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

Schmich F, Szczurek E, Kreibich S, Dilling S, Andritschke D, Casanova A, Low SH, Eicher S, Muntwiler S, Emmenlauer M, Rämö P, Conde-Alvarez R, von Mering C, Hardt WD, Dehio C, Beerenwinkel N - Genome Biol. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland. fabian.schmich@bsse.ethz.ch.

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.

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Related in: MedlinePlus

Gene-specific phenotypes (GSPs; red) estimated from off-target-confounded RNAi screens. a Schematic representation of a knockdown screen. RNAi reagents (e.g., siRNAs) target their intended on-target (black solid arrow) and additional off-target (grey dashed line arrows) genes. Each gene has a hidden GSP, whereas the observed reagent-specific phenotypes (RSPs; violet) correspond to the combined effect of on- and off-target genes. b Unlike RSPs, deconvoluted GSPs are expected to exhibit high concordance between distinct libraries containing different reagents targeting the same genes
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Fig1: Gene-specific phenotypes (GSPs; red) estimated from off-target-confounded RNAi screens. a Schematic representation of a knockdown screen. RNAi reagents (e.g., siRNAs) target their intended on-target (black solid arrow) and additional off-target (grey dashed line arrows) genes. Each gene has a hidden GSP, whereas the observed reagent-specific phenotypes (RSPs; violet) correspond to the combined effect of on- and off-target genes. b Unlike RSPs, deconvoluted GSPs are expected to exhibit high concordance between distinct libraries containing different reagents targeting the same genes

Mentions: In fact, despite improved algorithms for the design of RNAi reagents [8], chemical modifications of reagents [9, 10], and the development of computational methods to improve reproducibility and to minimize the risk of reporting false positive hits [11–13], it remains challenging to identify the specific effect of each individual gene on the phenotype from RNAi screens. These limitations dampened initial excitement and raised concerns about the utility of the technology [11, 14]. Here, we address this challenge and introduce gespeR (for gene-specific phenotype estimator), a statistical model for the estimation of hidden gene-specific phenotypes (GSPs) from observed reagent-specific phenotypes (RSPs). We model the observed RSPs as the weighted sum of individual GSPs from all on- and off-target genes, where the weights are proportional to the strengths of gene knockdowns by a reagent (Fig. 1a). Unlike RSPs, the inferred GSPs are gene-specific, deconvoluted phenotypes, i.e., they are independent of the underlying RNAi library and hence highly reproducible between distinct RNAi libraries (Fig. 1b) and ultimately allow constructing unconfounded gene hit rankings for follow-up analyses.Fig. 1


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

Schmich F, Szczurek E, Kreibich S, Dilling S, Andritschke D, Casanova A, Low SH, Eicher S, Muntwiler S, Emmenlauer M, Rämö P, Conde-Alvarez R, von Mering C, Hardt WD, Dehio C, Beerenwinkel N - Genome Biol. (2015)

Gene-specific phenotypes (GSPs; red) estimated from off-target-confounded RNAi screens. a Schematic representation of a knockdown screen. RNAi reagents (e.g., siRNAs) target their intended on-target (black solid arrow) and additional off-target (grey dashed line arrows) genes. Each gene has a hidden GSP, whereas the observed reagent-specific phenotypes (RSPs; violet) correspond to the combined effect of on- and off-target genes. b Unlike RSPs, deconvoluted GSPs are expected to exhibit high concordance between distinct libraries containing different reagents targeting the same genes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597449&req=5

Fig1: Gene-specific phenotypes (GSPs; red) estimated from off-target-confounded RNAi screens. a Schematic representation of a knockdown screen. RNAi reagents (e.g., siRNAs) target their intended on-target (black solid arrow) and additional off-target (grey dashed line arrows) genes. Each gene has a hidden GSP, whereas the observed reagent-specific phenotypes (RSPs; violet) correspond to the combined effect of on- and off-target genes. b Unlike RSPs, deconvoluted GSPs are expected to exhibit high concordance between distinct libraries containing different reagents targeting the same genes
Mentions: In fact, despite improved algorithms for the design of RNAi reagents [8], chemical modifications of reagents [9, 10], and the development of computational methods to improve reproducibility and to minimize the risk of reporting false positive hits [11–13], it remains challenging to identify the specific effect of each individual gene on the phenotype from RNAi screens. These limitations dampened initial excitement and raised concerns about the utility of the technology [11, 14]. Here, we address this challenge and introduce gespeR (for gene-specific phenotype estimator), a statistical model for the estimation of hidden gene-specific phenotypes (GSPs) from observed reagent-specific phenotypes (RSPs). We model the observed RSPs as the weighted sum of individual GSPs from all on- and off-target genes, where the weights are proportional to the strengths of gene knockdowns by a reagent (Fig. 1a). Unlike RSPs, the inferred GSPs are gene-specific, deconvoluted phenotypes, i.e., they are independent of the underlying RNAi library and hence highly reproducible between distinct RNAi libraries (Fig. 1b) and ultimately allow constructing unconfounded gene hit rankings for follow-up analyses.Fig. 1

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland. fabian.schmich@bsse.ethz.ch.

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.

Show MeSH
Related in: MedlinePlus