dbDEPC

dbDEPC compiles and curates proteomics data on differentially expressed proteins in human cancers to support analysis of protein-level expression changes across cancer types.

Key Features:

• Curated Data Collection: Aggregates mass spectrometry (MS) results manually curated from peer-reviewed publications, initially from 65 studies and expanded to over 331 experiments encompassing 20 human cancer types and more than 4,000 protein entries.
• Integration with Genetic Data: Integrates low-throughput experimental data and cancer-associated genes from external databases and links proteins to structural genomic variations such as copy number variations and single nucleotide polymorphisms.
• Query and Annotation: Supports query by protein identifier, description, or sequence and provides detailed annotations of differential expression patterns observed in cancers.
• Experiment Metadata and Filtering: Includes experiment design information and advanced filtering options for customized analyses.
• Network Analysis: Provides a network analysis tool to explore relationships among differentially expressed proteins.

Scientific Applications:

• Differential Expression Identification: Identify proteins reported as differentially expressed in specific cancer types.
• Comparative Proteomics: Compare proteomic findings across studies to detect consistent expression changes.
• Expression Visualization: Visualize protein expression heatmaps across multiple cancer types.
• Biomarker and Genotype-Phenotype Linking: Link protein expression changes to genetic aberrations to support identification of potential biomarkers.

Methodology:

Aggregates and manually curates MS experiment results from peer-reviewed publications, integrates low-throughput experimental data and cancer-associated gene databases, annotates proteins with structural genomic variations including copy number variations and single nucleotide polymorphisms, and provides experiment-design metadata plus network analysis functionality.