MetMap enables genome-scale Methyltyping for determining methylation states in populations.
MetMap enables genome-scale Methyltyping for determining methylation states in populations.
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DOI：
10.1371/journal.pcbi.1000888
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发表时间：
2010-08-19
影响因子：
4.3
通讯作者：
Pachter L
中科院分区：
文献类型：
--
作者：
Singer M;Boffelli D;Dhahbi J;Schönhuth A;Schroth GP;Martin DI;Pachter L
使用高通量测序分析基因组尺度甲基化模式的能力使得进行关联研究以确定表观遗传变异和表型之间的关系成为可能。虽然亚硫酸氢盐测序可以在高分辨率上确定甲基组，但成本限制了其在比较和群体研究中的应用。MethylSeq是一种基于甲基化敏感限制性内切酶产生的片段末端测序的方法，是一种甲基化（甲基化状态的调查）方法，是一种位点特异性和成本效益高的替代全基因组亚硫酸氢盐测序方法。尽管有其优势，但甲基seq的使用受到甲基seq数据偏差的限制，这些偏差使甲基型的确定复杂化。在这里，我们介绍了一种统计方法，MetMap，它可以从MethylSeq实验中产生校正的位点特异性甲基化状态，并注释整个基因组中的未甲基化岛。MetMap整合了基因组序列信息和实验数据，在统计上健全和有凝聚力的贝叶斯网络。它推断了个体基因组和跨区域的甲基化程度，并作为物种内和物种间比较甲基化分析的框架。我们通过直接亚硫酸氢盐测序验证了MetMap的推断，表明位点和岛屿的甲基化状态是准确推断的。我们使用MetMap分析了来自四个人类中性粒细胞样本的MethylSeq数据，发现了新的、高度未甲基化的岛，这些岛对于基于序列的注释策略是不可见的。MethylSeq和MetMap的结合是一种强大而经济的工具，用于确定适合比较和关联研究的基因组尺度甲基型。在脊椎动物中，DNA中胞嘧啶残基的甲基化与与DNA相关的蛋白质一起调节基因活性。寻求将特定甲基化模式与疾病联系起来的大规模全基因组比较研究将需要数百或数千个样本，因此需要测定全基因组甲基化的经济方法。其中一种方法是MethylSeq，它通过对甲基化敏感限制性内切酶产生的DNA片段的末端进行高通量测序，以特定位点的分辨率对胞嘧啶甲基化进行采样。MethylSeq的低成本和简单的实现使其能够用于大规模的比较研究，但该方法固有的偏差抑制了对其产生的数据的解释。在这里，我们提出MetMap，这是一个统计框架，首先解释甲基seq数据中的偏差，然后生成适合用于比较研究的数据分析。我们发现，MethylSeq和MetMap可以一起用于确定整个基因组的甲基化谱，并鉴定可能参与基因调控的新的非甲基化区域。以合理的成本进行足够规模的比较研究的能力有望揭示胞嘧啶甲基化与表型之间关系的新见解。
The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies. In the vertebrates, methylation of cytosine residues in DNA regulates gene activity in concert with proteins that associate with DNA. Large-scale genomewide comparative studies that seek to link specific methylation patterns to disease will require hundreds or thousands of samples, and thus economical methods that assay genomewide methylation. One such method is MethylSeq, which samples cytosine methylation at site-specific resolution by high-throughput sequencing of the ends of DNA fragments generated by methylation-sensitive restriction enzymes. MethylSeq's low cost and simplicity of implementation enable its use in large-scale comparative studies, but biases inherent to the method inhibit interpretation of the data it produces. Here we present MetMap, a statistical framework that first accounts for the biases in MethylSeq data and then generates an analysis of the data that is suitable for use in comparative studies. We show that MethylSeq and MetMap can be used together to determine methylation profiles across the genome, and to identify novel unmethylated regions that are likely to be involved in gene regulation. The ability to conduct comparative studies of sufficient scale at a reasonable cost promises to reveal new insights into the relationship between cytosine methylation and phenotype.
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影响因子：
46.9
作者：
Ball, Madeleine P.;Li, Jin Billy;Gao, Yuan;Lee, Je-Hyuk;LeProust, Emily M.;Park, In-Hyun;Xie, Bin;Daley, George Q.;Church, George M.
通讯作者：
Church, George M.
影响因子：
14.9
作者：
Bock C;Walter J;Paulsen M;Lengauer T
通讯作者：
Lengauer T
影响因子：
14.9
作者：
Karolchik, D;Hinrichs, AS;Kent, WJ
通讯作者：
Kent, WJ
影响因子：
14.9
作者：
CLARK, SJ;HARRISON, J;FROMMER, M
通讯作者：
FROMMER, M
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL