gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000105423Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Genome BiologyBand
Seiten / Artikelnummer
Verlag
BioMed CentralThema
RNAi; SiRNA; Off-target; Confounded; Phenotype; Deconvolution; Statistical model; Hit prioritization; Pathogen infection screenOrganisationseinheit
03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
03589 - Hardt, Wolf-Dietrich / Hardt, Wolf-Dietrich