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NetBID2 provides comprehensive hidden driver analysis

Author

Listed:
  • Xinran Dong

    (St. Jude Children’s Research Hospital
    Children’s Hospital of Fudan University)

  • Liang Ding

    (St. Jude Children’s Research Hospital)

  • Andrew Thrasher

    (St. Jude Children’s Research Hospital)

  • Xinge Wang

    (St. Jude Children’s Research Hospital
    University of Illinois at Chicago)

  • 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
    St. Jude Children’s Research Hospital)

  • Xu Yang

    (St. Jude Children’s Research Hospital)

  • Isabel Risch

    (St. Jude Children’s Research Hospital
    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)

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 .

Suggested Citation

  • 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-, 2023. "NetBID2 provides comprehensive hidden driver analysis," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38335-6
    DOI: 10.1038/s41467-023-38335-6
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    References listed on IDEAS

    as
    1. Xiaotu Ma & Yu Liu & Yanling Liu & Ludmil B. Alexandrov & Michael N. Edmonson & Charles Gawad & Xin Zhou & Yongjin Li & Michael C. Rusch & John Easton & Robert Huether & Veronica Gonzalez-Pena & Mark , 2018. "Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours," Nature, Nature, vol. 555(7696), pages 371-376, March.
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    3. Xingrong Du & Jing Wen & Yanyan Wang & Peer W. F. Karmaus & Alireza Khatamian & Haiyan Tan & Yuxin Li & Cliff Guy & Thanh-Long M. Nguyen & Yogesh Dhungana & Geoffrey Neale & Junmin Peng & Jiyang Yu & , 2018. "Hippo/Mst signalling couples metabolic state and immune function of CD8α+ dendritic cells," Nature, Nature, vol. 558(7708), pages 141-145, June.
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