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COCOA: coordinate covariation analysis of epigenetic heterogeneity
A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487606/ https://www.ncbi.nlm.nih.gov/pubmed/32894181 http://dx.doi.org/10.1186/s13059-020-02139-4 |
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author | Lawson, John T. Smith, Jason P. Bekiranov, Stefan Garrett-Bakelman, Francine E. Sheffield, Nathan C. |
author_facet | Lawson, John T. Smith, Jason P. Bekiranov, Stefan Garrett-Bakelman, Francine E. Sheffield, Nathan C. |
author_sort | Lawson, John T. |
collection | PubMed |
description | A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor (http://bioconductor.org/packages/COCOA). |
format | Online Article Text |
id | pubmed-7487606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74876062020-09-15 COCOA: coordinate covariation analysis of epigenetic heterogeneity Lawson, John T. Smith, Jason P. Bekiranov, Stefan Garrett-Bakelman, Francine E. Sheffield, Nathan C. Genome Biol Method A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor (http://bioconductor.org/packages/COCOA). BioMed Central 2020-09-07 /pmc/articles/PMC7487606/ /pubmed/32894181 http://dx.doi.org/10.1186/s13059-020-02139-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Method Lawson, John T. Smith, Jason P. Bekiranov, Stefan Garrett-Bakelman, Francine E. Sheffield, Nathan C. COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title | COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title_full | COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title_fullStr | COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title_full_unstemmed | COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title_short | COCOA: coordinate covariation analysis of epigenetic heterogeneity |
title_sort | cocoa: coordinate covariation analysis of epigenetic heterogeneity |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487606/ https://www.ncbi.nlm.nih.gov/pubmed/32894181 http://dx.doi.org/10.1186/s13059-020-02139-4 |
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