Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Gene clustering and enrichment analysis are common in microarray data analysis.
• These methods identify functional themes and regulatory motifs but are often used separately.
• Integrating these approaches presents a significant computational challenge.

Purpose of the Study:

• To develop an integrated algorithm for gene expression profile analysis.
• To discover molecular functions and elucidate transcriptional logics.
• To link gene expression patterns with regulatory motifs.

Main Methods:

• Proposed an algorithm named gene set expression coherence analysis (GSECA).
• Selected functional gene sets based on expression coherences.
• Investigated functional clusters for enrichment of transcriptional regulatory motifs using modified gene set enrichment analysis.

Main Results:

• Successfully applied the algorithm to murine myogenesis and erythropoiesis expression profiles.
• Identified myocyte- and erythrocyte-related molecular functions.
• Discovered putative transcriptional regulators for the identified molecular functions.

Conclusions:

• The developed method provides an integrative approach for analyzing large-scale gene expression data.
• It generates testable hypotheses about gene regulation (e.g., regulator X controls function Y in condition Z).
• The GSECA algorithm is available as a free software package.