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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) for pathogen entry estimated by gespeR from two distinct genome-wide Qiagen sub-libraries are biologically meaningful. a Scatterplots of reagent-specific and estimated gene-specific phenotypes between the pathogens B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability. Unlike RSPs, GSPs exhibit biologically expected high correlation between (pathogen-independent) Viability phenotypes and only low to moderate correlation for Infectivity. b Gene set enrichment analysis: pathways significantly enriched at a false discovery rate (FDR) smaller than 0.25 for decreased Infectivity and gene lists from gespeR GSPs, haystack, RSA, and ISPs for all pathogens. Canonical pathway databases: R Reactome, K KEGG, ST Signal transduction KE. Pathways, such as focal adhesion or integrin- and TGF-β-signaling, shown to play a crucial role in pathogen entry, are enriched exclusively for GSPs; 62.5 % of pathways enriched for ISPs are also enriched for GSPs. RSA gene rankings are exclusively enriched for three pathways, while haystack rankings did not show sufficient overlap with any tested gene set (minimum overlap n = 15)
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Fig4: Gene-specific phenotypes (GSPs) for pathogen entry estimated by gespeR from two distinct genome-wide Qiagen sub-libraries are biologically meaningful. a Scatterplots of reagent-specific and estimated gene-specific phenotypes between the pathogens B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability. Unlike RSPs, GSPs exhibit biologically expected high correlation between (pathogen-independent) Viability phenotypes and only low to moderate correlation for Infectivity. b Gene set enrichment analysis: pathways significantly enriched at a false discovery rate (FDR) smaller than 0.25 for decreased Infectivity and gene lists from gespeR GSPs, haystack, RSA, and ISPs for all pathogens. Canonical pathway databases: R Reactome, K KEGG, ST Signal transduction KE. Pathways, such as focal adhesion or integrin- and TGF-β-signaling, shown to play a crucial role in pathogen entry, are enriched exclusively for GSPs; 62.5 % of pathways enriched for ISPs are also enriched for GSPs. RSA gene rankings are exclusively enriched for three pathways, while haystack rankings did not show sufficient overlap with any tested gene set (minimum overlap n = 15)

Mentions: In order to assess inter-pathogen concordance between GSPs estimated by gespeR, we compared GSPQ estimates for B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability, defined as the normalized cell count after infection, between two distinct genome-wide Qiagen sub-libraries (Fig. 4a). Unlike RSPs, GSPs showed strong correlation for Viability and weak correlation for Infectivity. This phenomenon is biologically expected, because gene knockdowns with a strong effect on the growth rate of cells (Viability) are largely pathogen-independent. Infectivity, in contrast, is pathogen dependent and subtle correlation may be explained by a few shared components between different pathogen entry mechanisms.Fig. 4


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) for pathogen entry estimated by gespeR from two distinct genome-wide Qiagen sub-libraries are biologically meaningful. a Scatterplots of reagent-specific and estimated gene-specific phenotypes between the pathogens B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability. Unlike RSPs, GSPs exhibit biologically expected high correlation between (pathogen-independent) Viability phenotypes and only low to moderate correlation for Infectivity. b Gene set enrichment analysis: pathways significantly enriched at a false discovery rate (FDR) smaller than 0.25 for decreased Infectivity and gene lists from gespeR GSPs, haystack, RSA, and ISPs for all pathogens. Canonical pathway databases: R Reactome, K KEGG, ST Signal transduction KE. Pathways, such as focal adhesion or integrin- and TGF-β-signaling, shown to play a crucial role in pathogen entry, are enriched exclusively for GSPs; 62.5 % of pathways enriched for ISPs are also enriched for GSPs. RSA gene rankings are exclusively enriched for three pathways, while haystack rankings did not show sufficient overlap with any tested gene set (minimum overlap n = 15)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Gene-specific phenotypes (GSPs) for pathogen entry estimated by gespeR from two distinct genome-wide Qiagen sub-libraries are biologically meaningful. a Scatterplots of reagent-specific and estimated gene-specific phenotypes between the pathogens B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability. Unlike RSPs, GSPs exhibit biologically expected high correlation between (pathogen-independent) Viability phenotypes and only low to moderate correlation for Infectivity. b Gene set enrichment analysis: pathways significantly enriched at a false discovery rate (FDR) smaller than 0.25 for decreased Infectivity and gene lists from gespeR GSPs, haystack, RSA, and ISPs for all pathogens. Canonical pathway databases: R Reactome, K KEGG, ST Signal transduction KE. Pathways, such as focal adhesion or integrin- and TGF-β-signaling, shown to play a crucial role in pathogen entry, are enriched exclusively for GSPs; 62.5 % of pathways enriched for ISPs are also enriched for GSPs. RSA gene rankings are exclusively enriched for three pathways, while haystack rankings did not show sufficient overlap with any tested gene set (minimum overlap n = 15)
Mentions: In order to assess inter-pathogen concordance between GSPs estimated by gespeR, we compared GSPQ estimates for B. abortus and S. typhimurium for Infectivity and the auxiliary phenotype of Viability, defined as the normalized cell count after infection, between two distinct genome-wide Qiagen sub-libraries (Fig. 4a). Unlike RSPs, GSPs showed strong correlation for Viability and weak correlation for Infectivity. This phenomenon is biologically expected, because gene knockdowns with a strong effect on the growth rate of cells (Viability) are largely pathogen-independent. Infectivity, in contrast, is pathogen dependent and subtle correlation may be explained by a few shared components between different pathogen entry mechanisms.Fig. 4

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