MethylAction: detecting differentially methylated regions that distinguish biological subtypes.
MethylAction: detecting differentially methylated regions that distinguish biological subtypes.
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DOI：
10.1093/nar/gkv1461
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发表时间：
2016-01-08
影响因子：
14.9
通讯作者：
Ting AH
中科院分区：
文献类型：
--
作者：
Bhasin JM;Hu B;Ting AH
DNA甲基化差异捕获了生物亚型之间分子和基因调控状态的大量信息。基于富集的下一代测序方法，如mbd分离基因组测序（MiGS）和MeDIP-seq，在研究DNA甲基化全基因组以区分生物亚型方面具有吸引力。然而，目前的分析工具不能为分析三组或更大的研究设计提供最佳特征。MethylAction通过检测涉及任意数量组的比较中统计上显著的高甲基化和低甲基化的所有可能模式来解决这一需求。至关重要的是，显著性是在差异甲基化区域（DMRs）水平上建立的，自举决定了与每种模式相关的错误发现率（fdr）。我们在良性前列腺癌和三种临床前列腺癌亚型的四组比较中证明了这种功能，并表明自举式fdr在选择最稳健的dmr模式方面非常有用。与仅限于两组比较的现有工具相比，MethylAction通过亚硫酸氢盐全基因组测序证实的强差异甲基化测量检测到更多的DMRs，并在交叉队列比较中提供了精度和召回率之间的更好平衡。MethylAction作为R包可在http://jeffbhasin.github.io/methylaction获得。
DNA methylation differences capture substantial information about the molecular and gene-regulatory states among biological subtypes. Enrichment-based next generation sequencing methods such as MBD-isolated genome sequencing (MiGS) and MeDIP-seq are appealing for studying DNA methylation genome-wide in order to distinguish between biological subtypes. However, current analytic tools do not provide optimal features for analyzing three-group or larger study designs. MethylAction addresses this need by detecting all possible patterns of statistically significant hyper- and hypo- methylation in comparisons involving any number of groups. Crucially, significance is established at the level of differentially methylated regions (DMRs), and bootstrapping determines false discovery rates (FDRs) associated with each pattern. We demonstrate this functionality in a four-group comparison among benign prostate and three clinical subtypes of prostate cancer and show that the bootstrap FDRs are highly useful in selecting the most robust patterns of DMRs. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons. MethylAction is available as an R package at http://jeffbhasin.github.io/methylaction.