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Megadepth: efficient coverage quantification for BigWigs and BAMs.
Wilks, Christopher; Ahmed, Omar; Baker, Daniel N; Zhang, David; Collado-Torres, Leonardo; Langmead, Ben.
Afiliação
  • Wilks C; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Ahmed O; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Baker DN; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Zhang D; Department of Molecular Neuroscience Institute of Neurology, University College London (UCL), London WC1E 6BT, UK.
  • Collado-Torres L; NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London WC1E 6BT, UK.
  • Langmead B; Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health University College London, London WC1E 6BT, UK.
Bioinformatics ; 37(18): 3014-3016, 2021 09 29.
Article em En | MEDLINE | ID: mdl-33693500
MOTIVATION: A common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. RESULTS: Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19 000 GTExV8 BigWig files in approximately 1 h using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genoma / Genômica Idioma: En Revista: Bioinformatics Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genoma / Genômica Idioma: En Revista: Bioinformatics Ano de publicação: 2021 Tipo de documento: Article