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Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses
Oladele A Oluwayiose1, Haotian Wu2, Feng Gao2
1Department of Obstetrics and Gynecology, School of Medicine, C.S. Mott Center for Human Growth and Development, Wayne State University, Detroit, MI 48201, USA.
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 However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.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).
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