SynVectorDB: embedding-based retrieval system for synthetic biology parts.

ABSTRACT: Synthetic biology part discovery faces significant challenges due to inconsistent data organization and limited semantic search capabilities across existing repositories. We developed SynVectorDB, an embedding-based retrieval system that addresses these limitations through methodological innovations in data integration and AI-driven semantic search. Our approach integrates 19 850 biological parts from multiple sources (Addgene, iGEM Registry, laboratory collections), implementing systematic curation protocols that resulted in 7656 parts achieving verified status through literature-based validation and reliability assessment. We introduce a novel three-level hierarchical classification system organizing parts into functionally coherent categories (DNA Elements, RNA Elements, Coding Sequence

SUBMITTER: Li H 

PROVIDER: S-EPMC12805114 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature