Insights into mammalian transcription control by systematic analysis of ChIP sequencing data.

TitleInsights into mammalian transcription control by systematic analysis of ChIP sequencing data.
Publication TypeJournal Article
Year of Publication2018
AuthorsDevailly, G, Joshi, A
JournalBMC Bioinformatics
Volume19
IssueSuppl 14
Pagination409
Date Published2018 Nov 20
ISSN1471-2105
Abstract

BACKGROUND: Transcription regulation is a major controller of gene expression dynamics during development and disease, where transcription factors (TFs) modulate expression of genes through direct or indirect DNA interaction. ChIP sequencing has become the most widely used technique to get a genome wide view of TF occupancy in a cell type of interest, mainly due to established standard protocols and a rapid decrease in the cost of sequencing. The number of available ChIP sequencing data sets in public domain is therefore ever increasing, including data generated by individual labs together with consortia such as the ENCODE project.RESULTS: A total of 1735 ChIP-sequencing datasets in mouse and human cell types and tissues were used to perform bioinformatic analyses to unravel diverse features of transcription control. 1- We used the Heat*seq webtool to investigate global relations across the ChIP-seq samples. 2- We demonstrated that factors have a specific genomic location preferences that are, for most factors, conserved across species. 3- Promoter proximal binding of factors was more conserved across cell types while the distal binding sites are more cell type specific. 4- We identified combinations of factors preferentially acting together in a cellular context. 5- Finally, by integrating the data with disease-associated gene loci from GWAS studies, we highlight the value of this data to associate novel regulators to disease.CONCLUSION: In summary, we demonstrate how ChIP sequencing data integration and analysis is powerful to get new insights into mammalian transcription control and demonstrate the utility of various bioinformatic tools to generate novel testable hypothesis using this public resource.

DOI10.1186/s12859-018-2377-x
Alternate JournalBMC Bioinformatics
PubMed ID30453943
PubMed Central IDPMC6245581