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Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occu...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895346/ https://www.ncbi.nlm.nih.gov/pubmed/26818008 http://dx.doi.org/10.1186/s12859-015-0846-z |
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author | Kumar, Sunil Bucher, Philipp |
author_facet | Kumar, Sunil Bucher, Philipp |
author_sort | Kumar, Sunil |
collection | PubMed |
description | BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process. METHODS: Our study primarily relies on recent data published by the ENCODE consortium. Five dissimilar TFs assayed in multiple cell-types were selected as examples: CTCF, JunD, REST, GABP and USF2. We used two types of candidate target sites: (a) predicted sites obtained by scanning the whole genome with a position weight matrix, and (b) cell-type specific peak lists provided by ENCODE. Quantitative in vivo occupancy levels in different cell-types were based on ChIP-seq data for the corresponding TFs. In parallel, we computed a number of associated sequence-intrinsic and experimental features (histone modification, DNase I hypersensitivity, etc.) for each site. Machine learning algorithms were then used in a binary classification and regression framework to predict site occupancy and binding strength, for the purpose of assessing the relative importance of different contextual features. RESULTS: We observed striking differences in the feature importance rankings between the five factors tested. PWM-scores were amongst the most important features only for CTCF and REST but of little value for JunD and USF2. Chromatin accessibility and active histone marks are potent predictors for all factors except REST. Structural DNA parameters, repressive and gene body associated histone marks are generally of little or no predictive value. CONCLUSIONS: We define a general and extensible computational framework for analyzing the importance of various DNA-intrinsic and chromatin-associated features in determining cell-type specific TF binding to target sites. The application of our methodology to ENCODE data has led to new insights on transcription regulatory processes and may serve as example for future studies encompassing even larger datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0846-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4895346 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48953462016-06-10 Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features Kumar, Sunil Bucher, Philipp BMC Bioinformatics Proceedings BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process. METHODS: Our study primarily relies on recent data published by the ENCODE consortium. Five dissimilar TFs assayed in multiple cell-types were selected as examples: CTCF, JunD, REST, GABP and USF2. We used two types of candidate target sites: (a) predicted sites obtained by scanning the whole genome with a position weight matrix, and (b) cell-type specific peak lists provided by ENCODE. Quantitative in vivo occupancy levels in different cell-types were based on ChIP-seq data for the corresponding TFs. In parallel, we computed a number of associated sequence-intrinsic and experimental features (histone modification, DNase I hypersensitivity, etc.) for each site. Machine learning algorithms were then used in a binary classification and regression framework to predict site occupancy and binding strength, for the purpose of assessing the relative importance of different contextual features. RESULTS: We observed striking differences in the feature importance rankings between the five factors tested. PWM-scores were amongst the most important features only for CTCF and REST but of little value for JunD and USF2. Chromatin accessibility and active histone marks are potent predictors for all factors except REST. Structural DNA parameters, repressive and gene body associated histone marks are generally of little or no predictive value. CONCLUSIONS: We define a general and extensible computational framework for analyzing the importance of various DNA-intrinsic and chromatin-associated features in determining cell-type specific TF binding to target sites. The application of our methodology to ENCODE data has led to new insights on transcription regulatory processes and may serve as example for future studies encompassing even larger datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0846-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-11 /pmc/articles/PMC4895346/ /pubmed/26818008 http://dx.doi.org/10.1186/s12859-015-0846-z Text en © Kumar and Bucher. 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 | Proceedings Kumar, Sunil Bucher, Philipp Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title | Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title_full | Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title_fullStr | Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title_full_unstemmed | Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title_short | Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features |
title_sort | predicting transcription factor site occupancy using dna sequence intrinsic and cell-type specific chromatin features |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895346/ https://www.ncbi.nlm.nih.gov/pubmed/26818008 http://dx.doi.org/10.1186/s12859-015-0846-z |
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