Detalle Publicación

gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens
Autores: Schmich, F.; Szczurek, E.; Kreibich, S.; Dilling, S.; Andritschke, D.; Casanova, A.; Low, S.H.; Eicher, S.; Muntwiler, S.; Emmenlauer, M.; Rämö, P.; Conde Álvarez, Raquel; von Mering, C.; Hardt, W.D.; Dehio, C.; Beerenwinkel, N.
Título de la revista: GENOME BIOLOGY
ISSN: 1474-7596
Volumen: 16
Páginas: 220
Fecha de publicación: 2015
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-beta signaling. gespeR is available as a Bioconductor R-package.