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Making Sense of the Epigenome Using Data Integration Approaches

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure e...

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Autores principales: Cazaly, Emma, Saad, Joseph, Wang, Wenyu, Heckman, Caroline, Ollikainen, Miina, Tang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390500/
https://www.ncbi.nlm.nih.gov/pubmed/30837884
http://dx.doi.org/10.3389/fphar.2019.00126
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author Cazaly, Emma
Saad, Joseph
Wang, Wenyu
Heckman, Caroline
Ollikainen, Miina
Tang, Jing
author_facet Cazaly, Emma
Saad, Joseph
Wang, Wenyu
Heckman, Caroline
Ollikainen, Miina
Tang, Jing
author_sort Cazaly, Emma
collection PubMed
description Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.
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spelling pubmed-63905002019-03-05 Making Sense of the Epigenome Using Data Integration Approaches Cazaly, Emma Saad, Joseph Wang, Wenyu Heckman, Caroline Ollikainen, Miina Tang, Jing Front Pharmacol Pharmacology Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies. Frontiers Media S.A. 2019-02-19 /pmc/articles/PMC6390500/ /pubmed/30837884 http://dx.doi.org/10.3389/fphar.2019.00126 Text en Copyright © 2019 Cazaly, Saad, Wang, Heckman, Ollikainen and Tang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Cazaly, Emma
Saad, Joseph
Wang, Wenyu
Heckman, Caroline
Ollikainen, Miina
Tang, Jing
Making Sense of the Epigenome Using Data Integration Approaches
title Making Sense of the Epigenome Using Data Integration Approaches
title_full Making Sense of the Epigenome Using Data Integration Approaches
title_fullStr Making Sense of the Epigenome Using Data Integration Approaches
title_full_unstemmed Making Sense of the Epigenome Using Data Integration Approaches
title_short Making Sense of the Epigenome Using Data Integration Approaches
title_sort making sense of the epigenome using data integration approaches
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390500/
https://www.ncbi.nlm.nih.gov/pubmed/30837884
http://dx.doi.org/10.3389/fphar.2019.00126
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