IBDTransDB

IBDTransDB aggregates curated transcriptomic datasets for inflammatory bowel disease to enable comparative analyses, pathway enrichment, gene-signature identification, cell-type deconvolution, and meta-analysis to prioritize genes and pathways related to IBD pathogenesis and treatment response.

Key Features:

• Manually Curated Database: Comprises 34 transcriptomic datasets encompassing 2,932 samples and 122 differential comparisons.
• Advanced Query System: Supports 35 keywords across five attributes, including tissue type and treatment modalities such as infliximab, enabling complex multi-criteria queries.
• IBDExplore: Provides visualization of differential gene lists, pathway enrichment analyses, gene-signature identification, and cell-type deconvolution from individual datasets.
• IBDCompare: Enables comparisons of selected genes or pathways across multiple datasets and conditions to assess consistency and variability of transcriptomic responses.
• IBDIntegrate: Performs meta-analyses to prioritize genes or pathways based on user-selected datasets and conditions.

Scientific Applications:

• Comparative transcriptomic analysis: Compare gene- and pathway-level responses across studies and conditions, including treatment responses such as infliximab.
• Meta-analysis and prioritization: Synthesize evidence across datasets to rank candidate genes and pathways for follow-up.
• Mechanistic discovery: Integrate transcriptomic profiles with other omics data types to uncover complex mechanisms underlying IBD pathogenesis.
• Therapeutic target identification: Support identification and prioritization of novel drug targets and biomarkers related to IBD and treatment response.

Methodology:

Manual curation of 34 transcriptomic datasets (2,932 samples) yielding 122 differential comparisons; a keyword-based query system using 35 keywords across five attributes; and analytical modules that perform differential gene-list visualization, pathway enrichment, gene-signature identification, cell-type deconvolution, cross-dataset comparisons, and meta-analysis for gene/pathway prioritization.