Abstract
BackgroundBig data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization.ResultsTo address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users.
Highlights
Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation
We identified several interesting candidate pathways for human immunodeficiency virus 1 (HIV-1) and hepatitis C virus (HCV), including known targets such as the mediator complex or members of the heterogeneous nuclear ribonucleoprotein subunits in HIV infection, or MAP kinases and heat shock proteins in HCV infection
Given the long and often largely non-overlapping hit lists from RNA interference (RNAi) screens targeting viral infection, a central aim of our analysis was to select a small number of most significant, infection-relevant host protein subnetworks for further manual analysis, and to pick most promising candidates from the original screens for functional characterization
Summary
Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. The analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. Protein interaction networks, virus-host interaction networks and other heterogeneous data have increased tremendously [29,30,31,32,33,34] This offers novel ways to interpret hit lists from RNAi experiments from a network perspective, by integrating individual hits in their systemic context. Being less dependent on individual genes, but rather focusing on pathways, may shed new light onto virus-specific and generic host processes facilitating or restricting infection, and may prove a promising approach to identify potential host targets for antiviral drug development
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