Aclust2.0

Aclust2.0 performs unsupervised regional DNA methylation analysis on Illumina Infinium array data (450K and EPIC) and mouse array datasets using an R-based processing pipeline.

Key Features:

• Unsupervised clustering algorithm: Groups neighboring methylation sites using an unsupervised clustering approach that accounts for the spatial context of methylation patterns.
• Array compatibility: Supports Illumina Infinium 450K and EPIC human arrays and mouse array datasets.
• Five-step R-based pipeline: Implements a five-step processing pipeline in R covering preprocessing, clustering, regional analysis, statistical evaluation, and visualization.
• Regional methylation analysis: Focuses on detection and characterization of regionally coordinated DNA methylation patterns relevant to epigenetic regulation.

Scientific Applications:

• Molecular epidemiology: Identify associations between regional DNA methylation patterns and health outcomes or environmental exposures.
• Mouse model investigations: Characterize regional methylation in mouse arrays to investigate epigenetic mechanisms in disease models.
• Epigenetic regulation and disease research: Detect regionally coordinated methylation modifications associated with gene regulation and disease states.

Methodology:

Data preprocessing (cleaning and normalization), unsupervised clustering of methylation sites, regional analysis of clustered regions, statistical evaluation of cluster significance, and generation of visual outputs and reports.