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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

Prioritizing biological pathways by recognizing context in time-series gene expression data.

Jusang Lee¹, Kyuri Jo¹, Sunwon Lee²

¹Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

BMC Bioinformatics

|February 4, 2017

Summary

This summary is machine-generated.

This study introduces ContextTRAP, a new method for pathway analysis that combines gene expression data with scientific literature. ContextTRAP prioritizes relevant biological pathways more effectively than existing tools.

Keywords:

Literature information Pathway Pathway analysis Pathway prioritization Time series

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Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Pathway analysis of transcriptome data aims to identify perturbed pathways but often struggles due to genes participating in multiple pathways.
The complexity of gene-pathway relationships, where single genes can affect numerous pathways, hinders accurate identification of context-specific biological relevance.
Integrating literature information offers a complementary approach to enhance pathway analysis accuracy.

Purpose of the Study:

To develop a novel method for prioritizing biological pathways by integrating pathway analysis results with literature-derived evidence.
To address the challenge of identifying truly relevant pathways in gene expression studies.
To improve the reliability of pathway analysis by leveraging both statistical significance and contextual relevance.

Main Methods:

A new algorithm was developed, combining pathway analysis results (significance) with literature evidence (relevance) to prioritize pathways.
The approach introduces Context Score and Impact Score, which are merged into a single score for pathway ranking.
The framework was implemented as ContextTRAP, utilizing existing tools TRAP and BEST.

Main Results:

The proposed method significantly ranked truly relevant pathways higher compared to existing pathway analysis tools.
Experiments conducted on two datasets demonstrated the superior performance of the novel approach.
The integration of literature information effectively disambiguated pathway relevance in complex biological contexts.

Conclusions:

ContextTRAP provides a robust framework for pathway-based analysis of gene expression data.
Users can specify the biological experiment's context using keywords, enhancing the tool's applicability.
ContextTRAP is available as a web tool, facilitating its use in biological research.