Identifying cooperative transcription factors in yeast using multiple data sources

Area of Science:

• * Molecular Biology
• * Genomics
• * Bioinformatics

Background:

• * Transcriptional regulation involves complex interactions between multiple transcription factors (TFs).
• * Understanding TF cooperativity is crucial for deciphering gene expression mechanisms.
• * Existing methods utilize various data types but have not incorporated nucleosome occupancy data.

Purpose of the Study:

• * To develop a novel computational method for inferring TF pair cooperativity.
• * To integrate TF-gene regulation, TF binding site (TFBS), and nucleosome occupancy data.
• * To identify novel cooperative TF pairs and evaluate the method's performance against existing approaches.

Main Methods:

• * Integrated TF-gene regulation, TFBS, and genome-wide nucleosome occupancy data.
• * Developed a scoring system based on common target genes and nucleosome depletion at TFBSs.
• * Validated predictions using protein-protein interactions and functional annotations.

Main Results:

• * Identified 27 cooperative TF pairs in yeast, including 8 novel pairs.
• * The novel pairs were supported by protein-protein interactions and functional co-annotations.
• * The developed method demonstrated superior performance compared to 11 existing methods.

Conclusions:

• * The novel method effectively identifies cooperative TF pairs in yeast.
• * The findings provide insights into transcriptional regulation mechanisms.
• * The approach outperforms existing methods and identifies novel TF interactions.