Pan Heng, Elemento Olivier
Department of Physiology and Biophysics, Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, New York, NY, 10021, USA.
Methods Mol Biol. 2018;1711:27-53. doi: 10.1007/978-1-4939-7493-1_3.
Epigenetic modifications play a key role in cellular development and tumorigenesis. Recent large-scale genomic studies have shown that mutations in players of the epigenetic machinery and concomitant perturbation of epigenomic patterning are frequent events in tumors. Among epigenetic marks, DNA methylation is one of the best studied. Hyper- and hypo-methylation events of specific regulatory elements (such as promoters and enhancers) are sometimes thought to be correlated with expression of nearby genes. High-throughput bisulfite converted sequencing is currently the technology of choice for studying DNA methylation in base-pair resolution and on whole-genome scale. Such broad and high-resolution coverage investigations of the epigenome provide unprecedented opportunities to analyze DNA methylation patterns, which are correlated with tumorigenesis, tumor evolution, and tumor progression. However, few computational pipelines are available to the public to perform systematic DNA methylation analysis. Utilizing open source tools, we here describe a comprehensive computational methodology to thoroughly analyze DNA methylation patterns during tumor evolution based on bisulfite converted sequencing data, including intra-tumor methylation heterogeneity.
表观遗传修饰在细胞发育和肿瘤发生中起关键作用。最近的大规模基因组研究表明,表观遗传机制相关因子的突变以及随之而来的表观基因组模式紊乱在肿瘤中是常见事件。在表观遗传标记中,DNA甲基化是研究得最为深入的之一。特定调控元件(如启动子和增强子)的高甲基化和低甲基化事件有时被认为与附近基因的表达相关。高通量亚硫酸氢盐转化测序是目前在碱基对分辨率和全基因组规模上研究DNA甲基化的首选技术。对表观基因组进行如此广泛且高分辨率的覆盖研究为分析与肿瘤发生、肿瘤演变和肿瘤进展相关的DNA甲基化模式提供了前所未有的机会。然而,可供公众使用的用于进行系统DNA甲基化分析的计算流程很少。利用开源工具,我们在此描述一种全面的计算方法,用于基于亚硫酸氢盐转化测序数据彻底分析肿瘤演变过程中的DNA甲基化模式,包括肿瘤内甲基化异质性。