Oladele A Oluwayiose1, Haotian Wu2, Feng Gao2, Andrea A Baccarelli2, Tamar Sofer3, J Richard Pilsner1,4. 1. Department of Obstetrics and Gynecology, School of Medicine, C.S. Mott Center for Human Growth and Development, Wayne State University, Detroit, MI 48201, USA. 2. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA. 3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. 4. Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48201, USA.
Abstract
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 However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.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).
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 However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.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).
Authors: Pan Du; Xiao Zhang; Chiang-Ching Huang; Nadereh Jafari; Warren A Kibbe; Lifang Hou; Simon M Lin Journal: BMC Bioinformatics Date: 2010-11-30 Impact factor: 3.169
Authors: Oladele A Oluwayiose; Haotian Wu; Hachem Saddiki; Brian W Whitcomb; Laura B Balzer; Nicole Brandon; Alexander Suvorov; Rahil Tayyab; Cynthia K Sites; Lisa Hill; Chelsea Marcho; J Richard Pilsner Journal: Sci Rep Date: 2021-02-05 Impact factor: 4.379
Authors: Ruth Pidsley; Elena Zotenko; Timothy J Peters; Mitchell G Lawrence; Gail P Risbridger; Peter Molloy; Susan Van Djik; Beverly Muhlhausler; Clare Stirzaker; Susan J Clark Journal: Genome Biol Date: 2016-10-07 Impact factor: 13.583