Chang Ya-Ting Sabrina, Yen Ming-Ren, Chen Pao-Yang
Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Front Bioinform. 2022 Feb 10;2:815289. doi: 10.3389/fbinf.2022.815289. eCollection 2022.
DNA methylation is one of the most studied epigenetic modifications that has applications ranging from transcriptional regulation to aging, and can be assessed by bisulfite sequencing (BS-seq) or enzymatic methyl sequencing (EM-seq) at single base-pair resolution. The permutations of methylation statuses given by aligned reads reflect the methylation patterns of individual cells. These patterns at specific genomic locations are sought to be indicative of cellular heterogeneity within a cellular population, which are predictive of developments and diseases; therefore, methylation heterogeneity has potentials in early detection of these changes. Computational methods have been developed to assess methylation heterogeneity using methylation patterns formed by four consecutive CpGs, but the nature of shotgun sequencing often give partially observed patterns, which makes very limited data available for downstream analysis. While many programs are developed to impute genome-wide methylation levels, currently there is only one method developed for recovering partially observed methylation patterns; however, the program needs lots of data to train and cannot be used directly; therefore, we developed a probabilistic-based imputation method that uses information from neighbouring sites to recover partially observed methylation patterns speedily. It is demonstrated to allow for the evaluation of methylation heterogeneity at 15% more regions genome-wide with high accuracy for data with moderate depth. To make it more user-friendly we also provide a computational pipeline for genome-screening, which can be used in both evaluating methylation levels and profiling methylation patterns genomewide for all cytosine contexts, which is the first of its kind. Our method allows for accurate estimation of methylation levels and makes evaluating methylation heterogeneity available for much more data with reasonable coverage, which has important implications in using methylation heterogeneity for monitoring changes within the cellular populations that were impossible to detect for the assessment of development and diseases.
DNA甲基化是研究最为深入的表观遗传修饰之一，其应用范围涵盖转录调控到衰老等领域，并且可以通过亚硫酸氢盐测序（BS-seq）或酶促甲基测序（EM-seq）在单碱基对分辨率下进行评估。比对读数给出的甲基化状态排列反映了单个细胞的甲基化模式。特定基因组位置的这些模式被认为可指示细胞群体内的细胞异质性，而细胞异质性可预测发育和疾病；因此，甲基化异质性在早期检测这些变化方面具有潜力。已经开发了计算方法来使用由四个连续的CpG形成的甲基化模式评估甲基化异质性，但鸟枪法测序的性质常常会产生部分观察到的模式，这使得可用于下游分析的数据非常有限。虽然已经开发了许多程序来估算全基因组甲基化水平，但目前只有一种方法用于恢复部分观察到的甲基化模式；然而，该程序需要大量数据进行训练且不能直接使用；因此，我们开发了一种基于概率的估算方法，该方法利用来自相邻位点的信息快速恢复部分观察到的甲基化模式。对于中等深度的数据，该方法被证明能够在全基因组范围内多15%的区域以高精度评估甲基化异质性。为了使其更便于用户使用，我们还提供了一个用于基因组筛选的计算流程，该流程可用于评估甲基化水平和全基因组分析所有胞嘧啶背景下的甲基化模式，这是同类中的首个。我们的方法能够准确估计甲基化水平，并使评估甲基化异质性可用于更多具有合理覆盖度的数据，这对于利用甲基化异质性监测细胞群体内无法通过发育和疾病评估检测到的变化具有重要意义。
