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

  • Schmich F Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland. fabian.schmich@bsse.ethz.ch.
  • Szczurek E Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland. ewa.szczurek@bsse.ethz.ch.
  • Kreibich S Department of Biology, ETH, Zurich, Switzerland. saskia.kreibich@micro.biol.ethz.ch.
  • Dilling S Department of Biology, ETH, Zurich, Switzerland. sabrina.dilling@micro.biol.ethz.ch.
  • Andritschke D Department of Biology, ETH, Zurich, Switzerland. daniel.andritschke@micro.biol.ethz.ch.
  • Casanova A Biozentrum, University of Basel, Basel, Switzerland. alain.casanova@unibas.ch.
  • Low SH Biozentrum, University of Basel, Basel, Switzerland. shyan.low@unibas.ch.
  • Eicher S Biozentrum, University of Basel, Basel, Switzerland. simone.eicher@unibas.ch.
  • Muntwiler S Biozentrum, University of Basel, Basel, Switzerland. simone.muntwiler@unibas.ch.
  • Emmenlauer M Biozentrum, University of Basel, Basel, Switzerland. mario.emmenlauer@unibas.ch.
  • Rämö P Biozentrum, University of Basel, Basel, Switzerland. pauli.ramo@unibas.ch.
  • Conde-Alvarez R Institute for Tropical Health and Departamento de Microbiología y Parasitología, Universidad de Navarra, Pamplona, Spain. rconde@unav.es.
  • von Mering C SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. mering@imls.uzh.ch.
  • Hardt WD Department of Biology, ETH, Zurich, Switzerland. wolf-dietrich.hardt@micro.biol.ethz.ch.
  • Dehio C Biozentrum, University of Basel, Basel, Switzerland. christoph.dehio@unibas.ch.
  • Beerenwinkel N Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland. niko.beerenwinkel@bsse.ethz.ch.
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  • 2015-10-09
Published in:
  • Genome biology. - 2015
Bartonella henselae
Brucella abortus
Gene Knockdown Techniques
HeLa Cells
Humans
Models, Statistical
Phenotype
RNA Interference
RNA, Small Interfering
Salmonella typhimurium
Signal Transduction
Software
Transforming Growth Factor beta
English 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.
Language
  • English
Open access status
gold
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Persistent URL
https://sonar.ch/global/documents/36783
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