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Related Experiment Video

Updated: Jun 8, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

NetBID2 provides comprehensive hidden driver analysis.

Xinran Dong^1,2, Liang Ding¹, Andrew Thrasher¹

¹Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.

Nature Communications

|May 4, 2023

Summary

This summary is machine-generated.

NetBID2 identifies "hidden" gene drivers missed by traditional methods. This network-based tool analyzes multi-omics data to uncover novel drivers of diseases like cancer.

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Published on: December 7, 2021

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Many disease-driving genes, termed "hidden drivers," evade detection by standard genomic or gene expression analyses.
These drivers may operate through mechanisms like post-translational modifications, influencing phenotypes such as tumorigenesis.

Purpose of the Study:

To introduce NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), a novel algorithm and toolkit.
To enable the identification of previously undetectable "hidden" drivers by integrating multi-omics data and network activity.

Main Methods:

NetBID2 employs a comprehensive algorithm to reverse-engineer context-specific interactomes.
It integrates network activity inferred from large-scale multi-omics data for robust driver identification.
The toolkit offers enhanced data visualization and sophisticated statistical analyses for result interpretation.

Main Results:

NetBID2 successfully identified hidden drivers that traditional methods could not detect.
The study demonstrated the toolkit's efficacy using three distinct hidden driver examples.
Deployment of NetBID2 Viewer, Runner, and Cloud apps facilitates end-to-end analysis across 145 gene regulatory and signaling networks.

Conclusions:

NetBID2 provides a powerful, data-driven approach for uncovering "hidden" drivers in complex biological systems.
The toolkit significantly advances multi-omics data analysis for disease research, particularly in cancer.
NetBID2 enhances biological discovery through accessible, interactive, and shareable network analysis tools.