Organellerdir

Organellerdir is a proposed standardized directory of cellular organelles designed for computer-readable representation of organelle identity, structure, function, and interactions. The term blends organelle with directory, signaling a cataloged, navigable resource used in bioinformatics and education. The concept envisions a central schema and federated repositories that describe each organelle and its connections within the cell.

Data model: Each entry records a unique identifier, preferred name, synonyms, lineage (organelle family), localization, morphology, molecular composition, functional roles, transport routes, contact sites, and regulatory properties. Annotations reference established ontologies (Gene Ontology, Uberon) and literature-backed evidence levels. Cross-organellar links capture interactions and metabolic dependencies. Versioning and provenance ensure reproducibility.

Structure and scope: Core organelles (nucleus, mitochondrion, plastid/chloroplast, endoplasmic reticulum, Golgi apparatus, lysosome, peroxisome, vesicles) plus subcompartments and membrane contact sites. Species coverage ranges from model organisms to broader taxa; data may be specialized for developmental stage or cell type.

Applications: Data integration for systems biology models, simulation engines, and educational tools; enables queries such as “which organelles participate in calcium storage?” or “which organelles share contact sites with the ER?” and supports mapping to metabolic pathways and gene products.

Interoperability: Aligns with bio-ontologies and data standards; supports APIs, SBML-compatible exports, and graph formats for knowledge representations; release histories and citation tracking are maintained to ensure traceability.

Status: Organellerdir remains a conceptual framework or nascent project in the literature, not a universally adopted standard. Various research groups maintain or prototype organelle directories within broader knowledge graphs or cell biology databases.

See also: organelle, Gene Ontology, Uberon, Systems Biology Ontology, knowledge graph, SBML.