HighAltitudeOmicsDB

HighAltitudeOmicsDB provides a curated compendium of genes and proteins associated with high-altitude hypoxia, annotated with experimental context and functional interaction data to support omics-level analysis of acclimatization and pathology.

Key Features:

• Curated Data Compilation: Contains curated genes and proteins experimentally validated as associated with high-altitude conditions, annotated with regulation direction (up/down), fold change, control groups (low landers or high landers), duration and altitude of exposure, tissue of expression, source organism, and level of hypoxia.
• Experimental Validation: Includes experimental metadata such as place/country of study, ethnicity, geographical location, and literature references with links to respective publications.
• Disease and Drug Associations: Collates disease and drug associations related to high-altitude conditions to link biomolecules with clinical relevance.
• Pathway Analysis: Provides Gene Ontology (GO) and KEGG pathway associations for HA-associated genes and proteins.
• Protein-Protein Interaction Networks: Constructs Protein-Protein Interaction (PPI) networks representing molecular interactions among HA-associated proteins.
• Semantic Similarity Analysis: Computes GO semantic similarities among interactors to support mechanistic interpretation of biomolecular responses.

Scientific Applications:

• Identification of Biomarkers: Enables identification of diagnostic, therapeutic, and predictive biomarkers for High Altitude Illnesses (HAIs) through analysis of associated genes and proteins.
• Mechanistic Insights: Supports exploration of molecular mechanisms of acclimatization to hypoxia via pathway, PPI, and semantic similarity analyses.
• Comparative Analysis: Facilitates comparison of HA-associated biomolecules across studies, populations, exposure durations, altitudes, and tissues.
• Therapeutic Development: Aids identification of potential therapeutic targets by linking HA-associated biomolecules with disease and drug associations.

Methodology:

Integration of bioinformatics tools to construct PPI networks, extract GO semantic similarities, and map genes/proteins to Gene Ontology (GO) and KEGG pathway associations.