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Insights into mammalian transcription control by systematic analysis of ChIP sequencing data
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245581/ https://www.ncbi.nlm.nih.gov/pubmed/30453943 http://dx.doi.org/10.1186/s12859-018-2377-x |
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author | Devailly, Guillaume Joshi, Anagha |
author_facet | Devailly, Guillaume Joshi, Anagha |
author_sort | Devailly, Guillaume |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6245581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62455812018-11-26 Insights into mammalian transcription control by systematic analysis of ChIP sequencing data Devailly, Guillaume Joshi, Anagha BMC Bioinformatics Research 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. BioMed Central 2018-11-20 /pmc/articles/PMC6245581/ /pubmed/30453943 http://dx.doi.org/10.1186/s12859-018-2377-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Devailly, Guillaume Joshi, Anagha Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title | Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title_full | Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title_fullStr | Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title_full_unstemmed | Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title_short | Insights into mammalian transcription control by systematic analysis of ChIP sequencing data |
title_sort | insights into mammalian transcription control by systematic analysis of chip sequencing data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245581/ https://www.ncbi.nlm.nih.gov/pubmed/30453943 http://dx.doi.org/10.1186/s12859-018-2377-x |
work_keys_str_mv | AT devaillyguillaume insightsintomammaliantranscriptioncontrolbysystematicanalysisofchipsequencingdata AT joshianagha insightsintomammaliantranscriptioncontrolbysystematicanalysisofchipsequencingdata |