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bioRxiv · 10.1101/064535

A scored human protein-protein interaction network to catalyze genomic interpretation

Taibo Li·Rasmus Wernersson·Rasmus Borup Hansen·Heiko Horn·Johnathan M Mercer·Greg Slodkowicz·Christopher Workman·Olga Regina·Kristoffer Rapacki·Hans-Henrik Staerfeldt·Soren Brunak·Thomas S Jensen·Kasper Lage
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Abstract

Human protein-protein interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human protein-protein interaction network (InWeb_IM). Juxtaposed with five comparable resources, InWeb_IM has 2.8 times more interactions (~585K) and a superior functional signal showing that the added interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.

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Taibo Li, Rasmus Wernersson, Rasmus Borup Hansen, Heiko Horn, Johnathan M Mercer, Greg Slodkowicz, Christopher Workman, Olga Regina, Kristoffer Rapacki, Hans-Henrik Staerfeldt, Soren Brunak, Thomas S Jensen, Kasper Lage. 2016-07-19. A scored human protein-protein interaction network to catalyze genomic interpretation. https://doi.org/10.1101/064535

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