Genomni provides an evidence‑aware genomics platform that automatically annotates and prioritizes genetic variants by linking them to curated biomedical literature, clinical guidelines, and functional studies. Its machine‑learning models generate pathogenicity scores and present results in an interactive dashboard with real‑time evidence updates, while APIs and SDKs enable integration into existing bioinformatics pipelines and electronic health records.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers and clinicians often struggle to interpret large-scale genomic data without integrated evidence linking genetic variants to clinical outcomes, leading to time‑consuming manual analysis and potential missed insights.
Solution
Genomni offers an evidence‑aware genomics platform that automatically annotates and prioritizes genetic variants using curated biomedical literature, clinical databases, and functional studies. The system applies machine‑learning models to assess variant pathogenicity and relevance to specific diseases, presenting results in an interactive dashboard that supports hypothesis generation and decision making. By integrating real‑time evidence updates, Genomni ensures users work with the most current knowledge without extensive manual curation. The platform also provides APIs for seamless incorporation into existing bioinformatics pipelines and electronic health record systems.
Target Audience
Primary users are genomic researchers, clinical geneticists, and biotech companies that need rapid, evidence‑based interpretation of sequencing data.
Features
- Automated variant annotation with links to peer‑reviewed studies, clinical guidelines, and functional assays
- Machine‑learning driven pathogenicity scoring that incorporates multi‑source evidence
- Interactive web dashboard for visual exploration of variant impact across genes and phenotypes
- Real‑time evidence updates to keep annotations current as new research emerges
- RESTful API and SDKs for integration with laboratory information systems and research workflows
- Compliance with data security standards for handling sensitive genomic information