- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
Background 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. Results 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. Conclusions 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. Show more
Journal / seriesBMC Genomics
Pages / Article No.
SubjectHigh-throughput high-content RNAi screening; Pathogen entry; Linear mixed model; Hit detection
Organisational unit03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
03589 - Hardt, Wolf-Dietrich / Hardt, Wolf-Dietrich
03495 - Helenius, Ari
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
02891 - ScopeM / ScopeM
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