accessibility: null
additionDate: '2017-01-13T13:13:56Z'
biotoolsCURIE: biotools:bwa
biotoolsID: bwa
collectionID:
- BWA
confidence_flag: null
cost: null
credit:
- email: bio-bwa-help@sourceforge.net
  fundrefid: null
  gridid: null
  name: bwa team
  note: null
  orcidid: null
  rorid: null
  typeEntity: Person
  typeRole:
  - Primary contact
  url: null
- email: me@liheng.org
  fundrefid: null
  gridid: null
  name: Heng Li
  note: null
  orcidid: http://orcid.org/0000-0003-4874-2874
  rorid: null
  typeEntity: Person
  typeRole:
  - Contributor
  url: http://www.liheng.org/
description: Fast, accurate, memory-efficient aligner for short and long sequencing
  reads
documentation:
- note: null
  type:
  - User manual
  url: http://bio-bwa.sourceforge.net/bwa.shtml
download:
- note: null
  type: Tool wrapper (CWL)
  url: https://gitlab.com/sibyllewohlgemuth/cwl_files/raw/master/bwa_index.cwl
  version: null
- note: null
  type: Tool wrapper (CWL)
  url: https://gitlab.com/sibyllewohlgemuth/cwl_files/raw/master/bwa_mem.cwl
  version: null
- note: null
  type: Container file
  url: https://docker-ui.genouest.org/app/#/container/bioconda/bwa
  version: null
- note: null
  type: Container file
  url: https://anaconda.org/bioconda/bwa
  version: null
editPermission:
  authors:
  - swohlgemuth
  type: group
elixirCommunity: []
elixirNode: []
elixirPlatform: []
elixir_badge: 0
function:
- cmd: null
  input:
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTA
      uri: http://edamontology.org/format_1929
  note: Index database sequences in the FASTA format. database
  operation:
  - term: Genome indexing
    uri: http://edamontology.org/operation_3211
  output:
  - data:
      term: Genome index
      uri: http://edamontology.org/data_3210
    format: []
- cmd: null
  input:
  - data:
      term: Genome index
      uri: http://edamontology.org/data_3210
    format: []
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTQ
      uri: http://edamontology.org/format_1930
  note: Align 70bp-1Mbp query sequences with the BWA-MEM algorithm. Briefly, the algorithm
    works by seeding alignments with maximal exact matches (MEMs) and then extending
    seeds with the affine-gap Smith-Waterman algorithm (SW). The BWA-MEM algorithm
    performs local alignment. It may produce multiple primary alignments for different
    part of a query sequence. This is a crucial feature for long sequences. However,
    some tools such as Picards markDuplicates does not work with split alignments.
    One may consider to use option -M to flag shorter split hits as secondary.
  operation:
  - term: Sequence alignment
    uri: http://edamontology.org/operation_0292
  output:
  - data:
      term: Alignment
      uri: http://edamontology.org/data_1916
    format: []
- cmd: null
  input:
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTA search results format
      uri: http://edamontology.org/format_1332
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTQ
      uri: http://edamontology.org/format_1930
  note: Find the SA coordinates of the input reads. reference sequence query sequence
  operation:
  - term: Read mapping
    uri: http://edamontology.org/operation_3198
  - term: Sequence alignment
    uri: http://edamontology.org/operation_0292
  output:
  - data:
      term: Sequence coordinates
      uri: http://edamontology.org/data_2012
    format: []
- cmd: null
  input:
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTA
      uri: http://edamontology.org/format_1929
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTQ
      uri: http://edamontology.org/format_1930
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format: []
  note: Generate alignments in the SAM format given single-end reads. Repetitive hits
    will be randomly chosen reference sequence query sequence
  operation:
  - term: Generation
    uri: http://edamontology.org/operation_3429
  - term: Sequence alignment
    uri: http://edamontology.org/operation_0292
  output:
  - data:
      term: Sequence alignment
      uri: http://edamontology.org/data_0863
    format:
    - term: SAM
      uri: http://edamontology.org/format_2573
- cmd: null
  input:
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTA
      uri: http://edamontology.org/format_1929
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format: []
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTQ
      uri: http://edamontology.org/format_1930
  note: Generate alignments in the SAM format given paired-end reads. Repetitive read
    pairs will be placed randomly. reference sequence
  operation:
  - term: Generation
    uri: http://edamontology.org/operation_3429
  - term: Sequence alignment
    uri: http://edamontology.org/operation_0292
  output:
  - data:
      term: Sequence alignment
      uri: http://edamontology.org/data_0863
    format:
    - term: SAM
      uri: http://edamontology.org/format_2573
- cmd: null
  input:
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTA
      uri: http://edamontology.org/format_1929
  - data:
      term: Sequence
      uri: http://edamontology.org/data_2044
    format:
    - term: FASTQ
      uri: http://edamontology.org/format_1930
  note: Align query sequences in the in.fq file reference sequence
  operation:
  - term: Sequence alignment
    uri: http://edamontology.org/operation_0292
  output:
  - data:
      term: Sequence alignment
      uri: http://edamontology.org/data_0863
    format: []
homepage: http://bio-bwa.sourceforge.net
homepage_status: 0
language:
- C
lastUpdate: '2024-11-25T14:09:49.679089Z'
license: MIT
link: []
maturity: Mature
name: BWA
operatingSystem:
- Linux
- Mac
otherID: []
owner: seqwiki_import
publication:
- doi: 10.1093/bioinformatics/btp324
  metadata:
    abstract: 'Motivation: The enormous amount of short reads generated by the new
      DNA sequencing technologies call for the development of fast and accurate read
      alignment programs. A first generation of hash table-based methods has been
      developed, including MAQ, which is accurate, feature rich and fast enough to
      align short reads from a single individual. However, MAQ does not support gapped
      alignment for single-end reads, which makes it unsuitable for alignment of longer
      reads where indels may occur frequently. The speed of MAQ is also a concern
      when the alignment is scaled up to the resequencing of hundreds of individuals.
      Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment
      package that is based on backward search with Burrows-Wheeler Transform (BWT),
      to efficiently align short sequencing reads against a large reference sequence
      such as the human genome, allowing mismatches and gaps. BWA supports both base
      space reads, e.g. from Illumina sequencing machines, and color space reads from
      AB SOLiD machines. Evaluations on both simulated and real data suggest that
      BWA is ∼10-20× faster than MAQ, while achieving similar accuracy. In addition,
      BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format.
      Variant calling and other downstream analyses after the alignment can be achieved
      with the open source SAMtools software package. © 2009 The Author(s).'
    authors:
    - name: Li H.
    - name: Durbin R.
    citationCount: 33667
    date: '2009-07-01T00:00:00Z'
    journal: Bioinformatics
    title: Fast and accurate short read alignment with Burrows-Wheeler transform
  note: null
  pmcid: PMC2705234
  pmid: '19451168'
  type:
  - Primary
  version: null
- doi: 10.1186/1471-2105-14-184
  metadata:
    abstract: 'Background: The development of next-generation sequencing instruments
      has led to the generation of millions of short sequences in a single run. The
      process of aligning these reads to a reference genome is time consuming and
      demands the development of fast and accurate alignment tools. However, the current
      proposed tools make different compromises between the accuracy and the speed
      of mapping. Moreover, many important aspects are overlooked while comparing
      the performance of a newly developed tool to the state of the art. Therefore,
      there is a need for an objective evaluation method that covers all the aspects.
      In this work, we introduce a benchmarking suite to extensively analyze sequencing
      tools with respect to various aspects and provide an objective comparison.Results:
      We applied our benchmarking tests on 9 well known mapping tools, namely, Bowtie,
      Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP, Novoalign, and mrsFAST (mrFAST) using
      synthetic data and real RNA-Seq data. MAQ and RMAP are based on building hash
      tables for the reads, whereas the remaining tools are based on indexing the
      reference genome. The benchmarking tests reveal the strengths and weaknesses
      of each tool. The results show that no single tool outperforms all others in
      all metrics. However, Bowtie maintained the best throughput for most of the
      tests while BWA performed better for longer read lengths. The benchmarking tests
      are not restricted to the mentioned tools and can be further applied to others.Conclusion:
      The mapping process is still a hard problem that is affected by many factors.
      In this work, we provided a benchmarking suite that reveals and evaluates the
      different factors affecting the mapping process. Still, there is no tool that
      outperforms all of the others in all the tests. Therefore, the end user should
      clearly specify his needs in order to choose the tool that provides the best
      results. © 2013 Hatem et al.; licensee BioMed Central Ltd.'
    authors:
    - name: Hatem A.
    - name: Bozdag D.
    - name: Toland A.E.
    - name: Catalyurek U.V.
    citationCount: 146
    date: '2013-06-07T00:00:00Z'
    journal: BMC Bioinformatics
    title: Benchmarking short sequence mapping tools
  note: null
  pmcid: PMC3694458
  pmid: '23758764'
  type:
  - Benchmarking study
  version: null
- doi: 10.1016/j.ygeno.2017.03.001
  metadata:
    abstract: Massive data produced due to the advent of next-generation sequencing
      (NGS) technology is widely used for biological researches and medical diagnosis.
      The crucial step in NGS analysis is read alignment or mapping which is computationally
      intensive and complex. The mapping bias tends to affect the downstream analysis,
      including detection of polymorphisms. In order to provide guidelines to the
      biologist for suitable selection of aligners; we have evaluated and benchmarked
      5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping
      bias based on characteristics of 5 microbial genomes. Two million simulated
      read pairs of various sizes (36 bp, 50 bp, 72 bp, 100 bp, 125 bp, 150 bp, 200
      bp, 250 bp and 300 bp) were aligned. Specific alignment features such as sensitivity
      of mapping, percentage of properly paired reads, alignment time and effect of
      tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster
      alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively
      slower. Most of the aligners showed high sensitivity towards long reads (> 100
      bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both
      short reads (36 bp, 50 bp, 72 bp) and long reads (> 100 bp) mappings; It also
      showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum.
      The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy
      were markedly higher. None of the aligners outperforms the others in the benchmark,
      however the aligners perform differently with genome characteristics. We expect
      that the results from this study will be useful for the end user to choose aligner,
      thus enhance the accuracy of read mapping.
    authors:
    - name: Thankaswamy-Kosalai S.
    - name: Sen P.
    - name: Nookaew I.
    citationCount: 54
    date: '2017-07-01T00:00:00Z'
    journal: Genomics
    title: Evaluation and assessment of read-mapping by multiple next-generation sequencing
      aligners based on genome-wide characteristics
  note: null
  pmcid: null
  pmid: '28286147'
  type: []
  version: null
- doi: 10.1186/1471-2164-15-264
  metadata:
    abstract: 'Background: The rapid evolution in high-throughput sequencing (HTS)
      technologies has opened up new perspectives in several research fields and led
      to the production of large volumes of sequence data. A fundamental step in HTS
      data analysis is the mapping of reads onto reference sequences. Choosing a suitable
      mapper for a given technology and a given application is a subtle task because
      of the difficulty of evaluating mapping algorithms.Results: In this paper, we
      present a benchmark procedure to compare mapping algorithms used in HTS using
      both real and simulated datasets and considering four evaluation criteria: computational
      resource and time requirements, robustness of mapping, ability to report positions
      for reads in repetitive regions, and ability to retrieve true genetic variation
      positions. To measure robustness, we introduced a new definition for a correctly
      mapped read taking into account not only the expected start position of the
      read but also the end position and the number of indels and substitutions. We
      developed CuReSim, a new read simulator, that is able to generate customized
      benchmark data for any kind of HTS technology by adjusting parameters to the
      error types. CuReSim and CuReSimEval, a tool to evaluate the mapping quality
      of the CuReSim simulated reads, are freely available. We applied our benchmark
      procedure to evaluate 14 mappers in the context of whole genome sequencing of
      small genomes with Ion Torrent data for which such a comparison has not yet
      been established.Conclusions: A benchmark procedure to compare HTS data mappers
      is introduced with a new definition for the mapping correctness as well as tools
      to generate simulated reads and evaluate mapping quality. The application of
      this procedure to Ion Torrent data from the whole genome sequencing of small
      genomes has allowed us to validate our benchmark procedure and demonstrate that
      it is helpful for selecting a mapper based on the intended application, questions
      to be addressed, and the technology used. This benchmark procedure can be used
      to evaluate existing or in-development mappers as well as to optimize parameters
      of a chosen mapper for any application and any sequencing platform. © 2014 Caboche
      et al.; licensee BioMed Central Ltd.'
    authors:
    - name: Caboche S.
    - name: Audebert C.
    - name: Lemoine Y.
    - name: Hot D.
    citationCount: 66
    date: '2014-04-05T00:00:00Z'
    journal: BMC Genomics
    title: 'Comparison of mapping algorithms used in high-throughput sequencing: Application
      to Ion Torrent data'
  note: null
  pmcid: PMC4051166
  pmid: '24708189'
  type:
  - Benchmarking study
  version: null
- doi: 10.1093/bioinformatics/btu146
  metadata:
    abstract: 'Motivation: Next-generation sequencing has become an important tool
      in molecular biology. Various protocols to investigate genomic, transcriptomic
      and epigenomic features across virtually all species and tissues have been devised.
      For most of these experiments, one of the first crucial steps of bioinformatic
      analysis is the mapping of reads to reference genomes. Results: Here, we present
      thorough benchmarks of our read aligner segemehl in comparison with other state-of-the-art
      methods. Furthermore, we introduce the tool lack to rescue unmapped RNA-seq
      reads which works in conjunction with segemehl and many other frequently used
      split-read aligners. © The Author 2014.'
    authors:
    - name: Otto C.
    - name: Stadler P.F.
    - name: Hoffmann S.
    citationCount: 71
    date: '2014-07-01T00:00:00Z'
    journal: Bioinformatics
    title: Lacking alignments? The next-generation sequencing mapper segemehl revisited
  note: null
  pmcid: null
  pmid: '24626854'
  type: []
  version: null
- doi: 10.1093/bioinformatics/btp698
  metadata:
    abstract: 'Motivation: Many programs for aligning short sequencing reads to a
      reference genome have been developed in the last 2 years. Most of them are very
      efficient for short reads but inefficient or not applicable for reads >200 bp
      because the algorithms are heavily and specifically tuned for short queries
      with low sequencing error rate. However, some sequencing platforms already produce
      longer reads and others are expected to become available soon. For longer reads,
      hashingbased software such as BLAT and SSAHA2 remain the only choices. Nonetheless,
      these methods are substantially slower than short-read aligners in terms of
      aligned bases per unit time. Results: We designed and implemented a new algorithm,
      Burrows-Wheeler Aligner''s Smith-Waterman Alignment (BWA-SW), to align long
      sequences up to 1Mb against a large sequence database (e.g. the human genome)
      with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more
      accurate than BLAT, and is several to tens of times faster than both. Availability:
      http://bio-bwa.sourceforge.net. Contact: rd@sanger.ac.uk © The Author(s) 2010.
      Published by Oxford University Press.'
    authors:
    - name: Li H.
    - name: Durbin R.
    citationCount: 8584
    date: '2010-01-15T00:00:00Z'
    journal: Bioinformatics
    title: Fast and accurate long-read alignment with Burrows-Wheeler transform
  note: null
  pmcid: PMC2828108
  pmid: '20080505'
  type: []
  version: null
relation: []
toolType:
- Command-line tool
- Workbench
topic:
- term: Mapping
  uri: http://edamontology.org/topic_0102
validated: 1
version: []
