Making Sense of the Epigenome Using Data Integration Approaches

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http://hdl.handle.net/10138/299981

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Cazaly , E , Saad , J , Wang , W , Heckman , C , Ollikainen , M & Tang , J 2019 , ' Making Sense of the Epigenome Using Data Integration Approaches ' , Frontiers in Pharmacology , vol. 10 , 126 . https://doi.org/10.3389/fphar.2019.00126

Title: Making Sense of the Epigenome Using Data Integration Approaches
Author: Cazaly, Emma; Saad, Joseph; Wang, Wenyu; Heckman, Caroline; Ollikainen, Miina; Tang, Jing
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Clinicum
University of Helsinki, Medicum
Date: 2019-02-19
Language: eng
Number of pages: 15
Belongs to series: Frontiers in Pharmacology
ISSN: 1663-9812
URI: http://hdl.handle.net/10138/299981
Abstract: 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.
Subject: epigenetics
data integration
functional annotation
drug discovery
data resources
profiling techniques
BODY-MASS INDEX
DNA METHYLATION
MENDELIAN RANDOMIZATION
COLORECTAL-CANCER
GENE-EXPRESSION
FUNCTIONAL INTERPRETATION
EPIGENETIC CHANGES
WIDE ASSOCIATION
MESSENGER-RNA
CAUSAL ROLE
317 Pharmacy
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