Debian Med Biology packages

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

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

abPOA is an extended version of Partial Order Alignment (POA) that performs adaptive banded dynamic programming (DP) with an SIMD implementation. abPOA can perform multiple sequence alignment (MSA) on a set of input sequences and generate a consensus sequence by applying the heaviest bundling algorithm to the final alignment graph.

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

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

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

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.

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.

This program searches for and removes remnant adapter sequences from High- Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into (longer) consensus sequences. Additionally, the AdapterRemoval may be used to recover a consensus adapter sequence for paired-ended data, for which this information is not available.

Adun is a biomolecular simulator that also includes data management and analysis capabilities. It was developed at the Computational Biophysics and Biochemistry Laboratory, a part of the Research Unit on Biomedical Informatics of the UPF.

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

The AEGeAn Toolkit is designed for the Analysis and Evaluation of Genome Annotations. The toolkit includes a variety of analysis programs, e.g. for comparing distinct sets of gene structure annotations (ParsEval), computation of gene loci (LocusPocus) and more.

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.

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/.

ALTER (ALignment Transformation EnviRonment) is a tool to transform between multiple sequence alignment formats. ALTER focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats.

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.

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.

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.

This is the andi program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because andi does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.

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).

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

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

It supports the following input formats:

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

Files may be compressed with:

 1. gzip, typically .gz
 2. bzip2, typically .bz2
 3. zip, typically .zip

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.

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.

ARIBA is a tool that identifies antibiotic resistance genes by running local assemblies. The input is a FASTA file of reference genes and paired sequencing reads. ARIBA reports which of the reference genes were found, plus detailed information on the quality of the assemblies and any variants between the sequencing reads and the reference genes.

ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Artemis is a genome browser and annotation tool that allows visualisation of sequence features, next generation data and the results of analyses within the context of the sequence, and also its six-frame translation.

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

ArtificialFastqGenerator takes the reference genome (in FASTA format) as input and outputs artificial FASTQ files in the Sanger format. It can accept Phred base quality scores from existing FASTQ files, and use them to simulate sequencing errors. Since the artificial FASTQs are derived from the reference genome, the reference genome provides a gold-standard for calling variants (Single Nucleotide Polymorphisms (SNPs) and insertions and deletions (indels)). This enables evaluation of a Next Generation Sequencing (NGS) analysis pipeline which aligns reads to the reference genome and then calls the variants.

Get statistics from a list of files.

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

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

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

atac computes a one-to-one pairwise alignment of large DNA sequences. It first finds the unique k-mers in each sequence, chains them to larger blocks, and fills in spaces between blocks. It was written primarily to transfer annotations between different assemblies of the human genome.

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

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

This package is part of the Kmer suite.

A toolkit for measuring and comparing ATAC-seq results, made in the Parker lab at the University of Michigan. They wrote it to help understand how well their ATAC-seq assays had worked, and to make it easier to spot differences that might be caused by library prep or sequencing.

Atropos is tool for specific, sensitive, and speedy trimming of NGS reads. It is a fork of the venerable Cutadapt read trimmer, with the primary improvements being:

  1. Multi-threading support, including an extremely fast "parallel
     write" mode.
  2. Implementation of a new insert alignment-based trimming algorithm
     for paired-end reads that is substantially more sensitive and
     specific than the original Cutadapt adapter alignment-based
     algorithm. This algorithm can also correct mismatches between the
     overlapping portions of the reads.
  3. Options for trimming specific types of data (miRNA, bisulfite-seq).
  4. A new command ('detect') that will detect adapter sequences and
     other potential contaminants.
  5. A new command ('error') that will estimate the sequencing error
     rate, which helps to select the appropriate adapter- and quality-
     trimming parameter values.
  6. A new command ('qc') that generates read statistics similar to
     FastQC. The trim command can also compute read statistics both
     before and after trimming (using the '--stats' option).
  7. Improved summary reports, including support for serialization
     formats (JSON, YAML, pickle), support for user-defined templates
     (via the optional Jinja2 dependency), and integration with MultiQC.
  8. The ability to merge overlapping reads (this is experimental and
     the functionality is limited).
  9. The ability to write the summary report and log messages to
     separate files.
 10. The ability to read SAM/BAM files and read/write interleaved
     FASTQ files.
 11. Direct trimming of reads from an SRA accession.
 12. A progress bar, and other minor usability enhancements.

The nextstrain project is an attempt to make flexible informatic pipelines and visualization tools to track ongoing pathogen evolution as sequence data emerges. The nextstrain project derives from nextflu, which was specific to influenza evolution.

nextstrain is comprised of three components:

• fauna: database and IO scripts for sequence and serological data
• augur: informatic pipelines to conduct inferences from raw data
• auspice: web app to visualize resulting inferences

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

AutoDock je prvním typickým programem provádějícím simulace uspořádání malých chemických ligand do větších receptorů bílkovin. U předchozích verzí byl kladen důraz především na ligandy než na proteiny. Poslední verze 4 používá k uspořádání vybraných řetězců také určité pružnosti, např. bere v potaz tzv. rotamery.

Program AutoDock provádí uspořádání ligand do množin popisujících cílový protein. AutoGrid vypočítává právě tyto uspořádání.

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

AutoDockSuite se zabývá molekulární analýzou slučování nenších chemických látek do jejich receptorů se známou trojrozměrnou strukturou.

Program AutoGrid provádí předvýpočty pro sloučení ligandu do sady mřížek, které znázorňují působení proteinu na jednotku vydání. Působení těchto vlivů na ligand je následně analyzováno programem AutoDock.

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

Features include:

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

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

Axe very rapidly selects the optimal barcode present in a sequence read, even in the presence of sequencing errors. The algorithm is able to handle combinatorial barcoding, barcodes of differing length, and several mismatches per barcode.

The BaitFisher package consists of two programs: BaitFisher and BaitFilter.

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

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

BAli-Phy estimates multiple sequence alignments and evolutionary trees from unaligned DNA, amino acid, or codon sequences. BAli-Phy uses MCMC to estimate evolutionary trees, positive selection, and branch lengths while averaging over alternative alignments. BAli-Phy can display alignment ambiguity graphically in an alignment uncertainty (AU) plot.

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

BALLView provides fast OpenGL-based visualization of molecular structures, molecular mechanics methods (minimization, MD simulation using the AMBER, CHARMM, and MMFF94 force fields), calculation and visualization of electrostatic properties (FDPB) and molecular editing features.

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

Remove gene-specific primer sequences from SAM/BAM alignments of PCR amplicons by soft-clipping.

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

This package provides some Python3 tools for common BAM file manipulations.

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)

Bandage is a GUI program that allows users to interact with the assembly graphs made by de novo assemblers such as Velvet, SPAdes, MEGAHIT and others.

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

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

The package is relevant to the field of genome assembly.

Barrnap (BAsic Rapid Ribosomal RNA Predictor) predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

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

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

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

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

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

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

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

A bioinformatics tool for constructing the compacted de Bruijn graph from sequencing data.

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

BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

Beads is a program for spot detection on 2-D gel images. It is based on an analogy with beads flowing uphill on the surface of the gel image and on the analysis of their paths (Langella & Zivy, 2008).

Beagle performs genotype calling, genotype phasing, imputation of ungenotyped markers, and identity-by-descent segment detection. Genotypic imputation works on phased haplotypes using a Li and Stephens haplotype frequency model. Beagle also implements the Refined IBD algorithm for detecting homozygosity-by-descent (HBD) and identity-by-descent (IBD) segments.

BEAST is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. Included is a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

BEAST is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. Included is a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

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

BEDOPS is a suite of tools to address common questions raised in genomic studies, mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

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.

Belvu is a multiple sequence alignment viewer and phylogenetic tool with an extensive set of user-configurable modes to color residues.

• View multiple sequence alignments.
• Residues can be coloured by conservation, with user-configurable cutoffs and colours.
• Residues can be coloured by residue type (user-configurable).
• Colour schemes can be imported or exported.
• Swissprot (or PIR) entries can be fetched by double clicking.
• The position in the alignment can be easily tracked.
• Manual deletion of rows and columns.
• Automatic editing of rows and columns based on customisable criteria:
  • removal of all-gap columns;
  • removal of all gaps;
  • removal of redundant sequences;
  • removal of a column by a user-specified percentage of gaps;
  • filtering of sequences by percent identity;
  • removal of sequences by a user-specified percentage of gaps;
  • removal of partial sequences (those starting or ending with gaps); and
  • removal of columns by conservation (with user-specified upper/lower cutoffs).
• The alignment can be saved in Stockholm, Selex, MSF or FASTA format.
• Distance matrices between sequences can be generated using a variety of distance metrics.
• Distance matrices can be imported or exported.
• Phylogenetic trees can be constructed based on various distance-based tree reconstruction algorithms.
• Trees can be saved in New Hampshire format.
• Belvu can perform bootstrap phylogenetic reconstruction.

eXpress is a streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data. It is based on an online-EM algorithm that results in space (memory) requirements proportional to the total size of the target sequences and time requirements that are proportional to the number of sampled fragments. Thus, in applications such as RNA-Seq, eXpress can accurately quantify much larger samples than other currently available tools greatly reducing computing infrastructure requirements. eXpress can be used to build lightweight high-throughput sequencing processing pipelines when coupled with a streaming aligner (such as Bowtie), as output can be piped directly into eXpress, effectively eliminating the need to store read alignments in memory or on disk.

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

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

Bifrost is a command-line tool for sequencing that features a broad range of functions, such as indexing, editing, and querying the graph, and includes a graph coloring method that maps each k-mer of the graph to the genomes it occurs in.

• Build, index, color and query the compacted de Bruijn graph
• No need to build the uncompacted de Bruijn graph
• Reads or assembled genomes as input
• Output graph in GFA (can be visualized with Bandage), FASTA or binary
• Graph cleaning: short tip clipping, etc.
• Multi-threaded
• No parameters to estimate with other tools
• Exact or approximate k-mer search of queries

Eagle estimates haplotype phase either within a genotyped cohort or using a phased reference panel. The basic idea of the Eagle1 algorithm is to harness identity-by-descent among distant relatives—which is pervasive at very large sample sizes but rare among smaller numbers of samples—to rapidly call phase using a fast scoring approach. In contrast, the Eagle2 algorithm analyzes a full probabilistic model similar to the diploid Li-Stephens model used by previous HMM-based methods.

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

Efficient tool for clustering and assembling short reads, especially for RAD.

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

Bio-Tradis contains a set of tools to analyse the output from TraDIS analyses.

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

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

Bio-vcf provides a domain specific language (DSL) for processing the VCF format. Record named fields can be queried with regular expressions, e.g.

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

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

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

This package contains some tools for processing BAM files, including

  bamsormadup:  parallel sorting and duplicate marking
  bamcollate2:  reads BAM and writes BAM reordered such that alignment
                or collated by query name
  bammarkduplicates: reads BAM and writes BAM with duplicate alignments
                marked using the BAM flags field
  bammaskflags: reads BAM and writes BAM while masking (removing) bits
                from the flags column
  bamrecompress: reads BAM and writes BAM with a defined compression
                 setting. This tool is capable of multi-threading.
  bamsort:       reads BAM and writes BAM resorted by coordinates or
                 query name
  bamtofastq:    reads BAM and writes FastQ; output can be collated
                 or uncollated by query name

Syntax highlighting for computational biology to bring you intuitively close to your data. BioSyntax supports .sam, .flagstat, .vcf, .fasta, .fastq, .faidx , .clustal, .pdb, .gtf, .bed files & more.

This is a metapackage depending on all bioSyntax plugins.

BitSeq is an application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions. An advantage of this approach is the ability to account for both technical uncertainty and intrinsic biological variance in order to avoid false DE calls. The technical contribution to the uncertainty comes both from finite read-depth and the possibly ambiguous mapping of reads to multiple transcripts.

Basic local alignment with successive refinement (BLASR) is a method for mapping single-molecule sequencing reads against a reference genome. Such reads are thousands of bases long, with divergence between them and the genome being dominated by insertion and deletion error.

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

• Overview section showing the positions of genes and alignments around the alignment window
• Detail section showing the actual alignment of protein or nucleotide sequences to the genomic DNA sequence.
• View alignments against both strands of the reference sequence.
• View sequences in nucleotide or protein mode; in protein mode, Blixem will display the three-frame translation of the reference sequence.
• Residues are highlighted in different colours depending on whether they are an exact match, conserved substitution or mismatch.
• Gapped alignments are supported, with insertions and deletions being highlighted in the match sequence.
• Matches can be sorted and filtered.
• SNPs and other variations can be highlighted in the reference sequence.
• Poly(A) tails can be displayed and poly(A) signals highlighted in the reference sequence.

The BOLT-LMM software package currently consists of two main algorithms, the BOLT-LMM algorithm for mixed model association testing, and the BOLT-REML algorithm for variance components analysis (i.e., partitioning of SNP-heritability and estimation of genetic correlations).

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

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

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).

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

Boxshade je program sloužící k vytváření pěkně vypadajících tiskových sestav z různých uspořádání proteinů a sekvencí DNA. Program neřeší vlastní uspořádání a vyžaduje vstupní soubor, který je vytvořen tímto programem či ručně upraven příslušnými nástroji.

Boxshade čte sekvenční uspořádání ze souborů PILEUP-MSF, CLUSTAL-ALN, MALIGNED-data a ESEE-save (omezených na maximálně 150 sekvencí s maximálně 10000 položkami). Program umí různá stínování na stejné/podobné zbytky. Výstup je možno zobrazovat na obrazovce ve formátech: ANSI/VT100, PS/EPS, RTF, HPGL, ReGIS, LJ250-printer, ASCII, xFIG, PICT a HTML.

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

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

The following programs are included:

• BppML for maximum likelihood analysis,
• BppSeqGen for sequences simulation,
• BppAncestor for ancestral states reconstruction,
• BppDist for distance methods,
• BppPars for parsimony analysis,
• BppSeqMan for file conversion and sequence manipulation,
• BppConsense for building consensus tree and computing bootstrap values,
• BppReRoot for tree rerooting.
• BppTreeDraw for tree drawing.
• BppAlnScore for comparing alignments and computing alignment scores.
• BppMixedLikelihoods for computing site per site likelihoods of components of mixture models.
• BppPopGen for population genetics analyses.

BRIG can display circular comparisons between a large number of genomes, with a focus on handling genome assembly data.

• Images show similarity between a central reference sequence and other sequences as concentric rings.
• BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
• Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
• Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
• BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously