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The Sequence Alignment/Map format and SAMtools

Bioinformatics(2009)10.1093/bioinformatics/btp352Source: DataRank Database

The Sequence Alignment/Map format and SAMtools is a research paper published in Bioinformatics (2009). On theSindex it has a DataRank of 1.7. It has been cited 66,179 times. Its calibrated FAIR score is 68/100.

N/A
1.7DataRank · unranked
1.7
Open Access66179 citations · base score 11.1
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

SummaryThe Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.Availabilityhttp://samtools.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.

      68FAIR score
      F Findable
      100
      A Accessible
      70
      I Interoperable
      50
      R Reusable
      50
      Top 5% 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 (10)

      Alec Wysoker,Tim Fennell,Jue RuanORCID,Nils Homer,Richard DurbinORCID

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