NetBID2 provides comprehensive hidden driver analysis

Xinran Dong, Liang Ding, Andrew Thrasher, Xinge Wang, Jingjing Liu, Qingfei Pan, Jordan Rash, Yogesh Dhungana, Xu Yang, Isabel Risch, Yuxin Li, Lei Yan, Michael Rusch, Clay McLeod, Koon-Kiu Yan, Junmin Peng, Hongbo Chi, Jinghui Zhang and Jiyang Yu ()
Additional contact information
Xinran Dong: St. Jude Children’s Research Hospital
Liang Ding: St. Jude Children’s Research Hospital
Andrew Thrasher: St. Jude Children’s Research Hospital
Xinge Wang: St. Jude Children’s Research Hospital
Jingjing Liu: St. Jude Children’s Research Hospital
Qingfei Pan: St. Jude Children’s Research Hospital
Jordan Rash: St. Jude Children’s Research Hospital
Yogesh Dhungana: St. Jude Children’s Research Hospital
Xu Yang: St. Jude Children’s Research Hospital
Isabel Risch: St. Jude Children’s Research Hospital
Yuxin Li: Centre for Proteomics and Metabolomics, St. Jude Children’s Research Hospital
Lei Yan: St. Jude Children’s Research Hospital
Michael Rusch: St. Jude Children’s Research Hospital
Clay McLeod: St. Jude Children’s Research Hospital
Koon-Kiu Yan: St. Jude Children’s Research Hospital
Junmin Peng: Centre for Proteomics and Metabolomics, St. Jude Children’s Research Hospital
Hongbo Chi: St. Jude Children’s Research Hospital
Jinghui Zhang: St. Jude Children’s Research Hospital
Jiyang Yu: St. Jude Children’s Research Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Many signaling and other genes known as “hidden” drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

Date: 2023
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DOI: 10.1038/s41467-023-38335-6

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