Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses

Area of Science:

• Genomics
• Bioinformatics
• Epigenetics

Background:

• Computational tools are essential for analyzing regional DNA methylation from Illumina Infinium array data.
• The original Aclust algorithm was an early unsupervised method for clustering neighboring methylation sites on 27K and 450K arrays.
• Outdated dependencies made the original Aclust non-operational for newer Infinium EPIC and mouse arrays.

Purpose of the Study:

• To develop a streamlined and updated computational pipeline for regional DNA methylation analysis.
• To create a user-friendly and versatile tool applicable to both human and mouse array data.
• To enhance the analysis of molecular epidemiological and mouse studies through improved DNA methylation profiling.

Main Methods:

• Development of Aclust2.0, a five-step streamlined computational pipeline.
• Adaptation for analysis of human Infinium 450K and EPIC array data.
• Adaptation for analysis of mouse array data.

Main Results:

• Aclust2.0 offers a user-friendly and versatile platform for regional DNA methylation analyses.
• The pipeline successfully processes data from both human (450K, EPIC) and mouse arrays.
• Aclust2.0 addresses the limitations of previous versions, enabling analysis with newer array technologies.

Conclusions:

• Aclust2.0 is a valuable update to existing DNA methylation analysis tools.
• The pipeline facilitates advanced regional methylation studies in human and mouse models.
• Aclust2.0 enhances the utility of Infinium array data for molecular epidemiology and research.