Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark.
Laboratory for Genomics and Bioinformatics, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil.
Bioinformatics. 2017 Feb 15;33(4):549-551. doi: 10.1093/bioinformatics/btw657.
Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.
Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical P -values through randomization tests, even for big datasets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, P -values and empirical P -values, and it corrects for multiple testing.
DiMmeR is publicly available at http://dimmer.compbio.sdu.dk .
Supplementary data are available at Bioinformatics online.
全基因组关联研究（EWAS）生成了大量的流行病学数据集。它们旨在检测可能影响转录基因活性的差异甲基化 DNA 区域，从而调节代谢过程。到目前为止，最广泛使用的技术是 Illumina Methylation BeadChip，它在一组患者与对照组的 CpG 二核苷酸环境中测量 450（850）万个胞嘧啶的甲基化水平。有许多生物信息学工具可用于原始数据分析。然而，其中大多数工具需要在编程语言 R 方面具有一些知识，没有用户界面，并且不提供从原始数据到统计学上显著的差异甲基化区域（DMR）和相关基因的所有必要步骤来指导用户。
在这里，我们介绍了 DiMmeR（多重差异甲基化区域的发现），这是第一个免费的独立软件，它通过用户友好的图形用户界面（GUI），以交互方式引导科学家完成 EWAS 数据分析的全过程。它提供了针对 Illumina 450K 和 850K EPIC 芯片数据的并行化统计方法，用于有效识别 DMR。DiMmeR 通过随机化检验计算经验 P 值，即使对于包含数百个患者和数千个排列的大型数据集，也可以在标准台式 PC 上在几分钟内计算出经验 P 值。它独立于任何第三方库，计算回归系数、P 值和经验 P 值，并进行多重检验校正。
DiMmeR 可在 http://dimmer.compbio.sdu.dk 上公开获取。
补充数据可在 Bioinformatics 在线获取。
