Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications.
Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications.
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分析表观遗传学数据的计算方法和下一代测序方法：方法和应用的分析。
DOI：
10.1016/j.ymeth.2020.09.008
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Tollefsbol TO
中科院分区：
文献类型：
--
作者：
Arora I;Tollefsbol TO
表观遗传学主要由调节基因组相互作用的特征组成，从而在大量的生物过程中发挥关键作用。表观遗传机制如DNA甲基化和组蛋白修饰通过调节DNA在细胞核中的包装来影响基因表达。大量的研究强调了通过全基因组研究和高通量方法分析表观遗传学数据的重要性，从而为癌症等基于表观遗传学的疾病提供了关键见解。最近的进展已经朝着将表观遗传学研究转化为高通量方法，如基因组规模分析。其中，生物信息学在实现表观遗传学相关的计算研究中起着关键作用。尽管表观基因组分析取得了重大进展，但了解各种表观遗传修饰如染色质修饰和DNA甲基化如何调节基因表达仍具有挑战性。下一代测序（NGS）提供准确和平行的测序，从而使研究人员能够理解表观基因组谱。在这篇综述中，我们总结了不同的计算方法，如机器学习和其他生物信息学工具，公开可用的数据库和资源，以确定与表观遗传机制相关的关键修饰。此外，该综述还重点介绍了使用NGS方法进行表观基因组分析的最新方法，包括文库制备，不同的测序平台和分析技术，以评估各种表观遗传修饰，如DNA甲基化和组蛋白修饰。我们还提供了生物信息学工具和计算策略的详细信息，负责分析表观遗传学的大规模数据。
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression by modulating the packaging of DNA in the nucleus. A plethora of studies have emphasized the importance of analyzing epigenetics data through genome-wide studies and high-throughput approaches, thereby providing key insights towards epigenetics-based diseases such as cancer. Recent advancements have been made towards translating epigenetics research into a high throughput approach such as genome-scale profiling. Amongst all, bioinformatics plays a pivotal role in achieving epigenetics-related computational studies. Despite significant advancements towards epigenomic profiling, it is challenging to understand how various epigenetic modifications such as chromatin modifications and DNA methylation regulate gene expression. Next-generation sequencing (NGS) provides accurate and parallel sequencing thereby allowing researchers to comprehend epigenomic profiling. In this review, we summarize different computational methods such as machine learning and other bioinformatics tools, publicly available databases and resources to identify key modifications associated with epigenetic machinery. Additionally, the review also focuses on understanding recent methodologies related to epigenome profiling using NGS methods ranging from library preparation, different sequencing platforms and analytical techniques to evaluate various epigenetic modifications such as DNA methylation and histone modifications. We also provide detailed information on bioinformatics tools and computational strategies responsible for analyzing large scale data in epigenetics.
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影响因子：
64.5
作者：
Alekseyenko AA;Peng S;Larschan E;Gorchakov AA;Lee OK;Kharchenko P;McGrath SD;Wang CI;Mardis ER;Park PJ;Kuroda MI
通讯作者：
Kuroda MI
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3.5
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Brasa S;Mueller A;Jacquemont S;Hahne F;Rozenberg I;Peters T;He Y;McCormack C;Gasparini F;Chibout SD;Grenet O;Moggs J;Gomez-Mancilla B;Terranova R
通讯作者：
Terranova R
影响因子：
8.8
作者：
Lauss, M.;Visne, I.;Weinhaeusel, A.;Vierlinger, K.;Noehammer, C.;Kriegner, A.
通讯作者：
Kriegner, A.
影响因子：
1.8
作者：
Bogdanović O;Veenstra GJ
通讯作者：
Veenstra GJ