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LiBiNorm: an htseq-count analogue with improved normalisation of Smart-seq2 data and library preparation diagnostics.
Dyer, Nigel P; Shahrezaei, Vahid; Hebenstreit, Daniel.
Afiliación
  • Dyer NP; School of Life Sciences, University of Warwick, Coventry, UK.
  • Shahrezaei V; Department of Mathematics, Imperial College London, London, UK.
  • Hebenstreit D; School of Life Sciences, University of Warwick, Coventry, UK.
PeerJ ; 7: e6222, 2019.
Article en En | MEDLINE | ID: mdl-30740268
Protocols for preparing RNA sequencing (RNA-seq) libraries, most prominently "Smart-seq" variations, introduce global biases that can have a significant impact on the quantification of gene expression levels. This global bias can lead to drastic over- or under-representation of RNA in non-linear length-dependent fashion due to enzymatic reactions during cDNA production. It is currently not corrected by any RNA-seq software, which mostly focus on local bias in coverage along RNAs. This paper describes LiBiNorm, a simple command line program that mimics the popular htseq-count software and allows diagnostics, quantification, and global bias removal. LiBiNorm outputs gene expression data that has been normalized to correct for global bias introduced by the Smart-seq2 protocol. In addition, it produces data and several plots that allow insights into the experimental history underlying library preparation. The LiBiNorm package includes an R script that allows visualization of the main results. LiBiNorm is the first software application to correct for the global bias that is introduced by the Smart-seq2 protocol. It is freely downloadable at http://www2.warwick.ac.uk/fac/sci/lifesci/research/libinorm.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline Idioma: En Revista: PeerJ Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline Idioma: En Revista: PeerJ Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos