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Related Experiment Video

Updated: Jun 12, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Protocol for identifying key genes using network-based approach as an alternative to differential expression

Piyush Agrawal¹, Sridhar Hannenhalli²

¹Department of Medical Research, SRM Medical College Hospital & Research Centre, SRMIST, Kattankulathur, Chennai, India.

|December 5, 2024

Summary

This summary is machine-generated.

Identifying key genes in disease etiology and transcriptomic changes is crucial. Our PathExt tool uses a network-based approach and ripple centrality scores to pinpoint these critical genes effectively.

Keywords:

Bioinformatics Cancer Computer sciences Genomics Systems biology

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Last Updated: Jun 12, 2025

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Characterizing genes involved in disease etiology is essential for understanding biological processes.
Global transcriptomic changes are often mediated by key regulatory genes.
Identifying these genes aids in disease mechanism elucidation and therapeutic target discovery.

Purpose of the Study:

To present a protocol for identifying key genes associated with disease etiology and transcriptomic changes.
To introduce PathExt, a novel network-based tool for gene characterization.
To detail the methodology for utilizing PathExt for pathway and gene analysis.

Main Methods:

Protocol implementation for installing necessary libraries.
Data preparation guidelines for input into the PathExt tool.
Detailed procedures for running PathExt and analyzing results using ripple centrality scores.

Main Results:

Successful application of the PathExt tool to identify key genes.
Characterization of differential pathways based on network analysis.
Quantification of gene importance using ripple centrality scores.

Conclusions:

The PathExt tool provides an effective network-based method for identifying key genes in biological contexts.
The protocol facilitates the analysis of transcriptomic data to understand disease etiology.
Ripple centrality scores offer a robust metric for ranking gene significance.