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

Schmich, Fabian and Szczurek, Ewa and Kreibich, Saskia and Dilling, Sabrina and Andritschke, Daniel and Casanova, Alain and Low, Shyan Huey and Eicher, Simone and Muntwiler, Simone and Emmenlauer, Mario and Rämö, Pauli and Conde-Alvarez, Raquel and von Mering, Christian and Hardt, Wolf-Dietrich and Dehio, Christoph and Beerenwinkel, Niko. (2015) gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens. Genome Biology, 16 (220). pp. 1-12.

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Official URL: http://edoc.unibas.ch/39832/

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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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Infection Biology > Molecular Microbiology (Dehio)
UniBasel Contributors:Dehio, Christoph
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:BioMed Central
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:15 Nov 2017 10:05
Deposited On:30 May 2016 06:03

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