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RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts.
Graubert, Aaron; Aguet, François; Ravi, Arvind; Ardlie, Kristin G; Getz, Gad.
Afiliação
  • Graubert A; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Aguet F; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Ravi A; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Ardlie KG; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Getz G; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Bioinformatics ; 37(18): 3048-3050, 2021 09 29.
Article em En | MEDLINE | ID: mdl-33677499
ABSTRACT

SUMMARY:

Post-sequencing quality control is a crucial component of RNA sequencing (RNA-seq) data generation and analysis, as sample quality can be affected by sample storage, extraction and sequencing protocols. RNA-seq is increasingly applied to cohorts ranging from hundreds to tens of thousands of samples in size, but existing tools do not readily scale to these sizes, and were not designed for a wide range of sample types and qualities. Here, we describe RNA-SeQC 2, an efficient reimplementation of RNA-SeQC (DeLuca et al., 2012) that adds multiple metrics designed to characterize sample quality across a wide range of RNA-seq protocols. AVAILABILITY AND IMPLEMENTATION The command-line tool, documentation and C++ source code are available at the GitHub repository https//github.com/getzlab/rnaseqc. Code and data for reproducing the figures in this paper are available at https//github.com/getzlab/rnaseqc2-paper. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / RNA Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / RNA Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article