustering software
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The open source clustering software available here contains clustering routines that can be used to analyze gene expression data. Routines for hierarchical (pairwise simple, complete, average, and centroid linkage) clustering, k-means and k-medians clustering, and 2D self-organizing maps are included. The routines are available in the form of a C clustering library, an extension module to Python, a module to Perl, as well as an enhanced version of Cluster, which was originally developed by Michael Eisen of Berkeley Lab. The C clustering library and the associated extension module for Python was released under the Python license. The Perl module was released under the Artistic License. Cluster 3.0 is covered by the original Cluster/TreeView license.

This package only contains the command line and motif gui versions of Cluster 3.0.

Please cite: M. J. L. de Hoon, S. Imoto, J. Nolan and S. Miyano: Open Source Clustering Software. (PubMed,eprint) Bioinformatics 20(9):1453-1454 (2004)
cufflinks
Transcript assembly, differential expression and regulation for RNA-Seq
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Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one.

This package provides the binary of cufflinks and associated tools, i.e. compress_gtf, cuffcompare, cuffdiff, cuffmerge, cuffnorm, cuffquant and gtf_to_sam.

Please cite: Cole Trapnell, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J van Baren, Steven L Salzberg, Barbara J Wold and Lior Pachter: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. (PubMed) Nature Biotechnology 28(5):511-515 (2010)
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embassy-phylip
EMBOSS conversions of the programs in the phylip package
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This package is the adaptation of the PHYLIP package in which its programs can operate with the biological sequence formats and databases of the European Molecular Biology Open Software Suite (EMBOSS). The software packages adapted for EMBOSS are called EMBASSY.

PHYLIP (the PHYLogeny Inference Package) is a package of programs for inferring phylogenies (evolutionary trees). Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites and fragments, distance matrices, and discrete characters.

The EMBASSY PHYLIP programs all have the prefix "f" to distinguish them from the original programs and avoid namespace conflict.

raccoon
preparation of in silico drug screening projects
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The field of computational biology is all about modeling physiochemical entities. Structural biology is about how those entities look in 3D and behave. And we have a drug when we can change that behaviour in a way that we want it to be changed - and only that behaviour.

Some good part of the characterisation of promising drug-like compounds for their interaction with a larger protein can be done on computers. This package helps with getting collections of small ligands prepared to be fitted against a particular protein of known structure. That docking itself is then to be performed by autodock or autodock-vina.

Raccoon may not find the drug, but it may well find a lead to it. To have this package with Debian shall help smaller biochemistry labs and grants an opportunity for the general public to educate itself and/or actively join in to help the world .... just a bit.

Please cite: Sandro Cosconati, Stefano Forli, Alex L. Perryman, Rodney Harris, David S. Goodsell and Arthur J. Olson: Virtual screening with AutoDock: theory and practice. (PubMed,eprint) Expert Opinion on Drug Discovery 5(6):597–607 (2010)
relion-cuda
parallel toolkit for 3D reconstructions in cryo-electron microscopy
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RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy.

RELION provides a GUI, several command-line tools in parallel (MPI) and serial versions, optionally with CUDA/GPU support.

relion-cuda provides the serial and parallel (MPI) command-line tools with CUDA/GPU support.

relion-gui-cuda
toolkit for 3D reconstructions in cryo-electron microscopy (gui apps)
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RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy.

RELION provides a GUI, several command-line tools in parallel (MPI) and serial versions, optionally with CUDA/GPU support.

relion-gui-cuda provides the graphical user interface with CUDA/GPU support.

seq-gen
simulate the evolution of nucleotide or amino acid sequences
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Seq-Gen is a program that will simulate the evolution of nucleotide or amino acid sequences along a phylogeny, using common models of the substitution process. A range of models of molecular evolution are implemented including the general reversible model. State frequencies and other parameters of the model may be given and site-specific rate heterogeneity may also be incorporated in a number of ways. Any number of trees may be read in and the program will produce any number of data sets for each tree. Thus large sets of replicate simulations can be easily created. It has been designed to be a general purpose simulator that incorporates most of the commonly used (and computationally tractable) models of molecular sequence evolution.

Please cite: A. Rambaut and N. C. Grassly: Seq-Gen: An application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. (PubMed,eprint) Comput. Appl. Biosci. 13(3):235-238 (1997)
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seqcluster
analysis of small RNA in NGS data
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Seqcluster investigates small RNA sequences of all sorts in RNA sequencing data. This is especially helpful for the identification of RNA that is neither coding nor belonging to the already well-established group of miRNA, towards many tools feel constrained to.

Please cite: Lorena Pantano, Marc R. Friedländer, Georgia Escaramís, Esther Lizano, Joan Pallarès-Albanell, Isidre Ferrer, Xavier Estivill and Eulàlia Martí: Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis. (PubMed) Bioinformatics (2015)
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sift
predicts if a substitution in a protein has a phenotypic effect
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SIFT is a sequence homology-based tool that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. SIFT is based on the premise that protein evolution is correlated with protein function. Positions important for function should be conserved in an alignment of the protein family, whereas unimportant positions should appear diverse in an alignment.

Please cite: Prateek Kumar, Steven Henikoff and Pauline C Ng: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. (PubMed,eprint) Nature Protocols 4(7):1073-81 (2009)
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solvate
arranges water molecules around protein structures
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For molecular dynamics simulations it is sometimes appropriate not to model in the vacuum but to have the proteins surrounded by their solvent. This program computes the location of water molecules such that the resulting PDB files become suitable for further analyses.

trnascan-se
detection of transfer RNA genes in genomic sequence
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tRNAscan-SE identifies 99-100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases. Two previously described tRNA detection programs are used as fast, first-pass prefilters to identify candidate tRNAs, which are then analyzed by a highly selective tRNA covariance model. This work represents a practical application of RNA covariance models, which are general, probabilistic secondary structure profiles based on stochastic context-free grammars. tRNAscan-SE searches at ~ 30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudogenes.

Please cite: Todd M Lowe and Sean R Eddy: tRNAscan-SE: A program for improved detection of transfer RNA genes in genomic sequence. (PubMed,eprint) Nucleic Acids Research 25(5):955-964 (1997)
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varscan
variant detection in next-generation sequencing data
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Variant detection in massively parallel sequencing. For one sample, calls SNPs, indels, and consensus genotypes. For tumor-normal pairs, further classifies each variant as Germline, Somatic, or LOH, and also detects somatic copy number changes.

Please cite: Daniel C. Koboldt, Qunyuan Zhang, David E. Larson, Dong Shen, Michael D. McLellan, Ling Lin, Christopher A. Miller, Elaine R. Mardis, Li Ding and Richard K. Wilson: VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing". (PubMed,eprint) Genome Res. 22(3):568-576 (2012)
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vdjtools
framework for post-analysis of B/T cell repertoires
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VDJtools is an open-source Java/Groovy-based framework designed to facilitate analysis of immune repertoire sequencing (RepSeq) data. VDJtools computes a wide set of statistics and is able to perform various forms of cross-sample analysis. Both comprehensive tabular output and publication-ready plots are provided.

The main aims of the VDJtools Project are:

  • To ensure consistency between post-analysis methods and results
  • To save the time of bioinformaticians analyzing RepSeq data
  • To create an API framework facilitating development of new RepSeq analysis applications
  • To provide a simple enough command line tool so it could be used by immunologists and biologists with little computational background
Please cite: M Shugay, D.V. Bagaev, M.A. Turchaninova, D.A. Bolotin, O.V. Britanova, E.V. Putintseva, M.V. Pogorelyy, V.I. Nazarov VI, I.V. Zvyagin, V.I. Kirgizova, K.I. Kirgizov, E.V. Skorobogatova and D.M. Chudakov: VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires. (PubMed,eprint) PLoS Comput Biol. 11(11):e1004503 (2015)
vienna-rna
RNA sequence analysis
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The Vienna RNA Package consists of a C code library and several stand-alone programs for the prediction and comparison of RNA secondary structures. It is developed and maintained by the group of Ivo Hofacker in Vienna.

RNA secondary structure prediction through energy minimization is the most used function in the package. It provides three kinds of dynamic programming algorithms for structure prediction:

  • the minimum free energy algorithm of (Zuker & Stiegler 1981) which yields a single optimal structure,
  • the partition function algorithm of (McCaskill 1990) which calculates base pair probabilities in the thermodynamic ensemble, and the suboptimal folding algorithm of (Wuchty et.al 1999) which generates all suboptimal structures within a given energy range of the optimal energy.

For secondary structure comparison, the package contains several measures of distance (dissimilarities) using either string alignment or tree-editing (Shapiro & Zhang 1990). Finally, is provided an algorithm to design sequences with a predefined structure (inverse folding). The RNAforester package is a tool for aligning RNA secondary structures and it's user interface integrates to those of the tools of the Vienna RNA package.

Please cite: Ronny Lorenz, Stephan H. Bernhart, Christian Höner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F. Stadler and Ivo L. Hofacker: ViennaRNA Package 2.0. (eprint) Algorithms for Molecular Biology 6(1):26 (2011)
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Packaging has started and developers might try the packaging code in VCS

acacia
Error-corrector for pyrosequenced amplicon reads.
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Acacia is a java program developed to quickly and conservatively correct errors, whilst simultaneously de-replicating, amplicon sequences.

The main purpose of Acacia is to correct the over-call, under-call errors prevalent in Roche 454 GS-FLX data, and more recently, with the Titanium chemistry. Acacia will only ectively correct errors in amplicons - as it assumes that the 5' end of the sequences start at the same position, the MID, followed by the primer. Acacia uses empirically-derived models to identify homopolymer regions where there are more `errors' than expected by chance - these imply that the differences are due to population differences rather than error-induced polymorphisms.

Nat Methods. 2012 Apr 27;9(5):425-6. doi: 10.1038/nmeth.1990. Fast, accurate error-correction of amplicon pyrosequences using Acacia. Bragg L, Stone G, Imelfort M, Hugenholtz P, Tyson GW.

Please cite: Lauren Bragg, Glenn Stone, Michael Imelfort, Philip Hugenholtz and Gene W. Tyson: Fast, accurate error-correction of amplicon pyrosequences using Acacia. (PubMed) Nature Methods 9(5):425-426 (2012)
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agat
another GFF analysis toolkit
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Suite of tools to handle gene annotations in any GTF/GFF format.

It has the power to check, fix, pad missing information of any kind of gtf and gff to create complete, sorted and standardised gff3 format.

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amos-assembler
modular whole genome assembler
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The AMOS consortium is committed to the development of open-source whole genome assembly software. The project acronym (AMOS) represents our primary goal - to produce A Modular, Open-Source whole genome assembler. Open-source so that everyone is welcome to contribute and help build outstanding assembly tools, and modular in nature so that new contributions can be easily inserted into an existing assembly pipeline. This modular design will foster the development of new assembly algorithms and allow the AMOS project to continually grow and improve in hopes of eventually becoming a widely accepted and deployed assembly infrastructure. In this sense, AMOS is both a design philosophy and a software system.

Please cite: Michael C. Schatz, Adam M. Phillippy, Daniel D. Sommer, Arthur L. Delcher, Daniela Puiu, Giuseppe Narzisi, Steven L. Salzberg and Mihai Pop: Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies. (PubMed,eprint) Briefings in Bioinformatics (2011)
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apollo
genome annotation viewer and editor
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Apollo is a genome annotation viewer and editor. It was developed as a collaboration between the Berkeley Drosophila Genome Project (part of the FlyBase consortium) and The Sanger Institute in Cambridge, UK. Apollo allows researchers to explore genomic annotations at many levels of detail, and to perform expert annotation curation, all in a graphical environment. It was used by the FlyBase biologists to construct the Release 3 annotations on the finished Drosophila melanogaster genome, and is also a primary vehicle for sharing these annotations with the community. The Generic Model Organism Database (GMOD) project, which aims to provide a complete ready-to-use toolkit for analyzing whole genomes, has adopted Apollo as its annotation workbench.

Please cite: Susanna E. Lewis, Steve M. J. Searle, Naomi Harris, M. Gibson, V Lyer, J. Richter, C. Wiel, L. Bayraktaroglir, Ewan Birney, M. A. Crosby, J. S. Kaminker, B. B. Matthews, S. E. Prochnik, C. D. Smithy, J. L. Tupy, G. M. Rubin, S. Misra, Chris J. Mungall and Michelle E. Clamp: Apollo: a sequence annotation editor. (PubMed,eprint) Genome Biology 3(12):research0082-0082.14 (2002)
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arvados
managing and analyzing biomedical big data
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Arvados is an open source platform for managing, processing, and sharing genomic and other large scientific and biomedical data. With Arvados, bioinformaticians run and scale compute-intensive workflows, developers create biomedical applications, and IT administrators manage large compute and storage resources.

axparafit
optimized statistical analysis of host-parasite coevolution
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AxParafit is a highly optimized version of Pierre Legendre's Parafit program for statistical analysis of host-parasite coevolution. AxParafit has been parallelized with MPI (Message Passing Interface) for compute clusters and was used to carry out the largest co-evolutionary analysis to date for the paper describing the software.

axpcoords
highly optimized and parallelized porting of pcoords
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AxPcoords is an highly optimized versions of Pierre Legendre's DistPCoA program for statistical analysis of host-parasite coevolution.

AxPcoords is a fast, LAPACK-based implementation of DistPCoA (see http://www.bio.umontreal.ca/Casgrain/en/labo/distpcoa.html) which is another program by Pierre Legendre, it conducts a principal coordinates analysis. This program is required for the pipeline that conducts a full host-parasite co-phylogenetic analysis in combination with AxParafit.

bagpipe
genomewide LD mapping
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Bagpipe is a program for performing genomewide linkage disequilibrium mapping of quantitative trait loci in populations whose genome structure can be accommodated in the HAPPY framework [Mott00]. This includes most diploid crosses where the founders of the individuals have known genotypes.

  • Bagpipe is a simplified and streamlined version of Bagphenotype that does not currently include resample model averaging (RMA) capabilities.
  • Bagpipe can help fit single locus regression models (with or without random effects) to marker intervals whose genetic ancestry is inferred using the HAPPY software.
  • Bagpipe cannot help you decide what is a sensible model to fit.
  • Bagpipe does not currently accommodate populations with significant population structure, except through the specification of simple random intercepts based on unpatterned covariance matrices.
  • Bagpipe is named after the Scottish wind instrument "the bagpipes" and after Bagphenotype, which in turn was a PIPEline for BAGging-based multiple QTL analysis of phenoTYPEs. Bagphenotype was in turn based on software written by Richard Mott and William Valdar to analyze heterogeneous stock mice in [Valdar06].
  • Bagpipe is experimental software, is provided free of charge subject to copyleft restrictions, and comes with no guarantees whatsoever.
Please cite: Richard Mott, Christopher J. Talbot, Maria G. Turri, Allan C. Collins and Jonathan Flint: A method for fine mapping quantitative trait loci in outbred animal stocks. (PubMed) Proc Natl Acad Sci U S A. 97(23):12649-54 (200)
bax2bam
Convert legacy PacBio Bax.H5, Bas.H5, and Ccs.H5 files to the new PacBio BAM format
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The program bax2bam converts the legacy PacBio basecall format (bax.h5) into the BAM basecall format.

biceps
error-tolerant peptide identification
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BICEPS is tool for the error-tolerant identification of peptides based on a statistical regularization scheme. It balances possible improvements in peptide-spectrum-matches by allowing substitutions against the increased risk of false positives. BICEPS can identify peptides containing two or more substitutions as occuring e.g. in cross-species searches.

Please cite: Bernhard Y. Renard‡and Buote Xu, Marc Kirchner, Franziska Zickmann‡and Dominic Winter, Simone Korten‡and Norbert W. Brattig‡and Amit Tzur, Fred A. Hamprecht and Hanno Steen: Overcoming Species Boundaries in Peptide Identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS). (PubMed,eprint) Mol Cell Proteomics ;11(7):M111.014167 (2012)
Remark of Debian Med team: Mentioned at http://www.renard.it/, developed in RKI
bigsdb
Bacterial Isolate Genome Sequence Database
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The Bacterial Isolate Genome Sequence Database (BIGSdb) is a scalable, web-accessible database system designed to store and analyse linked phenotypic and genotypic information in a computationally efficient manner. Sequence data can range from single sequence reads to multiple contigs generated by whole genome sequencing technologies. The system incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into schemes for isolate characterisation or for evolutionary or functional analyses.

Please cite: Keith A. Jolley and Martin C.J. Maiden: BIGSdb: Scalable analysis of bacterial genome variation at the population level. (PubMed,eprint) BMC Bioinformatics 11:595 (2010)
bismark
bisulfite read mapper and methylation caller
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Bismark is a program to map bisulfite treated sequencing reads to a genome of interest and perform methylation calls in a single step. The output can be easily imported into a genome viewer, such as SeqMonk, and enables a researcher to analyse the methylation levels of their samples straight away. It's main features are:

  • Bisulfite mapping and methylation calling in one single step
  • Supports single-end and paired-end read alignments
  • Supports ungapped and gapped alignments
  • Alignment seed length, number of mismatches etc. are adjustable
  • Output discriminates between cytosine methylation in CpG, CHG and CHH context
Please cite: Felix Krueger and Simon R. Andrews: Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. (PubMed,eprint) Bioinformatics 27(11):1571-1572 (2011)
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blat
BLAST-Like Alignment Tool
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BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 25 bases or more. It may miss more divergent or shorter sequence alignments. It will find perfect sequence matches of 25 bases, and sometimes find them down to 20 bases. BLAT on proteins finds sequences of 80% and greater similarity of length 20 amino acids or more. In practice DNA BLAT works well on primates, and protein blat on land vertebrates.

BLAT is not BLAST. DNA BLAT works by keeping an index of the entire genome in memory. The index consists of all non-overlapping 11-mers except for those heavily involved in repeats. The index takes up a bit less than a gigabyte of RAM. The genome itself is not kept in memory, allowing BLAT to deliver high performance on a reasonably priced Linux box. The index is used to find areas of probable homology, which are then loaded into memory for a detailed alignment. Protein BLAT works in a similar manner, except with 4-mers rather than 11-mers. The protein index takes a little more than 2 gigabytes.

Please cite: W. Jim Kent: BLAT--the BLAST-like alignment tool. (PubMed,eprint) Genome Research 12(4):656-64 (2002)
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blobology
tool set for the visualisation of genome assemblies
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Tools for making blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step.

blobtools consist of a series of tools that can be used to

  • collate information associated with an assembly file, such as:
    • sequence ID
    • sequence length
    • GC-content
    • coverage information
    • taxonomy information (sequence similarity search hits)
    • user-defined categories
  • visualise information using blobplots, covplots and/or readcovplots.
  • extract information into human- and computer-readable files
  • produce paper-ready figures
Please cite: Sujai Kumar, Martin Jones, Georgios Koutsovoulos, Michael Clarke and Mark Blaxter: Blobology: exploring raw genome data for contaminants, symbionts, and parasites using taxon-annotated GC-coverage plots. (PubMed,eprint) frontiers in Genetics 4:237 (2013)
braker
annotating protein coding genes in genomes.
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Genomic DNA controls the behaviour of biological cells. Understanding it, and its variations, facilitates the molecular pathology of diseases. braker.pl can either run with a genome sequence, only; or with additional alignments for short transcriptome reads against the genome; or with additional protein sequences of closely related species; or with evidence from the alignment of protein sequences of distantly related species. The package provides the means to interpret genomic sequences in FASTA format from fungi, plants and animals.

Please cite: Katharina J. Hoff, Simone Lange, Alexandre Lomsadze, Mark Borodovsky and Mario Stanke: BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.. (PubMed,eprint) Bioinformatics. 32(5):767-769 (2016)
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card-rgi
analysis of genome sequences using the Resistance Gene Identifier
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The Comprehensive Antibiotic Resistance Database ("CARD") provides data, models, and algorithms relating to the molecular basis of antimicrobial resistance. The CARD provides curated reference sequences and SNPs organized via the Antibiotic Resistance Ontology ("ARO"). These data can be browsed on the website or downloaded in a number of formats. These data are additionally associated with detection models, in the form of curated homology cut-offs and SNP maps, for prediction of resistome from molecular sequences. These models can be downloaded or can be used for analysis of genome sequences using the Resistance Gene Identifier ("RGI"), either online or as a stand-alone tool.

Please cite: Baofeng Jia, Amogelang R. Raphenya, Brian Alcock, Nicholas Waglechner, Peiyao Guo, Kara K. Tsang, Briony A. Lago, Biren M. Dave, Sheldon Pereira, Arjun N. Sharma, Sachin Doshi, Mélanie Courtot, Raymond Lo, Laura E. Williams, Jonathan G. Frye, Tariq Elsayegh, Daim Sardar, Erin L. Westman, Andrew C. Pawlowski, Timothy A. Johnson, Fiona S.L. Brinkman, Gerard D. Wright and Andrew G. McArthur: CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. (PubMed,eprint) Nucleic Acids Research 45(D1):D566-D573 (2017)
cellprofiler
quantitatively measure phenotypes from images automatically
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CellProfiler is cell image analysis software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically.

cinema
multi-sequence alignment editor and viewer
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It has been designed to be as extensible as possible. Notes of this extensibility can be found in "EXTENDING_CINEMA", and the "cinema-module" sub-directory.

Cinema currently has limited support for various sequence formats, although its easy to add new ones. A large number of alignments in the appropriate format can be found as part of the align compendium at

condetri
straight-forward trimming of FASTQ sequences
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This package is a simplistic contribution to the wealth of tools for trimming of sequences of current Next-Generation-Sequencing data. It was developed in the context of de novo whole-genome assembly.

The tool reads from the 3'-end and extract reads (or read pairs) of good quality. If the reads are paired, the filtering is done pairwise, and if one read in a pair has low quality, the remaining read is saved as single end.

Please cite: Linnéa Smeds and Axel Künstner: ConDeTri - A Concent Dependent Read Trimmer for Illumina Data. (PubMed,eprint) PLoS ONE 6(10):e26314 (2011)
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contrafold
CONditional TRAining for RNA Secondary Structure Prediction
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For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondarystructure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models.

CONTRAfold is a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. By incorporating most of the features found in typical thermodynamic models, CONTRAfold achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction.

Please cite: Chuong B. Do, Daniel A. Woods1 and Serafim Batzoglou: CONTRAfold: RNA secondary structure prediction without physics-based models. (PubMed,eprint) Bioinformatics 22(14):e90-e98 (2006)
coot
model building program for macromolecular crystallography
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This is a program for constructing atomic models of macromolecules from x-ray diffraction data. Coot displays electron density maps and molecular models and allows model manipulations such as idealization, refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers. Validation tools such as Ramachandran and geometry plots are available to the user. This package provides a Coot build with embedded Python support.

Please cite: P. Emsley, B. Lohkamp, W. G. Scott and K. Cowtan: Features and development of Coot. (eprint) Acta Crystallographica Section D 66(4):486-501 (2010)
covpipe
pipeline to generate consensus sequences from NGS reads
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CovPipe is a pipeline to generate consensus sequences from NGS reads based on a reference sequence. The pipeline is tailored to be used for SARS-CoV-2 data, but may be used for other viruses.

Genomic variants of your NGS data in comparison to a reference will be determined. These variants will be included into the reference and form the consensus sequences. See below for further details on the determined set of consensus sequences.

crossbow
Genotyping from short reads using cloud computing
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Crossbow is a scalable software pipeline for whole genome resequencing analysis. It combines Bowtie, an ultrafast and memory efficient short read aligner, and SoapSNP, an accurate genotyper, within Hadoop to distribute and accelerate the computation with many nodes. The pipeline can accurately analyze over 35x coverage of a human genome in one day on a 10-node local cluster, or in 3 hours for about $100 using a 40-node, 320-core cluster rented from Amazon's EC2 utility computing service.

Please cite: Ben Langmead, Michael C Schatz, Jimmy Lin, Mihai Pop and Steven L Salzberg: Searching for SNPs with cloud computing. (PubMed,eprint) Genome Biology 10:R134 (2009)
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crux-toolkit
toolkit for tandem mass spectrometry analysis
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The Crux mass spectrometry analysis toolkit is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data. The toolkit includes several search engines for both standard and cross-linked database search, as well as a variety of pre- and post-processing engines for assigning high-resolution precursor masses to spectra, assigning statistical confidence estimates to spectra, peptides and proteins, and performing label free quantification.

Please cite: Sean McIlwain, Kaipo Tamura, Attila Kertesz-Farkas, Charles E. Grant, Benjamin Diament, Barbara Frewen, J. Jeffry Howbert, Michael R. Hoopmann, Lukas Käll, Jimmy K. Eng, Michael J. MacCoss and William Stafford Noble: Crux: rapid open source protein tandem mass spectrometry analysis. (PubMed) 2014 13(10):4488-4491 (Journal of Proteome Research)
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cytoscape
visualizing molecular interaction networks
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Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration and visualization.

dazzle
Java-based DAS server
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Dazzle is a general purpose server for the Distributed Annotation System (DAS) protocol. It is implemented as a Java servlet, using the BioJava APIs. Dazzle is a modular system which uses small "datasource" plugins to provide access to a range of databases. Several general-purpose plugins are included in the package, and it it straightforward to develop new plugins to connect to your own databases.

Information on DAS is available from http://www.biodas.org/

deepbinner
demultiplexing barcoded Oxford Nanopore sequencing reads
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Deepbinner is a tool for demultiplexing barcoded Oxford Nanopore sequencing reads. It does this with a deep convolutional neural network classifier, using many of the architectural advances that have proven successful in image classification. Unlike other demultiplexers (e.g. Albacore and Porechop), Deepbinner identifies barcodes from the raw signal (a.k.a. squiggle) which gives it greater sensitivity and fewer unclassified reads.

Reasons to use Deepbinner:

  • To minimise the number of unclassified reads (use Deepbinner by itself).
  • To minimise the number of misclassified reads (use Deepbinner in conjunction with Albacore demultiplexing).
  • You plan on running signal-level downstream analyses, like Nanopolish. Deepbinner can demultiplex the fast5 files which makes this easier. Reasons to not use Deepbinner:
  • You only have basecalled reads not the raw fast5 files (which Deepbinner requires).
  • You have a small/slow computer. Deepbinner is more computationally intensive than Porechop.
  • You used a sequencing/barcoding kit other than the ones Deepbinner was trained on.
Please cite: Ryan R Wick, Louise M Judd and Kathryn E Holt: Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks. (PubMed,eprint) bioRxiv 14(11):e1006583 (2018)
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dendroscope
analyzing and visualizing rooted phylogenetic trees and networks
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Dendroscope 3 is a new program for working with rooted phylogenetic trees and networks. It provides a number of methods for drawing and comparing rooted phylogenetic networks, and for computing them from rooted trees. The program can be used interactively or in command-line mode.

Please cite: Daniel H. Huson and Celine Scornavacca: Dendroscope 3: An Interactive Tool for Rooted Phylogenetic Trees and Networks. (PubMed,eprint) Systematic Biology 61(6):1061–1067 (2012)
diann
data-independent acquisition (DIA) proteomics data processing
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DIA-NN - a universal software for data-independent acquisition (DIA) proteomics data processing by Demichev, Ralser and Lilley labs. In 2018, DIA-NN opened a new chapter in proteomics, introducing a number of algorithms which enabled reliable, robust and quantitatively accurate large-scale experiments using high-throughput methods.

Please cite: Vadim Demichev, Christoph B. Messner, Spyros I. Vernardis, Kathryn S. Lilley and Markus Ralser: DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. (PubMed) Nature Methods 17(1):41-44 (2019)
ecell
Concept and environment for constructing virtual cells on computers
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The E-Cell Project is an international research project aiming at developing necessary theoretical supports, technologies and software platforms to allow precise whole cell simulation.

The E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells, architected by Kouichi Takahashi and written by a team of developers.

The core part of the system, E-Cell Simulation Environment version 3, allows many components driven by multiple algorithms with different timescales to coexist.

E-Cell System consists of the following three major parts:

  • E-Cell Simulation Environment (or E-Cell SE)
  • E-Cell Modeling Environment (or E-Cell ME)
  • E-Cell Analysis Toolkit

This package contains all these parts, only the documentation is distributed separately.

ensembl
basic Ensembl genome browser
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Ensembl is a joint project of the Sanger Center and the European Bioinformatics Institute, an outstation of the European Molecular Biology Laboratory, (EMBL-EBI) that are sharing a campus in Hinxton near Cambridge, UK. It presents the sequence data for the yet available complete genomes of many vertebrates and is helped by many sister-projects to cover also plants, invertebrates and bacteria.

This package provides a basic installation of Ensembl. It comprises a full copy of the public Ensembl website, minus Blast and SSAHA, and minus BioMart. It uses UniSearch instead of the engine used on the public site for searching by keyword. It connects directly to the public databases hosted by the EBI/Sanger.

This is meant as an easy way to get a basic Ensembl installation working on Debian. It can then be customised to local requirements.

Note that Ensembl has two odd dependencies: bioperl1.2.3 and libparallel-useragent-perl. Those are not required for routine browsing, but the bioperl1.2.3 library performs the parsing of BLAST outputs. Version 1.2.3 is in conflict with any other existing bioperl installation and forces you to effectively downgrade.

libwww-perl5.808 will conflict with the latest libwww-perl installation and thus force a downgrade to 5.808, which will disable many other tools on your system. Therefore it is advisable NOT to install this package in parallel with any other software, and/or use a virtual machine or dedicated machine.

WARNING: Requires internet connection both to install and to run, as it connects to the Sanger/EBI database servers during both installation and at runtime.

Remark of Debian Med team: Ensembl was removed from Debian due #645487

Ensembl used to be in Debian experimental branch but was removed for formal reasons which are explained in http://bugs.debian.org/645487

ensembl-vep
Variant Effect Predictor predicting the functional effects of genomic variants
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The Ensembl Variant Effect Predictor predicts the functional effects of genomic variants. It has three components:

  • VEP (Variant Effect Predictor) predicts the functional effects of genomic variants.
  • Haplosaurus uses phased genotype data to predict whole-transcript haplotype sequences.
  • Variant Recoder translates between different variant encodings.
Please cite: William McLaren, Laurent Gil, Sarah E. Hunt, Harpreet Singh Riat, Graham R. S. Ritchie, Anja Thormann, Paul Flicek and Fiona Cunningham: The Ensembl Variant Effect Predictor. (PubMed,eprint) Genome Biology 17(1):122 (2016)
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euler-sr
correcting errors in short gene sequence reads and assembling them
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The EULER-SR assembly package contains a suite of programs for correcting errors in short reads and assembling them. Our assembler may take as input classical Sanger reads, 454 sequences, and Illumina reads.

Please cite: Mark J. Chaisson and Pavel A. Pevzner: Short read fragment assembly of bacterial genomes. (PubMed,eprint) Genome Research 18(2):324-30 (2008)
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euler2
de novo repeat classification and fragment assembly
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Repetitive sequences make up a significant fraction of almost any genome and an important and still open question in bioinformatics is how to represent all repeats in DNA sequences. We propose a radically new approach to repeat classification that is motivated by the fundamental topological notion of quotient spaces. A torus or Klein bottle are examples of quotient spaces that can be obtained from a square by gluing some points. Our new repeat classification algorithm is based on the observation that the alignment-induced quotient space of a DNA sequence compactly represents all sequence repeats. This observation leads to a simple and efficient solution of the repeat classification problem as well as new approaches to fragment assembly and multiple alignment.

Please cite: Pavel A. Pevzner, Haixu Tang and Glenn Tesler: De novo repeat classification and fragment assembly. RECOMB '04 Proceedings of the eighth annual international conference on Research in computational molecular biology :213-222 (2004)
exabayes
bayesian phylogenetic tree inference for large-scale analyses
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ExaBayes is a tool for Bayesian phylogenetic analyses. It implements a Markov chain Monte Carlo sampling approach that allows to determine the posterior probability of a tree (resp., topology) and various evolutionary model parameters, for instance, branch lengths or substitution rates. Similar approaches are implemented in beast-mcmc or mrbayes. ExaBayes has heavily drawn inspiration specifically from the latter one.

ExaBayes comes with the most commonly used evolutionary models, such as the generalized time reversible model (GTR) of character substitution, the discretized Gamma-model of among site rate heterogeneity and estimates trees with unconstrained branch lengths. For clocked tree models or less parameter-rich substitution models, we refer you to the established tools.

The distinguishing feature of ExaBayes is its capability to handle enormous datasets efficiently. ExaBayes provides an implementation of data parallelism using the Message Passing Interface (MPI). This means, that if you conduct your analysis on a computing cluster composed of several machines (a.k.a. nodes), the memory needed to evaluate the likelihood of trees and parameters given a large alignment can be spread out across multiple computing nodes. In conclusion, the size of the concatenated alignment ExaBayes can handle is only limited by the combined main memory of your entire computing cluster.

Please cite: Andre J. Aberer, Kassian Kobert and Alexandros Stamatakis: ExaBayes: Massively Parallel Bayesian Tree Inference for the Whole-Genome Era. (PubMed,eprint) Molecular Biology and Evolution 31(10):2553-2556 (2014)
ffp
Feature Frequency Profile Phylogeny
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FFP (Feature frequency profile) is an alignment free comparison tool for phylogenetic analysis and text comparison. It can be applied to nucleotide sequences, complete genomes, proteomes and even used for text comparison.

Please cite: Gregory E. Sims and Sung-Hou Kim: Whole-genome phylogeny of Escherichia coli/Shigella group by feature frequency profiles (FFPs). (PubMed,eprint) Proc Natl Acad Sci U S A. 108(20):8329-34 (2011)
fieldbioinformatics
pipeline with virus identification with Nanopore sequencer
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This is the ARTIC bioinformatics pipeline for working with virus sequencing data, sequenced with nanopore. It implements a complete bioinformatics protocol to take the output from the Nanopore sequencer and determine consensus genome sequences. Includes basecalling, de-multiplexing, mapping, polishing and consensus generation.

An outbreak of SARS-CoV-2, Ebola, ... something unknown? This software is field-proven.

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flappie
flip-flop basecaller for Oxford Nanopore reads
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Basecall Fast5 reads using flip-flop basecalling.

Features

  • Flip-flop basecalling for the MinION platform

  • R9.4.1 (Native or PCR libraries)

  • R10C (PCR libraries only)
  • Basecalling of 5mC in CpG context for R9.4.1, PromethION platform
forester
Graphical vizualiation tool Archaeopteryx
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Archaeopteryx is a software tool for the visualization, analysis, and editing of potentially large and highly annotated phylogenetic trees. It can be used both as applet (ArchaeopteryxA and ArchaeopteryxE) and as a standalone application.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html

BioLinux was following the upstream name change to archaeopteryx and thus the package is called bio-linux-archaeopteryx there.

The binary package is full of JARs without source.

galaxy
scientific workflow and data integration platform for computational biology
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Galaxy is a scientific workflow, data integration, and data and analysis persistence and publishing platform that aims to make computational biology accessible to research scientists that do not have computer programming or systems administration experience. Although it was initially developed for genomics research, it is largely domain agnostic and is now used as a general bioinformatics workflow management system.

Please cite: Marcel Martin: Cutadapt removes adapter sequences from high-throughput sequencing reads. (eprint) EMBnet.journal 17(1):10-12 (2015)
gatk
The Genome Analysis Toolkit
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The Genome Analysis Toolkit or GATK is a software package developed at the Broad Institute to analyze high-throughput sequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size.

Please cite: Aaron McKenna, Matthew Hanna, Eric Banks, Andrey Sivachenko, Kristian Cibulskis, Andrew Kernytsky, Kiran Garimella, David Altshuler, Stacey Gabriel, Mark Daly and Mark A. DePristo: The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. (PubMed,eprint) Genome Research 20(9):1297-303 (2010)
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gerp++
identifies constrained elements in multiple alignments
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GERP is a package for analyzing evolutionary rates and finding constrained elements in a multiple alignment. It uses the notion of "rejected substitutions" (RS) in order to quantify constraint at individual positions as well as over elements spanning multiple positions.

GERP consists of two main components: gerpcol, which analyzes multiple alignments and computes RS scores for all positions, and gerpelem, which finds constrained elements given the RS scores produced by gerpcol.

Please cite: Eugene V. Davydov, David L. Goode, Marina Sirota, Gregory M. Cooper, Arend Sidow and Serafim Batzoglou: Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++. (PubMed,eprint) PLOS Computational Biology 6(12):1-13 (2010)
gramalign
multiple alignment of biological sequences
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GramAlign is a time-efficient progressive Multiple Sequence Alignment (MSA) algorithm. The novelty of GramAlign comes from the sequence distance estimation step, whereby distances are determined by the natural grammar present in nucleotide and amino acid sequences.

Please cite: D. J. Russell, H. H. Otu and K. Sayood: Grammar-based distance in progressive multiple sequence alignment. (eprint) BMC Bioinformatics 9:306 (2008)
graphbin
refined binning of metagenomic contigs using assembly graphs
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GraphBin is a NGS data-based metagenomic contig bin refinment tool that makes use of the contig connectivity information from the assembly graph to bin contigs. It utilizes the binning result of an existing binning tool and a label propagation algorithm to correct mis-binned contigs and predict the labels of contigs which are discarded due to short length.

Please cite: Vijini Mallawaarachchi, Anuradha Wickramarachchi and Yu Lin: GraphBin: refined binning of metagenomic contigs using assembly graphs. (PubMed) Bioinformatics 36:3307-3313 (2020)
graphmap2
highly sensitive and accurate mapper for long, error-prone reads
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GraphMap2 is a highly sensitive and accurate mapper for long, error- prone reads. The mapping algorithm is designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps

95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads.

Please cite: Ivan Sović, Mile Šikić, Andreas Wilm, Shannon Nicole Fenlon, Swaine Chen and Niranjan Nagarajan: Fast and sensitive mapping of nanopore sequencing reads with GraphMap. (PubMed,eprint) Nature Communications 7(11307) (2016)
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haploview
Analysis and visualization of LD and haplotype maps
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This tools assists in the analysis of the nucleotide variation in a population. Such investigations are performed to determine genes and genetic pathways that are associated with diseases. This is an early stage in the quest for new drugs.

Please cite: Jeffrey C. Barrett: Haploview: Visualization and analysis of SNP genotype data. (PubMed,eprint) Cold Spring Harb Protoc. 2009(10):pdb.ip71 (2009)
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hawkeye
Interactive Visual Analytics Tool for Genome Assemblies
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Genome assembly remains an inexact science. Even when accomplished with the best software available, the assembly of a genome often contains numerous errors, both small and large. Hawkeye is a visual analytics tool for genome assembly analysis and validation, designed to aid in identifying and correcting assembly errors. Hawkeye blends the best practices from information and scientific visualization to facilitate inspection of large-scale assembly data while minimizing the time needed to detect mis-assemblies and make accurate judgments of assembly quality.

All levels of the assembly data hierarchy are made accessible to users, along with summary statistics and common assembly metrics. A ranking component guides investigation towards likely mis-assemblies or interesting features to support the task at hand. Wherever possible, high-level overviews, dynamic filtering, and automated clustering are leveraged to focus attention and highlight anomalies in the data. Hawkeyes effectiveness has been proven on several genome projects, where it has been used both to improve quality and to validate the correctness of complex genomes.

Hawkeye is compatible with most widely used assemblers, including Phrap, ARACHNE, Celera Assembler, Newbler, AMOS, and assemblies deposited in the NCBI Assembly Archive.

Please cite: Michael C. Schatz, Adam M. Phillippy, Daniel D. Sommer, Arthur L. Delcher, Daniela Puiu, Giuseppe Narzisi, Steven L. Salzberg and Mihai Pop: Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies. (PubMed,eprint) Briefings in Bioinformatics (2011)
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htqc
Quality control and filtration for illumina sequencing data
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HTQC is a toolkit including statistics tool for illumina high-throughput sequencing data, and filtration tools for sequence quality, length, tail quality, etc..

Please cite: Xi Yang, Di Liu, Fei Liu, Jun Wu, Jing Zou, Xue Xiao, Fangqing Zhao and Baoli Zhu: HTQC: a fast quality control toolkit for Illumina sequencing data. (PubMed,eprint) BMC Bioinformatics 14:33 (2013)
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idefix
index checking for improved demultiplexing of NGS data
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IDeFIX is a tool for demultiplexing Illumina NGS data.

It reports inconsistencies between the raw data and the Sample Sheet, checks for duplicates of indices/ index combinations in the latter and removes unwanted characters from it. Apart from messages printed on the terminal, IDeFIX creates an IDeFIX_Report.csv containing the indices/ index combinations from the raw data and their abundance as well as their count in the Sample Sheet and the corresponding Index ID(s). This file is stored in the project folder.

inspect
mass-spectrometry database search tool
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Inspect is a MS/MS database search tool specifically designed to address two crucial needs of the proteomics comminuty: post-translational modification identification and search speed. The program is available as a free download or online in the ProteoSAFe webserver. The online interface is coordinated with other proteomics software developed in the lab, like PepNovo

Typical database searches do not deal well with the dynamic nature of the proteome. Post-translational modifications, alternative splicing, and laboratory chemisty all affect protein behavior and make spectrum interpretation more challenging. The primary challenge is that the "virtual database" of all modified peptides undergoes a combinatorial explosion when a broad range of modifications is allowed. This affects search running time. A secondary challenge is that in this richer database, there are many more close "relatives" for each peptide. This affects scoring accuracy, since differentiating between correct and incorrect identifications is more difficult.

InsPecT addresses several algorithmic problems in order to identify modified proteins.

InsPecT uses peptide sequence tags (PSTs) to filter the database. InsPecT has an internal tag generator, but can accept tags generated by other tools (e.g. Pepnovo, GutenTAG). Because de novo is imperfect, multiple tags are produced for each spectrum, to ensure that (at least) one tag is corrrect. These PSTs are extremely efficient filters, even in the context of up to a dozen possible modifications. Tag-based filtering can also be combined with the "two-pass" filtering pioneered by X!Tandem, where from one search provides a list of proteins (a mini- database) for a more detailed search.

Unanticipated modifications are common in proteomics. InsPecT implements the MS-Alignment algorithm for "blind" spectral search, with no bias toward anticipated modification types. This search has been applied to annotate heavily-modified proteins such as crystallins.

Please cite: Stephen Tanner, Hongjun Shu, Ari Frank, Ling-Chi Wang, Ebrahim Zandi, Marc Mumby, Pavel A. Pevzner and Vineet Bafna: InsPecT: Fast and accurate identification of post-translationally modified peptides from tandem mass spectra. (PubMed) Anal Chem. 77(14):4626-39 (2005)
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jbrowse
genome browser with an AJAX-based interface
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JBrowse is a genome browser with an AJAX-based interface. JBrowse renders most tracks using client side JavaScript and JSON as its data transfer format. JBrowse is the official successor to GBrowse.

kempbasu
significance tests for comparing digital gene expression profiles
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This package implements the significance tests for comparing digital gene profiles described in the article:

Varuzza et al. "Significance tests for comparing digital gene expression profiles"

They provide two programs: kemp for the frequentist test and basu for the Bayesian test, and some auxiliary scripts.

mach-haplotyper
Markov Chain based SNP haplotyper
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Recent advancements in chip-based DNA genotyping allow to infer DNA variants that are not part of the chip but known to be associated with a combination of SNPs that are measured.

Please cite: Yun Li, Cristen J. Willer, Jun Ding, Paul Scheet and Gonçalo R. Abecasis: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. (PubMed) Genetic Epidemiology 34(8):816-34 (2010)
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mage2tab
MAGE-MLv1 converter and visualiser
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This tool-kit is part of MR_T, a framework for import or export various of MAGE (MicroArray Gene Expression) documents (MAGE-MLv1, MAGE-TAB, SOFT, MINiML) from or into databases like GUS (the Genomics Unified Schema, www.gusdb.org).

This package provides the following programs:

 mage2tab     — MAGE-MLv1 to MAGE-TAB converter
 mage2graph   — GraphViz-based mage data visualisation tool
 mage-checker — Validation tool
manta
structural variant and indel caller for mapped sequencing data
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Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium- sized indels and large insertions within a single efficient workflow. The method is designed for rapid analysis on standard compute hardware: NA12878 at 50x genomic coverage is analyzed in less than 20 minutes on a 20 core server, and most WGS tumor/normal analyses can be completed within 2 hours. Manta combines paired and split-read evidence during SV discovery and scoring to improve accuracy, but does not require split- reads or successful breakpoint assemblies to report a variant in cases where there is strong evidence otherwise. It provides scoring models for germline variants in small sets of diploid samples and somatic variants in matched tumor/normal sample pairs. There is experimental support for analysis of unmatched tumor samples as well. Manta accepts input read mappings from BAM or CRAM files and reports all SV and indel inferences in VCF 4.1 format.

Please cite: Xiaoyu Chen, Ole Schulz-Trieglaff, Richard Shaw, Bret Barnes, Felix Schlesinger, Morten Källberg, Anthony J. Cox, Semyon Kruglyak and Christopher T. Saunders: Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. (PubMed,eprint) Bioinformatics 32(8):1220-1222 (2015)
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marginphase
simultaneous haplotyping and genotyping
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MarginPhase is a program for simultaneous haplotyping and genotyping. It is an experimental, open source implementation written in C and developed to work primarily with nanopore data. The MarginPhase workflow includes an alignment summation step. This differentiates it from WhatsHap, which performs a local realignment around analyzed sites. MarginPhase can also phase genotypic variants simultaneously after filtering out the sites that are likely homozygous. MarginPhase’s output includes a BAM which encodes the phasing of each read, including which phase set it is in, which haplotype it belongs to, and what of the aligned portion falls into each phase set. Reads which span a phase set boundary have information for both encoded in them.

Please cite: Jana Ebler, Marina Haukness, Trevor Pesout, Tobias Marschall and Benedict Paten: Haplotype-aware diplotyping from noisy long reads. (PubMed,eprint) Genome Biol 20(1):116 (2019)
martj
distributed data integration system for biological data
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BioMart is a simple, distributed data integration system with powerful query capabilities. The BioMart data model has been applied to the following data sources: UniProt Proteomes, Macromolecular Structure Database (MSD), Ensembl, Vega, and dbSNP.

It has been designed to provide researchers with an easy and interactive access to both the wealth of data available on the Internet and for in house data integration. BioMart is a successor to the generic query system originally developed for the Ensembl genome database (EnsMart). Building on its success, BioMart, has now been applied to other biological databases.

medaka
sequence correction provided by ONT Research
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Medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.

Features

  • Requires only basecalled data. (.fasta or .fastq)
  • Improved accurary over graph-based methods (e.g. Racon).
  • 50X faster than Nanopolish (and can run on GPUs).
  • Methylation aggregation from Guppy .fast5 files.
  • Benchmarks are provided here.
  • Includes extras for implementing and training bespoke correction networks.
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meme
search for common motifs in DNA or protein sequences
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MEME (Multiple EM for Motif Elicitation) is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs.

MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif.

Please cite: Timothy L. Bailey, Nadya Williams, Chris Misleh and Wilfred W. Li: MEME: discovering and analyzing DNA and protein sequence motifs. (PubMed,eprint) Nucleic Acids Research 34(Web Server issue):W369–W373 (2006)
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mesquite
modular system for evolutionary analysis
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Mesquite is modular, extendible software for evolutionary biology, designed to help biologists organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics, while others do non-phylogenetic multivariate analysis. Because it is modular, the analyses available depend on the modules installed.

Mesquite also has many features for managing and processing data, including processing of chromatograms, sequence alignment, editing of morphometric data, and others.

metabit
analysing microbial profiles from high-throughput sequencing shotgun data
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MetaBIT is an integrative and automated metagenomic pipeline for analysing microbial profiles from high-throughput sequencing shotgun data.

The metaBIT pipeline proposes tools for visualising microbial profiles (barplots, heatmaps) and performing a range of statistical analyses (diversity indices, hierarchical clustering and principal coordinate analysis). It uses as input fastq files containing trimmed reads from shotgun high through-put sequencing.

Please cite: Guillaume Louvel, Clio Der Sarkissian, Kristian Hanghøj and Ludovic Orlando: metaBIT, an integrative and automated metagenomic pipeline for analysing microbial profiles from high-throughput sequencing shotgun data. (PubMed) Molecular Ecology Resources (2016)
modeller
Protein structure modeling by satisfaction of spatial restraints
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MODELLER is used for homology or comparative modeling of protein three-dimensional structures (1). The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints (2, 3), and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc.

Please cite: M.A. Marti-Renom, A. Stuart, A. Fiser, R. Sánchez and F. Melo, A. Sali.: Comparative protein structure modeling of genes and genomes. (PubMed) Annu Rev Biophys Biomol Struct. 29:291-325 (2000)
Remark of Debian Med team: The package is created independently from Debian Med or Debian Science.

The source code is not generally available. Hence, most users are limited to the compiled versions of MODELLER. The program is distributed as a single install file that contains scripts, libraries, examples, documentation (in PDF and HTML formats) and executables for the supported platforms and operating systems. Please refer to the relevant section below for your platform:

The program comes as closed source, only free for academia, see http://salilab.org/modeller/registration.html.

molekel
Advanced Interactive 3D-Graphics for Molecular Sciences
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Molekel is an open-source multi-platform molecular visualization program.

Some of the features are:

  • Different methods to speed-up rendering of molecules with support for billboards and view-dependent level of detail techniques
  • Programmable shaders; standard shaders to enhance rendering quality, outline contours and perform sketch-like renderings are provided
  • Visualization of residues (ribbon or schematic)
  • Complete control over the generation of molecular surfaces (bounding box and resolution)
  • Visualization of the following surfaces:
  • Orbitals
  • Iso-surface from density matrix
  • Iso-surface from Gaussian cube grid data
  • SAS
  • SES
  • Van der Waals
  • Animation of molecular surfaces
  • Animation of vibrational modes
  • Export high resolution images for 300+ DPI printing
  • Export to PostScript and PDF
  • Export animation
  • Plane widget to visualize a scalar field: the plane can be freely moved in 3d space and the points on the plane surface will be colored according to the value of the scalar field: a cursor can be moved on the plane surface to show the exact value of the field at a specific point in space.
mosaik-aligner
reference-guided aligner for next-generation sequencing
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MosaikBuild converts various sequence formats into Mosaik’s native read format. MosaikAligner pairwise aligns each read to a specified series of reference sequences. MosaikSort resolves paired-end reads and sorts the alignments by the reference sequence coordinates. Finally, MosaikText converts alignments to different text-based formats.

At this time, the workflow consists of supplying sequences in FASTA, FASTQ, Illumina Bustard & Gerald, or SRF file formats and producing results in the BLAT axt, the BAM/SAM, the UCSC Genome Browser bed, or the Illumina ELAND formats.

mpsqed
alignment editor and multiplex pyrosequencing assay designer
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Molecular-based diagnostic assays are the gold standard for infectious diseases today, since they allow a rapid and sensitive identification and typing of various pathogens. While PCR can be designed to be specific for a certain pathogen, a subsequent sequence analysis is frequently required for confirmation or typing. The design of appropriate PCR-based assays is a complex task, especially when conserved discriminating polymorphisms are rare or if the number of types which need to be differentiated is high. One extremely useful but underused method for this purpose is the multiplex pyrosequencing technique. mPSQed is a program developed at the Robert Koch Institute and targeted at facilitating the creation of such assays.

Please cite: Piotr Wojtek Dabrowski and Andreas Nitsche: mPSQed: A Software for the Design of Multiplex Pyrosequencing Assays. (PubMed,eprint) PLoS One 7(6):e38140 (2012)
mugsy
multiple whole genome alignment tool
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Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.

Please cite: Samuel V. Angiuoli and Steven L. Salzberg: Mugsy: fast multiple alignment of closely related whole genomes. (PubMed,eprint) Bioinformatics 27(3):334-342 (2011)
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mview
biological sequence alignment conversion
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mview is a command line utility that extracts and reformats the results of a sequence database search or a multiple alignment, optionally adding HTML markup for web page layout. It can also be used as a filter to extract and convert searches or alignments to common formats.

Inputs:

  • Sequence database search: BLAST, FASTA suites.
  • Multiple sequence alignment: CLUSTAL, HSSP, MSF, FASTA, PIR, MAF Outputs:

  • HTML, FASTA, CLUSTAL, MSF, PIR, RDB (tab-separated).

The redundancy of that source tree with existing JS packages needs to be evaluated. In the interim, the package shall remain in experimental.

Please cite: Nigel P. Brown, C. Leroy and Christian Sander: MView: A Web compatible database search or multiple alignment viewer. (PubMed,eprint) Bioinformatics 14(4):380-381 (1998)
nano-snakemake
detection of structural variants in genome sequencing data
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To "have a genetic variation" may mean many different things. Technically most straight forward to investigate are changes to single positions in the long DNA chains - every chromosome is a single polymer of nucleic acids. This is also what we have most data from for many diseases.

But sometimes, DNA that looks completely the same when looking at short reads at the time (and not feeling lucky), the position looked at may be inverted on the chromosome. Or it may be a copy of the original site and not a "real" single-nucleotide polymorphism (SNP). Or it may have translocated to another chromosome.

These are examples for structural changes to the DNA. Individuals may never notice them. Or there may be a higher chances to develop a disease or it may affect fertility. Technologies like the Nanopore have emerged that can read longer segments of the DNA, so one can see multiple copies of the same gene in the same read or at least can assemble the DNA fragments read in a way to then align the reads non-ambiguously and support the analysis of such copy-number variations (CNVs).

This snakemake pipeline on nanopore whole genome sequencing data provides a complete structural variant analysis. Steps implemented and tools wrapped comprise:

  • fast: minimap2 alignment with Sniffles and SVIM SV calling
  • precise: ngmlr alignment with Sniffles SV calling
  • minimap2: minimap2 alignment with Sniffles, SVIM, NanoSV and npInv SV calling
  • minimap2_pbsv: minimap2 alignment with pbsv-specific parameters with pbsv, SVIM, NanoSV and npInv SV calling
  • ngmlr: ngmlr with Sniffles, NanoSV, SVIM and npInv SV calling
  • last-prepare: create a LAST index and train aligner parameters using last-train
  • last: LAST alignment with tandem-genotypes STR calling
Please cite: Wouter De Coster, Peter De Rijk, Arne De Roeck, Tim De Pooter, Svenn D'Hert, Mocja Strazisar, Sleegers Kristel and Christine Van Broeckhoven: Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome. Genome Res. (2019.0)
nanocall
Basecaller for Oxford Nanopore Sequencing Data
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The highly portable Oxford Nanopore MinION sequencer has enabled new applications of genome sequencing directly in the field. However, the MinION currently relies on a cloud computing platform, Metrichor (metrichor.com), for translating locally generated sequencing data into basecalls.

Nanocall allows offline and private analysis of MinION data. Nanocall is the first freely-available, open-source basecaller for Oxford Nanopore sequencing data and does not require an internet connection. Using R7.3 chemistry, on two E.coli and two human samples, with natural as well as PCR-amplified DNA, Nanocall reads have ~68% identity, directly comparable to Metrichor "1D" data. Further, Nanocall is efficient, processing ~2500Kbp of sequence per core hour using the fastest settings, and fully parallelized. Using a 4 core desktop computer, Nanocall could basecall a MinION sequencing run in real time. Metrichor provides the ability to integrate the "1D" sequencing of template and complement strands of a single DNA molecule, and create a "2D" read. Nanocall does not currently integrate this technology, and addition of this capability will be an important future development. In summary, Nanocall is the first open-source, freely available, off-line basecaller for Oxford Nanopore sequencing data.

Please cite: Matei David, Lewis Jonathan Dursi, Delia Yao, Paul C Boutros and Jared T Simpson: Nanocall: An Open Source Basecaller for Oxford Nanopore Sequencing Data. (PubMed,eprint) Bioinformatics (2016)
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nanocomp
compare multiple runs of long biological sequences
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NanoClmp compares multiple runs of long read sequencing data and alignments. It creates violin plots or box plots of length, quality and percent identity and creates dynamic, overlaying read length histograms and a cumulative yield plot.

This package installs the 'NanoCalc' executable.

Please cite: Wouter De Coster, Svenn D’Hert, Darrin T Schultz, Marc Cruts and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. (PubMed,eprint) Bioinformatics 34(15):2666–2669 (2018)
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nanoplot
plotting scripts for long read sequencing data
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NanoPlot provides plotting scripts for long read sequencing data.

These scripts perform data extraction from Oxford Nanopore sequencing data in the following formats:

  • fastq files (optionally compressed)
  • fastq files generated by albacore, guppy or MinKNOW containing additional information (optionally compressed)
  • sorted bam files
  • sequencing_summary.txt output table generated by albacore, guppy or MinKnow basecalling (optionally compressed)
  • fasta files (optionally compressed)
  • multiple files of the same type can be offered simultaneously
Please cite: Wouter De Coster, Svenn D'Hert, Darrin T Schultz, Marc Cruts and Christine Van Broeckhoven: NanoPack: visualizing and processing long-read sequencing data. (PubMed,eprint) Bioinformatics 34(15):2666-2669 (2018)
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ncbi-magicblast
RNA-seq mapping tool
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Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read- pairing is ignored.

Please cite: Grzegorz M. Boratyn, Jean Thierry-Mieg, Danielle Thierry-Mieg, Ben Busby and Thomas L. Madden: Magic-BLAST, an accurate RNA-seq aligner for long and short reads. (PubMed,eprint) BMC Bioinformatics 20(1):405 (2019)
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nextsv
automated structural variation detection for long-read sequencing
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NextSV is an computational pipeline that allows structural variant (SV) calling from PacBio sequencing data using PBhoney and Sniffles. NextSV takes FASTA or FASTQ files as input. Once the SV caller is selected by user, NextSV automatically chooses the compatible aligner and performs mapping. The alignments will be automatically sorted and then presented to the SV caller. Users can change the parameters by modifying its configuration file. When the analysis is finished, NextSV will examine the FASTA/FASTQ, BAM, and result files and generate a report showing various statistics. If more than both callers are selected, NextSV will format the raw result files (.tails, .spots, or .vcf files) into bed files and generate the intersection or union call set for the purpose of higher accuracy or sensitivity.

Please cite: Li Fang, Jiang Hu, Depeng Wang and Kai Wang: Evaluation on Detection of Structural Variants by Low-Coverage Long-Read Sequencing. bioRxiv (2016)
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ngila
global pairwise alignments with logarithmic and affine gap costs
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Ngila is an application that will find the best alignment of a pair of sequences using log-affine gap costs, which are the most biologically realistic gap costs.

Ngila implements the Miller and Myers (1988) algorithm in order to find a least costly global alignment of two sequences given homology costs and a gap cost. Two versions of the algorithm are included: holistic and divide-and-conquer. The former is faster but the latter utilizes less memory. Ngila starts with the divide-and-conquer method but switches to the holistic method for subsequences smaller than a user-established threshold. This improves its speed without substantially increasing memory requirements. Ngila also allows users to assign costs to end gaps that are smaller than costs for internal gaps. This is important for aligning using the free-end-gap method.

Please cite: Reed A. Cartwright: Ngila: global pairwise alignments with logarithmic and affine gap costs. (PubMed,eprint) Bioinformatics 23(11):1427-1428 (2007)
ngsqctoolkit
toolkit for the quality control of next generation sequencing data
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NGS QC Toolkit: A toolkit for the quality control (QC) of next generation sequencing (NGS) data. The toolkit comprises of user-friendly stand alone tools for quality control of the sequence data generated using Illumina and Roche 454 platforms with detailed results in the form of tables and graphs, and filtering of high-quality sequence data. It also includes few other tools, which are helpful in NGS data quality control and analysis.

Please cite: Ravi K. Patel and Mukesh Jain: NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data. (PubMed,eprint) PLoS One 7(2):e30619 (2012)
nw-align
global protein sequence alignment
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NWalign is simple and robust alignment program for protein sequence-to-sequence alignments based on the standard Needleman-Wunsch dynamic programming algorithm. The implementation is performed in FORTRAN.

This program was tested at 2014-02-01 by Daniel Barker at the Debian Med sprint and was not functional according to his test.

oases
de novo transcriptome assembler for very short reads
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Oases is a de novo transcriptome assembler designed to produce transcripts from short read sequencing technologies, such as Illumina, SOLiD, or 454 in the absence of any genomic assembly. Oases uploads a preliminary assembly produced by Velvet, and clusters the contigs into small groups, called loci. It then exploits the paired-end read and long read information, when available, to construct transcript isoforms.

Please cite: Marcel H. Schulz, Daniel R. Zerbino, Martin Vingron and Ewan Birney: Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. (PubMed,eprint) Bioinformatics 28(8):1086-1092 (2012)
Registry entries: Bio.tools  SciCrunch 
omegamap
describing selection and recombination in sequences
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OmegaMap is a program for detecting natural selection and recombination in DNA or RNA sequences. It is based on a model of population genetics and molecular evolution. The signature of natural selection is determined by the relative excess of non-synonymous to synonymous polymorphisms. The signature of recombination is detected from the patterns of linkage disequilibrium.

Please cite: Daniel J. Wilson and G. McVean: Estimating diversifying selection and functional constraint in the presence of recombination.. (PubMed,eprint) Genetics 172(3):1411-1425 (2006)
Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
oncofuse
predicting oncogenic potential of gene fusions
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Oncofuse is a framework designed to estimate the oncogenic potential of de-novo discovered gene fusions. It uses several hallmark features and employs a bayesian classifier to provide the probability of a given gene fusion being a driver mutation.

Please cite: Mikhail Shugay, Iñigo Ortiz de Mendíbil, José L. Vizmanos and Francisco J. Novo: Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions. (PubMed,eprint) Bioinformatics 29(20):2539–2546 (2013)
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optitype
precision HLA typing from next-generation sequencing data
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OptiType is a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate 4-digit HLA genotyping predictions from NGS data by simultaneously selecting all major and minor HLA Class I alleles.

Please cite: András Szolek, Benjamin Schubert, Christopher Mohr, Marc Sturm, Magdalena Feldhahn and Oliver Kohlbacher: OptiType: precision HLA typing from next-generation sequencing data. (PubMed,eprint) Bioinformatics 30(23):3310–3316 (2014)
paipline
Pipeline for the Automatic Identification of Pathogens
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This program is designed to search for pathogen nucleic acid sequences in NGS datasets. It needs databases in the format provided by the database- updater found under https://gitlab.com/andreas.andrusch/database-updater.

Please cite: Andreas Andrusch, Piotr W. Dabrowski, Jeanette Klenner, Simon H. Tausch, Claudia Kohl, Abdalla A. Osman, Bernhard Y. Renard and Andreas Nitsche: PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples. (eprint) Bioinformatics 34(17):i715-i721 (2018)
pangolin
Phylogenetic Assignment of Named Global Outbreak LINeages
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Pangolin runs a multinomial logistic regression model trained against lineage assignments based on GISAID data.

Legacy pangolin runs using a guide tree and alignment hosted at cov-lineages/lineages. Some of this data is sourced from GISAID, but anonymised and encrypted to fit with guidelines. Appropriate permissions have been given and acknowledgements for the teams that have worked to provide the original SARS-CoV-2 genome sequences to GISAID are also hosted here.

Registry entries: Bioconda 
partitionfinder
choses partitioning schemes and models of molecular evolution for sequence data
Responsible: Kevin Murray (Andreas Tille)
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PartitionFinder and PartitionFinderProtein are Python programs for simultaneously choosing partitioning schemes and models of molecular evolution for sequence data. You can use them before running a phylogenetic analysis, in order to decide how to divide up your sequence data into separate blocks before analysis, and to simultaneously perform model selection on each of those blocks.

patristic
Calculate patristic distances and comparing the components of genetic change
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Patristic overcomes some logistic barriers to analysing signals in sequences. In additional to calculating patristic distances, it provides plots for any combination of matrices, calculates commonly used statistics, allows data such as isolation dates to be entered and reorders matrices with matching species or gene labels. It will be used to analyse rates of mutation and substitutional saturation and the evolution of viruses.

Please cite: Mathieu Fourment and Mark J Gibbs: PATRISTIC: a program for calculating patristic distances and graphically comparing the components of genetic change. (PubMed,eprint) BMC Evolutionary Biology 6:1 (2006)
pcma
fast and accurate multiple sequence alignment based on profile consistency
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PCMA (profile consistency multiple sequence alignment) is a progressive multiple sequence alignment program that combines two different alignment strategies. Highly similar sequences are aligned in a fast way as in ClustalW, forming pre-aligned groups. The T-Coffee strategy is applied to align the relatively divergent groups based on profile–profile comparison and consistency. The scoring function for local alignments of pre-aligned groups is based on a novel profile–profile comparison method that is a generalization of the PSI-BLAST approach to profile–sequence comparison. PCMA balances speed and accuracy in a flexible way and is suitable for aligning large numbers of sequences.

Please cite: Jimin Pei, Ruslan Sadreyev and Nick V. Grishin: PCMA: fast and accurate multiple sequence alignment based on profile consistency. (PubMed,eprint) Bioinformatics 19(3):427-428 (2003)
Remark of Debian Med team: Precondition for T-Coffee

see http://wiki.debian.org/DebianMed/TCoffee

Check with authors about licensing, they adopted code from clustalw which is now free. Thus a change might be possible

phylophlan
microbial Tree of Life using 400 universal proteins
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PhyloPhlAn is a computational pipeline for reconstructing highly accurate and resolved phylogenetic trees based on whole-genome sequence information. The pipeline is scalable to thousands of genomes and uses the most conserved 400 proteins for extracting the phylogenetic signal. PhyloPhlAn also implements taxonomic curation, estimation, and insertion operations.

The main features of PhyloPhlAn are:

  • completely automatic, as the user needs only to provide the (unannotated) protein sequences of the input genomes (as multifasta files of peptides - not nucleotides)
  • very high topological accuracy and resolution because of the use of up to 400 previously identified most conserved proteins
  • the possibility of integrating new genomes in the already reconstructed most comprehensive tree of life (3,171 microbial genomes)
  • taxonomy estimation for the newly inserted genomes
  • taxonomic curation for the produced phylogenetic trees
Please cite: Nicola Segata, Daniela Börnigen, Xochitl C. Morgan and Curtis Huttenhower: PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. (PubMed,eprint) Nature Communications 4:2304 (2013)
Remark of Debian Med team: usearch can not be replaced since vsearch does not work with proteins

See https://lists.debian.org/debian-med/2016/05/msg00091.html

phyloviz-core
phylogenetic inference and data visualization for sequence based typing methods
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Phyloviz allows the analysis of sequence-based typing methods that generate allelic profiles and their associated epidemiological data.

For representing the possible evolutionary relationships between strains identified by allelic profiles it uses the goeBURST algorithm, a refinement of eBURST algorithm proposed by Feil et al., and its expansion to generate a complete minimum spanning tree (MST).

Phyloviz is being developed in a modular way to allow its expansion with novel data analysis algorithms and new visualization modules.

Capabilities

  • Modularity allows the creation of plugins to analyse different types of data
  • Allows the visualization of data overlaid onto goeBURST and MST results
  • Confidence assessment of each link in the graph
  • Query the data and see the query results directly onto the graphs
  • Search your data set using regular expressions to select what to display
  • Export the results as images in various formats: eps, png, gif, pdf, etc
Please cite: Alexandre P Francisco, Cátia Vaz, Pedro T Monteiro, José Melo-Cristino, Mário Ramirez and João A Carriço: PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods. (PubMed,eprint) BMC Bioinformatics 13(1):87 (2012)
Remark of Debian Med team: There are several plugins to package

The download page http://www.phyloviz.net/wiki/plugins/ lists several plugins that should be packaged (single or as bundle) as well.

pigx-scrnaseq
pipeline for checkpointed and distributed scRNA-seq analyses
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This package provides a automated workflow for the automated analysis of single-cell RNA-seq experiments. A series of well-accecpted tools are connected in Python scripts and controlled via snakemake. This supports the parallel execution of these workflows and provides checkpointing, such that interrupted workflows can take up their work again.

pipasic
Protein Abundance Correction in Metaproteomic Data
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Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise due to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions.

pipasic (peptide intensity-weighted proteome abundance similarity correction) is a tool which corrects identification and spectral counting based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor.

Please cite: Anke Penzlin, Martin S. Lindner, Joerg Doellinger, Piotr Wojtek Dabrowski, Andreas Nitsche and Bernhard Y. Renard: Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics. (PubMed,eprint) Bioinformatics 30(12):i149–i156 (2014)
plato
Analysis, translation, and organization of large-scale genetic data
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PLATO is an acronym for "PLatform for the Analysis, Translation, and Organization of large-scale data". Recent technological advances enable the study of hundreds of thousands of human single-nucleotide polymorphisms at the population level. Because strategies for analyzing these data have not kept pace with the laboratory methods that generate the data, it is unlikely that these advances will immediately lead to an improved understanding of the genetic contribution to common human disease and drug response. Currently, no single analytical method allows us to extract all available information from a whole-genome association study. In fact, no single method can be optimal for all datasets, especially when the genetic architecture for diseases can vary substantially, as is certainly the case. Therefore, an integrative platform is needed to accommodate multiple analytical methods for analysis as we learn more about genetic architecture. As a result, we are developing a system for the analysis of genome-wide association data that will incorporate several analytical approaches as filters to allow a scientist to choose whatever analytical methods they wish to apply. PLATO (PLatform for the Analysis, Translation, and Organization of large-scale data) will incorporate a number of filters to select the important SNPs in a genome-wide association study.

Whole-genome Association Study Pipeline (WASP) has recently been absorbed into PLATO. WASP was designed to aid in retrieving, evaluating, formatting, and analyzing genotypic and clinical data from the latest large-scale genotyping studies. WASP implements a battery of quality control procedures to assess the data. Among the currently available procedures are the examination of marker and sample genotyping efficiency, allele frequency calculations, checks of Mendelian error (if applicable) and gender discrepancies (based on available chromosome X and Y genotypes), and tests of Hardy-Weinberg Equilibrium. Additionally, WASP can retrieve and format data for other software programs such as the Graphical Representation of Relationships (GRR) program, or STRUCTURE, and depending on the nature of the samples and the depth of examination the user desires to pursue. Beyond the quality control aspect of this application, WASP can perform standard tests of association using the Transmission Disequilibrium Test TDT for family-based datasets and the chi-square test of association for case-control datasets.

Please cite: Benjamin J. Grady, Eric Torstenson, Scott M. Dudek, Justin Giles, David Sexton and Marylyn D. Ritchie: Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data. (PubMed,eprint) Proceedings of the Pacific Symposium :315-26 (2010)
pomoxis
analysis components from Oxford Nanopore Research
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Pomoxis comprises a set of basic bioinformatic tools tailored to nanopore sequencing. Notably tools are included for generating and analysing draft assemblies. Many of these tools are used by the research data analysis group at Oxford Nanopore Technologies.

Features

  • Wraps third party tools with known good default parameters and methods of use.
  • Creates an isolated environment with all third-party tools.
  • Streamlines common short analysis chains.
  • Integrates into katuali for performing more complex analysis pipelines.
Registry entries: Bioconda 
profit - wnpp
Protein structure alignment
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ProFit is designed to be the ultimate protein least squares fitting program. It has many features including flexible specification of fitting zones and atoms, calculation of RMS over different zones or atoms, RMS-by-residue calculation, on-line help facility, etc.

A symbolic link is provided to have the binary name back to how it is historically correct.

Remark of Debian Med team: The authors need to change the license, still.
psipred
protein secondary structure prediction
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Version: 4.01-1

PSIPRED is a simple and accurate secondary structure prediction method, incorporating two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST (Position Specific Iterated - BLAST). Using a very stringent cross validation method to evaluate the method's performance, PSIPRED 2.6 achieves an average Q3 score of 80.7%.

Please cite: David T. Jones: Protein secondary structure prediction based on position-specific scoring matrices. (PubMed) Journal of Molecular Biology 292(2):195-202 (1999)
pssh2
set of scripts for mapping protein sequence to structure
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pssh2 creates sequence-to-structure alignments based on hhblits profiles built for the query sequence. pssh2 consists of scripts to run the hhblits queries and parse the output. You also need the pdb_full database downloaded from rostlab.org: ftp://rostlab.org/pssh2/pdb_full/

This package provides the script files needed to run within PredictProtein. They are all called in the correct order in pp_pssh2. It also contains scripts to run independent of PredictProtein. It assumes you have a mysql database to store information. The configuration information is kept in pssh2.conf

pufferfish
Efficient index for the colored, compacted, de Bruijn graph
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Pufferfish is a new time and memory-efficient data structure for indexing a compacted, colored de Bruijn graph (ccdBG).

Though the de Bruijn Graph (dBG) has enjoyed tremendous popularity as an assembly and sequence comparison data structure, it has only relatively recently begun to see use as an index of the reference sequences (e.g. deBGA, kallisto). Particularly, these tools index the compacted dBG (cdBG), in which all non-branching paths are collapsed into individual nodes and labeled with the string they spell out. This data structure is particularly well-suited for representing repetitive reference sequences, since a single contig in the cdBG represents all occurrences of the repeated sequence. The original positions in the reference can be recovered with the help of an auxiliary "contig table" that maps each contig to the reference sequence, position, and orientation where it appears as a substring. The deBGA paper has a nice description how this kind of index looks (they call it a unipath index, because the contigs we index are unitigs in the cdBG), and how all the pieces fit together to be able to resolve the queries we care about. Moreover, the cdBG can be built on multiple reference sequences (transcripts, chromosomes, genomes), where each reference is given a distinct color (or colour, if you're of the British persuasion). The resulting structure, which also encodes the relationships between the cdBGs of the underlying reference sequences, is called the compacted, colored de Bruijn graph (ccdBG). This is not, of course, the only variant of the dBG that has proven useful from an indexing perspective. The (pruned) dBG has also proven useful as a graph upon which to build a path index of arbitrary variation / sequence graphs, which has enabled very interesting and clever indexing schemes like that adopted in GCSA2. Also, thinking about sequence search in terms of the dBG has led to interesting representations for variation-aware sequence search backed by indexes like the vBWT (implemented in the excellent gramtools package).

purple
Picking Unique Relevant Peptides for viraL Experiments
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Emerging virus diseases present a global threat to public health. To detect viral pathogens in time-critical scenarios, accurate and fast diagnostic assays are required. Such assays can now be established using mass spectrometry-based targeted proteomics, by which viral proteins can be rapidly detected from complex samples down to the strain level with high sensitivity and reproducibility. Developing such targeted assays involves tedious steps of peptide candidate selection, peptide synthesis, and assay optimization. Peptide selection requires extensive preprocessing by comparing candidate peptides against a large search space of background proteins. Purple (Picking Unique Relevant Peptides for viraL Experiments) is a software tool for selecting target-specific peptide candidates directly from given proteome sequence data.

Purple enables peptide candidate selection across various taxonomic levels and filtering against backgrounds of varying complexity. Its functionality is demonstrated using data from different virus species and strains. Purple enables building taxon-specific targeted assays and paves the way to time-efficient and robust viral diagnostics using targeted proteomics.

This is the command line version of purple.

Please cite: Johanna Lechner, Felix Hartkopf, Pauline Hiort, Andreas Nitsche Marica Grossegesse, Joerg Doellinger, Bernhard Y. Renard and Thilo Muth: Purple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomics. (PubMed,eprint) Viruses 11(6):536 (2019)
q2-composition
QIIME2 plugin for Compositional statistics
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-deblur
QIIME2 plugin to wrap the Deblur software for sequence quality control
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License: BSD-3-clause
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Version: 2023.9.0-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Amnon Amir, Daniel McDonald, Jose A. Navas-Molina, Evguenia Kopylova, James T. Morton, Zhenjiang Zech Xu, Eric P. Kightley, Luke R. Thompson, Embriette R. Hyde, Antonio Gonzalez and Rob Knight: Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. (PubMed,eprint) mSystems 2 (2017)
q2-diversity
QIIME2 plugin for core diversity analysis
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License: BSD-3-clause
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Version: 2021.8.0-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. Functionality is made available through QIIME 2 plugins.

This plugin provides the means to statistically assess the diversity of microbiota. This has direct clinical interest, since with whatever we eat or have antibiotics applied, the survival of different groups of bacteria/yeasts will be affected. From these relative abundances of strains that constribute the microbiome, most prominently, comparisons within a group of samples (or an individual) determines the alpha diversity and between (groups of) samples the beta diversity is inspected.

This package is key to most workflows in qiime.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-gneiss
QIIME2 plugin for Compositional Data Analysis and Visualization
Versions of package q2-gneiss
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License: BSD-3-clause
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Version: 2020.11.1-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-longitudinal
QIIME2 plugin for longitudinal studies and paired comparisons
Versions of package q2-longitudinal
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License: BSD-3-clause
Debian package not available
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Version: 2023.9.1+ds-1

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

  • Integrated and automatic tracking of data provenance
  • Semantic type system
  • Plugin system for extending microbiome analysis functionality
  • Support for multiple types of user interfaces (e.g. API, command line, graphical)

QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.

QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (eprint) Nature Biotechnology 37 (2019)
q2-vsearch
QIIME 2 plugin for clustering and dereplicating with vsearch
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A QIIME 2 plugin that wraps the vsearch application, and provides methods for clustering and dereplicating features and sequences.

Please cite: Torbjørn Rognes, Tomáš Flouri, Ben Nichols, Christopher Quince and Frédéric Mahé: VSEARCH: a versatile open source tool for metagenomics. PeerJ 4 (2016)
qtlreaper
QTL analysis for expression data
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License: GPL-2+
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QTL Reaper is software, written in C and compiled as a Python module, for rapidly scanning microarray expression data for QTLs. It is essentially the batch-oriented version of WebQTL. It requires, as input, expression data from members of a set of recombinant inbred lines and genotype information for the same lines. It searches for an association between each expression trait and all genotypes and evaluates that association by a permutation test. For the permutation test, it performs only as many permutations as are necessary to define the empirical P-value to a reasonable precision. It also performs bootstrap resampling to estimate the confidence region for the location of a putative QTL.

The reaper module is used underneath the http://genenetwork.org site.

qualimap
evaluating next generation sequencing alignment data
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Qualimap 2 provides both a Graphical User Interface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

Supported types of experiments include:

  • Whole-genome sequencing
  • Whole-exome sequencing
  • RNA-seq (speical mode available)
  • ChIP-seq

Qualimap examines sequencing alignment data in SAM/BAM files according to the features of the mapped reads and provides an overall view of the data that helps to the detect biases in the sequencing and/or mapping of the data and eases decision-making for further analysis.

Qualimap provides multi-sample comparison of alignment and counts data.

  • Fast analysis accross the reference of genome coverage and nucleotide distribution;
  • Easy to interpret summary of the main properties of the alignment data;
  • Analysis of the reads mapped inside/outside of the regions provided in GFF format;
  • Computation and analysis of read counts obtained from intersectition of read alignments with genomic features;
  • Analysis of the adequasy of the sequencing depth in RNA-seq experiments;
  • Multi-sample comparison of alignment and counts data;
  • Clustering of epigenomic profiles.
Please cite: Fernando García-Alcalde, Konstantin Okonechnikov, José Carbonell, Luis M. Cruz, Stefan Götz, Sonia Tarazona, Joaquín Dopazo, Thomas F. Meyer and Ana Conesa: Qualimap: evaluating next-generation sequencing alignment data. (PubMed,eprint) Bioinformatics 28(20):2678-2679 (2012)
Registry entries: Bio.tools  SciCrunch  Bioconda 
quast
Quality Assessment Tool for Genome Assemblies
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License: GPL-2
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QUAST evaluates genome assemblies. For metagenomes, please see MetaQUAST project. It works both with and without a given reference genome. The tool accepts multiple assemblies, thus it allows for comparisons.

Please cite: Alla Mikheenko, Andrey Prjibelski, Vladislav Saveliev, Dmitry Antipov and Alexey Gurevich: Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34(13):i142-i150 (2018)
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r-bioc-mofa2
Multi-Omics Factor Analysis v2
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The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.

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r-bioc-org.mm.eg.db
genome wide annotation for Mouse
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Genome wide annotation for Mouse, primarily based on mapping using Entrez Gene identifiers.

r-cran-drinsight
drug repurposing on transcriptome sequencing data
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The package's name is an acronym for "Drug Repurposing Integration and Systematic Investigation of Genomic High Throughput Data", which pretty much describes it: This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.

Please cite: Jinyan Chan, Xuan Wang, Jacob A Turner, Nicole E Baldwin and Jinghua Gu: Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing. (PubMed,eprint) Bioinformatics 35(16):2818–2826 (2019)
r-other-apmswapp
GNU R Pre- and Postprocessing For Affinity Purification Mass Spectrometry
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The reliable detection of protein-protein-interactions by affinity purification mass spectrometry (AP-MS) is a crucial stepping stone for the understanding of biological processes. The main challenge in a typical AP-MS experiment is to separate true interaction proteins from contaminants by contrasting counts of proteins binding to specific baits with counts of negative controls.

Please cite: Martina Fischer, Susann Zilkenat, Roman G. Gerlach, Samuel Wagner and Bernhard Y. Renard: Pre- and post-processing workflow for affinity purification mass spectrometry data. (PubMed) Journal of Proteome Research 13(5):2239-49 (2014)
r-other-fastbaps
A fast genetic clustering algorithm that approximates a Dirichlet Process Mixture model
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Takes a multiple sequence alignment as input and clusters according to the 'no-admixture' model. It combines ideas from the Bayesian Hierarchical Clustering algorithm of Heller et al. and hierBAPS to produce a rapid and accurate clustering algorithm.

Please cite: Gerry Tonkin-Hill, John A Lees, Stephen D Bentley, Simon D W Frost and Jukka Corander: Fast hierarchical Bayesian analysis of population structure. (PubMed,eprint) Nucleic Acids Research 47(11):5539–5549 (2019)
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raxml-ng
phylogenetic tree inference tool which uses maximum-likelihood
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RAxML Next Generation: faster, easier-to-use and more flexible

RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion. Its search heuristic is based on iteratively performing a series of Subtree Pruning and Regrafting (SPR) moves, which allows to quickly navigate to the best-known ML tree. RAxML-NG is a successor of RAxML (Stamatakis 2014) and leverages the highly optimized likelihood computation implemented in libpll (Flouri et al. 2014).

RAxML-NG offers improvements in speed, flexibility and user-friendliness over the previous RAxML versions. It also implements some of the features previously available in ExaML (Kozlov et al. 2015), including checkpointing and efficient load balancing for partitioned alignments.

repeatmasker
screen DNA sequences for interspersed repeats
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RepeatMasker is a program that screens DNA sequences for interspersed repeats and low complexity DNA sequences. The output of the program is a detailed annotation of the repeats that are present in the query sequence as well as a modified version of the query sequence in which all the annotated repeats have been masked (default: replaced by Ns). Sequence comparisons in RepeatMasker are performed by the program cross_match, an efficient implementation of the Smith-Waterman-Gotoh algorithm developed by Phil Green, or by WU-Blast developed by Warren Gish.

Please cite: Sébastien Tempel: Using and Understanding RepeatMasker. (PubMed) Methods Mol. Biol. 859:29-51 (2012)
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roadtrips
case-control association testing with unknown population and pedigree structure
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ROADTRIPS performs single-SNP, case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses an empirical covariance matrix calculated from genomewide SNP data to correct for unknown population and pedigree structure, while maintaining high power by taking advantage of known pedigree information when it is available. The program is applicable to association studies with completely general combinations of related and unrelated individuals. Analysis can be performed genomewide (currently just for autosomes).

ROADTRIPS is suitable for applications such as:

  • correcting for possible population structure and/or misspecified relationships in the context of case-control association testing in samples of unrelated individuals and/or related individuals with well- characterized pedigrees
  • case-control association testing in samples from isolated populations for which pedigree information is limited or unavailable
Please cite: Timothy Thornton and Mary Sara McPeek: ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure. (PubMed) American Journal of Human Genetics 86(2):172–184 (2010)
rosa
Removal of Spurious Antisense in biological RNA sequences
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In stranded RNA-Seq experiments it is possible to detect and measure antisense transcription, important since antisense transcripts impact gene transcription in several different ways. Stranded RNA-Seq determines the strand from which an RNA fragment originates, and so can be used to identify where antisense transcription may be implicated in gene regulation.

However, spurious antisense reads are often present in experiments, and can manifest at levels greater than 1% of sense transcript levels. This is enough to disrupt analyses by causing false antisense counts to dominate the set of genes with high antisense transcription levels.

The RoSA (Removal of Spurious Antisense) tool detects the presence of high levels of spurious antisense transcripts, by:

  • analysing ERCC spike-in data to find the ratio of antisense:sense transcripts in the spike-ins; or
  • using antisense and sense counts around splice sites to provide a set of gene-specific estimates; or
  • both.

Once RoSA has an estimate of the spurious antisense, expressed as a ratio of antisense:sense counts, RoSA will calculate a correction to the antisense counts based on the ratio. Where a gene-specific estimate is available for a gene, it will be used in preference to the global estimate obtained from either spike-ins or spliced reads.

This package provides the library for the statistics suite R.

rsat
Regulatory Sequence Analysis Tools
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RSAT is a series of modular computer programs specifically designed for the detection of regulatory signals in non-coding sequences.

RSAT servers have been up and running since 1997. The project was initiated by Jacques van Helden, and is now pursued by the RSAT team.

Please cite: Alejandra Medina-Rivera, Matthieu Defrance, Olivier Sand, Carl Herrmann, Jaime A. Castro-Mondrago, Jeremy Delerce, Sébastien Jaeger, Christophe Blanchet, Pierre Vincens, Christophe Caron, Daniel M. Staines, Bruno Contreras-Moreira, Marie Artufel, Lucie Charbonnier-Khamvongsa, Céline Hernandez, Denis Thieffry, Morgane Thomas-Chollier and Jacques van Helden: RSAT 2015: Regulatory Sequence Analysis Tools. (PubMed,eprint) Nucleic Acids Res. 43(W1):W50-W56 (2015)
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sailfish
RNA-seq expression estimation
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RNA-seq is a technology to read at least parts of individual RNA sequences of a tissue sample. After assigning these reads to genes that are likely responsible to have coded for them (mapping), this gives an insight (estimate) about how much these genes have been active (expressed) in that sample. The trickier bits in that process to address is the similarity of genes and the genes being capable to variably but deterministically skip parts of their sequence to be read (introns). A single variantly spliced gene may then yield different sequences (isoforms) and the RNA-seq evaluation better informs about this. It may be relevant for a disease.

Sailfish is particularly good (efficient) in this process. It tricks the complexity by introducing an intermediate level of artificial very short reads to which the alternative splicing is of no concern. That can then be addressed by "telephone-book"-like hashing techniques that are easy and lightning fast. The final presentation is then found to be competitive with established mappers like eXpress and Cufflinks.

Please cite: Rob Patro, Stephen M Mount and Carl Kingsford: Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. (PubMed) Nature Biotechnology 32(5):462-464 (2014)
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sap
Pairwise protein structure alignment via double dynamic programming
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In contrast to DNA, proteins exhibit an apparently unlimited variety of structure. This is a necessary requirement of the vast array of differing functions that they perform in the maintainance of life, again, in contrast to the relatively static archival function of DNA. Not only do we observe a bewildering variety of form but even within a common structure, there is variation in the lengths and orientation substructures. Such variation is both a reflection on the very long time periods over which some structures have diverged and also a consequence of the fact that proteins cannot be completely rigid bodies but must have flexibility to accommodate the structural changes that are almost always necessary for them to perform their functions. These aspects make comparing structure and finding structural similarity over long divergence times very difficult. Indeed, computationally, the problem of recognizing similarity is one of three-dimensional pattern recognition, which is a notoriously difficult problem for computers to perform. In this chapter, guidance is provided on the use of a flexible structure comparison method that overcomes many of the problems of comparing protein structures that may exhibit only weak similarity.

Please cite: William R. Taylor: Protein Structure Comparison Using SAP. (PubMed) 143:19-32 (2000)
Remark of Debian Med team: Precondition for T-Coffee

see http://wiki.debian.org/DebianMed/TCoffee

seq-seq-pan
workflow for the SEQuential alignment of SEQuences
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Seq-seq-pan is a framework that provides methods for adding or removing new genomes from a set of aligned genomes and uses these to construct a whole genome alignment. Throughout the sequential workflow the alignment is optimized for generating a representative linear presentation of the aligned set of genomes, that enables its usage for annotation and in downstream analyses.

Please cite: Christine Jandrasits, Piotr W. Dabrowski, Stephan Fuchs and Bernhard Y. Renard: seq-seq-pan: building a computational pan-genome data structure on whole genome alignment. (PubMed,eprint) BMC Genomics 19(1):47 (2018)
Remark of Debian Med team: Needs blat which is not re-distributable
seqwish
alignment to variation graph inducer
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Seqwish implements a lossless conversion from pairwise alignments between sequences to a variation graph encoding the sequences and their alignments. As input we typically take all-versus-all alignments, but the exact structure of the alignment set may be defined in an application specific way. This algorithm uses a series of disk-backed sorts and passes over the alignment and sequence inputs to allow the graph to be constructed from very large inputs that are commonly encountered when working with large numbers of noisy input sequences. Memory usage during construction and traversal is limited by the use of sorted disk-backed arrays and succinct rank/select dictionaries to record a queryable version of the graph.

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signalalign
HMM-HDP models for MinION signal alignments
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MinION signal-level alignment and methylation detection using hidden Markov Models with hierarchical Dirichlet process kmer learning.

Nanopore sequencing is based on the principal of isolating a nanopore in a membrane separating buffered salt solutions, then applying a voltage across the membrane and monitoring the ionic current through the nanopore. The Oxford Nanopore Technologies (ONT) MinION sequences DNA by recording the ionic current as DNA strands are enzymatically guided through the nanopore. SignalAlign will align the ionic current from the MinION to a reference sequence using a trainable hidden Markov model (HMM). The emissions model for the HMM can either be the table of parametric normal distributions provided by ONT or a hierarchical Dirichlet process (HDP) mixture of normal distributions. The HDP models enable mapping of methylated bases to your reference sequence.

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sina
reference based multiple sequence alignment
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SINA is a tool to add sequences to an existing multiple sequence alignment. It needs about 1 second on a single core to add one 16S full length sequence (about 100k/h on a 32-core workstation). It was developed to create the multi-million sequence alignment that is the core of the SILVA SSU and LSU rRNA databases.

Please cite: Elmar Pruesse, Jörg Peplies and Frank Oliver Glöckner: SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. (PubMed,eprint) Bioinformatics 28(14):1823–1829 (2012)
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sistr
Salmonella In Silico Typing Resource (SISTR)
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The Salmonella In Silico Typing Resource (SISTR) commandline tool allows serovar predictions from whole-genome sequence assemblies by determination of antigen gene and cgMLST gene alleles using BLAST. Mash MinHash can also be used for serovar prediction.

Please cite: Catherine E. Yoshida, Peter Kruczkiewicz, Chad R. Laing, Erika J. Lingohr, Victor P. J. Gannon, John H. E. Nash and Eduardo N. Taboada: The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies. (PubMed,eprint) PLoS One 11(1):e0147101 (2016)
situs
Modeling of atomic resolution structures into low-resolution density maps
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Situs is an award-winning program package for the modeling of atomic resolution structures into low-resolution density maps e.g. from electron microscopy, tomography, or small angle X-ray scattering. The software supports both rigid-body and flexible docking using a variety of fitting strategies. Situs is developed by Willy Wriggers and collaborators: biomachina.org.

sourmash
tools for comparing DNA sequences with MinHash sketches
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Compute MinHash signatures for nucleotide (DNA/RNA) and protein sequences.

MinHash sketches provide a lightweight way to store “signatures” of large DNA or RNA sequence collections, and then compare or search them using a Jaccard index. MinHash sketches can be used to identify samples, find similar samples, identify data sets with shared sequences, and build phylogenetic trees (Ondov et al. 2015).

sourmash provides a command line script, a Python library, and a CPython module for MinHash sketches

Please cite: C. Titus Brown and Luiz Irber: sourmash: a library for MinHash sketching of DNA. (eprint) The Journal of Open Source Software 1(5):27 (2016)
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sparta
automated reference-based bacterial RNA-seq Transcriptome Analysis
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SPARTA is a workflow aimed at analyzing single-end Illumina RNA-seq data. The workflow combines several tools: Trimmomatic (read trimming/adapter removal), FastQC (read quality analysis), Bowtie (mapping reads to the reference genome), HTSeq (transcript/gene feature abundance counting), and edgeR (differential gene expression analysis). Within the differential gene expression analysis step, batch effects can be detected and the user is warned of the potential, unintended additional variable. The analysis procedure is outlined below.

Please cite: Benjamin K. Johnson, Matthew B. Scholz, Tracy K. Teal and Robert B. Abramovitch: SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis. (PubMed,eprint) BMC Bioinformatics 17:66 (2016)
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ssaha
Sequence Search and Alignment by Hashing Algorithm
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SSAHA is a software tool for very fast matching and alignment of DNA sequences. It achieves its fast search speed by converting sequence information into a `hash table' data structure, which can then be searched very rapidly for matches.

SSAHA is the only free software of its category (fast search of nearly indentical sequences). The popular alternative, BLAT, is restricted to non-commercial use.

Please cite: Zemin Ning, Anthony J. Cox and James C. Mullikin: SSAHA: A Fast Search Method for Large DNA Databases. (PubMed,eprint) Genome Research 11(10):1725–1729 (2001)
Remark of Debian Med team: Successor for ssaha2 available: smalt

The program smalt is from the same author is according to its author faster and more precise than ssaha2 (except for sequences > 2000bp)

strap
Comfortable and intuitive protein alignment editor / viewer
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Strap is started from the menu "Science" in the "Applications" menu or with the shell command strap_protein_alignment. Alignments can be manually edited or computed automatically using sequence and/or structure based methods. Information can be attached to proteins and residue selections such as free text notes, balloon messages, cross-references as well as 3D and PDF display styles. Alignments can be exported in several formats: Multiple-Fasta, ClustalW, MSF, HSSP, Jalview. Decorated alignments with residue annotations and secondary structure cartoons can be exported to PDF, HTML and Word-processors to create figures in publication quality - see http://3d-alignment.eu/ for details. Strap is an integrated environment for Bioinformatics tools and resources like DAS sequence features, 3D-visualization, structure prediction and Blast search. It is scriptable and extendable and can be used by other programs to display sequence alignments and 3D-superpositions.

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strap-base
essential files for the interactive alignment viewer and editor Strap
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Most users should install the package strap which in addition installs required Debian packages for alignment computation and 3D visualization. Strap-base provides utilities for protein and alignment file format conversion: strap_to_clustal, strap_to_msf, strap_to_fasta, strap_to_multiple_fasta. It also provides interactive alignment visualization and HTML export for other bioinformatics software. The command strap_base with the option

 -script=file or -script=named_pipe opens a new interactive alignment
view. Named pipes are the basis for interprocess communication. The

command strap_to_html is a command line tool to produce decorated and annotated alignments suitable for web-browsers with script commands as explained in http://www.bioinformatics.org/strap/alignment-to-html.html.

strelka
strelka2 germline and somatic small variant caller
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Strelka2 is a fast and accurate small variant caller optimized for analysis of germline variation in small cohorts and somatic variation in tumor/normal sample pairs. The germline caller employs an efficient tiered haplotype model to improve accuracy and provide read-backed phasing, adaptively selecting between assembly and a faster alignment- based haplotyping approach at each variant locus. The germline caller also analyzes input sequencing data using a mixture-model indel error estimation method to improve robustness to indel noise. The somatic calling model improves on the original Strelka method for liquid and late- stage tumor analysis by accounting for possible tumor cell contamination in the normal sample. A final empirical variant re-scoring step using random forest models trained on various call quality features has been added to both callers to further improve precision.

Please cite: Sangtae Kim, Konrad Scheffler, Aaron L. Halpern, Mitchell A. Bekritsky, Eunho Noh, Morten Källberg, Xiaoyu Chen, Yeonbin Kim, Doruk Beyter, Peter Krusche and Christopher T. Saunders: Strelka2: fast and accurate calling of germline and somatic variants. (PubMed) Nature Methods 15(8):591–594 (2018)
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tab2mage
submitting large microarray experiment datasets to public repository database
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Tab2MAGE is a software package written and supported by the ArrayExpress curation team, which aims to ease the process of submitting large microarray experiment datasets to our public repository database. To this end, Tab2MAGE currently includes two tools, the tab2mage.pl script itself, and a data file checking script, expt_check.pl. With these scripts it is possible to perform an initial data file validation against an array design (e.g., in the form of an "Array Description File" or ADF), and then to generate MAGE-ML using these data files alongside a separate spreadsheet providing MIAME-compliant sample annotation.

tacg
command line program for finding patterns in nucleic acids
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tacg is a character-based, command line tool for unix-like operating systems for pattern-matching in nucleic acids and performing some of the basic protein manipulations. It was originally designed for restriction enzyme analysis of DNA, but has been extended to other types of matching. It now handles degenerate sequence input in a variety of matching approaches, as well as patterns with errors, regular expressions and TRANSFAC-formatted matrices.

It was designed to be a grep for DNA and like the original grep, its capabilities have grown so that now the author has to keep calling up the help page to figure out which flags (now ~50) mean what. tacg is NOT a GUI application in any sense. However, it's existance as a strictly command-line tool lends itself well to Webification and wrapping by various GUI tools and it is now distributed with a web interface form and a Perl CGI handler. Additionally, it can easily be integrated into editors that support shell commands such as nedit.

Please cite: Harry J Mangalam: tacg - a grep for DNA. (PubMed,eprint) BMC Bioinformatics 3:8 (2002)
tandem-genotypes
compare lengths of duplications in DNA sequences
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Only a fraction of the DNA in the human genome (and that of other species that are not optimised for the speed of their replication like viruses) are coding for genes. Other parts may be binding to transcription factors or direct the expression of genes in other ways - these other bits may not be fully understood, and parts may indeed be "junk", but this is only until user finds a clinical feature to be statistically associated with something statistically noteworthy in the DNA sequences.

Features that somehow seem informative when looking at the DNA are whatever does not look like random. This tool looks at a special case of repeats - short regions that are duplicated, i.e. they appear as a tandem. When these repeats are longer, then these would be referred as microsatellites.

When interested in structural variations, within a family/population or to compare cancer tissue with benign samples, you may also want to look at these repeats. This software determines the lengths (and changes to the lengths) across samples and knows how to present this graphically.

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tide
SEQUEST Searching for Peptide Identification from Tandem Mass Spectra
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Tide implements the SEQUEST algorithm for peptide identification and that achieves a dramatic speedup over Crux and SEQUEST. The optimization strategies detailed here employ a combination of algorithmic and software engineering techniques to achieve speeds up to 170 times faster than a recent version of SEQUEST that uses indexing. For example, on a single Xeon CPU, Tide searches 10,000 spectra against a tryptic database of 27,499 C.\ elegans proteins at a rate of 1,550 spectra per second, which compares favorably with a rate of 8.8 spectra per second for a recent version of SEQUEST with index running on the same hardware.

Please cite: Benjamin J. Diament and William Stafford Noble: Faster SEQUEST Searching for Peptide Identification from Tandem Mass Spectra. (PubMed) Journal of Proteome Research 10(9):3871–3879 (2011)
tigr-glimmer-mg
finding genes in environmental shotgun DNA sequences
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Glimmer-MG is a system for finding genes in environmental shotgun DNA sequences. Glimmer-MG (Gene Locator and Interpolated Markov ModelER - MetaGenomics) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA.

Please cite: David R. Kelley, Bo Liu, Arthur L. Delcher, Mihai Pop and Steven L. Salzberg: Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering. (PubMed) Nucleic Acids Research 40(1):e9 (2012)
tn-seqexplorer
explore and analyze Tn-seq data for prokaryotic genomes
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Tn-seq Explorer allows users to explore and analyze Tn-seq data for prokaryotic (bacterial or archaeal) genomes. It implements two alternative methods for identification of essential genes and provides additional tools to investigate the Tn-seq data. The primary goal of the data analysis is to study fitness by identifying genes that are essentia

Please cite: Sina Solaimanpour, Felipe Sarmiento and Jan Mrázek: Tn-Seq Explorer: A Tool for Analysis of High-Throughput Sequencing Data of Transposon Mutant Libraries. (PubMed,eprint) PLoS ONE 10(5):e0126070 (2015)
ufasta
utility to manipulate fasta files
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Description of ufasta subcommands:

  • one: remove the new lines in the data section. Hence, all the sequences are written on one line. In some sense, it is the opposite of the format subcommand.
  • format: reformat the data sections. The data is written in lines of the same length, it can changes the content in upper/lower case.
  • sizes: print the amount of sequence in each section
  • head: like UNIX head. Display the first 10 sequences
  • tail: like UNIX tail. Display the last 10 sequences
  • rc: reverse complement every sequence
  • n50, stats: display stats about the sequences: N50, E size, total size, etc.
  • extract: extract a sequence whose header match given names
  • hsort, sort: sort file based on header content
  • dsort: sort the data sections
  • hgreap: output sequences whose header match the regular expression
  • dgresp: output sequences whose sequence match the regular expression
  • split: split a fasta file into many files
umap
quantify genome and methylome mappability
Versions of package umap
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VCS1.0.0-1all
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License: GPL-3.0
Debian package not available
Git
Version: 1.0.0-1

Umap identifies uniquely mappable regions of any genome. Its Bismap extension identifies mappability of the bisulfite converted genome (methylome).

Please cite: Mehran Karimzadeh, Carl Ernst, Anshul Kundaje and Michael M. Hoffman: Umap and Bismap: quantifying genome and methylome mappability. (PubMed,eprint) Nucleic Acids Res. 46(20):e120 (2018)
Registry entries: Bioconda 
unc-fish
Fast Identification of Segmental Homology
Versions of package unc-fish
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VCS1.0+dfsg-1all
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License: LicenseAgreementFISH
Debian package not available
Git
Version: 1.0+dfsg-1

FISH is software for identifying regions of common ancestry between genome maps. Fast identification and statistical evaluation of segmental homologies in comparative maps.

Development and maintenance of FISH is supported by funding from the National Science Foundation (Plant Genome Research Program Grants DBI-0110069 and DBI-0227314 to TJV and DMS-0102008 to PPC).

Please cite: Peter P. Calabrese, Sugata Chakravarty and Todd J. Vision: Fast identification and statistical evaluation of segmental homologies in comparative maps. (PubMed,eprint) Bioinformatics 19(Suppl 1):i74-i80 (2003)
varmatch
robust matching of small genomic variant datasets
Versions of package varmatch
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VCS0+git20180126.5c6fed5+dfsg-1all
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License: GPL-3.0+
Debian package not available
Git
Version: 0+git20180126.5c6fed5+dfsg-1

Small variant calling is an important component of many analyses, and, in many instances, it is important to determine the set of variants which appear in multiple callsets. Variant matching is complicated by variants that have multiple equivalent representations. Normalization and decomposition algorithms have been proposed, but are not robust to different representation of complex variants. The VarMatch algorithm is robust to different representation of complex variants and is particularly effective in low complexity regions or those dense in variants. VarMatch also provides summary statistics, annotations, and visualizations that are useful for understanding callers' performance.

Please cite: Chen Sun and Paul Medvedev: VarMatch: robust matching of small variant datasets using flexible scoring schemes. (PubMed) Bioinformatics 33(9):1301-1308 (2017)
vmd
presentation of traces of molecular dynamics runs
Versions of package vmd
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VCS1.9.1-3all
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License: University_of_Illinois_non-free
Debian package not available
Git
Version: 1.9.1-3

VMD stands for Visual Molecular Dynamics. While text books and even structure databases because of technical problems only present static pictures of proteins or DNA, for the understanding of the properties of those molecules their vibration or their movement in general is important.

The movements itself are calculated by molecular dynamics programs, such as NAMD (by the same group), Rosetta, BALLView or GROMACS. The latter two are already in the distribution, we have package build instructions for Rosetta.

VMD has a series of nice features, from displaying through animation to analysing. It can be scripted, clustered, and runs on all common OS. Its license does not allow to redistribute a Debian package. But to share these build instructions for such a package is just fine.

Please cite: W. Humphrey, A. Dalke and K. Schulten: VMD: visual molecular dynamics. (PubMed,eprint) Journal of Molecular Graphics 14(1):33-38 (1996)
zodiac-zeden
ZODIAC - Zeden's Organise DIsplay And Compute
Versions of package zodiac-zeden
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VCS0.6.5-1all
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License: GPL-3.0+
Debian package not available
Git
Version: 0.6.5-1

Zodiac is a molecular modelling suite for computation, analysis and display of molecular data. It features state-of-the-art tools for managing molecular databases, run molecular docking experiments, compute raytraced images and much more.

Unofficial packages built by somebody else

big-blast
Helper tool to run blast on large sequences
License: not specified

This script will chop up a large sequence, run blast on each bit and then write out an EMBL feature table and a MSPcrunch -d file containing the hits.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
estferret
processes, clusters and annotates EST data
License: to be clarified

ESTFerret processes, clusters and annotates EST data. It is user-configurable. Results are currently stored in a series of text tables. Annotation consists of searches against use r-defined blast databases, prosite, GO and allocation of EC numbers where possible.

EST-ferret is a user-configurable, automated pipeline for the convenient analysis of EST sequence data that includes all of the necessary steps for cleanup and trimming, submission to external sequence repositories, clustering, identification by BLAST homology searches and by searches of protein domain databases, annotation with computer-addressable terms and production of outputs for direct entry into microarray analysis packages. It is composed of several widely used, open-source algorithms, including PHRED, CAP3, BLAST, and a range of sequence and annotation databases, including Gene Ontology and Conserved Domain Database to deliver a putative identity and a detailed annotation of each clone. It can be run either step-by-step to track the outputs, or as a single batch process. Users can easily edit the configuration file to define parameter settings.

This package has five major components: (1) ESTs coding system; (2) sequence processing; (3) sequence clustering; (4) sequence annotating and (5) storage and reporting of results. DNA trace files are renamed and converted into FASTA format, cleaned and submitted to dbEST(Boguski, et al, 1993). Sequence assembly uses two rounds of CAP3 to assemble the ESTs into groups corresponding to separate gene families and unique genes. Sequence identification and annotation is provided by a series of BLAST homology searches (Parallel_BLAST and Priority_BLAST) against user-defined sequence databases implemented with the NCBI BLASTALL algorithm. The BLAST results are parsed and annotation terms that reflect functional attributes are captured from Gene Ontology (The Gene Ontology Consortium, 2000), KEGG and Enzyme Commission (EC) databases and applied to each of the clones. CDD (and InterPro) searches are performed for seeking protein domains in the sequences. Other options are provided to run PatSearch, RepeatMasker and BLAT to find UTRs, repeats and EST candidates in genomes. Finally, the package generates analysis reports in a variety of flat file formats, sources of which can be serve as inputs for some gene annotation and gene expression profiling tools, and also as a MySQL database or web-browsable search tool.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
maxd
data warehouse and visualisation environment for genomic expression data
License: Artistic

Maxd is a data warehouse and visualisation environment for genomic expression data. It is being developed in the University of Manchester by the Microarray Bioinformatics Group.

Software components:

 maxdLoad2 - standards-compliant, highly customisable transcriptomics
             database
 maxdView  - modular and easily extensible data visualisation and
             analysis environment
 maxdSetup - installation management utility
Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
migrate
estimation of population sizes and gene flow using the coalescent
License: to be clarified

Migrate estimates effective population sizes and past migration rates between n population assuming a migration matrix model with asymmetric migration rates and different subpopulation sizes. Migrate uses maximum likelihood or Bayesian inference to jointly estimate all parameters. It can use the followind data types: sequence data using Felsenstein's 84 model with or without site rate variation, single nucleotide polymorphism data, microsatellite data using a stepwise mutation model or a brownian motion mutation model, and electrophoretic data using an 'infinite' allele model. The output can contain: Estimates of all migration rates and all population sizes, assuming constant mutation rates among loci or a gamma distributed mutation rate among loci. Profile likelihood tables, Percentiles, Likelihood-ratio tests, and simple plots of the log-likelihood surfaces for all populations and all loci.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
msatfinder
identification and characterization of microsatellites in a comparative genomic context
License: GPL

Msatfinder is a Perl script designed to allow the identification and characterization of microsatellites in a comparative genomic context. There is also an online manual, a discussion forum and an online interface where users can do searches in any number of DNA or protein sequences (as long as the maximum size of all sequences does not exceed 10MB). Nucleotide and amino acid sequences in GenBank, FASTA, EMBL and Swissprot formats are supported.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
oligoarrayaux
Prediction of Melting Profiles for Nucleic Acids
License: non-free (fre academical use)

OligoArrayAux is a subset of the UNAFold package for use with OligoArray (http://berry.engin.umich.edu/oligoarray2_1/). OligoArray is a free software that computes gene specific oligonucleotides for genome-scale oligonucleotide microarray construction. (It is not really specified what they mean with "free software". You can download the source code after registration: "registration is the only way for me to keep trace of OligoArray users and be able to send you a bug fix or a new release".)

The original UNAFold server is available at http://dinamelt.bioinfo.rpi.edu/download.php and you should probably read http://dinamelt.bioinfo.rpi.edu/ if you want to know more about "Prediction of Melting Profiles for Nucleic Acids".

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html

Finally it is hard to find some documentation what OligoArrayAux is really doing because it is only specified into relation to OligoArray (as precondition) and UNAFold (as subset of this) but BioLinux distribution http://envgen.nox.ac.uk/biolinux.html decided to package this and so it might make soem sense to list it here - further investigation is needed.

partigene
generating partial gemomes
License: GPL

PartiGene is part of the Edinburgh-EGTDC developed EST-software pipeline at the moment consisting of trace2dbEST, PartiGene, wwwPartiGene, port4EST and annot8r. PartiGene is a menu-driven, multi-step software tool which takes sequences (usually ESTs) and creates a dataabase of a non-redundant set of sequence objects (putative genes) which we term a partial genome.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
pfaat
Protein Family Alignment Annotation Tool
Responsible: BioLinux - Dan Swan
License: GPL

Pfaat is a Java application that allows one to edit, analyze, and annotate multiple sequence alignments. The annotation features are a key component as they provide a framework to for further sequence, structure and statistical analysis.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
prot4est
EST protein translation suite
License: GPL

prot4EST is a perl script that takes expressed sequence tags (ESTs) and translates them optimally to produce putative peptides. prot4EST intergrates a number of programs to overcome problems inherent with translating ESTs.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
python3-orange
Data mining framework
Responsible: Mitar
License: GPLv3

Orange is a component-based data mining software. It includes a range of data visualization, exploration, preprocessing and modeling techniques. It can be used through a nice and intuitive user interface or, for more advanced users, as a module for Python programming language.

qtlcart
map quantitative traits using a map of molecular markers
Responsible: BioLinux - Dan Swan
License: GPL

QTL Cartographer is a suite of programs to map quantitative traits using a map of molecular markers. It contains a set of programs that will aid in locating the genes that control quantitative traits using a molecular map of markers. It includes some programs to allow simulation studies of experiments.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
rbs-finder
find ribosome binding sites(RBS)
License: not specified

The program implements an algorithm to find ribosome binding sites(RBS) in the upstream regions of the genes annotated by Glimmer2, GeneMark, or other prokaryotic gene finders. If there is no RBS-like patterns in this region, program searches for a start codon having a RBS-like pattern ,in the same reading frame upstream or downstream and relocates start codon accordingly.

You can find more detailed information at http://nbc11.biologie.uni-kl.de/docbook/doc_userguide_bioinformatics_server/chunk/ch01s06.html

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
roche454ace2caf
convert GS20 or FLX assemblies into CAF format
License: not specified

Some tools to convert GS20 or FLX assemblies (454Contigs.ace) into CAF format so that these are correct viewable/editable/... whithin the staden package (gap4). You have then access to "hidden data", exact aligned trace and there positions, base values etc and whith staden-1-7-0 you have graphical access to the associated flowgramm traces (SFF format).

Description, Goals - please take a look at http://genome.imb-jena.de/software/roche454ace2caf/Poster_UserMeeting_GS20_Munich_070328.pdf

Remark of Debian Med team: The BioLinux distribution http://envgen.nox.ac.uk/biolinux.html

maintains a package called bio-linux-assembly-conversion-tools which contains caftools and roche2gap in one package with the following description:

Conversion tools for handling 454 assemblies.

This package contains code from different authors that allow sequence assemblies to be converted into formats such as CAF (Common Assembly Format) or GAP4. This package includes tools to convert assemblies from Newbler's ace format for loading into a gap4 assembly.

splitstree
Analyzing and Visualizing Evolutionary Data
License: to be clarified

Evolutionary data is most often presented as a phylogentic tree, the underlying assumption being that evolution is a branching process. However, real data is never ideal and thus doesn't always support a unique tree, but often supports more than one possible tree. Hence, it makes sense to consider tree reconstruction methods that produce a tree, if the given data heavily favors one tree over all others, but otherwise produces a more general graph that indicates different possible phylogenies. One such method is the Split Decomposition introduced by Hans-Juergen Bandelt and Andreas Dress (1992) and its variations. Another example is Spectral Analysis developed by Hendy, Penny and others.

These and other methods are implemented in the program SplitsTree, that I wrote with contributions from Dave Bryant, Mike Hendy, Holger Paschke, Dave Penny and Udo Toenges. It is based on the Nexus format.

Note: There is a new version 4.0 written from scratch at http://www.splitstree.org/ which requires a license key - so this is probably non-free. Version 3.2 which is linked above has some downloadable source code without any license or copyright statement - so it has to be clarified whether we are able to distribute this code or not.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
taverna
designing and executing myGrid workflows for bioinformatics
License: LGPL

The Taverna workbench is a free software tool for designing and executing workflows, created by the myGrid project, and funded through OMII-UK. Taverna allows users to integrate many different software tools, including web services, such as those provided by the National Center for Biotechnology Information, The European Bioinformatics Institute, the DNA Databank of Japan (DDBJ), SoapLab, BioMOBY and EMBOSS.

The Taverna Workbench provides a desktop authoring environment and enactment engine for scientific workflows expressed in Scufl (Simple Conceptual Unified Flow language). The Taverna enactment engine is also available separately, and other Scufl enactors are available including Moteur. The myExperiment social web site supports finding and sharing of workflows and has special support for Scufl workflows. The Taverna workbench, myExperiment and associated components are developed and maintained by the myGrid team, in collaboration with the open source community.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
taxinspector
browser for entries in the NCBI taxonomy
Responsible: BioLinux - Tim Booth
License: Artistic + other free licenses

TaxInspector is a browser for entries in the NCBI taxonomy. It is designed to run as a plugin to annotation software such as maxdLoad2 and Pedro, but also has a standalone mode.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html
tetra
tetranucleotide frequency calculator
License: free academic

The TETRA program can be used to calculate how well tetranucleotide usage patterns in DNA sequences correlate. Such correlations can provide valuable hints on the relatedne ss of DNA sequences, and are particularly useful for metagenomic sequences.

Remark of Debian Med team: This package ships with BioLinux http://envgen.nox.ac.uk/biolinux.html

No known packages available but some record of interest (WNPP bug)

btk-core - wnpp
biomolecule Toolkit C++ library
Responsible: Morten Kjeldgaard
License: GPL
Debian package not available

The Biomolecule Toolkit is a library for modeling biological macromolecules such as proteins, DNA and RNA. It provides a C++ interface for common tasks in structural biology to facilitate the development of molecular modeling, design and analysis tools.

mirbase - wnpp
The microRNA sequence database
Responsible: Charles Plessy
License: Public Domain
Debian package not available

The miRBase Sequence Database provides a searchable repository for published microRNA sequences and associated annotation, functionality previously provided by the microRNA Registry. miRBase also contains predicted miRNA target genes in miRBase Targets, and provides a gene naming and nomenclature function in the miRBase Registry.

Release 9.1 of the database contains 4449 entries representing hairpin precursor miRNAs, expressing 4274 mature miRNA products, in primates, rodents, birds, fish, worms, flies, plants and viruses.

This package will install the miRBase database for mySQL, EMBOSS, and/or ncbi-blast if you have the corresponding packages installed.

It is possible that mirbase will not be a package from the main archive, but will be autogenerated as part of a larger data packaging effort.

phylowin - wnpp
Graphical interface for molecular phylogenetic inference
License: unknown
Debian package not available

Phylo_win is a graphical colour interface for molecular phylogenetic inference. It performs neighbor-joining, parsimony and maximum likelihood methods and bootstrap with any of them. Many distances can be used including Jukes & Cantor, Kimura, Tajima & Nei, HKY, Galtier & Gouy (1995), LogDet for nucleotidic sequences, Poisson correction for protein sequences, Ka and Ks for codon sequences. Species and sites to include in the analysis are selected by mouse. Reconstructed trees can be drawn, edited, printed, stored and evaluated according to numerous criteria.

This program uses sources files from the Phylip program, which forbids its use for profit. Therfore, Phylo_win will unfortunately have to be distributed in contrib or non-free.

Remark of Debian Med team: Issuer of previous ITP said:

Because I could never figure out the license of Phylo_win, and because the upstream authors released SeaView 4, which provides similar functionalities, I will not package Phylo_win.

Probably it makes sense to remove this project from the prospective packages list.

No known packages available

amoscmp
comparative genome assembly package
License: Artistic
Debian package not available

A comparative assembler is a program that can assemble a set of shotgun reads from an organism by mapping them to the finished sequence of a related organism. Thus, a comparative assembler transforms the traditional overlap-layout-consensus approach to alignment-layout-consensus. The AMOScmp package uses the MUMmer program to perform a mapping of the reads to the reference genome, then processes the alignment results with a sophisticated layout program designed to take into account polymorphisms between the two genomes. For a detailed description of the algorithms involved please refer to the paper listed in the References section.

AMOScmp uses as AMOS messages as both the inputs and the outputs (see documentation). Two utilities are provided to process these files: tarchive2amos - a versatile converter from trace archive .seq, .qual, and .xml information into AMOS formatted data; amos2ace - a converter from AMOS formatted data to the .ACE assembly format. In addition, the AMOS::AmosLib Perl module is provided as a tool for users who prefer to write their own conversion utilities. Please see the documentation included with the distribution for more information.

AMOScmp is part of the AMOS package (see http://amos.sourceforge.net/)- a collaborative effort to develop a modular open-source framework for assembly development.

Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
annovar
annotate genetic variants detected from diverse genomes
License: Open Source for non-profit
Debian package not available

ANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform:

 1. Gene-based annotation: identify whether SNPs or CNVs cause protein coding
    changes and the amino acids that are affected. Users can flexibly use RefSeq
    genes, UCSC genes, ENSEMBL genes, GENCODE genes, or many other gene definition
    systems.
 2. Region-based annotations: identify variants in specific genomic regions,
    for example, conserved regions among 44 species, predicted transcription
    factor binding sites, segmental duplication regions, GWAS hits, database
    of genomic variants, DNAse I hypersensitivity sites, ENCODE
    H3K4Me1/H3K4Me3/H3K27Ac/CTCF sites, ChIP-Seq peaks, RNA-Seq peaks, or many
    other annotations on genomic intervals.
 3. Filter-based annotation: identify variants that are reported in dbSNP,
    or identify the subset of common SNPs (MAF>1%) in the 1000 Genome Project,
    or identify subset of non-synonymous SNPs with SIFT score>0.05, or many
    other annotations on specific mutations.
 4. Other functionalities: Retrieve the nucleotide sequence in any
    user-specific genomic positions in batch, identify a candidate gene list
    for Mendelian diseases from exome data, identify a list of SNPs from
    1000 Genomes that are in strong LD with a GWAS hit, and many other
    creative utilities.

In a modern desktop computer (3GHz Intel Xeon CPU, 8Gb memory), for 4.7 million variants, ANNOVAR requires ~4 minutes to perform gene-based functional annotation, or ~15 minutes to perform stepwise "variants reduction" procedure, making it practical to handle hundreds of human genomes in a day.

arachne
toolkit for Whole Genome Shotgun Assembly
License: free
Debian package not available

Arachne is a toolkit developed for Whole Genome Shotgun Assembly. Arachne consists of a comprehensive set of modules, including a central pipeline (Assemblez) that can be run on almost any genome to produce a draft assembly. Arachne's mandate explicitly includes accommodating difficult genomes with complications such as extreme size, repeats, and high polymorphism rates. In order to construct a reasonably well-connected assembly from such tricky genomes, Arachne provides further tools that can be used after the main module pipeline.

The Arachne code package has been under continuous development since 2000. It began with the classic "overlap-layout-consensus" paradigm and has since developed into a vast collection of tools, implemented in numerous modules, to analyze, visualize and manipulate assemblies. New and improved algorithms are becoming available on a regular basis.

asap
organize the data associated with a genome
Responsible: Andreas Tille
License: GPL
Debian package not available

Developments in genome-wide approaches to biological research have yielded greatly increased quantities of data, necessitating the cooperation of communities of scientists focusing on shared sets of data. ASAP leverages the internet and database technologies to meet these needs. ASAP is designed to organize the data associated with a genome from the early stages of sequence annotation through genetic and biochemical characterization, providing a vehicle for ongoing updates of the annotation and a repository for genome-scale experimental data. Development was motivated by the need to more directly involve a greater community of researchers, with their collective expertise, in keeping the genome annotation current and to provide a synergistic link between up-to-date annotation and functional genomic data. The system is continually under development at the Genome Evolution Lab with the stable, in-use, publicly available University of Wisconsin installation updated regularly.

Software development on ASAP began in early 2002, and ASAP has been continually improved up until the present day. A longstanding goal of the ASAP project was to make the source code of ASAP available so that other installations of ASAP could be implemented. As future ASAP installations come to pass, ASAP will be further extended to be inter-operable between sites.

bambus
hierarchical approach to building contig scaffolds
License: Artistic
Debian package not available

BAMBUS is the first publicly available scaffolding program. It orders and orients contigs into scaffolds based on various types of linking information. Additionally, BAMBUS allows the users to build scaffolds in a hierarchical fashion by prioritizing the order in which links are used. For more information please check out the online documentation.

Note that currently Bambus is undergoing a transition in order to be integrated with the AMOS package (see http://amos.sourceforge.net/)

Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
cactus
License: GPL
Debian package not available

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure easily enables parallel computation across different architectures and collaborative code development between different groups.

Cactus provides easy access to many cutting edge software technologies being developed in the academic research community, including the Globus Metacomputing Toolkit, HDF5 parallel file I/O, the PETSc scientific library, adaptive mesh refinement, web interfaces, and advanced visualization tools.

cdna-db
quality-control checking of finished cDNA clone sequences
License: Artistic
Debian package not available

cdna_db is a software system designed for quality-control checking of finished cDNA clone sequences, and their computational analysis. The combination of a relational db (MySQL) schema, and an object-orientated perl API make it easy to implement high-level analyses of these transcript sequences.

The cdna_db can store cDNA clone sequences, and ESTs and consensus/contig sequences also derived from these clones. These are then used by the system to check cDNA clone sequence identity etc (see deneral_doc.txt). For each clone multiple DNA sequence versions can be stored, if for instance, the finished DNA sequence is revised as part of the sequencing process.

A blast pipeline is implemented together with a job control system (with LSF underlying) so that multiple CPUs can be used in parallel to carry out the blasts of large datasets. The searches can be made incremental, so as more cDNA sequences are added to the databank, just the new clones are blasted.

Utility scripts are provided to delete previous search results, and dump cDNA clones sequences (such as those that passed the QC checking) from the cdna_db.

cmap
view comparisons of genetic and physical maps
License: Not specified
Debian package not available

CMap is a web-based tool that allows users to view comparisons of genetic and physical maps. The package also includes tools for curating map data.

contralign
parameter learning framework for protein pairwise sequence alignment
License: Public Domain
Debian package not available

CONTRAlign is an extensible and fully automatic parameter learning framework for protein pairwise sequence alignment based on pair conditional random fields. The CONTRAlign framework enables the development of feature-rich alignment models which generalize well to previously unseen sequences and avoid overfitting by controlling model complexity through regularization.

copycat
fast access to cophylogenetic analyses
License: Use of the program is free for academic purposes at an academic institute. For all other uses, please contact the authors.
Debian package not available

CopyCat provides an easy and fast access to cophylogenetic analyses. It incorporates a wrapper for the program ParaFit, which conducts a statistical test for the presence of congruence between host and parasite phylogenies. CopyCat offers various features, such as the creation of customized host-parasite association data and the computation of phylogenetic host/parasite trees based on the NCBI taxonomy.

e-hive
distributed processing system based on 'autonomous agents'
License: Not specified
Debian package not available

This is a distributed processing system based on 'autonomous agents' and Hive behavioural structure of Honey Bees . It implements all functionality of both data-flow graphs and block-branch diagrams which should allow it to codify any program, algorithm, or parallel processing job control system. It is not bound to any processing 'farm' system and can be adapted to any GRID.

exalt
phylogenetic generalized hidden Markov model for predicting alternatively spliced exons
License: Artistic
Debian package not available

ExAlt is a software program designed to predict alternatively spliced overlapping exons in genomic sequence. The program works in several ways depending on the available input. ExAlt can use information of existing gene structure as well as sequence conservation to improve the precision of it's predictions. ExAlt can also make predictions when only a single genomic sequence is available. ExAlt has been extensively tested on Drosophila melanogaster, but can be adapted to run on other species.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
excavator
gene expression data clustering
License: GPL
Debian package not available
Language: Java

Excavator is a program for gene expression data clustering. It uses a set of unique clustering algorithms developed by the Computational Systems Biology Lab (CSBL) at the University of Georgia. Excavator represents data internally as a minimum spanning tree and outputs results to the user through the use of a micro-array data window, graphs, and a dendrogram viewer.

Features

  • partitioning gene expressions profiles using multiple methods of clustering and definitions of distance between profiles.
  • automatic selection of the most plausible number of clusters in a data set
  • three different ways of viewing data: Micro-array, Gene Expression, and Dendrogram. As well as graphing individual genes from each cluster independently.
  • identification of genes with expression profiles similar to specified seed genes
  • cluster identification from a noisy background
  • numerical comparison between different clustering results of the same data set
  • runnable on command line as well as through a Java GUI
figaro
novel vector trimming software
License: Artistic
Debian package not available

Figaro is a software tool for identifying and removing the vector from raw DNA sequence data without prior knowledge of the vector sequence. By statistically modeling short oligonucleotide frequencies within a set of reads, Figaro is able to determine which DNA words are most likely associated with vector sequence. For a description of Figaro's algorithms please see our paper. Figaro is part of the AMOS suite.

Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
forge
genome assembler for mixed read types
License: Apache 2.0
Debian package not available

Forge Genome Assembler is a parallel, MPI based genome assembler for mixed read types.

Forge is a classic "Overlap layout consensus" genome assembler written by Darren Platt and Dirk Evers. Implemented in C++ and using the parallel MPI library, it runs on one or more machines in a network and can scale to very large numbers of reads provided there is enough collective memory on the machines used. It generates a full consensus alignment of all reads, can handle mixtures of sanger, 454 and illumina reads. There is some support for solid color space and it includes built in tools for vector trimming and contamination screening.

Forge and was originally developed at Exelixis and they have kindly agreed to place the software which underwent much subsequent development outside Exelixis, into the public domain. Forge works with most of the common MPI implementations.

Remark of Debian Med team: Competitor to MIRA2 and wgs-assembler

This package was requested by William Spooner whs@eaglegenomics.com as a competitor to MIRA2 and wgs-assembler.

gbrowse-syn
Generic Synteny Browser
License: Not specified
Debian package not available

GBrowse_syn, or the Generic Synteny Browser, is a GBrowse-based synteny browser designed to display multiple genomes, with a central reference species compared to two or more additional species. It can be used to view multiple sequence alignment data, synteny or co-linearity data from other sources against genome annotations provided by GBrowse. GBrowse_syn is included with the standard GBrowse package (version 1.69 and later). Working examples can be seen at TAIR and WormBase.

genemark
family of gene prediction programs
License: Academic License Agreement
Debian package not available

A family of gene prediction programs developed at Georgia Institute of Technology, Atlanta, Georgia, USA.

genesplicer
computational method for splice site prediction
License: Artistic
Debian package not available

A fast, flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been trained and tested successfully on Plasmodium falciparum (malaria), Arabidopsis thaliana, human, Drosophila, and rice . Training data sets for human and Arabidopsis thaliana are included. Use the GeneSplicer Web Interface to run GeneSplicer directly, or see below for instructions on downloading the complete system including source code.

There is no independent program to train GeneSplicer, but there is a way to obtain the necessary files by using the training procedure of GlimmerHMM.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
genetrack
genomic data storage and visualization framework
Responsible: Charles Plessy
License: MIT
Debian package not available

GeneTrack is a high performance bioinformatics data storage and analysis system designed to store genome wide information. It is currently used to analyze data obtained via high-throughput rapid sequencing platforms such as the 454 and Solexa as well as tiling array data based on various platforms.

genezilla
eukaryotic gene finder
License: Artistic
Debian package not available
Language: C++

GeneZilla is a state-of-the-art program for computational prediction of protein-coding genes in eukaryotic DNA, and is based on the Generalized Hidden Markov Model (GHMM) framework, similar to GENSCAN and GENIE. It is highly reconfigurable and includes software for retraining by the end-user. It is written in highly optimized C++ and runs under most UNIX/Linux platforms. The run time and memory requirements are linear in the sequence length, and are in general much better than those of competing systems, due to GeneZilla's novel decoding algorithm. Graph-theoretic representations of the high scoring open reading frames are provided, allowing for exploration of sub-optimal gene models. It utilizes Interpolated Markov Models (IMMs), Maximal Dependence Decomposition (MDD), and includes states for signal peptides, branch points, TATA boxes, CAP sites, and will soon model CpG islands as well.

GeneZilla is an open-source project hosted at bioinformatics.org and currently consists of ~20,000 lines of code. GeneZilla evolved out of the ab initio eukaryotic gene finder TIGRscan, which was developed at The Institute for Genomic Research over a 3-year period under NIH grants R01-LM06845 and R01-LM007938, and which served as the basis for the comparative gene finder TWAIN.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
genographer
read data and reconstruct them into a gel image
License: GPL
Debian package not available

This program will read in data from an ABI 3700, 3100, 377 or 373, CEQ 2000 or SCF and reconstruct them into a gel image which is straightened and sized. Bins can be defined easily and viewed as thumbnails, which allows for a fairly quick and easy way of scoring a gel.

The program is written in Java and uses the Java 1.3 API. Therefore, it should run on any machine that can run java.

glimmerhmm
Eukaryotic Gene-Finding System
License: Artistic
Debian package not available

GlimmerHMM is a new gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single). A basic user manual can be consulted here.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
gmv
comparative genome browser for Murasaki
License: GPL
Debian package not available

GMV is a comparative genome browser for Murasaki. GMV visualizes anchors from Murasaki, annotation data from GenBank files, and expression / prediction score from GFF files.

jigsaw
gene prediction using multiple sources of evidence
License: Artistic
Debian package not available

JIGSAW is a program designed to use the output from gene finders, splice site prediction programs and sequence alignments to predict gene models. The program provides an automated way to take advantage of the many succsessful methods for computational gene prediction and can provide substantial improvements in accuracy over an individual gene prediction program.

JIGSAW is available for all species. It is tested on Human, Rice (Oryza sativa), Arabidopsis thaliana , Brugia malayi, Cryptococcus neoformans, Entamoeba histolytica, Theileria parva, Aspergillus fumigatus, Plasmodium falciparum and Plasmodium yoelii.

The linear combiner option is now available in the current JIGSAW software distribution. This allows JIGSAW to be run without the use of training data. A weight is assigned to each evidence source, and gene predictions are based on a weighted voting scheme, yielding the best 'consensus' predictions.

Predictions are now available for the ENCODE regions in Human and viewable as custom tracks in the UCSC Human Genome Browser. Predictions available for the Human genome and viewable as custom tracks in the UCSC Human Genome Browser

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
maker2
annotate genomes and create genome databases
License: GPL / Artistic
Debian package not available

MAKER is a portable and easily configurable genome annotation pipeline. It's purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER is also easily trainable: outputs of preliminary runs can be used to automatically retrain its gene prediction algorithm, producing higher quality gene-models on seusequent runs. MAKER's inputs are minimal and its ouputs can be directly loaded into a GMOD database. They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER should prove especially useful for emerging model organism projects with minimal bioinformatics expertise and computer resources

metarep
JCVI Metagenomics Reports
License: MIT
Debian package not available

JCVI Metagenomics Reports (METAREP) is a new open source tool for high-performance comparative metagenomics. It provides a suite of web based tools to help scientists to view, query, browse and compare metagenomics annotation data derived from ORFs called on metagenomics reads.

METAREP supports browsing of functional and taxonomic assignments. Users can either specify fields, or logical combinations of fields to flexibly filter datasets on the fly. Users can compare multiple datasets at various functional and taxonomic levels applying statistical tests as well as hierarchical clustering, multidimensional scaling and heatmaps.

minimus
AMOS lightweight assembler
License: Artistic
Debian package not available

minimus is an assembly pipeline designed specifically for small data-sets, such as the set of reads covering a specific gene. Note that the code will work for larger assemblies (we have used it to assemble bacterial genomes), however, due to its stringency, the resulting assembly will be highly fragmented. For large and/or complex assemblies the execution of Minimus should be followed by additional processing steps, such as scaffolding.

minimus follows the Overlap-Layout-Consensus paradigm and consists of three main modules:

  • overlapper - computes the overlaps between the reads using a modified version of the Smith-Waterman local alignment algorithm
  • tigger - uses the read overlaps to generate the layouts of reads representing individual contigs
  • make-consensus - refines the layouts produced by the tigger to generate accurate multiple alignments within the reads

minimus uses as AMOS messages as both the inputs and the outputs (see documentation). Two utilities are provided to process these files: tarchive2amos - a versatile converter from trace archive .seq, .qual, and .xml information into AMOS formatted data; amos2ace - a converter from AMOS formatted data to the .ACE assembly format. In addition, the AMOS::AmosLib Perl module is provided as a tool for users who prefer to write their own conversion utilities. Please see the documentation included with the distribution for more information.

minimus is part of the AMOS package - a collaborative effort to develop a modular open-source framework for assembly development.

Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
mummergpu
High-throughput sequence alignment using Graphics Processing Units
License: Artistic
Debian package not available

The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.

MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU is a GPGPU drop-in replacement for MUMmer, using the GPUs in common workstations to simultaneously align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies.

Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
obo-edit
editor for biological ontologies
License: Artistic
Debian package not available

(Open Biological Ontologies) Obo-Edit supports the formal representation of biological entities and the specification of is-a (specialisation) and part-of relations. Amongst the databases cureated by this tool is the GeneOntology.

operondb
detect and analyze conserved gene pairs
License: to be clarified
Debian package not available

Comparison of complete microbial genomes reveals a large number of conserved gene clusters - sets of genes that have the same order in two or more different genomes. Such gene clusters often, but not always represent a co-transcribed unit, or operon. A method was developed to detect and analyze conserved gene pairs - pairs of genes that are located close on the same DNA strand in two or more bacterial genomes. For each conserved gene pair, an estimate of probability is calculated that the genes belong to the same operon. The algorithm takes into account several alternative possibilities. One is that functionally unrelated genes may have the same order due simply because they were adjacent in a common ancestor. Other possibilities are that genes may be adjacent in two genomes by chance alone, or due to horizontal transfer of the gene pair.

The method is modified from the one described in: Maria D. Ermolaeva, Owen White and Steven L. Salzberg. Prediction of Operons in Microbial Genomes. Nucleic Acids Research, 29, 1216-1221, (2001)

OperonDB was supported by the NIH under grant R01-LM007938 and by the NSF under grant DBI-0234704.

Remark of Debian Med team: Other sequence analysis tools (http://www.cbcb.umd.edu/software/);

no info about license or downloadable code found, but tried to contact authors.

phagefinder
heuristic computer program to identify prophage regions within bacterial genomes
License: GPL
Debian package not available
Language: Perl

It uses tab-delimited results from NCBI BLASTALL or WU BLASTP 2.0 searches against a collection of bacteriophage protein sequences and results from HMMSEARCH analysis of 441 phage-specific HMMs to locate prophage regions. By using FASTA33, MUMMER or BLASTN, it can find potential attachment (att) sites of the phage region(s). Data from tRNAscan-SE and Aragorn are used to determine whether a tRNA or tmRNA served as the putative target for integration. Additionally, by looking for the presence or absence of specific proteins using specific HMM models, Phage_Finder can predict whether the region is most likely prophage and which type (Mu, P2, or retron R73), an integrated element, a plasmid, or a degenerate phage region.

The goal of this project is to provide an open-sourced, standardized and automated system to identify and classify prophages within prokaryotic genomes. It is hoped that this package will facilitate future studies on the biology and evolution of these prophages by providing a level of microbial genome annotation that was previously void.

phpphylotree
draw phylogenetic trees
License: GPL
Debian package not available

PhpPhylotree is a web application that is able to draw phylogenetic trees. It produces an SVG (Scalable Vector Graphic) file from phylip/newick tree files.

phylographer
Graph Visualization Tool
License: GPL
Debian package not available
Language: Tcl/Tk

PhyloGrapher is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements). PhyloGrapher is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use PhyloGrapher to visualize any type of relations between elements. Used in conjunction with tcl_blast_parser, PhyloGrapher can represent the results of a BLAST search as a graph.

PhyloGrapher and tcl_blast_parser are useful tools to analyse BLAST biological sequence alignment reports (BLAST is provided by Debian's blast2 package).

Remark of Debian Med team: Outdated upstream, better alternatives available

The former packaging effort of this package was dropped. It seems that http://cytoscape.org/ is a reasonable replacement.

pyrophosphate-tools
for assembling and searching pyrophosphate sequence data
License: not specified
Debian package not available

Simple tools for assembling and searching high-density picolitre pyrophosphate sequence data.

rose
Region-Of-Synteny Extractor
License: Open Source
Debian package not available

ROSE is a program which identifies regions between two genomes which are likely to contain orthologous genes. The two genomes are given as two multi fasta files of DNA sequences. The PROmer program from the MUMmer package needs to be run first between the two genomes, and the resulting delta file is then input to ROSE. If a previous annotation is available for one or both genomes, then the coordinates of the annotated genes from a genome can be optionally given as input in a gff file. The gene coordinates will be used to guide the length of the regions produced by ROSE. By default, when finding a region of consistent alignments, ROSE will add a user-defined margin (1000 bp by default) on either side of that region. When a predicted gene overlaps an alignment we use the gene prediction to extend the boundaries of the output region.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
treebuilder3d
viewer of SAGE and other types of gene expression data
License: GPL
Debian package not available
Language: Java

TreeBuilder3D is an interactive viewer that allows organization of SAGE and other types of gene expression data such as microarrays into hierarchical dendrograms, or phenetic networks (the term 'phenetic' used as the analysis relies on principals, used in phylogenetic analysis by system biology). Might be used as a visual aid when analyzing differences in expression profiles of SAGE libraries, serves as an alternative to Venn diagrams.

tripal
collection of Drupal modules for genomic research
License: GPL ( as Drupal a derivative )
Debian package not available

Tripal is a collection of open-source freely available Drupal modules under development at CUGI and a member of the GMOD family of tools. Tripal serve as a web interface for the GMOD Chado database. Tripal intially started as a web front-end for the Marine Genomics Project (MG.org). Work on the interface is currently ongoing for the MG.org project as well as the Fagaceae Genomics Web, and other CUGI projects. Tripal is currently being implemented for the new Cacao Genome Database, and Citrus Genome Database and will be used for the Genome Database for Rosaceae. These latter three databases are projects of the Main Bioinformatics Laboratory at Washington State University

twain
syntenic genefinder employing a Generalized Pair Hidden Markov Model
License: Open Source
Debian package not available

TWAIN is a new syntenic genefinder which employs a Generalized Pair Hidden Markov Model (GPHMM) to predict genes in two closely related eukaryotic genomes simultaneously. It utilizes the MUMmer package to perform approximate alignment before applying a GPHMM based on an enhanced version of the TigrScan gene finder. TWAIN was written by Bill Majoros and Mihaela Pertea while at The Institute for Genomic Research (TIGR).

TWAIN consists of two components: (1) ROSE, the Region Of Synteny Extractor, which identifies contiguous regions likely to contain one or more syntenic genes, and (2) OASIS, a generalized pair hidden Markov model (GPHMM) for predicting genes in the regions identified by ROSE. The system utilizes approximate alignments constructed by the PROmer and NUCmer programs in the MUMmer package to assess approximate alignment scores efficiently. More detailed information on the architecture of this system will be made available soon. Slides from a talk at Computational Genomics 2004 are now available.

Remark of Debian Med team: Computational Gene Finding (http://www.cbcb.umd.edu/software/)
uniprime
workflow-based platform for universal primer design
Responsible: Charles Plessy
License: GPL-3+
Debian package not available

UniPrime automatically designs large sets of universal primers by simply inputting a GeneID reference. It automatically retrieves and aligns orthologous sequences from GenBank, identifies regions of conservation within the alignment and generates suitable primers that can amplify variable genomic regions. UniPrime differs from previous automatic primer design programs in that all steps of primer design are automated, saved and are phylogenetically limited. We have experimentally verified the efficiency and success of this program. UniPrime is an experimentally validated, fully automated program that generates successful cross-species primers that take into account the biological aspects of the PCR.

x-tandem-pipeline
peptide/protein identification from MS/MS mass spectra
License: GPL
Debian package not available
Language: Java

X!Tandem is an open-source software performing peptide/protein identification from MS/MS mass spectra. X!Tandem is fast and accurate, but the Global Proteome Machine (GPM) is relatively limited regarding the processing of identification results. X!Tandem pipeline is an alternative to the installation of the GPM on local servers. X!Tandem pipeline performs database searching and matching on a list of MS/MS runs in one shot, using a list of easily user selected paramaters and databases. X!Tandem pipeline also performs filtering of data according to statistical values at peptide and protein levels. The results are stored into TSV (Tab Separated Values) files. Moreover, redundancy of protein databases are fully filtered as follows :

  • proteins identified without specific peptides compared to others are eliminated;
  • proteins identified with the same pool of peptides are assembled;
  • proteins are grouped by function (identified with at least one common peptide), and the specific peptides for each sub-group of proteins are indicated.
*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 237444