%0 Generic
%A Amberkar, Sandeep
%C Heidelberg
%D 2014
%F heidok:17492
%R 10.11588/heidok.00017492
%T Integrative bioinformatics analyses of genome-wide  RNAi screens
%U https://archiv.ub.uni-heidelberg.de/volltextserver/17492/
%X In past few years, genome-wide RNAi screens have identified many novel genes involved in  diseases for many viruses such as Human Immunodeficiency Virus-1 (HIV-1), Hepatitis C virus  (HCV), West Nile Virus (WNV) and Influenza virus (IV). However, due to difference  in experimental conditions, usage of different viral strains and inherent biological noise, these  screens have shown low number of common or overlapping hits for a virus. Moreover, this  overlap gets poorer for similar studies on viruses of different families. Although these overlaps  are significant, their lower size restricts a comprehensive insight from a comparative analysis.  Thus, a direct comparison of gene hit-lists of RNAi screens may not always give meaningful  results. To address this problem we propose an integrative bioinformatics pipeline that allows  for network based meta-analysis of viral HT-RNAi screens. Initially, human protein interaction  network (PIN) generated by collating data from various public repositories, is subjected  to unsupervised clustering to determine functional modules. Those modules that are significantly  enriched in 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 then interpreted for their biological significance from enrichment analyses.  With our approach we could predict Tankyrase-1 as a potential novel hit within the functional  subnetworks, within the human PIN for Hepatitis C virus (HCV). and Human Immunodeficiency  Virus-1 (HIV-1), based on HDFs and HRFs identified in the corresponding genome-wide  RNAi screens of these viruses. Thus, our approach allows for a network based meta-analysis  of genome-wide screens to develop plausible hypotheses for novel regulatory mechanisms in  virus-host interactions based on RNAi screens.