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BioMed Central, Genome Biology, 1(16), 2015

DOI: 10.1186/s13059-015-0783-1

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

Journal article published in 2015 by Fabian Schmich, Ewa Szczurek, Saskia Kreibich, Sabrina Dilling, Daniel Andritschke, Alain Casanova, Shyan Huey Low, Simone Eicher, Simone Muntwiler, Mario Emmenlauer, Pauli Rämö, Raquel Conde-Alvarez ORCID, Christian von Mering, Wolf-Dietrich Hardt, Christoph Dehio and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.