DiseaseMeth

DiseaseMeth provides a comprehensive repository and analytical platform for exploration and analysis of DNA methylation alterations associated with human diseases to support identification of differentially methylated genes (DMGs) and disease biomarkers.

Key Features:

• Comprehensive Data Repository: Houses over 14,000 entries from more than 175 high-throughput experiments and gene-centric methylation data for 162 human diseases derived from multiple technologies and platforms.
• Search Capabilities: Enables querying by gene ID and disease name to identify differentially methylated genes (DMGs) and gene-disease relationships.
• Integrated Data Analysis: Provides cross-data set integration of methylation data for diseased and normal samples to facilitate identification of DMGs.
• Functional Enrichment Tools: Includes FunctionSearch for functional enrichment using localized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations.
• Unified Analysis Pipeline: Implements a unified analysis pipeline (version 3.0) to identify DMGs from stored original data, reporting 22,718 DMGs across 99 diseases.
• Analytical Modules: Offers Methylation Disease Correlation and Cancer Prognosis & Co-Methylation modules for disease correlation and cancer prognosis/co-methylation analyses.
• Experimental Associations from Literature: Incorporates manually curated experimentally confirmed associations mined from 1,472 PubMed papers, adding 448 new disease–methylation pairs.

Scientific Applications:

• Biomarker Discovery: Identification of candidate disease biomarkers through analysis of DMGs across multiple diseases and datasets.
• Disease Mechanism Studies: Investigation of the role of DNA methylation in disease initiation and progression.
• Prognosis and Translational Research: Support for cancer prognosis assessment and development of personalized medicine approaches based on methylation patterns.

Methodology:

Integrates data from various sources including methylation-specific PCR and genome-wide profiling technologies, employs statistical analysis methods, and applies a unified analysis pipeline to identify DMGs from original data.