An integrative bioinformatics pipeline that allows for a network based meta-analysis
An integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28506461 interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. Conclusions: We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.Keywords: Network analysis, RNAi screening, Virus-host interactionsBackgroundRNA interference (RNAi) has become an important workhorse of functional genomics, and genome-wide RNAi screens have been employed for example to identify genes involved in cell growth and viability, proliferation, differentiation, signaling or trafficking [1-9]. The technology has furthermore accelerated the discovery of novel host dependency factors (HDF) and host restriction factors (HRF) in viral infection [10-19]. However, while RNAi is a very powerful tool to identify genes involved in a*Correspondence: [email protected] 1 Institute of Medical Informatics and Biometry, Medical Faculty, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany 2 ViroQuant Research Group Modeling, BioQuant, Heidelberg University, INF 267, 69120 Heidelberg, Germany Full list of author information is available at the end of the articlespecific biological process, the placement of hits in their functional and spatiotemporal context in the underlying molecular processes remains a major challenge [20,21]. The interpretation of RNAi data in particular for virus screens is complicated further by the observed low overlap between identified host factors, even in different screens targeting the same virus [22-24]. This low overlap has been explained by different experimental conditions such as host cell type and viral strain used, transfection, incubation and infection time, and siRNA library used [24] as well as by technical artifacts arising from cell population context [25,26]. Furthermore, due to the typical setup of RNAi experiments with primary screens followed by secondary validation assays, it is likely that published hit lists are highly specific, but not very GW 4064 custom synthesis sensitive, further?2015 Amberkar and Kaderali; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28242652 otherwise stated.