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

Schmich, Fabian; Szczurek, Ewa; Kreibich, Saskia; Dilling, Sabrina; Andritschke, Daniel; Casanova, Alain; Low, Shyan Huey; Eicher, Simone; Muntwiler, Simone; Emmenlauer, Mario; Rämö, Pauli; Conde-Alvarez, Raquel; von Mering, Christian; Hardt, Wolf-Dietrich; Dehio, Christoph; Beerenwinkel, Niko

doi:10.1186/s13059-015-0783-1

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Schmich, F., Szczurek, E., Kreibich, S., Dilling, S., Andritschke, D., Casanova, A., Low, S. H., Eicher, S., Muntwiler, S., Emmenlauer, M., Rämö, P., Conde-Alvarez, R., von Mering, C., Hardt, W.-D., Dehio, C., & Beerenwinkel, N. (2015). gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens. Genome Biology, 16, 220. https://doi.org/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 tar