- Title：MAIB-Talk-016: NetBID2 provides comprehensive hidden driver analysis
- Date：10:00pm US East time, 06/10/2023
- Date：10:00am Beijing time, 06/11/2023
- Zoom ID：933 1613 9423
- Zoom PWD：416262
- Zoom: https://uwmadison.zoom.us/meeting/register/tJcudu-prTIuGNda1MsF8PKyRQlnGn06TP2E
Presentation Record(Previous Presentation will be showed here if the video is not released for this talk)
Dr. Jiyang Yu
Dissecting molecular (re)wiring and underpinning hidden drivers within and between cells/systems toward clinical translation. Biological processes operate through molecular networks at the cellular level and cell–cell communication networks at the tissue/organ level. Deciphering the wiring and rewiring of these networks under normal and pathologic conditions is a fundamental goal of biomedical research. Our lab is focused on developing data-driven systems biology algorithms to integrate bulk, single-cell, and spatial omics data to decipher these (re)wiring events and hidden drivers underpinning biological processes in health and disease. Ultimately, we aim to translate the hidden driver discoveries into therapeutic targets, biomarkers, and combination therapies for cancer and other disorders.
Computational Biology Department Comprehensive Cancer Center St. Jude Graduate School of Biomedical Sciences
Other softwares developed by Dr. Yu and his team:
scMINER, Single-cell mutual information network engineered ranger (scMINER) is a toolbox for single-cell analysis based on mutual information.
NetBID, NetBID is a data-driven network-based algorithm for Bayesian inference of drivers.
SJARACNe, SJARACNe is a scalable tool for gene network reverse engineering from bulk or single-cell gene expression data.
ScreenBEAM, ScreenBEAM is a meta-analysis tool for functional genomic screens via Bayesian hierarchical modeling.
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.
The paper introduces NetBID2, a comprehensive algorithm and toolkit designed to identify hidden drivers that play a role in phenotypes such as tumorigenesis through post-translational modification or other mechanisms. These drivers may not exhibit genetic or epigenetic alterations or show differential expression at the mRNA or protein levels, making them challenging to detect using conventional genomics or differential expression approaches.NetBID2 addresses this limitation by reverse-engineering context-specific interactomes and integrating network activity inferred from large-scale multi-omics data. It enables the identification of hidden drivers that would otherwise remain undetected. The toolkit has undergone significant improvements from its previous version, offering versatile data visualization and sophisticated statistical analyses. These enhancements greatly aid researchers in result interpretation through end-to-end multi-omics data analysis.The paper showcases the capabilities of NetBID2 through three examples of hidden drivers. To facilitate analysis, real-time interactive visualization, and cloud-based data sharing, the authors provide NetBID2 Viewer, Runner, and Cloud apps. These apps support 145 context-specific gene regulatory and signaling networks across normal tissues as well as pediatric and adult cancers. Users can access these resources to conduct end-to-end analysis, visualize results interactively, and share data via the cloud.The authors emphasize that NetBID2 is freely available to the research community at https://jyyulab.github.io/NetBID. By leveraging NetBID2, researchers can overcome the limitations of traditional analyses and gain insights into hidden drivers that contribute to complex phenotypes, enhancing our understanding of diseases like cancer and facilitating the development of targeted therapies.
This paper is very important important:
1, Addressing the limitation of traditional approaches: The paper highlights the limitations of conventional genomics or differential expression approaches in identifying hidden drivers that contribute to complex phenotypes. By introducing NetBID2, a comprehensive algorithm and toolkit, the paper provides a novel solution to overcome this limitation and enables researchers to uncover these hidden drivers.
2, Integration of multi-omics data: NetBID2 integrates large-scale multi-omics data to infer network activity and context-specific interactomes. This integration allows researchers to gain a more holistic understanding of the molecular mechanisms underlying phenotypes, including diseases like cancer. By leveraging diverse data types, NetBID2 provides a comprehensive analysis framework for studying complex biological systems.
3, Empowering researchers with visualization and statistical analyses: The paper emphasizes the improvements made in NetBID2, including versatile data visualization and sophisticated statistical analyses. These enhancements facilitate result interpretation and data analysis, empowering researchers to gain meaningful insights from their multi-omics datasets. The provided NetBID2 Viewer, Runner, and Cloud apps further streamline the analysis process and enable real-time interactive visualization and data sharing.
4, Examples of hidden driver identification: The paper demonstrates the power of NetBID2 by presenting three examples of hidden drivers. These examples showcase the capability of NetBID2 to identify drivers that are not evident through traditional approaches. By highlighting specific cases, the paper illustrates the potential impact of NetBID2 in uncovering important molecular drivers in various biological contexts.
From this paper, researchers can learn the following:
1, Methodology for detecting hidden drivers: The paper introduces the methodology and algorithmic framework of NetBID2, providing researchers with insights into how they can identify hidden drivers that contribute to phenotypes. Researchers can understand the process of reverse-engineering context-specific interactomes and integrating multi-omics data to infer network activity.
2, Data integration and analysis techniques: The paper highlights the importance of integrating multi-omics data and provides guidance on how to leverage diverse data types effectively. Researchers can learn about the statistical analyses and visualization techniques employed in NetBID2 to gain a comprehensive understanding of their data.
3, Application of NetBID2 in different contexts: By presenting examples across normal tissues and pediatric and adult cancers, the paper demonstrates the versatility of NetBID2 in various biological contexts. Researchers can learn about the potential applications of NetBID2 in their own studies, particularly in uncovering hidden drivers related to complex diseases.
4, Overall, this paper is important as it introduces a novel approach to overcome the limitations of traditional analyses and provides researchers with a comprehensive toolkit for identifying hidden drivers. It offers insights into data integration, analysis techniques, and the potential impact of uncovering hidden drivers in various biological contexts.