A2 Refereed review article in a scientific journal
Making Sense of the Epigenome Using Data Integration Approaches
Authors: Cazaly Emma, Saad Joseph, Wang Wenyu Y., Heckman Caroline, Ollikainen Miina, Tang Jing
Publisher: FRONTIERS MEDIA SA
Publication year: 2019
Journal: Frontiers in Pharmacology
Journal name in source: FRONTIERS IN PHARMACOLOGY
Journal acronym: FRONT PHARMACOL
Article number: ARTN 126
Volume: 10
Number of pages: 15
ISSN: 1663-9812
DOI: https://doi.org/10.3389/fphar.2019.00126
Web address : https://www.frontiersin.org/articles/10.3389/fphar.2019.00126/full
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/39606957
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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