SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions

Area of Science:

• Genomics
• Computational Biology
• Epigenetics

Background:

• Aberrant DNA methylation in transcription factor binding sites causes abnormal gene regulation linked to human diseases.
• Most methylation-sensitive positions within these binding sites are currently unknown.
• Identifying these sites is crucial for understanding disease mechanisms.

Purpose of the Study:

• To introduce SEMplMe, a computational tool for predicting the impact of DNA methylation on transcription factor binding strength.
• To identify all methylation-sensitive positions within a transcription factor's motif.

Main Methods:

• SEMplMe integrates ChIP-seq and whole genome bisulfite sequencing data.
• It predicts the effects of methylation on transcription factor binding within specific motifs.

Main Results:

• SEMplMe successfully validates known methylation-sensitive and insensitive positions.
• It identifies cell-type-specific transcription factor binding influenced by methylation.
• Predictions for CTCF outperform SELEX-based methods.

Conclusions:

• SEMplMe provides accurate predictions of methylation effects on transcription factor binding.
• The tool can identify aberrant DNA methylation sites contributing to human diseases.
• It advances the understanding of epigenetic regulation in disease.