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Fast and accurate short read alignment with Burrows–Wheeler transform

Bioinformatics(2009)10.1093/bioinformatics/btp324Source: DataRank Database

Fast and accurate short read alignment with Burrows–Wheeler transform is a research paper published in Bioinformatics (2009). On theSindex it has a DataRank of 1.7. It has been cited 62,117 times. Its calibrated FAIR score is 80/100.

N/A
1.7DataRank · unranked
1.7
Open Access62117 citations · base score 11.0
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

MotivationThe 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.ResultsWe 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 approximately 10-20x 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.Availabilityhttp://maq.sourceforge.net.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      80FAIR score
      F Findable
      100
      A Accessible
      70
      I Interoperable
      100
      R Reusable
      50
      Top 1% by FAIRdeterministic✓ full text read

      Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      1.7

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Authors (2)