School of Medicine, Nankai University, Tianjin 300071, China.
Tianjin Key Laboratory of Human Development and Reproductive Regulation, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin 300199, China.
Int J Environ Res Public Health. 2021 Jul 28;18(15):7975. doi: 10.3390/ijerph18157975.
With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.
随着下一代测序技术的进步，基因组 DNA 的亚硫酸氢盐转化后测序已成为定量全基因组 DNA 甲基化的主要技术，具有单碱基分辨率。文献中提供了大量用于识别亚硫酸氢盐测序数据中差异甲基化区域的计算方法，并且还在不断开发更多的方法。在这里，我们专注于对常用差异甲基化分析方法的全面评估，并描述每种方法的潜在优势和局限性。我们发现，方法之间存在很大差异，没有一种方法在所有基准测试中始终排名第一。此外，平滑似乎并没有大大提高性能，而在 BS-seq 数据的计算分析中，少量重复比低测序深度产生更多的困难。数据分析和解释应格外小心，特别是当重复次数或测序深度有限时。
