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 .
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Authors | 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, Jiyang Yu |
Journal | Nature communications
(Nat Commun)
Vol. 14
Issue 1
Pg. 2581
(05 04 2023)
ISSN: 2041-1723 [Electronic] England |
PMID | 37142594
(Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
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Copyright | © 2023. The Author(s). |
Topics |
- Humans
- Bayes Theorem
- Genomics
- Algorithms
- Cell Transformation, Neoplastic
(genetics)
- Research Design
- Software
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