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Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen

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Item Type:Article
Title:Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen
Creators Name:Suratanee, A. and Schaefer, M.H. and Betts, M.J. and Soons, Z. and Mannsperger, H. and Harder, N. and Oswald, M. and Gipp, M. and Ramminger, E. and Marcus, G. and Männer, R. and Rohr, K. and Wanker, E. and Russell, R.B. and Andrade-Navarro, M.A. and Eils, R. and König, R.
Abstract:Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.
Keywords:Cluster Analysis, Gene Knockdown Techniques, Genomics, HeLa Cells, Phenotype, Protein Databases, Protein Interaction Maps, Proteins, ROC Curve, Small Interfering RNA
Source:PLoS Computational Biology
ISSN:1553-734X
Publisher:Public Library of Science (U.S.A.)
Volume:10
Number:9
Page Range:e1003814
Date:25 September 2014
Official Publication:https://doi.org/10.1371/journal.pcbi.1003814
PubMed:View item in PubMed

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