Detecting clustering and ordering binding patterns among transcription factors via point process models

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• ChIP-Seq technology provides extensive transcription factor (TF) binding data across diverse cell types and conditions.
• Understanding combinatorial TF binding is crucial for deciphering gene regulation mechanisms.

Purpose of the Study:

• To develop statistical methods for identifying clustering and ordering patterns among TF binding sites.
• To analyze TF binding patterns in ChIP-Seq data and their association with gene expression.

Main Methods:

• Utilized Ripley's K-function and its generalizations for pattern detection in TF binding sites.
• Applied methods to ChIP-Seq data from 91 TF pairs in mouse embryonic stem cells.

Main Results:

• Detected significant clustering patterns for most TF pairs, aligning with existing literature.
• Identified significant ordering preferences in binding sites for seven TFs relative to gene transcription direction.
• Demonstrated a correlation between TF binding patterns (clustering and ordering) and target gene expression.

Conclusions:

• The developed statistical methods effectively identify TF binding patterns from ChIP-Seq data.
• TF binding site clustering and ordering are prevalent and linked to gene expression.
• Findings offer novel insights into the co-regulatory roles of transcription factors.