Chang Ya-Ting Sabrina, Yen Ming-Ren, Chen Pao-Yang
Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Methods Mol Biol. 2023;2624:115-126. doi: 10.1007/978-1-0716-2962-8_8.
DNA methylation is studied extensively for its relations with several biological processes such as transcriptional regulation. While methylation levels are usually estimated per cytosine or genomic region, additional information on methylation heterogeneity can be obtained when considering stretches of successive cytosines on the same reads; however, the majority of methylomes suffer from low coverage of genomic regions with sequencing depths enough for accurate estimation of methylation heterogeneity using existing methods. Here we describe a probabilistic-based imputation method that makes use of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.
DNA甲基化因其与转录调控等多种生物学过程的关系而被广泛研究。虽然甲基化水平通常是针对每个胞嘧啶或基因组区域进行估计的，但当考虑同一条读段上连续胞嘧啶的延伸时，可以获得关于甲基化异质性的额外信息；然而，大多数甲基化组存在基因组区域覆盖度低的问题，其测序深度不足以使用现有方法准确估计甲基化异质性。在这里，我们描述了一种基于概率的插补方法，该方法利用来自相邻位点的甲基化信息来恢复部分观察到的甲基化模式。我们的方法和软件在所有评估中被证明速度更快、更准确。最终，我们的方法能够更简化地监测细胞群体内的表观遗传变化及其在疾病中的假定作用。
