Debian Med Project
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Summary
Next generation sequencing
Debian Med bioinformatics applications usable in Next Generation Sequencing

It aims at gettting packages which specialize in the processing or interpretation of data generated with next- (and later-) generation high-thoughput sequencing technologies.

Description

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

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

Links to other tasks

Debian Med Next generation sequencing packages

Official Debian packages with high relevance

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).

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

Please cite: Weichun Huang, Leping Li, Jason R. Myers and Gabor T. Marth: ART: a next-generation sequencing read simulator. (PubMed,eprint) Bioinformatics 28(4):593-594 (2012)
Registry entries: SciCrunch  OMICtools  bioconda 
Artfastqgenerator
outputs artificial FASTQ files derived from a reference genome
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ArtificialFastqGenerator takes the reference genome (in FASTA format) as input and outputs artificial FASTQ files in the Sanger format. It can accept Phred base quality scores from existing FASTQ files, and use them to simulate sequencing errors. Since the artificial FASTQs are derived from the reference genome, the reference genome provides a gold-standard for calling variants (Single Nucleotide Polymorphisms (SNPs) and insertions and deletions (indels)). This enables evaluation of a Next Generation Sequencing (NGS) analysis pipeline which aligns reads to the reference genome and then calls the variants.

Please cite: Matthew Frampton and Richard Houlston: Generation of Artificial FASTQ Files to Evaluate the Performance of Next-Generation Sequencing Pipelines. (PubMed,eprint) PLOSone 7(11):e49110 (2012)
Registry entries: OMICtools 
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)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Bcftools
genomic variant calling and manipulation of VCF/BCF files
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BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

Please cite: Petr Danecek and Shane A. McCarthy: BCFtools/csq: Haplotype-aware variant consequences. (2016)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
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)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Berkeley-express
Streaming quantification for high-throughput sequencing
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eXpress is a streaming tool for quantifying the abundances of a set of target sequences from sampled subsequences. Example applications include transcript-level RNA-Seq quantification, allele-specific/haplotype expression analysis (from RNA-Seq), transcription factor binding quantification in ChIP-Seq, and analysis of metagenomic data. It is based on an online-EM algorithm that results in space (memory) requirements proportional to the total size of the target sequences and time requirements that are proportional to the number of sampled fragments. Thus, in applications such as RNA-Seq, eXpress can accurately quantify much larger samples than other currently available tools greatly reducing computing infrastructure requirements. eXpress can be used to build lightweight high-throughput sequencing processing pipelines when coupled with a streaming aligner (such as Bowtie), as output can be piped directly into eXpress, effectively eliminating the need to store read alignments in memory or on disk.

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

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

Please cite: Adam Roberts and Lior Pachter: Streaming fragment assignment for real-time analysis of sequencing experiments. (PubMed) Nature Methods 10(1):71–73 (2013)
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Bio-rainbow
clustering and assembling short reads for bioinformatics
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Efficient tool for clustering and assembling short reads, especially for RAD.

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

Please cite: Zechen Chong, Jue Ruan and Chung-I. Wu: Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads.. (PubMed) Bioinformatics 28(21):2732-2737 (2012)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Blasr
mapping single-molecule sequencing reads
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Basic local alignment with successive refinement (BLASR) is a method for mapping single-molecule sequencing reads against a reference genome. Such reads are thousands of bases long, with divergence between them and the genome being dominated by insertion and deletion error.

Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
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)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Topics: Genomics
Bowtie2
ultrafast memory-efficient short read aligner
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is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes.

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

The package is enhanced by the following packages: bowtie2-examples
Please cite: Ben Langmead and Steven L Salzberg: Fast gapped-read alignment with Bowtie 2. (PubMed) Nature Methods 9:357–359 (2012)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Topics: Genomics
Bwa
Burrows-Wheeler Aligner
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BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

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

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

  • Detect overlaps in high-noise sequences using MHAP
  • Generate corrected sequence consensus
  • Trim corrected sequences
  • Assemble trimmed corrected sequences
Please cite: Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller and Adam M. Phillippy: Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. bioRxiv (2016)
Registry entries: Bio.Tools  SciCrunch  OMICtools  bioconda 
Remark of Debian Med team: Genome assembly and large-scale genome alignment (http://www.cbcb.umd.edu/software/)
Changeo
Repertoire clonal assignment toolkit (Python 3)
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Change-O is a collection of tools for processing the output of V(D)J alignment tools, assigning clonal clusters to immunoglobulin (Ig) sequences, and reconstructing germline sequences.

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

This package installs the library for Python 3.

Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Link to publication (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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Crac
integrated RNA-Seq read analysis
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CRAC is a tool to analyze High Throughput Sequencing (HTS) data in comparison to a reference genome. It is intended for transcriptomic and genomic sequencing reads. More precisely, with transcriptomic reads as input, it predicts point mutations, indels, splice junction, and chimeric RNAs (ie, non colinear splice junctions). CRAC can also output positions and nature of sequence error that it detects in the reads. CRAC uses a genome index. This index must be computed before running the read analysis. For this sake, use the command "crac-index" on your genome files. You can then process the reads using the command crac. See the man page of CRAC (help file) by typing "man crac". CRAC requires large amount of main memory on your computer. For processing against the Human genome, say 50 million reads of 100 nucleotide each, CRAC requires about 40 gigabytes of main memory. Check whether the system of your computing server is equipped with sufficient amount of memory before launching an analysis.

Please cite: Eliseos J. Mucaki, Natasha G. Caminsky, Ami M. Perri, Ruipeng Lu, Alain Laederach, Matthew Halvorsen, Joan H. M. Knoll and Peter K. Rogan: A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. (PubMed) BMS Medical Genomics 9:19 (2016)
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Cutadapt
Clean biological sequences from high-throughput sequencing reads
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Cutadapt helps with biological sequence clean tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

This package contains the user interface.

Please cite: Marcel Martin: Cutadapt removes adapter sequences from high-throughput sequencing reads. (eprint) EMBnet.journal 17(1):10-12 (2015)
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Daligner
local alignment discovery between long nucleotide sequencing reads
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These tools permit one to find all significant local alignments between reads encoded in a Dazzler database. The assumption is that the reads are from a Pacific Biosciences RS II long read sequencer. That is, the reads are long and noisy, up to 15% on average.

Please cite: Gene Myers: Efficient Local Alignment Discovery amongst Noisy Long Reads. 8701:52-67 (2014)
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Deepnano
alternative basecaller for MinION reads of genomic sequences
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DeepNano is alternative basecaller for Oxford Nanopore MinION reads based on deep recurrent neural networks.

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

Please cite: Vladimír Boža, Broňa Brejová and Tomáš Vinař: DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads. PLOS one (2017)
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Dindel
determines indel calls from short-read data
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Dindel is a program for calling small indels from short-read sequence data ('next generation sequence data'). It currently is designed to handle only Illumina data.

Dindel requires a BAM file containing the read-alignments as input. It then extracts candidate indels from the BAM file, and realigns the reads to candidate haplotypes consisting of these candidate indels. If there is sufficient evidence for an alternative haplotype to the reference, it will call an indel.

It is possible to test indels discovered with other methods using Dindel, for instance longer indels obtained through assembly methods. Dindel will then realign both mapped and unmapped reads to see if the candidate indel is supported by the reads.

Dindel outputs both genotype likelihoods and includes a script to convert these to a VCF file with indel and SNP calls.

There is basic support for outputting realigned BAM files for each realignment-window. These realigned BAM files can be used to call SNPs near (candidate) indels.

Please cite: Cornelis A. Albers, Gerton Lunter, Daniel G. MacArthur, Gilean McVean, Willem H. Ouwehand and Richard Durbin: Dindel: Accurate indel calls from short-read data. (PubMed,eprint) Genome Research 21(6):961-973 (2010)
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Topics: DNA structural variation
Discosnp
discovering Single Nucleotide Polymorphism from raw set(s) of reads
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Software discoSnp is designed for discovering Single Nucleotide Polymorphism (SNP) from raw set(s) of reads obtained with Next Generation Sequencers (NGS).

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

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

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

This program is superseded by DiscoSnp++.

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Dnaclust
tool for clustering millions of short DNA sequences
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dnaclust is a tool for clustering large number of short DNA sequences. The clusters are created in such a way that the "radius" of each clusters is no more than the specified threshold.

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

Please cite: Mohammadreza Ghodsi, Bo Liu and Mihai Pop: DNACLUST: accurate and efficient clustering of phylogenetic marker genes. (PubMed,eprint) BMC Bioinformatics 12:271 (2011)
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Dwgsim
short sequencing read simulator
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DWGSIM simulates short sequencing reads from modern sequencing platforms. DWGSIM generates base error rates using a parametric model, allowing a more realisic error profile. It was originally developed for use in evaluating short read aligners.

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Ea-utils
command-line tools for processing biological sequencing data
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Ea-utils provides a set of command-line tools for processing biological sequencing data, barcode demultiplexing, adapter trimming, etc.

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

Main Tools are:

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

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

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

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

Please cite: Erik Aronesty: Comparison of Sequencing Utility Programs. (eprint) The Open Bioinformatics Journal 7:1-8 (2013)
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Fastaq
FASTA and FASTQ file manipulation tools
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Fastaq represents a diverse collection of scripts that perform useful and common FASTA/FASTQ manipulation tasks, such as filtering, merging, splitting, sorting, trimming, search/replace, etc. Input and output files can be gzipped (format is automatically detected) and individual Fastaq commands can be piped together.

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Topics: Bioinformatics
Fastp
Ultra-fast all-in-one FASTQ preprocessor
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All-in-one FASTQ preprocessor, fastp provides functions including quality profiling, adapter trimming, read filtering and base correction. It supports both single-end and paired-end short read data and also provides basic support for long-read data.

Please cite: Shifu Chen, Yanqing Zhou, Yaru Chen and Jia Gu: fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884-i890 (2018)
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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
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Topics: Sequencing
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.

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Topics: Sequencing
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)
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Fml-asm
tool for assembling Illumina short reads in small regions
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Fml-asm is a command-line tool for assembling Illumina short reads in regions from 100bp to 10 million bp in size, based on the fermi-lite library. It is largely a light-weight in-memory version of fermikit without generating any intermediate files. It inherits the performance, the relatively small memory footprint and the features of fermikit. In particular, fermi-lite is able to retain heterozygous events and thus can be used to assemble diploid regions for the purpose of variant calling.

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

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

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

Please cite: Florent E. Angly, Dana Willner, Forest Rohwer, Philip Hugenholtz and Gene W. Tyson: Grinder: a versatile amplicon and shotgun sequence simulator. (PubMed,eprint) Nucleic Acids Research Epub ahead of print (2012)
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Hilive
realtime alignment of Illumina reads
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HiLive is a read mapping tool that maps Illumina HiSeq (or comparable) reads to a reference genome right in the moment when they are produced. This means, read mapping is finished as soon as the sequencer is finished generating the data.

Please cite: Martin S. Lindner, Benjamin Strauch, Jakob M. Schulze, Simon H. Tausch, Piotr W. Dabrowski, Andreas Nitsche and Bernhard Y. Renard: HiLive: real-time mapping of illumina reads while sequencing. (PubMed) Bioinformatics 33(6):917-919 (2017)
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Hinge
long read genome assembler based on hinging
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HINGE is a genome assembler that seeks to achieve optimal repeat resolution by distinguishing repeats that can be resolved given the data from those that cannot. This is accomplished by adding “hinges” to reads for constructing an overlap graph where only unresolvable repeats are merged. As a result, HINGE combines the error resilience of overlap-based assemblers with repeat-resolution capabilities of de Bruijn graph assemblers.

Please cite: Govinda M Kamath, Ilan Shomorony, Fei Xia, Thomas Courtade and David N Tse: HINGE: Long-read assembly achieves optimal repeat resolution. (PubMed,eprint) Genome Research (2017)
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Hisat2
graph-based alignment of short nucleotide reads to many genomes
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HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as against a single reference genome). Based on an extension of BWT for graphs a graph FM index (GFM) was designed and implementd. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM).

Please cite: Daehwan Kim, Ben Langmead and Steven L Salzberg: HISAT: a fast spliced aligner with low memory requirements. Nature Methods 12(4):357-360 (2015)
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Idba
iterative De Bruijn Graph short read assemblers
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IDBA stands for iterative de Bruijn graph assembler. In computational sequence biology, an assembler solves the puzzle coming from large sequencing machines that feature many gigabytes of short reads from a large genome.

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

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

Please cite: Yu Peng, Henry C. M. Leung, S. M. Yiu and Francis Y. L. Chin: IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. (PubMed,eprint) Bioinformatics 28(11):1420-1428 (2012)
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Igdiscover
analyzes antibody repertoires to find new V genes
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IgDiscover analyzes antibody repertoires and discovers new V genes from high-throughput sequencing reads. Heavy chains, kappa and lambda light chains are supported (to discover VH, VK and VL genes).

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

Please cite: Quentin Marcou, Thierry Mora and Aleksandra M. Walczak: High-throughput immune repertoire analysis with IGoR. (PubMed) Nature Communications 9(1):561 (2018)
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Iva
iterative virus sequence assembler
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IVA is a de novo assembler designed to assemble virus genomes that have no repeat sequences, using Illumina read pairs sequenced from mixed populations at extremely high depth.

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

Please cite: M. Hunt, A. Gall, S. H. Ong, J. Brener, B. Ferns, P. Goulder, E. Nastouli, J. A. Keane, P. Kellam and T. D. Otto: IVA: accurate de novo assembly of RNA virus genomes. (PubMed) Bioinformatics 31(14):2374-2376 (2015)
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Khmer
in-memory DNA sequence kmer counting, filtering & graph traversal
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khmer is a library and suite of command line tools for working with DNA sequence. It is primarily aimed at short-read sequencing data such as that produced by the Illumina platform. khmer takes a k-mer-centric approach to sequence analysis, hence the name.

Please cite: Michael R. Crusoe, Hussien F. Alameldin, Sherine Awad, Elmar Bucher, Adam Caldwell, Reed Cartwright, Amanda Charbonneau, Bede Constantinides, Greg Edvenson, Scott Fay, Jacob Fenton, Thomas Fenzl, Jordan Fish, Leonor Garcia-Gutierrez, Phillip Garland, Jonathan Gluck, Iván González, Sarah Guermond, Jiarong Guo, Aditi Gupta, Joshua R. Herr, Adina Howe, Alex Hyer, Andreas Härpfer, Luiz Irber, Rhys Kidd, David Lin, Justin Lippi, Tamer Mansour, Pamela McA'Nulty, Eric McDonald, Jessica Mizzi, Kevin D. Murray, Joshua R. Nahum, Kaben Nanlohy, Alexander Johan Nederbragt, Humberto Ortiz-Zuazaga, Jeramia Ory, Jason Pell, Charles Pepe-Ranney, Zachary N Russ, Erich Schwarz, Camille Scott, Josiah Seaman, Scott Sievert, Jared Simpson, Connor T. Skennerton, James Spencer, Ramakrishnan Srinivasan, Daniel Standage, James A. Stapleton, Joe Stein, Susan R Steinman, Benjamin Taylor, Will Trimble, Heather L. Wiencko, Michael Wright, Brian Wyss, Qingpeng Zhang, en zyme and C. Titus Brown: The khmer software package: enabling efficient sequence analysis. (2015)
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Kissplice
Detection of various kinds of polymorphisms in RNA-seq data
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KisSplice is a piece of software that enables the analysis of RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows one to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.

Please cite: Gustavo AT Sacomoto, Janice Kielbassa, Rayan Chikhi, Raluca Uricaru, Pavlos Antoniou, Marie-France Sagot, Pierre Peterlongo and Vincent Lacroix: KISSPLICE: de-novo calling alternative splicing events from RNA-seq data. (PubMed,eprint) BMC Bioinformatics 13((Suppl 6)):S5 (2012)
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Topics: RNA-seq; RNA splicing; Gene structure
Kraken
assigning taxonomic labels to short DNA sequences
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Kraken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts by other bioinformatics software to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. Kraken aims to achieve high sensitivity and high speed by utilizing exact alignments of k-mers and a novel classification algorithm.

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

The package is enhanced by the following packages: jellyfish1
Please cite: Derrick E Wood and Steven L Salzberg: Kraken: ultrafast metagenomic sequence classification using exact alignments. (PubMed,eprint) Genome Biol. 15(3):R46 (2014)
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Kraken2
taxonomic classification system using exact k-mer matches
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Kraken 2 is the newest version of Kraken, a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. The k-mer assignments inform the classification algorithm. [see: Kraken 1's Webpage for more details].

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

 1. Storage of Minimizers: Instead of storing/querying entire k-mers,
    Kraken 2 stores minimizers (l-mers) of each k-mer. The length of
    each l-mer must be ≤ the k-mer length. Each k-mer is treated by
    Kraken 2 as if its LCA is the same as its minimizer's LCA.
 2. Introduction of Spaced Seeds: Kraken 2 also uses spaced seeds to
    store and query minimizers to improve classification accuracy.
 3. Database Structure: While Kraken 1 saved an indexed and sorted list
    of k-mer/LCA pairs, Kraken 2 uses a compact hash table. This hash
    table is a probabilistic data structure that allows for faster
    queries and lower memory requirements. However, this data structure
    does have a <1% chance of returning the incorrect LCA or returning
    an LCA for a non-inserted minimizer. Users can compensate for this
    possibility by using Kraken's confidence scoring thresholds.
 4. Protein Databases: Kraken 2 allows for databases built from amino
    acid sequences. When queried, Kraken 2 performs a six-frame
    translated search of the query sequences against the database.
 5. 16S Databases: Kraken 2 also provides support for databases not
    based on NCBI's taxonomy. Currently, these include the 16S
    databases: Greengenes, SILVA, and RDP.
Please cite: Derrick E Wood and Steven L Salzberg: Kraken: ultrafast metagenomic sequence classification using exact alignments. (PubMed,eprint) Genome Biol. 15(3):R46 (2014)
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Last-align
genome-scale comparison of biological sequences
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LAST is software for comparing and aligning sequences, typically DNA or protein sequences. LAST is similar to BLAST, but it copes better with very large amounts of sequence data. Here are two things LAST is good at:

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

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

Please cite: Martin C. Frith, Raymond Wan and Paul Horton: Incorporating sequence quality data into alignment improves DNA read mapping. (PubMed,eprint) Nucl. Acids Res. 38(7):e100 (2010)
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Libvcflib-tools
C++ library for parsing and manipulating VCF files (tools)
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The Variant Call Format (VCF) is a flat-file, tab-delimited textual format intended to concisely describe reference-indexed variations between individuals. VCF provides a common interchange format for the description of variation in individuals and populations of samples, and has become the defacto standard reporting format for a wide array of genomic variant detectors.

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

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

This package contains several tools using the library.

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

Please cite: Yong Zhang, Tao Liu, Clifford A Meyer, Jérôme Eeckhoute, David S. Johnson, Bradley E. Bernstein, Chad Nussbaum, Richard M. Myers, Myles Brown, Wei Li and X Shirley Liu: Model-based Analysis of ChIP-Seq (MACS). (PubMed,eprint) Genome Biol. 9(9):R137 (2008)
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Mapdamage
tracking and quantifying damage patterns in ancient DNA sequences
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MapDamage is a computational framework written in Python and R, which tracks and quantifies DNA damage patterns among ancient DNA sequencing reads generated by Next-Generation Sequencing platforms.

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

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

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

Mapsembler2 may be used for (not limited to):

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

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

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

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

Please cite: Heng Li, Jue Ruan and Richard Durbin: Mapping short DNA sequencing reads and calling variants using mapping quality scores. (PubMed,eprint) Genome Research 18(11):1851-1858 (2008)
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Mhap
locality-sensitive hashing to detect long-read overlaps
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The MinHash Alignment Process (MHAP--pronounced MAP) is a reference implementation of a probabilistic sequence overlapping algorithm. Designed to efficiently detect all overlaps between noisy long-read sequence data. It efficiently estimates Jaccard similarity by compressing sequences to their representative fingerprints composed on min-mers (minimum k-mer).

Please cite: Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James P Drake, Jane M Landolin and Adam M Phillippy: Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. (PubMed) Nature Biotechnology 33(6):623–630 (2015)
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Microbiomeutil
Microbiome Analysis Utilities
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The microbiomeutil package comes with the following utilities:

  • ChimeraSlayer: ChimeraSlayer for chimera detection.
  • NAST-iEr: NAST-based alignment tool.
  • WigeoN: A reimplementation of the Pintail 16S anomaly detection utility
  • RESOURCES: Reference 16S sequences and NAST-alignments that the tools above leverage.
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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Mira-assembler
Whole Genome Shotgun and EST Sequence Assembler
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The mira genome fragment assembler is a specialised assembler for sequencing projects classified as 'hard' due to high number of similar repeats. For expressed sequence tags (ESTs) transcripts, miraEST is specialised on reconstructing pristine mRNA transcripts while detecting and classifying single nucleotide polymorphisms (SNP) occurring in different variations thereof.

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

The package provides the following executables: Binaries provided:

  • mira: for assembly of genome sequences
  • miramem: estimating memory needed to assemble projects.
  • mirabait: a "grep" like tool to select reads with kmers up to 256 bases.
  • miraconvert: is a tool to convert, extract and sometimes recalculate all kinds of data related to sequence assembly files.
Please cite: Bastien Chevreux, Thomas Pfisterer, Bernd Drescher, Albert J. Driesel, Werner E. G. Müller, Thomas Wetter and Sándor Suhai: Using the miraEST Assembler for Reliable and Automated mRNA Transcript Assembly and SNP Detection in Sequenced ESTs. (PubMed,eprint) Genome Research 14(6):1147-1159 (2004)
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Mothur
sequence analysis suite for research on microbiota
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Mothur seeks to develop a single piece of open-source, expandable software to fill the bioinformatics needs of the microbial ecology community. It has incorporated the functionality of dotur, sons, treeclimber, s-libshuff, unifrac, and much more. In addition to improving the flexibility of these algorithms, a number of other features including calculators and visualization tools were added.

Please cite: Patrick D Schloss, Sarah L Westcott, Thomas Ryabin, Justine R Hall, Martin Hartmann, Emily B Hollister, Ryan A Lesniewski, Brian B Oakley, Donovan H Parks, Courtney J Robinson, Jason W Sahl, Blaz Stres, Gerhard G Thallinger, David J Van Horn and Carolyn F Weber: Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. (PubMed) Appl Environ Microbiol 75(23):7537-7541 (2009)
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Nanopolish
consensus caller for nanopore sequencing data
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Nanopolish uses a signal-level hidden Markov model for consensus calling of nanopore genome sequencing data. It can perform signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more.

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Paleomix
pipelines and tools for the processing of ancient and modern HTS data
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The PALEOMIX pipelines are a set of pipelines and tools designed to aid the rapid processing of High-Throughput Sequencing (HTS) data: The BAM pipeline processes de-multiplexed reads from one or more samples, through sequence processing and alignment, to generate BAM alignment files useful in downstream analyses; the Phylogenetic pipeline carries out genotyping and phylogenetic inference on BAM alignment files, either produced using the BAM pipeline or generated elsewhere; and the Zonkey pipeline carries out a suite of analyses on low coverage equine alignments, in order to detect the presence of F1-hybrids in archaeological assemblages. In addition, PALEOMIX aids in metagenomic analysis of the extracts.

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

Please cite: Mikkel Schubert, Luca Ermini, Clio Der Sarkissian, Hákon Jónsson, Aurélien Ginolhac, Robert Schaefer, Michael D Martin, Ruth Fernández, Martin Kircher, Molly McCue, Eske Willerslev and Ludovic Orlando: Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. (PubMed) Nature Protocols 9(5):1056-82 (2014)
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Pbh5tools
tools for manipulating Pacific Biosciences HDF5 files
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This package provides functionality for manipulating and extracting data from cmp.h5 and bas.h5 files produced by the Pacific Biosciences sequencers. cmp.h5 files contain alignment information while bas.h5 files contain base-call information.

This package is part of the SMRTAnalysis suite.

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

PBHoney is part of the PBSuite.

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

PBJelly is part of the PBSuite.

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Pbsuite
software for Pacific Biosciences sequencing data
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The PBSuite contains two projects created for analysis of Pacific Biosciences long-read sequencing data.

  • PBJelly - genome upgrading tool
  • PBHoney - structural variation discovery
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Picard-tools
Command line tools to manipulate SAM and BAM files
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SAM (Sequence Alignment/Map) format is a generic format for storing large nucleotide sequence alignments. Picard Tools includes these utilities to manipulate SAM and BAM files:

 AddCommentsToBam                  FifoBuffer
 AddOrReplaceReadGroups            FilterSamReads
 BaitDesigner                      FilterVcf
 BamIndexStats                     FixMateInformation
 BamToBfq                          GatherBamFiles
 BedToIntervalList                 GatherVcfs
 BuildBamIndex                     GenotypeConcordance
 CalculateHsMetrics                IlluminaBasecallsToFastq
 CalculateReadGroupChecksum        IlluminaBasecallsToSam
 CheckIlluminaDirectory            LiftOverIntervalList
 CheckTerminatorBlock              LiftoverVcf
 CleanSam                          MakeSitesOnlyVcf
 CollectAlignmentSummaryMetrics    MarkDuplicates
 CollectBaseDistributionByCycle    MarkDuplicatesWithMateCigar
 CollectGcBiasMetrics              MarkIlluminaAdapters
 CollectHiSeqXPfFailMetrics        MeanQualityByCycle
 CollectIlluminaBasecallingMetrics MergeBamAlignment
 CollectIlluminaLaneMetrics        MergeSamFiles
 CollectInsertSizeMetrics          MergeVcfs
 CollectJumpingLibraryMetrics      NormalizeFasta
 CollectMultipleMetrics            PositionBasedDownsampleSam
 CollectOxoGMetrics                QualityScoreDistribution
 CollectQualityYieldMetrics        RenameSampleInVcf
 CollectRawWgsMetrics              ReorderSam
 CollectRnaSeqMetrics              ReplaceSamHeader
 CollectRrbsMetrics                RevertOriginalBaseQualitiesAndAddMateCigar
 CollectSequencingArtifactMetrics  RevertSam
 CollectTargetedPcrMetrics         SamFormatConverter
 CollectVariantCallingMetrics      SamToFastq
 CollectWgsMetrics                 ScatterIntervalsByNs
 CompareMetrics                    SortSam
 CompareSAMs                       SortVcf
 ConvertSequencingArtifactToOxoG   SplitSamByLibrary
 CreateSequenceDictionary          SplitVcfs
 DownsampleSam                     UpdateVcfSequenceDictionary
 EstimateLibraryComplexity         ValidateSamFile
 ExtractIlluminaBarcodes           VcfFormatConverter
 ExtractSequences                  VcfToIntervalList
 FastqToSam                        ViewSam
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Topics: Sequencing; Document, record and content management
Pirs
Profile based Illumina pair-end Reads Simulator
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The program pIRS can be used for simulating Illumina PE reads, with a series of characters generated by Illumina sequencing platform, such as insert size distribution, sequencing error(substitution, insertion, deletion), quality score and GC content-coverage bias.

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

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

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

Please cite: Xuesong Hu, Jianying Yuan, Yujian Shi, Jianliang Lu, Binghang Liu, Zhenyu Li, Yanxiang Chen, Desheng Mu, Hao Zhang, Nan Li, Zhen Yue, Fan Bai, Heng Li and Wei Fan: pIRS: Profile-based Illumina pair-end reads simulator. (PubMed,eprint) Bioinformatics 28(11):1533-5 (2012)
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Placnet
Plasmid Constellation Network project
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Placnet is a new tool for plasmid analysis in NGS projects. Placnet is optimized to work with Illumina sequences but it also works with 454, Iontorrent or any of the actual sequence technologies.

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

Please cite: Val F. Lanza, María de Toro, M. Pilar Garcillán-Barcia, Azucena Mora, Jorge Blanco, Teresa M. Coque and Fernando de la Cruz: Plasmid Flux in Escherichia coli ST131 Sublineages, Analyzed by Plasmid Constellation Network (PLACNET), a New Method for Plasmid Reconstruction from Whole Genome Sequences. (PubMed,eprint) PLOS 10(12):e1004766 (2014)
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Poretools
toolkit for nanopore nucleotide sequencing data
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poretools is a flexible toolkit for exploring datasets generated by nanopore sequencing devices from MinION for the purposes of quality control and downstream analysis. Poretools operates directly on the native FAST5 (a variant of the HDF5 standard) file format produced by ONT and provides a wealth of format conversion utilities and data exploration and visualization tools.

Please cite: Nicholas Loman and Aaron Quinlan: Poretools: a toolkit for analyzing nanopore sequence data. (PubMed,eprint) Bioinformatics 30(23):3399-3401 (2014)
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Python3-airr
Data Representation Standard library for antibody and TCR sequences
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This package provides a library by the AIRR community to for describing, reporting, storing, and sharing adaptive immune receptor repertoire (AIRR) data, such as sequences of antibodies and T cell receptors (TCRs). Some specific efforts include:

  • The MiAIRR standard for describing minimal information about AIRR datasets, including sample collection and data processing information.
  • Data representations (file format) specifications for storing large amounts of annotated AIRR data.
  • APIs for exposing a common interface to repositories/databases containing AIRR data.
  • A community standard for software tools which will allow conforming tools to gain community recognition.

This package installs the library for Python 3.

Python3-gffutils
Work with GFF and GTF files in a flexible database framework
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A Python package for working with and manipulating the GFF and GTF format files typically used for genomic annotations. Files are loaded into a sqlite3 database, allowing much more complex manipulation of hierarchical features (e.g., genes, transcripts, and exons) than is possible with plain-text methods alone.

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Python3-presto
toolkit for processing B and T cell sequences
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pRESTO is a toolkit for processing raw reads from high-throughput sequencing of B cell and T cell repertoires.

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

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Python3-pybedtools
Python 3 wrapper around BEDTools for bioinformatics work
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The BEDTools suite of programs is widely used for genomic interval manipulation or “genome algebra”. pybedtools wraps and extends BEDTools and offers feature-level manipulations from within Python.

This is the Python 3 version.

Please cite: R. K. Dale, B. S. Pedersen and A. R. Quinlan: Pybedtools: a flexible Python library for manipulating genomic datasets and annotations". Bioinformatics 27(24):3423-3424 (2011)
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Python3-sqt
SeQuencing Tools for biological DNA/RNA high-throughput data
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sqt is a collection of command-line tools for working with high-throughput sequencing data. Conceptionally not fixed to use any particular language, many sqt subcommands are currently implemented in Python. For them, a Python package is available with functions for reading and writing FASTA/FASTQ files, computing alignments, quality trimming, etc.

The following tools are offered:

  • sqt-coverage -- Compute per-reference statistics such as coverage and GC content
  • sqt-fastqmod -- FASTQ modifications: shorten, subset, reverse complement, quality trimming.
  • sqt-fastastats -- Compute N50, min/max length, GC content etc. of a FASTA file
  • sqt-qualityguess -- Guess quality encoding of one or more FASTA files.
  • sqt-globalalign -- Compute a global or semiglobal alignment of two strings.
  • sqt-chars -- Count length of the first word given on the command line.
  • sqt-sam-cscq -- Add the CS and CQ tags to a SAM file with colorspace reads.
  • sqt-fastamutate -- Add substitutions and indels to sequences in a FASTA file.
  • sqt-fastaextract -- Efficiently extract one or more regions from an indexed FASTA file.
  • sqt-translate -- Replace characters in FASTA files (like the 'tr' command).
  • sqt-sam-fixn -- Replace all non-ACGT characters within reads in a SAM file.
  • sqt-sam-insertsize -- Mean and standard deviation of paired-end insert sizes.
  • sqt-sam-set-op -- Set operations (union, intersection, ...) on SAM/BAM files.
  • sqt-bam-eof -- Check for the End-Of-File marker in compressed BAM files.
  • sqt-checkfastqpe -- Check whether two FASTQ files contain correctly paired paired-end data.
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Qcumber
quality control of genomic sequences
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QCPipeline is a tool for quality control. The workflow is as follows:

 1. Quality control with FastQC
 2. Trim Reads with Trimmomatic
 3. Quality control of trimmed reads with FastQC
 4. Map reads against reference using bowtie2
 5. Classify reads with Kraken
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Qiime
Quantitative Insights Into Microbial Ecology
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QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:

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

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

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

Please cite: Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A Bokulich, Christian Abnet, Gabriel A Al-Ghalith, Harriet Alexander, Eric J Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J Brislawn, C Titus Brown, Benjamin J Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily Cope, Ricardo Da Silva, Pieter C Dorrestein, Gavin M Douglas, Daniel M Durall, Claire Duvallet, Christian F Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M Gauglitz, Deanna L Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K Jarmusch, Lingjing Jiang, Benjamin Kaehler, Kyo Bin Kang, Christopher R Keefe, Paul Keim, Scott T Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan GI Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J McIver, Alexey V Melnik, Jessica L Metcalf, Sydney C Morgan, Jamie Morton, Ahmad Turan Naimey, Jose A Navas-Molina, Louis Felix Nothias, Stephanie B Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R Spear, Austin D Swafford, Luke R Thompson, Pedro J Torres, Pauline Trinh, Anupriya Tripathi, Peter J Turnbaugh, Sabah Ul-Hasan, Justin JJ van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C Weber, Chase HD Williamson, Amy D Willis, Zhenjiang Zech Xu, Jesse R Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight and J Gregory Caporaso: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. (PubMed,eprint) Nature Biotechnology 37:852 - 857 (2019)
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Topics: Microbial ecology
Quorum
QUality Optimized Reads of genomic sequences
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QuorUM enables to obtain trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. QuorUM provides best performance compared to other published error correctors in several metrics. QuorUM is efficiently implemented making use of current multi- core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core). The third-party assembler (SOAPdenovo) benefits significantly from using QuorUM error- corrected reads. QuorUM error corrected reads result in a factor of 1.1 to 4 improvement in N50 contig size compared to using the original reads with SOAPdenovo for the data sets investigated.

Please cite: Guillaume Marçais, James A. Yorke and Aleksey Zimin: QuorUM: An Error Corrector for Illumina Reads. (PubMed,eprint) PLoS One 10(6):e0130821 (2015)
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R-bioc-deseq2
R package for RNA-Seq Differential Expression Analysis
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Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Please cite: Michael I Love, Wolfgang Huber and Simon Anders: Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq}2. (eprint) Genome Biol 15(12) (2014)
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R-bioc-hilbertvis
GNU R package to visualise long vector data
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This tool allows one to display very long data vectors in a space-efficient manner, by organising it along a 2D Hilbert curve. The user can then visually judge the large scale structure and distribution of features simultaenously with the rough shape and intensity of individual features.

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

Please cite: Simon Anders: Visualization of genomic data with the Hilbert curve. (PubMed,eprint) Bioinformatics 25(10):1231-1235 (2009)
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R-bioc-metagenomeseq
GNU R statistical analysis for sparse high-throughput sequencing
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MetagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

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R-cran-alakazam
Immunoglobulin Clonal Lineage and Diversity Analysis
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Alakazam is part of the Immcantation analysis framework for Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) and provides a set of tools to investigate lymphocyte receptor clonal lineages, diversity, gene usage, and other repertoire level properties, with a focus on high-throughput immunoglobulin (Ig) sequencing.

Alakazam serves five main purposes:

  • Providing core functionality for other R packages in the Immcantation framework. This includes common tasks such as file I/O, basic DNA sequence manipulation, and interacting with V(D)J segment and gene annotations.
  • Providing an R interface for interacting with the output of the pRESTO and Change-O tool suites.
  • Performing lineage reconstruction on clonal populations of Ig sequences and analyzing the topology of the resultant lineage trees.
  • Performing clonal abundance and diversity analysis on lymphocyte repertoires.
  • Performing physicochemical property analyses of lymphocyte receptor sequences.
Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. (eprint) 31:3356–3358 (2017)
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R-cran-shazam
Immunoglobulin Somatic Hypermutation Analysis
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Provides a computational framework for Bayesian estimation of antigen-driven selection in immunoglobulin (Ig) sequences, providing an intuitive means of analyzing selection by quantifying the degree of selective pressure. Also provides tools to profile mutations in Ig sequences, build models of somatic hypermutation (SHM) in Ig sequences, and make model-dependent distance comparisons of Ig repertoires.

SHazaM is part of the Immcantation analysis framework for Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) and provides tools for advanced analysis of somatic hypermutation (SHM) in immunoglobulin (Ig) sequences. Shazam focuses on the following analysis topics:

  • Quantification of mutational load SHazaM includes methods for determine the rate of observed and expected mutations under various criteria. Mutational profiling criteria include rates under SHM targeting models, mutations specific to CDR and FWR regions, and physicochemical property dependent substitution rates.
  • Statistical models of SHM targeting patterns Models of SHM may be divided into two independent components: 1) a mutability model that defines where mutations occur and 2) a nucleotide substitution model that defines the resulting mutation. Collectively these two components define an SHM targeting model. SHazaM provides empirically derived SHM 5-mer context mutation models for both humans and mice, as well tools to build SHM targeting models from data.
  • Analysis of selection pressure using BASELINe The Bayesian Estimation of Antigen-driven Selection in Ig Sequences (BASELINe) method is a novel method for quantifying antigen-driven selection in high-throughput Ig sequence data. BASELINe uses SHM targeting models can be used to estimate the null distribution of expected mutation frequencies, and provide measures of selection pressure informed by known AID targeting biases.
  • Model-dependent distance calculations SHazaM provides methods to compute evolutionary distances between sequences or set of sequences based on SHM targeting models. This information is particularly useful in understanding and defining clonal relationships.
Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.. (PubMed,eprint) Bioinformatics 31(20):3356-3358 (2015)
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R-cran-tcr
Advanced Data Analysis of Immune Receptor Repertoires
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Cells of the immune system are the grand exception to the rule that all cells of an individuum have (mostly exact) copies of the same DNA. B cells (which produce antibodies) and T cells (which communicate with cells) however have a section of their DNA with genes of the groups V, D and J that are reorganised within the genomic DNA to provide the flexibility to deal with yet unknown pathogens.

This package provides a platform for the advanced analysis of T cell receptor repertoire data and its visualisations.

Please cite: Vadim I. Nazarov, Mikhail V. Pogorelyy, Ekaterina A. Komech, Ivan V. Zvyagin, Dmitry A. Bolotin, Mikhail Shugay, Dmitry M. Chudakov, Yury B. Lebedev and Ilgar Z. Mamedov: tcR: an R package for T cell receptor repertoire advanced data analysis. (eprint) BMC Bioinformatics 16:175 (2015)
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R-cran-tigger
Infers new Immunoglobulin alleles from Rep-Seq Data
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Summary: Infers the V genotype of an individual from immunoglobulin (Ig) repertoire-sequencing (Rep-Seq) data, including detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences.

High-throughput sequencing of B cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline V, D and J gene segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that utilize previously undetected alleles, whose frequency in the population is unclear.

TIgGER is a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual (including novel alleles) from V(D)J-rearrange sequences. TIgGER can then infer a subject’s genotype from these sequences, and use this genotype to correct the initial V(D)J allele assignments.

The application of TIgGER continues to identify a surprisingly high frequency of novel alleles in humans, highlighting the critical need for this approach. TIgGER, however, can and has been used with data from other species.

Core Abilities:

  • Detecting novel alleles
  • Inferring a subject’s genotype
  • Correcting preliminary allele calls

Required Input

  • A table of sequences from a single individual, with columns containing the following:
  • V(D)J-rearranged nucleotide sequence (in IMGT-gapped format)
  • Preliminary V allele calls
  • Preliminary J allele calls
  • Length of the junction region
  • Germline Ig sequences in IMGT-gapped fasta format (e.g., as those downloaded from IMGT/GENE-DB)

The former can be created through the use of IMGT/HighV-QUEST and Change-O.

Please cite: Namita T. Gupta, Jason A. Vander Heiden, Mohamed Uduman, Daniel Gadala-Maria, Gur Yaari and Steven H. Kleinstein: Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. (eprint) 31:3356–3358 (2017)
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Rna-star
ultrafast universal RNA-seq aligner
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Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, the authors experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy.

Please cite: Alexander Dobin, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson and Thomas R. Gingeras: STAR: ultrafast universal RNA-seq aligner. (PubMed,eprint) Bioinformatics 29(1):15-21 (2012)
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Topics: Sequence analysis
Rtax
Classification of sequence reads of 16S ribosomal RNA gene
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Short-read technologies for microbial community profiling are increasingly popular, yet previous techniques for assigning taxonomy to paired-end reads perform poorly. RTAX provides rapid taxonomic assignments of paired-end reads using a consensus algorithm.

Please cite: David A. W. Soergel, Neelendu Dey, Rob Knight and Steven E. Brenner: Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. (PubMed,eprint) The ISME Journal 6:1440–1444 (2012)
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Salmon
wicked-fast transcript quantification from RNA-seq data
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Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. Salmon achieves is accuracy and speed via a number of different innovations, including the use of lightweight alignments (accurate but fast-to-compute proxies for traditional read alignments) and massively-parallel stochastic collapsed variational inference. The result is a versatile tool that fits nicely into many different pipelines. For example, you can choose to make use of the lightweight alignments by providing Salmon with raw sequencing reads, or, if it is more convenient, you can provide Salmon with regular alignments (e.g. computed with your favorite aligner), and it will use the same wicked-fast, state-of-the-art inference algorithm to estimate transcript-level abundances for your experiment.

Please cite: Rob Patro, Geet Duggal and Carl Kingsford: Accurate, fast, and model-aware transcript expression quantification with Salmon. (eprint) bioRxiv (2015)
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Sambamba
tools for working with SAM/BAM data
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Sambamba positions itself as a performant alternative to samtools and provides tools for

  • Powerful filtering with sambamba view --filter
  • Picard-like SAM header merging in the merge tool
  • Optional for operations on whole BAMs
  • Fast copying of a region to a new file with the slice tool
  • Duplicate marking/removal, using the Picard criteria
Please cite: Artem Tarasov, Albert J. Vilella, Edwin Cuppen, Isaac J. Nijman and Pjotr Prins: Sambamba: fast processing of NGS alignment formats. (PubMed,eprint) Bioinformatics 31(12):2032-2034 (2015)
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Samblaster
marks duplicates, extracts discordant/split reads
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Current "next-generation" sequencing technologies cannot tell what exact sequence they will be reading. They take what is available. And if some sequences are read very often, then this needs some extra biomedical thinking. The genome could for instance be duplicated.

samblaster is a fast and flexible program for marking duplicates in read-id grouped paired-end SAM files. It can also optionally output discordant read pairs and/or split read mappings to separate SAM files, and/or unmapped/clipped reads to a separate FASTQ file. When marking duplicates, samblaster will require approximately 20MB of memory per 1M read pairs.

Please cite: Gregory G. Faust and Ira M. Hall: SAMBLASTER: fast duplicate marking and structural variant read extraction. (PubMed,eprint) Bioinformatics 30(17):2503-2505 (2014)
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Samtools
processing sequence alignments in SAM, BAM and CRAM formats
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Samtools is a set of utilities that manipulate nucleotide sequence alignments in the binary BAM format. It imports from and exports to the ascii SAM (Sequence Alignment/Map) and CRAM formats, does sorting, merging and indexing, and allows one to retrieve reads in any regions swiftly. It is designed to work on a stream, and is able to open a BAM or CRAM (not SAM) file on a remote FTP or HTTP server.

The package is enhanced by the following packages: libbio-samtools-perl
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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Scoary
pangenome-wide association studies
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Scoary is designed to take the gene_presence_absence.csv file from Roary as well as a traits file created by the user and calculate the associations between all genes in the accessory genome and the traits. It reports a list of genes sorted by strength of association per trait.

Please cite: Ola Brynildsrud, Jon Bohlin, Lonneke Scheffer and Vegard Eldholm: Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. (PubMed,eprint) Genome Biology 17(238) (2016)
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Scythe
Bayesian adaptor trimmer for sequencing reads
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Scythe uses a Naive Bayesian approach to classify contaminant substrings in sequence reads. It considers quality information, which can make it robust in picking out 3'-end adapters, which often include poor quality bases.

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Seqprep
stripping adaptors and/or merging paired reads of DNA sequences with overlap
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SeqPrep is a program to merge paired end Illumina reads that are overlapping into a single longer read. It may also just be used for its adapter trimming feature without doing any paired end overlap. When an adapter sequence is present, that means that the two reads must overlap (in most cases) so they are forcefully merged. When reads do not have adapter sequence they must be treated with care when doing the merging, so a much more specific approach is taken. The default parameters were chosen with specificity in mind, so that they could be ran on libraries where very few reads are expected to overlap. It is always safest though to save the overlapping procedure for libraries where you have some prior knowledge that a significant portion of the reads will have some overlap.

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Seqtk
Fast and lightweight tool for processing sequences in the FASTA or FASTQ format
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Currently, seqtk supports quality based trimming with the phred algorithm, converting fastq to fasta, reverse complementing sequences, extracting or masking subsequences in regions given in a BED/name list file, and more. It contains a subsampling module to sample exactly n sequences or a fraction of sequences.

Seqtk supports both fasta and fastq input files, which can be optionally gzip compressed.

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Sga
de novo genome assembler that uses string graphs
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The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

SGA is a de novo assembler for DNA sequence reads. It is based on Gene Myers' string graph formulation of assembly and uses the FM-index/Burrows-Wheeler transform to efficiently find overlaps between sequence reads.

Please cite: Jared T. Simpson and Richard Durbin: Efficient de novo assembly of large genomes using compressed data structures.. (PubMed,eprint) Genome Res 22(3):549-555 (2012)
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Sickle
windowed adaptive trimming tool for FASTQ files using quality
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Most modern sequencing technologies produce reads that have deteriorating quality towards the 3'-end. Incorrectly called bases here negatively impact assembles, mapping, and downstream bioinformatics analyses.

Sickle is a tool that uses sliding windows along with quality and length thresholds to determine when quality is sufficiently low to trim the 3'-end of reads. It will also discard reads based upon the length threshold. It takes the quality values and slides a window across them whose length is 0.1 times the length of the read. If this length is less than 1, then the window is set to be equal to the length of the read. Otherwise, the window slides along the quality values until the average quality in the window drops below the threshold. At that point the algorithm determines where in the window the drop occurs and cuts both the read and quality strings there. However, if the cut point is less than the minimum length threshold, then the read is discarded entirely.

Sickle supports four types of quality values: Illumina, Solexa, Phred, and Sanger. Note that the Solexa quality setting is an approximation (the actual conversion is a non-linear transformation). The end approximation is close.

Sickle also supports gzipped file inputs.

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Smalt
Sequence Mapping and Alignment Tool
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SMALT efficiently aligns DNA sequencing reads with a reference genome. Reads from a wide range of sequencing platforms, for example Illumina, Roche-454, Ion Torrent, PacBio or ABI-Sanger, can be processed including paired reads.

The software employs a perfect hash index of short words (< 20 nucleotides long), sampled at equidistant steps along the genomic reference sequences.

For each read, potentially matching segments in the reference are identified from seed matches in the index and subsequently aligned with the read using a banded Smith-Waterman algorithm.

The best gapped alignments of each read is reported including a score for the reliability of the best mapping. The user can adjust the trade-off between sensitivity and speed by tuning the length and spacing of the hashed words.

A mode for the detection of split (chimeric) reads is provided. Multi-threaded program execution is supported.

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Remark of Debian Med team: This can be regarded as successor of ssaha2

This program is from the same author as ssaha2 and according to its author faster and more precise than ssaha2 (except for sequences > 2000bp).

Smrtanalysis
software suite for single molecule, real-time sequencing
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SMRT® Analysis is a powerful, open-source bioinformatics software suite available for analysis of DNA sequencing data from Pacific Biosciences’ SMRT technology. Users can choose from a variety of analysis protocols that utilize PacBio® and third-party tools. Analysis protocols include de novo genome assembly, cDNA mapping, DNA base-modification detection, and long-amplicon analysis to determine phased consensus sequences.

This is a metapackage that depends on the components of SMRT Analysis.

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Snap-aligner
Scalable Nucleotide Alignment Program
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SNAP is a new sequence aligner that is 3-20x faster and just as accurate as existing tools like BWA-mem, Bowtie2 and Novoalign. It runs on commodity x86 processors, and supports a rich error model that lets it cheaply match reads with more differences from the reference than other tools. This gives SNAP up to 2x lower error rates than existing tools (in some cases) and lets it match larger mutations that they may miss. SNAP also natively reads BAM, FASTQ, or gzipped FASTQ, and natively writes SAM or BAM, with built-in sorting, duplicate marking, and BAM indexing.

Please cite: Matei Zaharia, William J. Bolosky, Kristal Curtis, Armando Fox, David Patterson, Scott Shenker, Ion Stoica, Richard M. Karp and Taylor Sittler: Faster and More Accurate Sequence Alignment with SNAP. (eprint) arXiv preprint arXiv:1111.5572 (2011)
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Sniffles
structural variation caller using third-generation sequencing
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Sniffles is a structural variation (SV) caller using third-generation sequencing data such as those from Pacific Biosciences or Oxford Nanopore platforms. It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis.

Please cite: Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler and Michael Schatz: Accurate detection of complex structural variations using single molecule sequencing. (eprint) bioRxiv (2017)
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Snp-sites
Binary code for the package snp-sites
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This program finds single nucleotide polymorphism (SNP) sites from multi-fasta alignment input files (which might be compressed). Its output can be in various widely used formats (Multi Fasta Alignment, Vcf, phylip).

The software has been developed at the Wellcome Trust Sanger Institute.

A Single Nucleotide - polymorphism (SNP, pronounced snip; plural snips) is a DNA sequence variation occurring when a Single Nucleotide — A, T, C or G — in the genome (or other shared sequence) differs between members of a biological species or paired chromosomes. For example, two sequenced DNA fragments from different individuals, AAGCCTA to AAGCTTA, contain a difference in a single nucleotide. In this case there are two alleles. Almost all common SNPs have only two alleles.

Please cite: Andrew J. Page, Ben Taylor, Aidan J. Delaney, Jorge Soares, Torsten Seemann, Jacqueline A. Keane and Simon R. Harris: SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. (eprint) Microbial Genomics 2(4) (2016)
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Topics: Genetic variation
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Snpomatic
fast, stringent short-read mapping software
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High throughput sequencing technologies generate large amounts of short reads. Mapping these to a reference sequence consumes large amounts of processing time and memory, and read mapping errors can lead to noisy or incorrect alignments.

SNP-o-matic is a fast, stringent short-read mapping software. It supports a multitude of output types and formats, for uses in filtering reads, alignments, sequence-based genotyping calls, assisted reassembly of contigs etc.

Please cite: Heinrich Magnus Manske and Dominic P. Kwiatkowski: SNP-o-matic. (PubMed,eprint) Bioinformatics 25(18):2434-2435 (2009)
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Topics: Genetic variation; Mapping
Soapdenovo
short-read assembly method to build de novo draft assembly
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SOAPdenovo is a novel short-read assembly method that can build a de novo draft assembly for the human-sized genomes. The program is specially designed to assemble Illumina GA short reads.

It creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost effective way.

This version is not maintained anymore, consider using soapdenovo2.

Please cite: Ruiqiang Li, Hongmei Zhu, Jue Ruan, Wubin Qian, Xiaodong Fang, Zhongbin Shi, Yingrui Li, Shengting Li, Gao Shan, Karsten Kristiansen, Songgang Li, Huanming Yang, Jian Wang and Jun Wang: De novo assembly of human genomes with massively parallel short read sequencing. (PubMed,eprint) Genome Research 20(2):265-72 (2009)
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Soapdenovo2
short-read assembly method to build de novo draft assembly
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SOAPdenovo is a novel short-read assembly method that can build a de novo draft assembly for the human-sized genomes. The program is specially designed to assemble Illumina GA short reads.

It creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost effective way.

Please cite: Ruibang Luo, Binghang Liu, Yinlong Xie, Zhenyu Li, Weihua Huang, Jianying Yuan, Guangzhu He, Yanxiang Chen, Qi Pan, Yunjie Liu, Jingbo Tang, Gengxiong Wu, Hao Zhang, Yujian Shi, Yong Liu, Chang Yu, Bo Wang, Yao Lu, Changlei Han, David W Cheung, Siu-Ming Yiu, Shaoliang Peng, Zhu Xiaoqian, Guangming Liu, Xiangke Liao, Yingrui Li, Huanming Yang, Jian Wang, Tak-Wah Lam and Jun Wang: SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Giga Science 1(1):18 (2012)
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Sortmerna
tool for filtering, mapping and OTU-picking NGS reads
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SortMeRNA is a biological sequence analysis tool for filtering, mapping and OTU-picking NGS reads. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering rRNA from metatranscriptomic data. Additional applications include OTU-picking and taxonomy assignation available through QIIME v1.9+ (http://qiime.org - v1.9.0-rc1). SortMeRNA takes as input a file of reads (fasta or fastq format) and one or multiple rRNA database file(s), and sorts apart rRNA and rejected reads into two files specified by the user. Optionally, it can provide high quality local alignments of rRNA reads against the rRNA database. SortMeRNA works with Illumina, 454, Ion Torrent and PacBio data, and can produce SAM and BLAST-like alignments.

Please cite: Evguenia Kopylova, Laurent Noé and Hélène Touzet: SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data". (PubMed,eprint) Bioinformatics 28(24):3211-3217 (2012)
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Spades
genome assembler for single-cell and isolates data sets
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The SPAdes – St. Petersburg genome assembler is intended for both standard isolates and single-cell MDA bacteria assemblies. It works with Illumina or IonTorrent reads and is capable of providing hybrid assemblies using PacBio and Sanger reads. You can also provide additional contigs that will be used as long reads.

Please cite: Anton Bankevich, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, Sergey I. Nikolenko, Son Pham, Andrey D. Prjibelski, Alexey V. Pyshkin, Alexander V. Sirotkin, Nikolay Vyahhi, Glenn Tesler, Max A. Alekseyev and Pavel A. Pevzner: SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. (PubMed,eprint) Journal of Computational Biology 19(5):455-477 (2012)
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Sprai
single-pass sequencing read accuracy improver
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Sprai is a tool to correct sequencing errors in single-pass reads for de novo assembly. It is originally designed for correcting sequencing errors in single-molecule DNA sequencing reads, especially in Continuous Long Reads (CLRs) generated by PacBio RS sequencers. The goal of Sprai is not maximizing the accuracy of error-corrected reads. Instead, Sprai aims at maximizing the continuity (i.e., N50 contig length) of assembled contigs after error correction.

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Sra-toolkit
utilities for the NCBI Sequence Read Archive
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Tools for reading the SRA archive, generally by converting individual runs into some commonly used format such as fastq.

The textual dumpers "sra-dump" and "vdb-dump" are provided in this release as an aid in visual inspection. It is likely that their actual output formatting will be changed in the near future to a stricter, more formalized representation[s]. PLEASE DO NOT RELY UPON THE OUTPUT FORMAT SEEN IN THIS RELEASE.

The "help" information will be improved in near future releases, and the tool options will become standardized across the set. More documentation will also be provided documentation on the NCBI web site.

Tool options may change in the next release. Version 1 tool options will remain supported wherever possible in order to preserve operation of any existing scripts.

Please cite: Rasko Leinonen, Ruth Akhtar, Ewan Birney, James Bonfield, Lawrence Bower, Matt Corbett, Ying Cheng, Fehmi Demiralp, Nadeem Faruque, Neil Goodgame, Richard Gibson, Gemma Hoad, Christopher Hunter, Mikyung Jang, Steven Leonard, Quan Lin, Rodrigo Lopez, Michael Maguire, Hamish McWilliam, Sheila Plaister, Rajesh Radhakrishnan, Siamak Sobhany, Guy Slater, Petra Ten Hoopen, Franck Valentin, Robert Vaughan, Vadim Zalunin, Daniel Zerbino and Guy Cochrane: Improvements to services at the European Nucleotide Archive. (PubMed,eprint) Nucleic Acids Research 38(Database issue):D39-45 (2010)
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Srst2
Short Read Sequence Typing for Bacterial Pathogens
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This program is designed to take Illumina sequence data, a MLST database and/or a database of gene sequences (e.g. resistance genes, virulence genes, etc) and report the presence of STs and/or reference genes.

Please cite: Michael Inouye, Harriet Dashnow, Lesley-Ann Raven, Mark B Schultz, Bernard J Pope, Takehiro Tomita, Justin Zobel and Kathryn E Holt: SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. (PubMed,eprint) Genome Medicine 6(11):90 (2014)
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Ssake
Genomová aplikace pro spojování miliónů velmi krátkých sekvencí DNA
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SSAKE (Short Sequence Assembly by K-mer search and 3′ read Extension) je aplikace pro agresivní montáž miliónů krátkých nukleotidových sekvencí progresivním hledáním perfektních 3′-most k-mers použitím prefixového DNA stromu. SSAKE je vytvořen tak, aby seskupil krátké sekvence do contig, které se používají pro charakterizaci nového seskupení.

Please cite: Rene L. Warren, Granger G. Sutton, Steven J. M. Jones and Robert A. Holt: Assembling millions of short DNA sequences using SSAKE. (PubMed,eprint) Bioinformatics 23(4):500-501 (2007)
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Topics: Sequence assembly
Stacks
pipeline for building loci from short-read DNA sequences
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Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.

Note that this package installs Stacks such that all commands must be run as: $ stacks

Please cite: Julian Catchen, Paul A. Hohenlohe, Susan Bassham, Angel Amores and William A. Cresko: Stacks: an analysis tool set for population genomics. (PubMed) Molecular Ecology 22(11):3124-40 (2013)
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Stringtie
assemble short RNAseq reads to transcripts
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The abundance of transcripts in a human tissue sample can be determined by RNA sequencing. The exact sequence sampled may be random, depending on the technology used. And it may be short, i.e. shorter than the transcript. At some point, many shorter reads need to be assembled to the model the complete transcripts.

StringTie knows how to assemble of RNA-Seq into potential transcripts without the need of a reference genome and provides a quantification also of the splice variants.

Please cite: Mihaela Pertea, Geo M. Pertea, Corina .M. Antonescu, Tsung-Cheng Chang, Joshua T. Mendell and Steven L. Salzberg: StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology 33:290–295 (2015)
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Subread
toolkit for processing next-gen sequencing data
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Subread aligner can be used to align both gDNA-seq and RNA-seq reads. Subjunc aligner was specified designed for the detection of exon-exon junction. For the mapping of RNA-seq reads, Subread performs local alignments and Subjunc performs global alignments.

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Sumaclust
fast and exact clustering of genomic sequences
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With the development of next-generation sequencing, efficient tools are needed to handle millions of sequences in reasonable amounts of time. Sumaclust is a program developed by the LECA. Sumaclust aims to cluster sequences in a way that is fast and exact at the same time. This tool has been developed to be adapted to the type of data generated by DNA metabarcoding, i.e. entirely sequenced, short markers. Sumaclust clusters sequences using the same clustering algorithm as UCLUST and CD- HIT. This algorithm is mainly useful to detect the 'erroneous' sequences created during amplification and sequencing protocols, deriving from 'true' sequences.

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Sumatra
fast and exact comparison and clustering of sequences
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With the development of next-generation sequencing, efficient tools are needed to handle millions of sequences in reasonable amounts of time. Sumatra is a program developed by the LECA. Sumatra aims to compare sequences in a way that is fast and exact at the same time. This tool has been developed to be adapted to the type of data generated by DNA metabarcoding, i.e. entirely sequenced, short markers. Sumatra computes the pairwise alignment scores from one dataset or between two datasets, with the possibility to specify a similarity threshold under which pairs of sequences that have a lower similarity are not reported. The output can then go through a classification process with programs such as MCL or MOTHUR.

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Tabix
generic indexer for TAB-delimited genome position files
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Tabix indexes files where some columns indicate sequence coordinates: name (usually a chromosome), start and stop. The input data file must be position sorted and compressed by bgzip (provided in this package), which has a gzip like interface. After indexing, tabix is able to quickly retrieve data lines by chromosomal coordinates. Fast data retrieval also works over network if an URI is given as a file name.

This version of tabix is built from the HTSlib source.

Please cite: Heng Li: Tabix: fast retrieval of sequence features from generic TAB-delimited files. (PubMed,eprint) Bioinformatics 27(5):718-719 (2011)
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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)
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Transrate-tools
helper for transrate
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Transrate is a library and command-line tool for quality assessment of de-novo transcriptome assemblies.

This package provides command line tools used by transrate to process BAM files.

Please cite: Richard Smith-Unna, Chris Boursnell, Rob Patro, Julian M. Hibberd and Steven Kelly: TransRate: reference-free quality assessment of de novo transcriptome assemblies.. (PubMed,eprint) Genome Research 26(8):1134-1144 (2016)
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Trimmomatic
flexible read trimming tool for Illumina NGS data
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Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data.The selection of trimming steps and their associated parameters are supplied on the command line.

The current trimming steps are:

  • ILLUMINACLIP: Cut adapter and other illumina-specific sequences from the read.
  • SLIDINGWINDOW: Perform a sliding window trimming, cutting once thes average quality within the window falls below a threshold.
  • LEADING: Cut bases off the start of a read, if below a threshold quality
  • TRAILING: Cut bases off the end of a read, if below a threshold quality
  • CROP: Cut the read to a specified length
  • HEADCROP: Cut the specified number of bases from the start of the read
  • MINLENGTH: Drop the read if it is below a specified length
  • TOPHRED33: Convert quality scores to Phred-33
  • TOPHRED64: Convert quality scores to Phred-64 It works with FASTQ (using phred + 33 or phred + 64 quality scores, depending on the Illumina pipeline used), either uncompressed or gzipp'ed FASTQ. Use of gzip format is determined based on the .gz extension.
Please cite: A.M. Bolger, M. Lohse and B. Usadel: Trimmomatic: a flexible trimmer for Illumina sequence data. (PubMed,eprint) Bioinformatics 30(15):2114-2120 (2014)
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Topics: Sequencing
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Trinityrnaseq
RNA-Seq De novo Assembly
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Trinity represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes.

Please cite: Manfred G Grabherr, Brian J Haas, Moran Yassour, Joshua Z Levin, Dawn A Thompson, Ido Amit, Xian Adiconis, Lin Fan, Raktima Raychowdhury, Qiandong Zeng, Zehua Chen, Evan Mauceli, Nir Hacohen, Andreas Gnirke, Nicholas Rhind, Federica di Palma, Bruce W Birren, Chad Nusbaum, Kerstin Lindblad-Toh, Nir Friedman and Aviv Regev: Full-length transcriptome assembly from RNA-Seq data without a reference genome.. (PubMed) Nature Biotechnology 29(7):644-652 (2011)
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Uc-echo
error correction algorithm designed for short-reads from NGS
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ECHO is an error correction algorithm designed for short-reads from next-generation sequencing platforms such as Illumina's Genome Analyzer II. The algorithm uses a Bayesian framework to improve the quality of the reads in a given data set by employing maximum a posteriori estimation.

Please cite: W.-C. Kao, A.H. Chan and Y.S. Song: ECHO: A reference-free short-read error correction algorithm. (PubMed,eprint) Genome Research 21:1181-1192 (2011)
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Topics: Data management; Sequencing
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)
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Velvet
Nucleic acid sequence assembler for very short reads
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Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.

Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired read information, if available, to retrieve the repeated areas between contigs.

Please cite: Daniel R. Zerbino and Ewan Birney: Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. (PubMed,eprint) Genome Research 18(5):821-829 (2008)
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Velvet-long
Nucleic acid sequence assembler for very short reads, long version
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Velvet is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.

Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired read information, if available, to retrieve the repeated areas between contigs.

This package installs special long-mode versions of Velvet, as recommended in the Velvet tutorials.

Please cite: Daniel R. Zerbino and Ewan Birney: Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. (PubMed,eprint) Genome Research 18(5):821-829 (2008)
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Velvetoptimiser
automatically optimise Velvet do novo assembly parameters
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VelvetOptimiser is a multi-threaded Perl script for automatically optimising the three primary parameter options (K, -exp_cov, -cov_cutoff) for the Velvet de novo sequence assembler.

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Vsearch
tool for processing metagenomic sequences
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Versatile 64-bit multithreaded tool for processing metagenomic sequences, including searching, clustering, chimera detection, dereplication, sorting, masking and shuffling

The aim of this project is to create an alternative to the USEARCH tool developed by Robert C. Edgar (2010). The new tool should:

  • have a 64-bit design that handles very large databases and much more than 4GB of memory
  • be as accurate or more accurate than usearch
  • be as fast or faster than usearch
Please cite: Torbjørn Rognes, Tomáš Flouri, Ben Nichols, Christopher Quince and Frédéric Mahé: VSEARCH: a versatile open source tool for metagenomics. (eprint) PeerJ 4:e2584
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Wham-align
Wisconsin's High-Throughput Alignment Method
Maintainer: Steffen Moeller
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This package provides functionality analogous to BWA or bowtie in aligning reads from next-generation DNA sequencing machines against a reference genome.

Please cite: Yinan Li, Allie Terrell and Jignesh M. Patel: WHAM: A High-throughput Sequence Alignment Method (eprint) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece (2011)
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Wigeon
reimplementation of the Pintail 16S DNA anomaly detection utility
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WigeoN examines the sequence conservation between a query and a trusted reference sequence, both in NAST alignment format. Based on the sequence identity between the query and the reference sequence, there is an expected amount of variation among the alignment. If the observed variation is greater than the 95% quantile of the distribution of variation observed between non-anomalous sequences, then it is flagged as an anomaly.

WigeoN is a flexible command-line based reimplementation of the Pintail algorithm Appl Environ Microbiol. 2005 Dec;7112:7724-36.

WigeoN is useful for flagging chimeras and anomalies only in near full-length 16S rRNA sequences. WigeoN lacks sensitivity with sequences less than 1000 bp.

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

WigeoN is part of the microbiomeutil suite.

The package is enhanced by the following packages: microbiomeutil-data
Please cite: Brian J. Haas, Dirk Gevers, Ashlee M. Earl, Mike Feldgarden, Doyle V. Ward, Georgia Giannoukos, Dawn Ciulla, Diana Tabbaa, Sarah K. Highlander, Erica Sodergren, Barbara Methé, Todd Z. DeSantis, The Human Microbiome Consortium, Joseph F. Petrosino, Rob Knight and Bruce W. Birren: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. (PubMed,eprint) Genome Research 21(3):494-504 (2011)
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Debian packages in contrib or non-free

Bcbio
toolkit for analysing high-throughput sequencing data
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This package installs the command line tools of the bcbio-nextgen toolkit implementing best-practice pipelines for fully automated high throughput sequencing analysis.

A high-level configuration file specifies inputs and analysis parameters to drive a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The project contributes a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.

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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)
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Igv
Integrative Genomics Viewer
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The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems.

Please cite: James T Robinson, Helga Thorvaldsdóttir, Wendy Winckler, Mitchell Guttman, Eric S Lander, Gad Getz and Jill P Mesirov: Integrative genomics viewer. (PubMed,eprint) Nature Biotechnology 29(1):24–26 (2011)
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Vdjtools
framework for post-analysis of B/T cell repertoires
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VDJtools is an open-source Java/Groovy-based framework designed to facilitate analysis of immune repertoire sequencing (RepSeq) data. VDJtools computes a wide set of statistics and is able to perform various forms of cross-sample analysis. Both comprehensive tabular output and publication-ready plots are provided.

The main aims of the VDJtools Project are:

  • To ensure consistency between post-analysis methods and results
  • To save the time of bioinformaticians analyzing RepSeq data
  • To create an API framework facilitating development of new RepSeq analysis applications
  • To provide a simple enough command line tool so it could be used by immunologists and biologists with little computational background
Please cite: Mikhail Shugay, Dmitriy V. Bagaev, Maria A. Turchaninova, Dmitriy A. Bolotin, Olga V. Britanova, Ekaterina V. Putintseva, Mikhail V. Pogorelyy, Vadim I. Nazarov, Ivan V. Zvyagin, Vitalina I. Kirgizova, Kirill I. Kirgizov, Elena V. Skorobogatova and Dmitriy M. Chudakov: VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires. (PubMed,eprint) PLoS Computational Biology 11(11):e1004503 (2015)
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Packaging has started and developers might try the packaging code in VCS

Mosaik-aligner
reference-guided aligner for next-generation sequencing
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Version: 2.2.30+20140627-1

MosaikBuild converts various sequence formats into Mosaik’s native read format. MosaikAligner pairwise aligns each read to a specified series of reference sequences. MosaikSort resolves paired-end reads and sorts the alignments by the reference sequence coordinates. Finally, MosaikText converts alignments to different text-based formats.

At this time, the workflow consists of supplying sequences in FASTA, FASTQ, Illumina Bustard & Gerald, or SRF file formats and producing results in the BLAT axt, the BAM/SAM, the UCSC Genome Browser bed, or the Illumina ELAND formats.

Umap
quantify genome and methylome mappability
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Umap identifies uniquely mappable regions of any genome. Its Bismap extension identifies mappability of the bisulfite converted genome (methylome).

Please cite: Mehran Karimzadeh, Carl Ernst, Anshul Kundaje and Michael M. Hoffman: Umap and Bismap: quantifying genome and methylome mappability. (PubMed,eprint) Nucleic Acids Res. 46(20):e120 (2018)

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Annovar
annotate genetic variants detected from diverse genomes
License: Open Source for non-profit
Debian package not available

ANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform:

 1. Gene-based annotation: identify whether SNPs or CNVs cause protein coding
    changes and the amino acids that are affected. Users can flexibly use RefSeq
    genes, UCSC genes, ENSEMBL genes, GENCODE genes, or many other gene definition
    systems.
 2. Region-based annotations: identify variants in specific genomic regions,
    for example, conserved regions among 44 species, predicted transcription
    factor binding sites, segmental duplication regions, GWAS hits, database
    of genomic variants, DNAse I hypersensitivity sites, ENCODE
    H3K4Me1/H3K4Me3/H3K27Ac/CTCF sites, ChIP-Seq peaks, RNA-Seq peaks, or many
    other annotations on genomic intervals.
 3. Filter-based annotation: identify variants that are reported in dbSNP,
    or identify the subset of common SNPs (MAF>1%) in the 1000 Genome Project,
    or identify subset of non-synonymous SNPs with SIFT score>0.05, or many
    other annotations on specific mutations.
 4. Other functionalities: Retrieve the nucleotide sequence in any
    user-specific genomic positions in batch, identify a candidate gene list
    for Mendelian diseases from exome data, identify a list of SNPs from
    1000 Genomes that are in strong LD with a GWAS hit, and many other
    creative utilities.

In a modern desktop computer (3GHz Intel Xeon CPU, 8Gb memory), for 4.7 million variants, ANNOVAR requires ~4 minutes to perform gene-based functional annotation, or ~15 minutes to perform stepwise "variants reduction" procedure, making it practical to handle hundreds of human genomes in a day.

Forge
genome assembler for mixed read types
License: Apache 2.0
Debian package not available

Forge Genome Assembler is a parallel, MPI based genome assembler for mixed read types.

Forge is a classic "Overlap layout consensus" genome assembler written by Darren Platt and Dirk Evers. Implemented in C++ and using the parallel MPI library, it runs on one or more machines in a network and can scale to very large numbers of reads provided there is enough collective memory on the machines used. It generates a full consensus alignment of all reads, can handle mixtures of sanger, 454 and illumina reads. There is some support for solid color space and it includes built in tools for vector trimming and contamination screening.

Forge and was originally developed at Exelixis and they have kindly agreed to place the software which underwent much subsequent development outside Exelixis, into the public domain. Forge works with most of the common MPI implementations.

Remark of Debian Med team: Competitor to MIRA2 and wgs-assembler

This package was requested by William Spooner whs@eaglegenomics.com as a competitor to MIRA2 and wgs-assembler.

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