GespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Schmich, Fabian; Szczurek, Ewa; Kreibich, Saskia; Dilling, Sabrina; Andritschke, Daniel; Casanova, Alain; Low, Shyan; Eicher, Simone; Muntwiler, Simone; Emmenlauer, Mario; Rämö, Pauli; Conde-Alvarez, Raquel; von Mering, Christian; Hardt, Wolf-Dietrich; Dehio, Christoph; Beerenwinkel, Niko
• Is Part Of: Genome biology. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-12
• Subjects: RNAi -- siRNA -- Off-target -- Confounded -- Phenotype -- Deconvolution -- Statistical model -- Hit prioritization -- Pathogen infection screen; Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-015-0783-1
• Languages: English
• ISSNs: 1474-760X

Abstract

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.