Bias tradeoffs in the creation and analysis of protein-protein interaction networks.
Networks constructed from aggregated protein-protein interaction data are commonplace in biology. But the studies these data are derived from were conducted with their own hypotheses and foci. Focusing on data from budding yeast present in BioGRID, we determine that many of the downstream signals present in network data are significantly impacted by biases in the original data. We determine the degree to which selection bias in favor of biologically interesting bait proteins goes down with study size, while we also find that promiscuity in prey contributes more substantially in larger studies. We analyze interaction studies over time with respect to data in the Gene Ontology and find that reproducibly observed interactions are less likely to favor multifunctional proteins. We find that strong alignment between co-expression and protein-protein interaction data occurs only for extreme co-expression values, and use this data to suggest candidates for targets likely to reveal novel biology in follow-up studies.


CONCLUSIONS
Protein-protein interaction data finds particularly heavy use in the interpretation of disease-causal variants. In principle, network data allows researchers to find novel commonalities among candidate genes. In this study, we detail several of the most salient biases contributing to aggregated protein-protein interaction databases. We find strong evidence for the role of selection and laboratory biases. Many of these effects contribute to the commonalities researchers find for disease genes. In order for characterization of disease genes and their interactions to not simply be an artifact of researcher preference, it is imperative to identify data biases explicitly. Based on this, we also suggest ways to move forward in producing candidates less influenced by prior knowledge. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?
Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, 500 Sunnyside Boulevard, Woodbury, NY 11797, United States. Electronic address: JGillis@cshl.edu.
Jesse
J
Gillis
Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, 500 Sunnyside Boulevard, Woodbury, NY 11797, United States. Electronic address: sballouz@cshl.edu.
Sara
S
Ballouz
Department of Psychiatry and Centre for High-Throughput Biology, University of British Columbia, 2185 East Mall., Vancouver, BC V6T 1Z4, Canada. Electronic address: paul@chibi.ubc.ca.
Paul
P
Pavlidis
D015982:Bias; D030562:Databases, Protein; D063990:Gene Ontology; D064251:Multifunctional Enzymes; D025941:Protein Interaction Mapping / Q000379:methods; D060066:Protein Interaction Maps; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
D064251:Multifunctional Enzymes; D029701:Saccharomyces cerevisiae Proteins
Bias; Co-expression; Gene Ontology; Multifunctionality; Networks; Protein–protein interaction