REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Pathway analysis is crucial for interpreting genome-wide transcription profiling.
• Traditional methods include over-representation analysis and gene set analysis.
• Genome-wide transcription factor binding data is increasingly available.

Purpose of the Study:

• To develop Regulatory Enrichment Pathway Analysis (REPA) for applying gene set analysis to transcription factor binding data.
• To infer associations between transcription factors and biological pathways.
• To enhance the interpretation of genome-wide transcription profiling studies.

Main Methods:

• Utilized ENCODE project transcription factor binding data.
• Employed gene sets from Molecular Signatures and KEGG databases.
• Applied gene set analysis principles to infer regulatory relationships.

Main Results:

• Achieved a recall of approximately 54% for inferred associations.
• 54% of REPA's predictions showed literature support.
• Identified potentially novel regulatory associations for future research.

Conclusions:

• REPA effectively infers transcription factor-pathway associations from binding data.
• The method enhances the interpretation of gene expression studies by identifying putative regulators.
• REPA offers a promising approach for guiding new research avenues in regulatory genomics.