Abstract
Transcription factors play a key role in the development of a disease. ChIP-sequencing has become a preferred technique to investigate genome-wide binding patterns of transcription factors in vivo. Although this technology has led to many important discoveries, the rapidly increasing number of publicly available ChIP-sequencing datasets still remains a largely unexplored resource. Using a compendium of 144 publicly available murine ChIP-sequencing datasets in blood, we show that systematic bioinformatic analysis can unravel diverse aspects of transcription regulation; from genome-wide binding preferences, finding regulatory partners and assembling regulatory complexes, to identifying novel functions of transcription factors and investigating transcription dynamics during development.
Highlights
The control of cell-type specific gene expression underlies development of all multicellular organisms, and is thought to be achieved through combinatorial interactions of transcription factors with gene regulatory sequences
Using a compendium of 144 publicly available murine chromatin immunoprecipitation (ChIP)-sequencing datasets in blood, we show that systematic bioinformatic analysis can unravel diverse aspects of transcription regulation; from genome-wide binding preferences, finding regulatory partners and assembling regulatory complexes, to identifying novel functions of transcription factors and investigating transcription dynamics during development
We recently reported a transcription factors (TFs) ChIP-Seq compendium containing 144 publicly available studies pertaining to the mouse blood system.[15]
Summary
The control of cell-type specific gene expression underlies development of all multicellular organisms, and is thought to be achieved through combinatorial interactions of transcription factors with gene regulatory sequences. Bespoke protocols are often developed by individual groups with specialist expertise, so that published ChIP-Seq studies commonly report binding maps for less than a handful of TFs4–10 and only a few larger studies reporting 10 or more factors[11,12] or a single factor across multiple cell types.[13] We have previously shown[14] that unlike gene expression data, ChIP-Seq datasets produced by different laboratories can be readily integrated This analysis revealed that genome wide transcription factor binding profiles are largely governed by cellular context. This includes identification of those TF-bound sites most likely to be functional, prediction of TF interactions and multicomponent complexes, specific functionality of individual TFs and the dynamics of transcriptional regulation during differentiation and development
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