Abstract
In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges.
Highlights
In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality
Guan et al (2012) constructed 107 tissue-specific networks for the laboratory mouse to be used in disease-gene prioritization[4]. They used a combination of training data from Gene Ontology (GO) and tissue-specific expression signatures to customize their networks before moving to predicting disease candidate genes
A theme that emerges from our review is brought out by the difference between the practices of computational biologists and those who use function prediction tools
Summary
A theme that emerges from our review is brought out by the difference between the practices of computational biologists and those who use function prediction tools (loosely defined) These differences should come as no real surprise, but it has important implications that we feel are not being attended to sufficiently. The concern about “how” is reflected in our demonstrations that gene multifunctionality and node degree effects are often more important in determining the outcome of a GBA analysis than details about the connections in the network[1]. These two realizations should affect practice, but they do not mean that the predictions one makes are always incorrect. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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