COMPARTMENTS

COMPARTMENTS integrates evidence from manually curated literature, high-throughput microscopy-based screens, automatic text mining, and sequence-based prediction methods to consolidate protein subcellular localization information for biological interpretation.

Key Features:

• Integration of Diverse Data Sources: Aggregates and harmonizes localization evidence from manually curated literature, high-throughput microscopy-based screens, automatic text mining, and sequence-based prediction methods.
• Common Protein Identifiers and Gene Ontology Mapping: Maps all gathered evidence onto common protein identifiers and standardized Gene Ontology (GO) terms to ensure consistent cross-referencing.
• Confidence Scoring System: Assigns confidence scores to each piece of evidence based on the type and source of the evidence.
• Visualization on Schematic Cell Model: Presents unified localization evidence for proteins on a schematic cell model for integrated visualization of localization assignments.
• Automated Update System: Maintains synchronized, up-to-date data through an automated update mechanism that applies mapping and scoring protocols.

Scientific Applications:

• Functional Genomics: Facilitates interpretation of protein function within cellular contexts by providing integrated localization evidence.
• Protein Engineering: Supports the design and evaluation of proteins with targeted subcellular localizations.
• Disease Research: Enables investigation of protein mislocalization and its implications in disease states using consolidated localization data.

Methodology:

Data aggregation is performed via an automated update system; evidence is mapped onto common protein identifiers and Gene Ontology (GO) terms and assigned confidence scores using defined mapping and scoring protocols.