ASTRA

ASTRA catalogs transcriptomic responses to stress in human cell lines, integrating and standardizing bulk RNA-seq datasets to advance research on conserved and stress-specific pathways, RNA biomarkers, and disease mechanisms.

Key Features:

• Stress category organization: Data are grouped into four major categories—oxidative stress, hypoxia, heat shock, and DNA damage—with subcategories corresponding to H2O2, low oxygen, increased temperature, and UV radiation as molecular stressors.
• RNA focus: The database focuses on messenger RNA and long noncoding RNA expression under diverse stress conditions.
• Gene-level query and analysis: Users can query gene-level expression, perform differential expression analyses, and visualize transcriptional dynamics across datasets, conditions, and cell-type groups.
• Comprehensive metadata annotation: Each dataset contains stressed and matched control samples annotated with cell line identity, tissue origin, cell-state classification (non-diseased, cancer, organoid), treatment parameters, and sequencing protocols.
• Coding and noncoding transcriptome profiling: ASTRA facilitates discovery of novel stress-responsive genes and RNA-mediated regulatory mechanisms.

Scientific Applications:

• Stress response pathway research: ASTRA supports investigation of conserved and stress-specific pathways in cellular stress responses.
• RNA biomarker discovery: The resource enables identification of transcriptomic signatures and RNA biomarkers associated with specific stress conditions.
• Disease mechanism studies: ASTRA advances understanding of cellular homeostasis, adaptation, and pathogenesis through stress-induced transcriptional profiling.
• Functional genomics and systems biology: The database supports systems-level analyses of stress-responsive coding and noncoding transcriptomes.
• Translational research: ASTRA bridges basic stress biology to clinical applications in disease contexts.

Methodology:

A harmonized computational pipeline ensures comparability across samples and studies. Datasets are integrated and standardized from the Gene Expression Omnibus (GEO).