Debian Med Project
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Summary
Cloud
Debian Med bioinformatics applications usable in cloud computing

This metapackage will install Debian packages related to molecular biology, structural biology and bioinformatics for use in life sciences, that do not depend on graphical toolkits and therefore can fit on system images for use in cloud computing clusters, where space can be limited.

The list to the right includes various software projects which are of some interest to the Debian Med Project. Currently, only a few of them are available as Debian packages. It is our goal, however, to include all software in Debian Med which can sensibly add to a high quality Debian Pure Blend.

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

Links to other tasks

Debian Med Cloud packages

Official Debian packages with high relevance

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)
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)
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)
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)
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)
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).

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)
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)
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
Please cite: Garrett M. Morris, Ruth Huey, William Lindstrom, Michel F. Sanner, Richard K. Belew, David S. Goodsell and Arthur J. Olson: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. (PubMed) Journal of Computational Chemistry 30(16):2785-2791 (2009)
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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)
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.

Please cite: Garrett M. Morris, Ruth Huey, William Lindstrom, Michel F. Sanner, Richard K. Belew, David S. Goodsell and Arthur J. Olson: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. (PubMed) Journal of Computational Chemistry 30(16):2785-2791 (2009)
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)
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)
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)
Biomaj
biological data-bank updater
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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.

Please cite: Olivier Filangi, Yoann Beausse, Anthony Assi, Ludovic Legrand, Jean-Marc Larré, Véronique Martin, Olivier Collin, Christophe Caron, Hugues Leroy and David Allouche: BioMAJ: a flexible framework for databanks synchronization and processing. (PubMed,eprint) Oxford Journals Bioinformatics 24(16):1823-1825 (2008)
Bioperl
Perl tools for computational molecular biology
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The Bioperl project is a coordinated effort to collect computational methods routinely used in bioinformatics into a set of standard CPAN-style, well-documented, and freely available Perl modules. It is well-accepted throughout the community and used in many high-profile projects, e.g., Ensembl.

The recommended packages are needed to run some of the included binaries, for a detailed explanation including the specific Perl modules please see README.Debian.

The suggested package enhances the manual pages.

Please cite: Jason E Stajich, David Block, Kris Boulez, Steven E Brenner, Stephen A Chervitz, Chris Dagdigian, Georg Fuellen, James G R Gilbert, Ian Korf, Hilmar Lapp, Heikki Lehvaslaiho, Chad Matsalla, Chris J Mungall, Brian I Osborne, Matthew R Pocock, Peter Schattner, Martin Senger, Lincoln D Stein, Elia Stupka, Mark D Wilkinson and Ewan Birney: The Bioperl toolkit: Perl modules for the life sciences.. (PubMed,eprint) Genome Res. 12(10):1611-1618 (2002)
Bioperl-run
BioPerl wrappers: scripts
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Contains scripts from the BioPerl-Run package. This package will also install all wrappable applications packaged in Debian. The ones that are not Free are "Suggested" by this package.

The package is enhanced by the following packages: kalign phyml mcl exonerate clustalw
Biosquid
utilities for biological sequence analysis
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SQUID is a library of C code functions for sequence analysis. It also includes a number of small utility programs to convert, show statistics, manipulate and do other functions on sequence files.

The original name of the package is "squid", but since there is already a squid on the archive (a proxy cache), it was renamed to "biosquid".

This package contains some tools to demonstrate the features of the SQUID library.

Blast2
Basic Local Alignment Search Tool
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The famous sequence alignment program. This is "official" NCBI version, #2. The blastall executable allows you to give a nucleotide or protein sequence to the program. It is compared against databases and a summary of matches is returned to the user.

Note that databases are not included in Debian; they must be retrieved manually.

The package is enhanced by the following packages: mcl
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
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)
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
Please cite: Ben Langmead and Steven L Salzberg: Fast gapped-read alignment with Bowtie 2. (PubMed) Nature Methods 9:357–359 (2012)
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

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)
Cain
simulations of chemical reactions
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Cain performs stochastic and deterministic simulations of chemical reactions. It can spawn multiple simulation processes to utilize multi-core computers. It stores models, methods, and simulation output (populations and reaction counts) in an XML format. In addition, SBML models can be imported and exported. The models and methods can be read from input files or edited within the program.

The GUI (Graphical User Interface) is written in Python and uses the wxPython toolkit. Most of the solvers are implemented as command line executables, written in C++, which are driven by Cain. This makes it easy to launch batch jobs. It also simplifies the process of adding new solvers. Cain offers a variety of solvers:

  • Gillespie's direct method.
  • Gillespie's first reaction method.
  • Gibson and Bruck's next reaction method.
  • Tau-leaping.
  • Hybrid direct/tau-leaping.
  • ODE integration.

This package provides the architecture independent files for cain

Please cite: Sean Mauch and Mark Stalzer: Efficient Formulations for Exact Stochastic Simulation of Chemical Systems. IEEE/ACM Trans. Comput. Biol. Bioinformatics 8(1):27-35 (2011)
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.

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.

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.

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)
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)
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, we recommend using 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)
Clustalo
General purpose multiple sequence alignment program for proteins
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Clustal-Omega is a general purpose multiple sequence alignment (MSA) program for dna/rna/proteins. It produces high quality MSAs and is capable of handling data-sets of hundreds of thousands of sequences in reasonable time.

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) Molecular Systems Biology 7(539) (2011)
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)
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).

ConCavity has many features. The default run of concavity is equivalent to ConCavity^L in the paper: 'Capra JA, Laskowski RA, Thornton JM, Singh M, and Funkhouser TA(2009) Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure. PLoS Comput Biol, 5(12).'.

score_conservation(1) from the conservation-code package can be used to calculate evolutionary sequence conservation for concavity.

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)
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)
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.

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)
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)
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.

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: A. Ceroni, A. Passerini, A. Vullo and P. Frasconi: DISULFIND: a disulfide bonding state and cysteine connectivity prediction server.. (PubMed) Nucleic Acids Res 34(Web Server issue):W177-81 (2006)
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 occurance 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)
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 (2) of DSSP is a rewrite that produces the same output as the original DSSP, but deals better with exceptions in PDB files and is much faster.

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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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)
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)
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
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)
Fastx-toolkit
FASTQ/A short nucleotide reads pre-processing tools
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The FASTX-Toolkit is a collection of command line tools for preprocessing short nucleotide reads in FASTA and FASTQ formats, usually produced by Next-Generation sequencing machines. The main processing of such FASTA/FASTQ files is mapping (aligning) the sequences to reference genomes or other databases using specialized programs like BWA, Bowtie and many others. However, it is sometimes more productive to preprocess the FASTA/FASTQ files before mapping the sequences to the genome—manipulating the sequences to produce better mapping results. The FASTX-Toolkit tools perform some of these preprocessing tasks.

Filo
FILe and stream Operations
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The following tools are available as part of the filo package:

groupBy – mimics the “groupBy” clause in database systems.

shuffle – randomize the order of lines in a file.

stats – computes descriptive statistic on a given column of a tab-delimited file or stream.

Because their name is too generic, ‘shuffle’ and ‘stats’ are relocated in /usr/lib/filo.

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)
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.

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)
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: ... BMC Bioinformatics (201?)
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.

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)
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)
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 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)
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)
Giira
RNA-Seq driven gene finding incorporating ambiguous reads
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GIIRA is a gene prediction method that identifies potential coding regions exclusively based on the mapping of reads from an RNA-Seq experiment. It was foremost designed for prokaryotic gene prediction and is able to resolve genes within the expressed region of an operon. However, it is also applicable to eukaryotes and predicts exon intron structures as well as alternative isoforms.

Please cite: Franziska Zickmann, Martin S. Lindner and Bernhard Y. Renard: GIIRA—RNA-Seq driven gene finding incorporating ambiguous reads. (PubMed,eprint) Bioinformatics (2013)
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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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)
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.

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)
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)
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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Idba
iterative De Bruijn Graph De Novo short read assembler for transcriptome
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IDBA-Tran is an iterative De Bruijn Graph De Novo short read assembler for transcriptome. It is purely de novo assembler based on only RNA sequencing reads. IDBA-Tran uses local assembly to reconstructing missing k-mers in low-expressed transcripts and then employs progressive cutoff on contigs to separate the graph into components. Each component corresponds to one gene in most cases and contains not many transcripts. A heuristic algorithm based on pair-end reads is then used to find the isoforms.

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)
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)
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.

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)
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: Timo Lassmann, Oliver Frings and Erik L. L. Sonnhammer: Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features. (PubMed,eprint) Nucl. Acids Res. 37(3):858-865 (2009)
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)
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)
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
Please cite: Simon C. Heath: Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. (PubMed,eprint) American Journal of Human Genetics 61(3):748-60 (1997)
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.

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)
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
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)
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)
Minia
short-read biological sequence assembler
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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. 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)
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)
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) occuring 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. Realised through link to mira.
  • convert_project: for converting project file types into other types
  • caf2fasta, caf2gbf, caf2text, caf2html, gbf2caf and gbf2fasta are some frequently used file converters (realised through links to convert_project)
  • scftool: set of tools useful when working with SCF trace files
  • fastatool: set of tools useful when working with FASTA trace files

Scripts provided:

  • fasta2frag: fragmenting sequences into smaller, overlapping subsequences. Useful for simulating shotgun sequences. Can create subsequences in both directions (/default) and also paired-end sequences.
  • fastaselect: given a FASTA file (and possibly a FASTA quality file) and a file with names of reads, select the sequences from the input FASTA (and quality file) and writes them to an output FASTA
  • fastqselect: like fastaselect, only for FASTQ
  • fixACE4consed: Consed has a bug which incapacitates it from reading consensus tags in ACE files written by the MIRA assembler (and possibly other programs). This script massages an ACE file so that consed can read the consensus tags.
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)
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.
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)
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)
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.

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)
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.

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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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)
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 discontigous 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.

Please cite: Schuler, Gregory D.: Sequence Mapping by Electronic PCR. (PubMed,eprint) Genome Research 7(5):541-550 (1997)
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 are in a separate package (blast2).

The package is enhanced by the following packages: mcl
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)
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)
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.

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.

Pdb2pqr
Preparation of protein structures for electrostatics calculations
Maintainer: Debian Med (Andreas Tille)
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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

This package also includes PropKa, a tool to modify the protonation state of protein structures in the Protein Data Bank (PDB) format to match a given pKa value. It can also be used to refine NMR structures, which often yield inaccurate pKa values for some residues.

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)
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)
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 pakcage also includes PhyTime.

Please cite: Stephane Guindon and Olivier Gascuel: A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood. (PubMed,eprint) Syst Biol 52(5):696-704 (2003)
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)
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: BamToBfq IlluminaBasecallsToSam BuildBamIndex MarkDuplicates CalculateHsMetrics MeanQualityByCycle CleanSam MergeBamAlignment CollectAlignmentSummaryMetrics MergeSamFiles CollectGcBiasMetrics NormalizeFasta CollectInsertSizeMetrics QualityScoreDistribution CollectRnaSeqMetrics ReplaceSamHeader CompareSAMs RevertSam CreateSequenceDictionary SamFormatConverter ExtractIlluminaBarcodes SamToFastq EstimateLibraryComplexity SortSam FastqToSam ValidateSamFile FixMateInformation ViewSam

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 p-link because of a name clash. Please read more about this in /usr/share/doc/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)
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.

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.

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)
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)
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) BMC Bioinformatics 11:134 (2010)
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)
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)
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)
Pynast
alignment of short DNA sequences
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The package provices a reimplementation of the Nearest Alignment Space Termination tool in Python. It was prepared for next generation sequencers.

Given a set of sequences and a template alignment, PyNAST will align the input sequences against the template alignment, and return a multiple sequence alignment which contains the same number of positions (or columns) as the template alignment. This facilitates the analysis of new sequences in the context of existing alignments, and additional data derived from existing alignments such as phylogenetic trees. Because any protein or nucleic acid sequences and template alignments can be provided, PyNAST is not limited to the analysis of 16s rDNA sequences.

Please cite: J. Gregory Caporaso, Kyle Bittinger, Frederic D. Bushman, Todd Z. DeSantis, Gary L. Andersen and Rob Knight: PyNAST: a flexible tool for aligning sequences to a template alignment. (PubMed) Bioinformatics 26:266-267. (2010)
Python-biopython
Python library for bioinformatics (implemented in Python 2)
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The Biopython Project is an international association of developers of freely available Python tools for computational molecular biology.

It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. The source code is made available under the Biopython License, which is extremely liberal and compatible with almost every license in the world. The project works along with the Open Bioinformatics Foundation, who generously provide web and CVS space for the project.

This package is targeting Python version 2.

Please cite: Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski and Michiel J. L. de Hoon: Biopython: freely available Python tools for computational molecular biology and bioinformatics. (PubMed,eprint) Bioinformatics 25(11):1422-1423 (2009)
Python-cogent
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)
Python3-biopython
Python library for bioinformatics (implemented in Python 3)
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The Biopython Project is an international association of developers of freely available Python tools for computational molecular biology.

It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. The source code is made available under the Biopython License, which is extremely liberal and compatible with almost every license in the world. The project works along with the Open Bioinformatics Foundation, who generously provide web and CVS space for the project.

This package is targeting Python version 3.

Please cite: Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski and Michiel J. L. de Hoon: Biopython: freely available Python tools for computational molecular biology and bioinformatics. (PubMed,eprint) Bioinformatics 25(11):1422-1423 (2009)
Qiime
Quantitative Insights Into Microbial Ecology
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QIIME (canonically pronounced ‘Chime’) is a pipeline for performing microbial community analysis that integrates many third party tools which have become standard in the field. A standard QIIME analysis begins with sequence data from one or more sequencing platforms, including

  • Sanger,
  • Roche/454, and
  • Illumina GAIIx. With all the underlying tools installed, of which not all are yet available in Debian (or any other Linux distribution), QIIME can perform

  • library de-multiplexing and quality filtering;

  • denoising with PyroNoise;
  • OTU and representative set picking with uclust, cdhit, mothur, BLAST, or other tools;
  • taxonomy assignment with BLAST or the RDP classifier;
  • sequence alignment with PyNAST, muscle, infernal, or other tools;
  • phylogeny reconstruction with FastTree, raxml, clearcut, or other tools;
  • alpha diversity and rarefaction, including visualization of results, using over 20 metrics including Phylogenetic Diversity, chao1, and observed species;
  • beta diversity and rarefaction, including visualization of results, using over 25 metrics including weighted and unweighted UniFrac, Euclidean distance, and Bray-Curtis;
  • summarization and visualization of taxonomic composition of samples using pie charts and histograms and many other features.

QIIME includes parallelization capabilities for many of the computationally intensive steps. By default, these are configured to utilize a mutli-core environment, and are easily configured to run in a cluster environment. QIIME is built in Python using the open-source PyCogent toolkit. It makes extensive use of unit tests, and is highly modular to facilitate custom analyses.

Please cite: J Gregory Caporaso, Justin Kuczynski, Stombaugh Jesse, Bittinger Kyle, Bushman Frederic D, Costello Elizabeth K, Fierer Noah, Pena Antonio Gonzalez, Goodrich Julia K, Gordon Jeffrey I, Huttley Gavin A, Kelley Scott T, Knights Dan, Koenig Jeremy E, Ley Ruth E, Lozupone Catherine A, McDonald Daniel, Muegge Brian D, Pirrung Meg, Reeder Jens, Sevinsky Joel R, Turnbaugh Peter J, Walters William A, Widmann Jeremy, Yatsunenko Tanya, Zaneveld Jesse and Knight Rob: QIIME allows analysis of high-throughput community sequencing data. (PubMed) Nature Methods 7:335 - 336 (2010)
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 and Gordon K. Smyth: Moderated statistical tests for assessing differences in tag abundance. (PubMed,eprint) Bioinformatics 23(21):2881-2887 (2007)
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)
R-cran-pvclust
Hierarchical Clustering with P-Values via Multiscale Bootstrap
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pvclust is a package for assessing the uncertainty in hierarchical cluster analysis. It provides AU (approximately unbiased) p-values as well as BP (boostrap probability) values computed via multiscale bootstrap resampling.

Please cite: Ryota Suzuki and Hidetoshi Shimodaira: Pvclust: an R package for assessing the uncertainty in hierarchical clustering. (PubMed,eprint) Bioinformatics 22(12):1540-1542 (2006)
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)
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.

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)
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)
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)
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)
Rtax
Classification of sequence reads of 16S ribosomal RNA gene
Maintainer: Med Packaging Team
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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)
Samtools
processing sequence alignments in SAM and BAM 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) format, does sorting, merging and indexing, and allows to retrieve reads in any regions swiftly. It is designed to work on a stream, and is able to open a BAM (not SAM) file on a remote FTP or HTTP server.

The package is enhanced by the following packages: libbio-samtools-perl
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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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)
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.
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)
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)
Smalt
Sequence Mapping and Alignment Tool
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SMALT efficiently aligns DNA sequencing reads with a reference genome. It 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.

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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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)
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 Luo1, Binghang Liu1, Yinlong Xie1, Zhenyu Li1, 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)
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.
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.

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)
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)
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: prank clustalw kalign mafft tm-align dialign-tx probcons muscle ncbi-blast+ poa
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)
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 chromosme), 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 version of tabix is built from the HTSlib source.

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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.

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)
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)
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
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)
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)
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)
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.

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)
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)
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)
Zalign
parallel local alignment of biological sequences
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zAlign is a local sequence aligner, especially intended for use with large biological DNA sequences, with more than 1Mbp (Millions of base pairs). It uses the Smith-Waterman exact algorithm with affine gap cost function to perform this task.

zAlign can be used both in distributed (clusters, for example) or standalone environments. Currently it has been tested on Linux and Sun Solaris, using both the MPICH (http://www.mcs.anl.gov/research/projects/mpi/mpich1/) and OpenMPI (http://www.open-mpi.org/) implementations. Ports for other Unix-like environments are highly considered.

Please cite: Rodolfo Bezerra Batista, Azzedine Boukerche and Alba Cristina Magalhaes Alves de Melo: A parallel strategy for biological sequence alignment in restricted memory space. Journal of Parallel and Distributed Computing 68(4):548-561 (2008)

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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: Jared T. Simpson, Kim Wong, Shaun D. Jackman, Jacqueline E. Schein, Steven J.M. Jones and İnanç Birol: ABySS: A parallel assembler for short read sequence data. (PubMed,eprint) Genome Research 19(6):1117-23 (2009)
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.

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)
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.

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, Colin K. Watanabe: GMAP: a genomic mapping and alignment program for mRNA and EST sequences. (eprint) Bioinformatics 21(9):1859-1875 (2005)

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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.

[Mott00] Mott R, Talbot CJ, Turri MG, Collins AC, Flint, J (2000) A method for fine mapping quantitative trait loci in outbred animal stocks. Proceedings of the National Academy of Sciences of the United States of America, 97(23), 12649-54. [Valdar06] Valdar W, Solberg LC, Gaugier D, Burnett S, Klenerman P, Cookson WO, Taylor M, Rawlins JNP, Mott R, Flint J (2006) Genome-wide genetic association of complex traits in outbred mice. Nature Genetics 38(8):879-87. PMID:16832355

R-other-valdar-bagphenotype.library
GNU R extension of the functionality of happy
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mapping QTLs in populations descended from known founders

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