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通过整合甲基化DNA免疫沉淀测序(MeDIP-seq)和甲基化敏感限制性内切酶测序(MRE-seq)进行全基因组DNA甲基化组综合分析

Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq.

作者信息

Xing Xiaoyun, Zhang Bo, Li Daofeng, Wang Ting

机构信息

The Edison Family Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University, 4515 McKinley Ave., St. Louis, MO, 63108, USA.

出版信息

Methods Mol Biol. 2018;1708:209-246. doi: 10.1007/978-1-4939-7481-8_12.

Abstract

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-the-art experimental strategies with state-of-the-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-methylcytidine 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 sequencing, relies on a collection of restriction enzymes that recognize CpG containing sequence motifs, 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 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.

摘要

了解DNA甲基化的作用通常需要以全基因组方式对这些修饰进行准确评估和比较。基于测序的DNA甲基化分析提供了一个前所未有的机会来绘制和比较完整的DNA CpG甲基化组。这些方法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,如MeDIP-seq、MBD-seq和MRE-seq。研究人员需要一种方法,该方法对输入DNA的量具有灵活性,能在基因组CpG覆盖范围、分辨率、定量准确性和成本之间提供适当平衡,并配有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的方案。我们首先介绍两种相互补充的实验方法。MeDIP-seq,即甲基化依赖性免疫沉淀后测序,使用抗甲基胞嘧啶抗体富集甲基化DNA片段,并使用大规模平行测序来揭示富集DNA的身份。MRE-seq,即甲基化敏感限制性内切酶消化后测序,依赖于一组识别含CpG序列基序的限制性内切酶,但仅在CpG未甲基化时切割。消化后的DNA片段在其末端富集未甲基化的CpG,这些CpG通过大规模平行测序得以揭示。两种计算方法都实施了整合MeDIP-seq和MRE-seq数据 的先进统计算法。M&M是一个用于检测两个样本之间差异甲基化区域的统计框架。methylCRF是一个机器学习框架,可在单个CpG分辨率下预测CpG甲基化水平,从而将MeDIP-seq和MRE-seq的分辨率和覆盖范围提高到与WGBS相当的水平,但成本仅为WGBS的不到5%。这些方法共同构成了一个有效、强大且经济实惠的全基因组DNA甲基化研究平台。

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