BayMeth：使用灵活的贝叶斯方法对亲和捕获测序数据进行改进的DNA甲基化定量
BayMeth: Improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach
摘要：
DNA甲基化（DNA甲基化）是表观遗传调节机制的重要组成部分，DNA甲基化模式的异常发生在许多疾病中，例如癌症。绘制和理解DNAme图谱为逆转异常状态提供了相当大的希望。有几种方法来分析DNAme，其在成本、分辨率和覆盖范围方面差异很大。甲基化DNA的亲和捕获和高通量测序在全基因组亚硫酸氢盐测序（WGBS）的高成本和甲基化阵列的低覆盖率之间取得了良好的平衡。然而，现有的方法不能充分区分低甲基化模式和低捕获效率，并且不能提供整合拷贝数变异（CNV）的灵活性。此外，没有提供不确定性估计，这可能被证明对组合来自多个协议的数据或传播到下游分析有用。我们提出了一个经验贝叶斯框架，使用一个完全甲基化（即SssI处理）的对照样本，将观察到的读段密度转换为区域甲基化估计。在我们的模型中，低效率的捕获可以通过较大的后验方差与低甲基化水平区分开来。此外，我们可以通过在泊松模型框架中引入乘法偏移来集成CNV。值得注意的是，我们的模型提供了甲基化水平的均值和方差的解析表达式，因此计算速度很快。我们的算法优于现有的方法，在偏差，均方误差和覆盖概率，如多个参考数据集上所示。虽然我们的方法提供了优势，即使没有SssI控制，相当大的改善是通过它的结合。我们的方法可应用于甲基化DNA亲和富集测定（例如MBD-seq、MeDIP-seq），并且软件实现可在Bioconductor Repitools包中获得。
英文摘要：
DNA methylation (DNAme) is a critical component of the epigenetic regulatory machinery and aberrations in DNAme patterns occur in many diseases, such as cancer. Mapping and understanding DNAme profiles offers considerable promise for reversing the aberrant states. There are several approaches to analyze DNAme, which vary widely in cost, resolution and coverage. Affinity capture and high-throughput sequencing of methylated DNA strike a good balance between the high cost of whole genome bisulphite sequencing (WGBS) and the low coverage of methylation arrays. However, existing methods cannot adequately differentiate between hypomethylation patterns and low capture efficiency, and do not offer flexibility to integrate copy number variation (CNV). Furthermore, no uncertainty estimates are provided, which may prove useful for combining data from multiple protocols or propagating into downstream analysis. We propose an empirical Bayes framework that uses a fully methylated (i.e. SssI treated) control sample to transform observed read densities into regional methylation estimates. In our model, inefficient capture can be distinguished from low methylation levels by means of larger posterior variances. Furthermore, we can integrate CNV by introducing a multiplicative offset into our Poisson model framework. Notably, our model offers analytic expressions for the mean and variance of the methylation level and thus is fast to compute. Our algorithm outperforms existing approaches in terms of bias, mean-squared error and coverage probabilities as illustrated on multiple reference datasets. Although our method provides advantages even without the SssI-control, considerable improvement is achieved by its incorporation. Our method can be applied to methylated DNA affinity enrichment assays (e.g MBD-seq, MeDIP-seq) and a software implementation is available in the Bioconductor Repitools package.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
