WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

Abstract

Background: In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations (“edges”), which can represent gene regulatory events essential to biological processes.

Method: We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations (“edges”) in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models.

Results: Our results showed WIPER can reliably discover both critical “well traversed in-path edges”, which are statistically more traversed than normal edges, and “peripheral in-path edges”, which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer’s disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation.

Conclusion: We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks.

Availability: The free WIPER API is described at discovery.informatics.uab.edu/wiper/

Graphical abstract

References

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0180-y.

ACKNOWLEDGEMENTS

We thank Jelai Wang and BioITX services at the University of Alabama at Birmingham (UAB) Informatics Institute for computing infrastructure support. The work is partly supported by the National Institute of Health funded Center for Clinical and Translational Science grant award (U54TR002731) to the University of Alabama at Birmingham (UAB), research start-up fund provided by the UAB Informatics Institute to Dr. Chen, the American Heart Association institutional data science fellowship award to the Informatics Institute of UAB, and the National Cancer Institute grant award (U01CA223976).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Zongliang Yue, Thanh Nguyen, Eric Zhang, Jianyi Zhang and Jake Y. Chen declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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