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

IF 12.3 1区生物学 Q1 Agricultural and Biological Sciences

Genome Biology Pub Date : 2015-10-07 DOI:10.1186/s13059-015-0783-1

Fabian Schmich, Ewa Szczurek, Saskia Kreibich, Sabrina Dilling, Daniel Andritschke, Alain Casanova, Shyan Huey Low, Simone Eicher, Simone Muntwiler, Mario Emmenlauer, Pauli Rämö, Raquel Conde-Alvarez, Christian von Mering, Wolf-Dietrich Hardt, Christoph Dehio, Niko Beerenwinkel

{"title":"gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.","authors":"Fabian Schmich, Ewa Szczurek, Saskia Kreibich, Sabrina Dilling, Daniel Andritschke, Alain Casanova, Shyan Huey Low, Simone Eicher, Simone Muntwiler, Mario Emmenlauer, Pauli Rämö, Raquel Conde-Alvarez, Christian von Mering, Wolf-Dietrich Hardt, Christoph Dehio, Niko Beerenwinkel","doi":"10.1186/s13059-015-0783-1","DOIUrl":null,"url":null,"abstract":"<p><p>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. </p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"16 ","pages":"220"},"PeriodicalIF":12.3000,"publicationDate":"2015-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-015-0783-1","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-015-0783-1","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}

引用次数: 34

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.

Abstract Image

查看原文本刊更多论文

用于反卷积脱靶混淆RNA干扰筛选的统计模型。

小干扰RNA (sirna)表现出强烈的脱靶效应，这混淆了RNA干扰筛选的基因水平解释，从而限制了它们在功能基因组学研究中的应用。在这里，我们提出gespeR，一个重建个体，基因特异性表型的统计模型。利用三家公司在三种病原体感染筛选中的115,878个siRNA，单个和汇集，我们证明了基于图像的表型反褶积大大提高了针对相同基因的独立siRNA集之间的可重复性。通过gespeR筛选和优先排序的基因被证实构成病原体进入机制和TGF-β信号传导的生物学相关成分。gespeR作为Bioconductor r包提供。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Genome Biology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY

CiteScore

25.50

自引率

3.30%

发文量

审稿时长

14 weeks

期刊介绍： Genome Biology is a leading research journal that focuses on the study of biology and biomedicine from a genomic and post-genomic standpoint. The journal consistently publishes outstanding research across various areas within these fields. With an impressive impact factor of 12.3 (2022), Genome Biology has earned its place as the 3rd highest-ranked research journal in the Genetics and Heredity category, according to Thomson Reuters. Additionally, it is ranked 2nd among research journals in the Biotechnology and Applied Microbiology category. It is important to note that Genome Biology is the top-ranking open access journal in this category. In summary, Genome Biology sets a high standard for scientific publications in the field, showcasing cutting-edge research and earning recognition among its peers.