Aclust2.0：一款经过改进的用于 Infinium 甲基化芯片数据分析的无监督 R 工具。
Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses.
机构信息
Department of Obstetrics and Gynecology, School of Medicine, C.S. Mott Center for Human Growth and Development, Wayne State University, Detroit, MI 48201, USA.
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
出版信息
Bioinformatics. 2022 Oct 14;38(20):4820-4822. doi: 10.1093/bioinformatics/btac583.
MOTIVATION
A wide range of computational packages has been developed for regional DNA methylation analyses of Illumina's Infinium array data. Aclust, one of the first unsupervised algorithms, was originally designed to analyze regional methylation of Infinium's 27K and 450K arrays by clustering neighboring methylation sites prior to downstream analyses. However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.
RESULTS
We have created Aclust2.0, a streamlined pipeline that involves five steps for the analyses of human (450K and EPIC) and mouse array data. Aclust2.0 provides a user-friendly pipeline and versatile for regional DNA methylation analyses for molecular epidemiological and mouse studies.
AVAILABILITY AND IMPLEMENTATION
Aclust2.0 is freely available on Github (https://github.com/OluwayioseOA/Alcust2.0.git).
动机
已经开发了广泛的计算软件包，用于分析 Illumina 的 Infinium 芯片数据的区域 DNA 甲基化。Aclust 是最早的无监督算法之一，最初是为了通过在下游分析之前对相邻的甲基化位点进行聚类，来分析 Infinium 的 27K 和 450K 芯片的区域甲基化。然而，Aclust 依赖于过时的软件包，这使得它在处理较新的 Infinium EPIC 和小鼠芯片时，基本上无法运行。
结果
我们创建了 Aclust2.0，这是一个简化的管道，包括五个步骤，用于分析人类（450K 和 EPIC）和小鼠的芯片数据。Aclust2.0 为分子流行病学和小鼠研究中的区域 DNA 甲基化分析提供了用户友好的、通用的管道。
可用性和实现
Aclust2.0 可在 Github 上免费获得（https://github.com/OluwayioseOA/Alcust2.0.git）。
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