Sun Zhifu, Cunningham Julie, Slager Susan, Kocher Jean-Pierre
Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN 55905, USA.
Medical Genome Facility, Mayo Clinic, Rochester, MN 55905, USA.
Epigenomics. 2015 Aug;7(5):813-28. doi: 10.2217/epi.15.21. Epub 2015 Sep 14.
Bisulfite treatment-based methylation microarray (mainly Illumina 450K Infinium array) and next-generation sequencing (reduced representation bisulfite sequencing, Agilent SureSelect Human Methyl-Seq, NimbleGen SeqCap Epi CpGiant or whole-genome bisulfite sequencing) are commonly used for base resolution DNA methylome research. Although multiple tools and methods have been developed and used for the data preprocessing and analysis, confusions remains for these platforms including how and whether the 450k array should be normalized; which platform should be used to better fit researchers' needs; and which statistical models would be more appropriate for differential methylation analysis. This review presents the commonly used platforms and compares the pros and cons of each in methylome profiling. We then discuss approaches to study design, data normalization, bias correction and model selection for differentially methylated individual CpGs and regions.
基于亚硫酸氢盐处理的甲基化微阵列(主要是Illumina 450K Infinium阵列)和下一代测序(简化代表性亚硫酸氢盐测序、安捷伦SureSelect人类甲基化测序、NimbleGen SeqCap Epi CpGiant或全基因组亚硫酸氢盐测序)常用于碱基分辨率的DNA甲基化组研究。尽管已经开发并使用了多种工具和方法进行数据预处理和分析,但这些平台仍存在一些困惑,包括450k阵列应如何以及是否进行标准化;应使用哪个平台以更好地满足研究人员的需求;以及哪种统计模型更适合差异甲基化分析。本综述介绍了常用平台,并比较了每个平台在甲基化组分析中的优缺点。然后,我们讨论了针对差异甲基化的单个CpG和区域的研究设计、数据标准化、偏差校正和模型选择方法。