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MeinteR:一种基于构象和顺式调控元件富集的 DNA 甲基化异常优先级框架。

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment.

机构信息

Lab. of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.

出版信息

Sci Rep. 2019 Dec 16;9(1):19148. doi: 10.1038/s41598-019-55453-8.

DOI:10.1038/s41598-019-55453-8
PMID:31844073
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6915744/
Abstract

DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic "deserts". In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.

摘要

DNA 甲基化研究随着单碱基分辨率阵列和亚硫酸氢盐测序方法的出现而得到了改进,使我们能够更深入地研究甲基化介导的机制。除了这些进展之外,许多生物信息学工具还解决了重要的计算挑战,涵盖了从 DNA 甲基化调用到多模态解释性分析的多个方面。然而,与检测驱动突变特征的分析框架相反,鉴定潜在的可操作的表观遗传事件仍然是一个未满足的需求。本工作描述了一种名为 MeinteR 的新型计算框架,该框架基于以下假设来优先考虑关键的 DNA 甲基化事件:关键的 DNA 甲基化异常更可能发生在富含具有独特结构特征的顺式作用调节元件的基因组底物上,而不是在基因组“荒漠”中。在这种情况下,该框架整合了功能顺式元件,例如转录因子结合位点、暂定剪接位点,以及构象特征,如 G-四联体和回文结构,以识别具有潜在转录调控影响的关键表观遗传异常。在多个公共癌症数据集上的评估结果表明,排名最高的与基因表达和已知驱动基因相关的基因座之间存在显著关联,从而首次能够基于高通量 DNA 甲基化数据进行具有高影响力的表观遗传变化的计算识别。

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本文引用的文献

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Bigmelon: tools for analysing large DNA methylation datasets.Bigmelon:用于分析大型甲基化数据集的工具。
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RnBeads 2.0: comprehensive analysis of DNA methylation data.RnBeads 2.0:全面的 DNA 甲基化数据分析。
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Incorporating methylation genome information improves prediction accuracy for drug treatment responses.纳入甲基化基因组信息可提高药物治疗反应的预测准确性。
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