Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq.
Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq.
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DOI：
10.1007/978-1-4939-7481-8_12
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wang T
中科院分区：
文献类型：
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作者：
Xing X;Zhang B;Li D;Wang T
了解 DNA 甲基化的作用通常需要在全基因组范围内准确评估和比较这些修饰。基于测序的 DNA 甲基化分析为绘制和比较完整的 DNA CpG 甲基化组提供了前所未有的机会。这些包括全基因组亚硫酸氢盐测序 (WGBS)、简化代表性亚硫酸氢盐测序 (RRBS) 以及基于富集的方法，例如 MeDIP-seq、MBD-seq 和 MRE-seq。研究人员需要一种能够灵活调整输入 DNA 数量的方法，在基因组 CpG 覆盖范围、分辨率、定量准确性和成本之间提供适当的平衡，并配备强大的生物信息学软件来分析数据。在本章中，我们描述了四种协议，它们将最先进的实验策略与最先进的计算算法相结合以实现这一目标。我们首先介绍两种互补的实验方法。 MeDIP-seq（即甲基化依赖性免疫沉淀随后测序）使用抗甲基胞嘧啶抗体来富集甲基化 DNA 片段，并使用大规模并行测序来揭示富集 DNA 的身份。 MRE-seq（甲基化敏感限制性内切酶消化后序列）依赖于一组限制性内切酶，这些内切酶可识别包含 CpG 的序列基序，但仅在 CpG 未甲基化时进行切割。消化的 DNA 片段在其末端富集未甲基化的 CpG，并且通过大规模并行测序揭示这些 CpG。这两种计算方法都实现了集成 MeDIP-seq 和 MRE-seq 数据的高级统计算法。 M&M 是一个统计框架，用于检测两个样本之间的差异甲基化区域。 methylCRF 是一种机器学习框架，可以在单个 CpG 分辨率下预测 CpG 甲基化水平，从而将 MeDIP-seq 和 MRE-seq 对 CpG 的分辨率和覆盖率提高到与 WGBS 相当的水平，但成本仅为 WGBS 的 5% 以下。这些方法共同构成了一个有效、强大且经济实惠的平台，用于研究全基因组 DNA 甲基化。
Understanding the role of DNA methylation often requires accurate assessment and comparison of these modifications in a genome-wide fashion. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA CpG methylomes. These include whole genome bisulfite sequencing (WGBS), Reduced-Representation Bisulfite- Sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. An investigator needs a method that is flexible with the quantity of input DNA, provides the appropriate balance among genomic CpG coverage, resolution, quantitative accuracy, and cost, and comes with robust bioinformatics software for analyzing the data. In this chapter we describe four protocols that combine state-of-art experimental strategies with state-of-art computational algorithms to achieve this goal. We first introduce two experimental methods that are complementary to each other. MeDIP-seq, or methylation dependent immunoprecipitation followed by sequencing, uses an anti-methyl-cytosine antibody to enrich for methylated DNA fragments, and uses massively parallel sequencing to reveal identity of enriched DNA. MRE-seq, or methylation sensitive restriction enzyme digestion followed by sequences, relies on a collection of restriction enzymes that recognize CpG containing sequence motif but only cut when the CpG is unmethylated. Digested DNA fragments enrich for unmethylated CpGs at their ends, and these CpGs are revealed by massively parallel sequencing. The two computational methods both implement advanced statistical algorithms that integrate MeDIP-seq and MRE-seq data. M&M is a statistical framework to detect differentially methylated regions between two samples. methylCRF is a machine learning framework that predicts CpG methylation levels at single CpG resolution, thus raising the resolution and coverage of MeDIP-seq and MRE-seq on CpGs to a comparable level of WGBS, but only incurring a cost of less than 5% of WGBS. Together these methods form an effective, robust, and affordable platform for the investigation of genome-wide DNA methylation.