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
Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland.
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Genome Biol. 2015 Oct 7;16:220. doi: 10.1186/s13059-015-0783-1.
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.
小干扰RNA（siRNA）具有很强的脱靶效应，这使得RNA干扰筛选的基因水平解释变得复杂，从而限制了它们在功能基因组学研究中的效用。在此，我们展示了gespeR，一种用于重建个体基因特异性表型的统计模型。我们使用来自三家公司的115,878个单链和混合的siRNA，进行了三项病原体感染筛选，结果表明基于图像的表型反卷积显著提高了针对相同基因的独立siRNA组之间的可重复性。通过gespeR选择并排序的基因得到了验证，并被证明构成了病原体进入机制和转化生长因子-β信号传导的生物学相关组成部分。gespeR可作为一个生物导体R包获取。
