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

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.