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

Abstract

BackgroundTranscription 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.ResultsHere, 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3450-3) contains supplementary material, which is available to authorized users.

A pipeline to identify TF combinatorial binding uncovers TEAD1 as an antagonist of tissue-specific transcription factors in human organogenesis

“…Several methods are available to explore the co-occurrence of TFs at regulatory regions (9–19). Some require ChIP-seq data of target TFs (10,12,18,19) or integrate TF and H3K27ac ChIP-seq experiments (13). These approaches are relatively context-specific as they rely on the availability of the target TF ChIP-seq, which relative to H3K27ac data, is less widely available.…”

“…TFBS can be represented by numerical matrices called position weight matrices (PWMs), which enable predictions of TF binding based on DNA sequence (8). Several methods are available to explore the co-occurrence of TFs at regulatory regions (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Some require ChIP-seq data of target TFs (10,12,18,19) or integrate TF and H3K27ac…”

“…Several methods are available to explore the co-occurrence of TFs at regulatory regions (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Some require ChIP-seq data of target TFs (10,12,18,19) or integrate TF and H3K27ac…”

Discovering a less-is-more effect to select transcription factor binding sites informative for motif inference

“…Second, the sites with strong binding signals may also contain motifs of many other TFs (e.g. the motifs of co-binding TFs) [33][34][35][36]. Such motifs may interfere with the target motif inference [36].…”