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BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates
BACKGROUND: Unraveling transcriptional regulatory networks is a central problem in molecular biology and, in this quest, chromatin immunoprecipitation and sequencing (ChIP-seq) technology has given us the unprecedented ability to identify sites of protein-DNA binding and histone modification genome...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574076/ https://www.ncbi.nlm.nih.gov/pubmed/26388941 http://dx.doi.org/10.1186/s13072-015-0028-2 |
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author | Ramachandran, Parameswaran Palidwor, Gareth A. Perkins, Theodore J. |
author_facet | Ramachandran, Parameswaran Palidwor, Gareth A. Perkins, Theodore J. |
author_sort | Ramachandran, Parameswaran |
collection | PubMed |
description | BACKGROUND: Unraveling transcriptional regulatory networks is a central problem in molecular biology and, in this quest, chromatin immunoprecipitation and sequencing (ChIP-seq) technology has given us the unprecedented ability to identify sites of protein-DNA binding and histone modification genome wide. However, multiple systemic and procedural biases hinder harnessing the full potential of this technology. Previous studies have addressed this problem, but a thorough characterization of different, interacting biases on ChIP-seq signals is still lacking. RESULTS: Here, we present a novel framework where the genome-wide ChIP-seq signal is viewed as being quantifiably influenced by different, measurable sources of bias, which can then be computationally subtracted away. We use a compendium of 123 human ENCODE ChIP-seq datasets to build regression models that tell us how much of a ChIP-seq signal can be attributed to mappability, GC-content, chromatin accessibility, and factors represented in input DNA and IgG controls. When we use the model to separate out these non-binding influences from the ChIP-seq signal, we obtain a purified signal that associates better to TF-DNA-binding motifs than do other measures of peak significance. We also carry out a multiscale analysis that reveals how ChIP-seq signal biases differ across different scales. Finally, we investigate previously reported associations between gene expression and ChIP-seq signals at transcription start sites. We show that our model can be used to discriminate ChIP-seq signals that are truly related to gene expression from those that are merely correlated by virtue of bias—in particular, chromatin accessibility bias, which shows up in ChIP-seq signals and also relates to gene expression. CONCLUSIONS: Our study provides new insights into the behavior of ChIP-seq signal biases and proposes a novel mitigation framework that improves results compared to existing techniques. With ChIP-seq now being the central technology for studying transcriptional regulation, it is most crucial to accurately characterize, quantify, and adjust for the genome-wide effects of biases affecting ChIP-seq. Our study also emphasizes that properly accounting for confounders in ChIP-seq data is of paramount importance for obtaining biologically accurate insights into the workings of the complex regulatory mechanisms in living organisms. R and MATLAB packages implementing the framework can be obtained from http://www.perkinslab.ca/Software.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13072-015-0028-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4574076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45740762015-09-19 BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates Ramachandran, Parameswaran Palidwor, Gareth A. Perkins, Theodore J. Epigenetics Chromatin Research BACKGROUND: Unraveling transcriptional regulatory networks is a central problem in molecular biology and, in this quest, chromatin immunoprecipitation and sequencing (ChIP-seq) technology has given us the unprecedented ability to identify sites of protein-DNA binding and histone modification genome wide. However, multiple systemic and procedural biases hinder harnessing the full potential of this technology. Previous studies have addressed this problem, but a thorough characterization of different, interacting biases on ChIP-seq signals is still lacking. RESULTS: Here, we present a novel framework where the genome-wide ChIP-seq signal is viewed as being quantifiably influenced by different, measurable sources of bias, which can then be computationally subtracted away. We use a compendium of 123 human ENCODE ChIP-seq datasets to build regression models that tell us how much of a ChIP-seq signal can be attributed to mappability, GC-content, chromatin accessibility, and factors represented in input DNA and IgG controls. When we use the model to separate out these non-binding influences from the ChIP-seq signal, we obtain a purified signal that associates better to TF-DNA-binding motifs than do other measures of peak significance. We also carry out a multiscale analysis that reveals how ChIP-seq signal biases differ across different scales. Finally, we investigate previously reported associations between gene expression and ChIP-seq signals at transcription start sites. We show that our model can be used to discriminate ChIP-seq signals that are truly related to gene expression from those that are merely correlated by virtue of bias—in particular, chromatin accessibility bias, which shows up in ChIP-seq signals and also relates to gene expression. CONCLUSIONS: Our study provides new insights into the behavior of ChIP-seq signal biases and proposes a novel mitigation framework that improves results compared to existing techniques. With ChIP-seq now being the central technology for studying transcriptional regulation, it is most crucial to accurately characterize, quantify, and adjust for the genome-wide effects of biases affecting ChIP-seq. Our study also emphasizes that properly accounting for confounders in ChIP-seq data is of paramount importance for obtaining biologically accurate insights into the workings of the complex regulatory mechanisms in living organisms. R and MATLAB packages implementing the framework can be obtained from http://www.perkinslab.ca/Software.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13072-015-0028-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-17 /pmc/articles/PMC4574076/ /pubmed/26388941 http://dx.doi.org/10.1186/s13072-015-0028-2 Text en © Ramachandran et al. 2015 Open AccessThis 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 Ramachandran, Parameswaran Palidwor, Gareth A. Perkins, Theodore J. BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title | BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title_full | BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title_fullStr | BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title_full_unstemmed | BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title_short | BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates |
title_sort | bidchips: bias decomposition and removal from chip-seq data clarifies true binding signal and its functional correlates |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574076/ https://www.ncbi.nlm.nih.gov/pubmed/26388941 http://dx.doi.org/10.1186/s13072-015-0028-2 |
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