DSDatlas

DSDatlas integrates curated and public DSD-related omics datasets with a Phenolyzer-based gene-disease knowledgebase to support gene discovery and biomarker identification in disorders of sex development (DSD).

Key Features:

• Knowledgebase categorization: Classifies DSD into four dominant classes, 15 subclasses, and 44 specific disease names.
• Gene prioritization: Implements Phenolyzer-based gene ranking using seed gene sets compiled from MalaCards, GeneCards, and DisGeNET to prioritize candidate genes associated with DSD terms.
• Integrated datasets: Aggregates 25 experimental datasets (24 public from GEO and SRA plus one self-generated) comprising 474 samples across 240 distinct DSD cases.
• Omics data types: Incorporates genomic and transcriptomic high‑throughput omics datasets for integrative analyses.
• Analytical capabilities: Supports differential expression analysis and gene mutation studies comparing DSD groups and control populations.
• Database backend: Maintains a centralized database for integrated data analysis of DSD-related omics and gene-disease associations.

Scientific Applications:

• Gene discovery: Prioritizes and identifies candidate genes implicated in diverse DSD conditions using integrated gene-disease evidence and omics data.
• Biomarker identification: Detects expression and mutation-based biomarkers for DSD through differential expression and mutation analyses.
• Pathogenesis investigation: Enables study of the genetic underpinnings and molecular heterogeneity across DSD classes, subclasses, and specific disease names.
• Comparative cohort analysis: Facilitates comparison of DSD cohorts and control populations across 240 cases and 474 samples to characterize molecular signatures.

Methodology:

Knowledgebase construction used Phenolyzer with seed gene sets sourced from MalaCards, GeneCards, and DisGeNET to rank candidate genes; the database integrates 24 public datasets from GEO and SRA plus one self-generated dataset and supports differential expression analysis and gene mutation studies.