Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transcription factors (TFs) form complexes to regulate gene expression.
• Existing databases primarily catalog TF complexes from limited experimental methods.
• Emerging ChIP-seq data offers opportunities to study condition-specific TF interactions.

Purpose of the Study:

• To develop a systematic pipeline for inferring condition-specific targets of human TF-TF complexes.
• To integrate ChIP-seq data and TF motifs for predicting TF complex regulatory networks.
• To construct a comprehensive database of condition-specific TF regulatory interactions.

Main Methods:

• Developed a systematic computational pipeline (CST pipeline).
• Integrated human ChIP-seq data and transcription factor motifs.
• Predicted TF complexes and their target genes.
• Validated predictions using external databases, ChIP-qPCR, RT-PCR, and gene ontology enrichment.

Main Results:

• Predicted 2,392 TF complexes.
• Identified 13,504 high-confidence and 127,994 low-confidence regulatory interactions.
• Validated the accuracy and biological relevance of predictions.
• Constructed a Condition-Specific Targets (CST) database.

Conclusions:

• Established a methodology for constructing the CST database.
• The CST database aids in analyzing transcriptional regulation.
• Facilitates the identification of novel TF-TF complex formation in specific conditions.
• Provides a user-friendly web interface for visualizing condition-specific regulatory networks.