Rämö Pauli, Drewek Anna, Arrieumerlou Cécile, Beerenwinkel Niko, Ben-Tekaya Houchaima, Cardel Bettina, Casanova Alain, Conde-Alvarez Raquel, Cossart Pascale, Csúcs Gábor, Eicher Simone, Emmenlauer Mario, Greber Urs, Hardt Wolf-Dietrich, Helenius Ari, Kasper Christoph, Kaufmann Andreas, Kreibich Saskia, Kühbacher Andreas, Kunszt Peter, Low Shyan Huey, Mercer Jason, Mudrak Daria, Muntwiler Simone, Pelkmans Lucas, Pizarro-Cerdá Javier, Podvinec Michael, Pujadas Eva, Rinn Bernd, Rouilly Vincent, Schmich Fabian, Siebourg-Polster Juliane, Snijder Berend, Stebler Michael, Studer Gabriel, Szczurek Ewa, Truttmann Matthias, von Mering Christian, Vonderheit Andreas, Yakimovich Artur, Bühlmann Peter, Dehio Christoph
Focal Area Infection Biology, Biozentrum, University of Basel, Klingelberstrasse 70, CH-4056 Basel, Switzerland.
BMC Genomics. 2014 Dec 22;15(1):1162. doi: 10.1186/1471-2164-15-1162.
Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.
We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.
Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.
大规模RNA干扰（RNAi）筛选已成为鉴定参与感兴趣生物学过程的基因的一项重要技术。然而，大规模RNAi筛选的质量常常因脱靶效应而下降。为了找到病原体入侵的具有统计学意义的效应基因，我们使用来自三家供应商的小干扰RNA（siRNA），通过基于图像的全激酶组siRNA筛选，系统地分析了八种病原体在人类宿主细胞中的入侵途径。我们提出了一种并行混合模型（PMM）方法，该方法可同时分析使用相同RNAi文库进行的几个不同的筛选。
我们表明，由于并行筛选，PMM在检测命中基因方面提高了统计功效。PMM允许纳入可根据RNAi质量的现有信息进行分配的siRNA权重。此外，PMM能够估计一个共享分数，该分数可用于将后续研究重点放在通用或特定的基因调节因子上。通过将PMM模型应用于我们的数据，我们为大多数研究的病原体发现了几个新的命中基因。
我们的结果表明，并行RNAi筛选可以改善单个筛选的结果。当大规模并行数据集越来越多地公开可用时，这一点目前尤其令人关注。我们全面的siRNA数据集为高内涵高通量siRNA筛选领域的进一步统计和生物学分析提供了一个公开的、免费可用的资源。
