GIDB

GIDB curates and integrates molecular signatures and supporting evidence for gastrointestinal (GI) cancers to enable multidimensional analysis of genomic, epigenomic, and transcriptomic alterations.

Key Features:

• Automated curation: Automated extraction and aggregation of molecular signatures and supporting evidence from literature and public datasets.
• Text mining: Integrated text mining of literature to identify gene-disease and molecular associations.
• Data mining: Integration and mining of publicly available molecular and clinical datasets.
• Comprehensive repository: Repository contents include 8,730 genes, 248 miRNAs, 58 lncRNAs, 320 copy number variations, 49 fusion genes, and 2,381 semantic networks.
• Parallel evidence-data integration: Parallel integration of supporting evidence and data for molecular signatures linked to GI cancer.
• Multilevel genomic characterization: Analysis layers include simple somatic mutations, gene expression, DNA methylation, copy number variation, fusion genes, and prognosis analysis.
• Semantic networks: Construction and inclusion of semantic networks representing relationships among entities in GI cancer.
• Timeline of discoveries: Timeline generation highlighting major molecular discoveries and historical knowledge development for genes in GI cancer.
• Heatmap analysis: Heatmap module for visualization and comparison of expression or alteration patterns across signatures.
• Network analysis: Network module for visualization and analysis of molecular interaction and association networks.

Scientific Applications:

• Biomarker discovery and validation: Identification and evidence aggregation for candidate diagnostic, predictive, or prognostic biomarkers in GI cancers.
• Comparative organ-level analysis: Comparative analysis of molecular signatures across oesophagus, stomach, liver, bile duct, pancreas, rectum, and colon cancers.
• Prognostic analysis: Integration of molecular data with prognosis analyses to identify survival-associated signatures.
• Precision medicine and basket trials: Support for molecular stratification and cross-tumor signature identification relevant to precision medicine and basket trial design.
• Field cancerization studies: Investigation of shared molecular alterations and field effects across GI organ systems.
• Research hotspot and historical analysis: Mapping of research hotspots and historical development of molecular knowledge in GI cancer.

Methodology:

Automated curation combined with integrated text mining and data mining of literature and public datasets, construction of semantic networks, and analyses of somatic mutations, gene expression, DNA methylation, copy number variation, fusion genes, and prognosis, plus generation of a timeline of molecular discoveries.