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gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens
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 phenotyp...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597449/ https://www.ncbi.nlm.nih.gov/pubmed/26445817 http://dx.doi.org/10.1186/s13059-015-0783-1 |
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author | Schmich, Fabian Szczurek, Ewa Kreibich, Saskia Dilling, Sabrina Andritschke, Daniel Casanova, Alain Low, Shyan Huey Eicher, Simone Muntwiler, Simone Emmenlauer, Mario Rämö, Pauli Conde-Alvarez, Raquel von Mering, Christian Hardt, Wolf-Dietrich Dehio, Christoph Beerenwinkel, Niko |
author_facet | Schmich, Fabian Szczurek, Ewa Kreibich, Saskia Dilling, Sabrina Andritschke, Daniel Casanova, Alain Low, Shyan Huey Eicher, Simone Muntwiler, Simone Emmenlauer, Mario Rämö, Pauli Conde-Alvarez, Raquel von Mering, Christian Hardt, Wolf-Dietrich Dehio, Christoph Beerenwinkel, Niko |
author_sort | Schmich, Fabian |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0783-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4597449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45974492015-10-08 gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens Schmich, Fabian Szczurek, Ewa Kreibich, Saskia Dilling, Sabrina Andritschke, Daniel Casanova, Alain Low, Shyan Huey Eicher, Simone Muntwiler, Simone Emmenlauer, Mario Rämö, Pauli Conde-Alvarez, Raquel von Mering, Christian Hardt, Wolf-Dietrich Dehio, Christoph Beerenwinkel, Niko Genome Biol Method 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0783-1) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-07 2015 /pmc/articles/PMC4597449/ /pubmed/26445817 http://dx.doi.org/10.1186/s13059-015-0783-1 Text en © Schmich et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Schmich, Fabian Szczurek, Ewa Kreibich, Saskia Dilling, Sabrina Andritschke, Daniel Casanova, Alain Low, Shyan Huey Eicher, Simone Muntwiler, Simone Emmenlauer, Mario Rämö, Pauli Conde-Alvarez, Raquel von Mering, Christian Hardt, Wolf-Dietrich Dehio, Christoph Beerenwinkel, Niko gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title | gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title_full | gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title_fullStr | gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title_full_unstemmed | gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title_short | gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens |
title_sort | gesper: a statistical model for deconvoluting off-target-confounded rna interference screens |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597449/ https://www.ncbi.nlm.nih.gov/pubmed/26445817 http://dx.doi.org/10.1186/s13059-015-0783-1 |
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