Determination and inference of eukaryotic transcription factor sequence specificity

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transcription factor (TF) DNA sequence preferences are crucial for gene regulation but are known for a small fraction of eukaryotic TFs.
• Understanding TF binding is essential for deciphering complex transcriptional networks.

Purpose of the Study:

• To determine DNA sequence preferences for a large number of eukaryotic TFs.
• To develop a method for inferring TF binding motifs for a broader range of TFs.
• To create a valuable resource for mapping eukaryotic transcriptional networks.

Main Methods:

• Determined DNA sequence preferences for over 1,000 TFs from 131 diverse eukaryotes, covering 54 DNA-binding domain (DBD) classes.
• Utilized sequence similarity of closely related DBDs to infer TF binding motifs.
• Analyzed enrichment of TF binding sites in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and promoter regions.

Main Results:

• Established DNA sequence preferences for >1,000 TFs, significantly expanding the known TF motif landscape.
• Developed a method to infer motifs for approximately 34% of known or predicted eukaryotic TFs based on DBD similarity.
• Demonstrated enrichment of measured and inferred TF binding sites in regulatory regions and identified TF binding site alterations by disease-associated SNPs.

Conclusions:

• This study provides a comprehensive resource of TF DNA sequence preferences and binding motifs across eukaryotes.
• The findings enable more accurate prediction of TF binding and facilitate the study of gene regulation.
• The developed motif library is a powerful tool for identifying TFs affected by genetic variations, including human disease risk alleles.