MeDIP-HMM：从高密度平铺阵列中识别独特 DNA 甲基化状态的全基因组方法。

MeDIP-HMM: genome-wide identification of distinct DNA methylation states from high-density tiling arrays.

机构信息

Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, Gatersleben, Germany.

出版信息

Bioinformatics. 2012 Nov 15;28(22):2930-9. doi: 10.1093/bioinformatics/bts562. Epub 2012 Sep 17.

DOI:10.1093/bioinformatics/bts562

PMID:22989518

Abstract

MOTIVATION

Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limit biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.

RESULTS

Here, we present a three-state hidden Markov model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM uses a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm, integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study with existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of methylome data, enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.

AVAILABILITY

MeDIP-HMM is available as part of the open-source Java library Jstacs (www.jstacs.de/index.php/MeDIP-HMM). Data files are available from the Jstacs website.

CONTACT

seifert@ipk-gatersleben.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

DNA 中的胞嘧啶甲基化是一种重要的表观遗传机制，涉及广泛真核生物中转录调控和基因组完整性的维持。用甲基化 DNA 免疫沉淀，然后与基因组平铺阵列杂交（MeDIP-chip）是一种经济高效且敏感的甲基组分析方法。然而，现有的生物信息学方法仅能对未甲基化和甲基化基因组区域进行二进制分类，从而限制了生物学解释。实际上，在给定的 DNA 片段内，DNA 甲基化水平可以根据甲基化胞嘧啶的数量和程度而有很大差异。因此，非常需要一种能够识别两种以上甲基化状态的方法。

结果

在这里，我们提出了一种用于分析 MeDIP-chip 数据的三状态隐马尔可夫模型（MeDIP-HMM）。MeDIP-HMM 使用高阶状态转移过程改进了对染色体区域之间空间依赖性的建模，允许对重复样本进行同时分析，并能够区分未甲基化、甲基化和高度甲基化的基因组区域。我们使用贝叶斯 Baum-Welch 算法训练 MeDIP-HMM，该算法整合了关于甲基化水平的先验知识。我们将 MeDIP-HMM 应用于拟南芥根甲基组的分析，并系统地研究了使用高阶 HMM 的好处。此外，我们还与现有的方法进行了深入的比较研究，并通过与当前对拟南芥甲基组的知识进行比较，证明了使用 MeDIP-HMM 的价值。我们发现 MeDIP-HMM 是一种快速而精确的甲基组数据分析方法，能够识别不同的 DNA 甲基化水平。最后，我们通过分析为鸡获得的启动子 DNA 甲基化数据提供了 MeDIP-HMM 具有普遍适用性的证据。