Development and application of an integrated allele-specific pipeline for methylomic and epigenomic analysis (MEA).
Development and application of an integrated allele-specific pipeline for methylomic and epigenomic analysis (MEA).
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综合等位基因特异性管道的开发和应用用于甲基乳腺分析（MEA）。
DOI：
10.1186/s12864-018-4835-2
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发表时间：
2018-06-15
期刊：
影响因子：
4.4
通讯作者：
Lorincz MC
中科院分区：
文献类型：
--
作者：
Richard Albert J;Koike T;Younesy H;Thompson R;Bogutz AB;Karimi MM;Lorincz MC
等位基因特异性的转录调控，包括印记基因，是哺乳动物正常发育所必需的。虽然控制印迹基因的调控区与DNA甲基化（DNAme）和特定组蛋白修饰相关，但由于缺乏可以处理和整合具有等位基因分辨率的多个表观基因组数据集的生物信息学包，因此通常不会在全基因组范围内研究等位基因分辨率下的转录和这些表观遗传标记之间的相互作用。此外，现有的ad-hoc软件仅考虑SNV用于等位基因特异性读段发现。这种限制忽略了潜在的信息性INDEL，其构成小鼠中SNV数量的约五分之一，并在等位基因特异性分析中引入了系统性参考偏倚。在这里，我们描述了MEA，一种INDEL感知的甲基组学和表观基因组等位基因特异性分析管道，它可以实现用户友好的数据探索，可视化和等位基因不平衡的解释。将MEA应用于小鼠胚胎数据集产生了强大的等位基因特异性DNAme图谱和低参考偏倚。我们验证了已知差异甲基化区域的等位基因特异性DNAme，并表明将此类甲基化数据与RNA和ChIP-seq数据集自动整合，可以产生直观的，多维的等位基因调控视图。MEA发现了许多新的动态甲基化位点，突出了我们管道的敏感性。此外，来自人脑的表观基因组数据集的处理和可视化揭示了H3 K27 ac和DNAme在印迹以及新的单等位基因表达基因处的预期等位基因特异性富集，突出了MEA用于整合不同来源的人类数据集以进行等位基因现象的全基因组分析的实用性。我们用于标准化等位基因特异性处理和不同表观基因组和甲基化数据集可视化的新管道能够快速分析和导航等位基因分辨率。MEA作为Docker容器在https://github.com/julienrichardalbert/MEA上免费提供。本文的在线版本（10.1186/s12864-018-4835-2）包含补充材料，可供授权用户使用。
Allele-specific transcriptional regulation, including of imprinted genes, is essential for normal mammalian development. While the regulatory regions controlling imprinted genes are associated with DNA methylation (DNAme) and specific histone modifications, the interplay between transcription and these epigenetic marks at allelic resolution is typically not investigated genome-wide due to a lack of bioinformatic packages that can process and integrate multiple epigenomic datasets with allelic resolution. In addition, existing ad-hoc software only consider SNVs for allele-specific read discovery. This limitation omits potentially informative INDELs, which constitute about one fifth of the number of SNVs in mice, and introduces a systematic reference bias in allele-specific analyses. Here, we describe MEA, an INDEL-aware Methylomic and Epigenomic Allele-specific analysis pipeline which enables user-friendly data exploration, visualization and interpretation of allelic imbalance. Applying MEA to mouse embryonic datasets yields robust allele-specific DNAme maps and low reference bias. We validate allele-specific DNAme at known differentially methylated regions and show that automated integration of such methylation data with RNA- and ChIP-seq datasets yields an intuitive, multidimensional view of allelic gene regulation. MEA uncovers numerous novel dynamically methylated loci, highlighting the sensitivity of our pipeline. Furthermore, processing and visualization of epigenomic datasets from human brain reveals the expected allele-specific enrichment of H3K27ac and DNAme at imprinted as well as novel monoallelically expressed genes, highlighting MEA’s utility for integrating human datasets of distinct provenance for genome-wide analysis of allelic phenomena. Our novel pipeline for standardized allele-specific processing and visualization of disparate epigenomic and methylomic datasets enables rapid analysis and navigation with allelic resolution. MEA is freely available as a Docker container at https://github.com/julienrichardalbert/MEA. The online version of this article (10.1186/s12864-018-4835-2) contains supplementary material, which is available to authorized users.
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