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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Zhi Huang1, Zhi Han2, Tongxin Wang3

  • 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA; Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.

Genomics, Proteomics & Bioinformatics
|March 11, 2021
PubMed
Summary

TSUNAMI is a new online tool for mining gene co-expression network (GCN) modules. It helps researchers find small, tightly connected gene sets for biomarker discovery and functional analysis.

Keywords:
Gene co-expression networkLmQCMNetwork miningSurvival analysisTranscriptomic data analysisWeb server

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene co-expression network (GCN) mining reveals gene interactions and aids in identifying disease biomarkers.
  • Existing tools lack user-friendly options for mining small, tightly connected GCN modules suitable for downstream analysis.

Purpose of the Study:

  • To develop an accessible online tool package for mining gene co-expression network modules.
  • To provide a user-friendly platform for identifying small, tightly connected gene modules and performing downstream gene set enrichment analysis.

Main Methods:

  • Developed TSUNAMI (Tools SUite for Network Analysis and MIning), an online GCN mining tool.
  • Incorporated the lmQCM algorithm for mining GCN modules from various omics data.
  • Integrated direct access to GEO and TCGA databases, user-input data options, and multiple co-expression analysis tools.
  • Included eigengene summarization, Enrichr enrichment analysis, and Circos plot visualization.

Main Results:

  • TSUNAMI offers a user-friendly web interface for real-time GCN module mining.
  • The tool supports diverse data types (microarray, RNA-seq) and public databases (GEO, TCGA).
  • It facilitates downstream analysis, including enrichment analysis and visualization, with flexible parameter options.

Conclusions:

  • TSUNAMI provides a comprehensive and user-friendly solution for GCN module mining and analysis.
  • The tool enhances the discovery of gene modules for biomarker identification and functional genomics research.
  • TSUNAMI is freely accessible online with source code available for broader research community adoption.