DiSignAtlas

DiSignAtlas provides comprehensive transcriptomics-based disease signatures for humans and mice to identify and characterize molecular biomarkers across diverse diseases.

Key Features:

• Dataset curation: Curation of 181,434 transcriptome profiles from studies covering 1,836 unique disease types and 10,306 case-control comparison datasets, including 328 single-cell RNA sequencing datasets.
• Differential expression signatures: Identification of 3,775,317 differentially expressed genes in humans and 1,723,674 in mice as disease signatures.
• Functional enrichment analysis: Downstream functional enrichment analysis to interpret biological implications of gene sets.
• Cell type analysis: Cell type analysis to investigate cellular heterogeneity within datasets.
• Signature correlation analysis: Correlation analysis of signatures to examine relationships between diseases and across species.
• Retrieval and comparison of signatures: Retrieval and comparison of disease signatures for cross-dataset and cross-species evaluation.

Scientific Applications:

• Diagnostic biomarker discovery: Use of transcriptomic signatures to identify diagnostic markers in human and mouse studies.
• Prognostic and predictive biomarker development: Application of signatures for prognostic and predictive marker identification.
• Therapeutic target identification: Use of disease signatures to highlight candidate therapeutic targets.
• Translational research and cross-species comparison: Support for translational studies and comparative analyses between human and mouse signatures.
• Disease mechanism exploration: Investigation of disease mechanisms via transcriptomics-derived molecular signatures.

Methodology:

Curation of 181,434 transcriptome profiles and 10,306 comparison datasets, analysis using established pipelines, and identification of differentially expressed genes yielding 3,775,317 human and 1,723,674 mouse signatures.