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HOME: a histogram based machine learning approach for effective identification of differentially methylated regions
BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521357/ https://www.ncbi.nlm.nih.gov/pubmed/31096906 http://dx.doi.org/10.1186/s12859-019-2845-y |
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author | Srivastava, Akanksha Karpievitch, Yuliya V. Eichten, Steven R. Borevitz, Justin O. Lister, Ryan |
author_facet | Srivastava, Akanksha Karpievitch, Yuliya V. Eichten, Steven R. Borevitz, Justin O. Lister, Ryan |
author_sort | Srivastava, Akanksha |
collection | PubMed |
description | BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. RESULTS: We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME. CONCLUSION: HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2845-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6521357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65213572019-05-23 HOME: a histogram based machine learning approach for effective identification of differentially methylated regions Srivastava, Akanksha Karpievitch, Yuliya V. Eichten, Steven R. Borevitz, Justin O. Lister, Ryan BMC Bioinformatics Methodology Article BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. RESULTS: We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME. CONCLUSION: HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2845-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-16 /pmc/articles/PMC6521357/ /pubmed/31096906 http://dx.doi.org/10.1186/s12859-019-2845-y Text en © The Author(s). 2019 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 | Methodology Article Srivastava, Akanksha Karpievitch, Yuliya V. Eichten, Steven R. Borevitz, Justin O. Lister, Ryan HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title | HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title_full | HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title_fullStr | HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title_full_unstemmed | HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title_short | HOME: a histogram based machine learning approach for effective identification of differentially methylated regions |
title_sort | home: a histogram based machine learning approach for effective identification of differentially methylated regions |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521357/ https://www.ncbi.nlm.nih.gov/pubmed/31096906 http://dx.doi.org/10.1186/s12859-019-2845-y |
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