Geisel School of Medicine
Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes.
Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database.
We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface.
Yang CC, Chen MH, Lin SY, Andrews EH, Cheng C, Liu CC, Chen JJ. Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data. BMC Genomics. 2017 Jan 10;18(1):61. doi: 10.1186/s12864-016-3450-3. PMID: 28068916; PMCID: PMC5223348.
Dartmouth Digital Commons Citation
Yang, Chia-Chun; Chen, Min-Hsuan; Lin, Sheng-Yi; Andrews, Erik H.; Cheng, Chao; and Chen, Jeremy J.W, "Inferring Condition-Specific Targets of Human TF-TF Complexes Using ChIP-seq Data" (2017). Dartmouth Scholarship. 2863.