gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens

Genome Biol. 2015 Oct 7;16:220. doi: 10.1186/s13059-015-0783-1.

Abstract

Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-β signaling. gespeR is available as a Bioconductor R-package.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bartonella henselae / genetics
  • Brucella abortus / genetics
  • Gene Knockdown Techniques*
  • HeLa Cells
  • Humans
  • Models, Statistical*
  • Phenotype
  • RNA Interference*
  • RNA, Small Interfering
  • Salmonella typhimurium / genetics
  • Signal Transduction
  • Software*
  • Transforming Growth Factor beta / physiology

Substances

  • RNA, Small Interfering
  • Transforming Growth Factor beta