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Related Experiment Video

Updated: Jun 29, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target

Archarlie Chou¹, Myesha Gilliland¹, Matt Reall¹

¹Department of Microbiology and Molecular Biology, Brigham Young University.

Journal of Visualized Experiments : Jove

|October 20, 2025

Summary

This summary is machine-generated.

This study presents a computational pipeline to identify therapeutic targets from RNA sequencing data. It prioritizes druggable targets and treatments by analyzing significant pathways and known drug interactions.

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Last Updated: Jun 29, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Published on: August 16, 2017

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Computational biology
Genomics
Pharmacogenomics

Background:

RNA sequencing (RNA-seq) data is crucial for understanding gene expression in diseases.
Identifying therapeutic targets from complex biological data remains a challenge.

Purpose of the Study:

To develop and validate a computational pipeline for identifying potential therapeutic targets from RNA-seq data.
To prioritize druggable targets and associated therapeutics based on pathway analysis and drug-target databases.

Main Methods:

Differential gene expression analysis using edgeR.
Signaling Pathway Impact Analysis (SPIA) for identifying significant pathways (p < 0.05).
Pathway2Targets algorithm integrating OpenTargets.org API for novel drug target scoring.

Main Results:

A multi-step pipeline for RNA-seq data analysis.
Identification of statistically significant pathways reflecting biological processes.
Prioritized lists of potential drug targets and associated therapeutics with weighted scores.

Conclusions:

The pipeline effectively identifies and prioritizes druggable targets from disease-specific gene expression profiles.
This approach offers mechanistic insights and facilitates drug discovery.
The method integrates pathway analysis with drug-target interaction data for enhanced therapeutic development.