Features
Automatic lineage capture for notebooks, scripts, functions, pipelines
Unified query interface across Parquet, Zarr, AnnData, SpatialData
Schema management and validation for biological datasets
Bio-registries and ontology support (Cell Ontology, Gene Ontology)
LIMS and ELN capabilities with markdown notes
Git-like branching and merging for change management
Zero-copy data sharing across databases and storage
Integration with Nextflow, Redun, Snakemake, Weights & Biases, MLFlow
Built-in role-based access control and audit logs
Support for local, S3, GCP, Azure, and R2 storage backends
Record.from_dataframe() ~2x speedup (db 2.7.0)
Excel support for sheet imports (hub 1.44.0)
Native SpatialData support
LaminR package for R traceability
Database mirror for Arc Virtual Cell Atlas (600M cells, 41TB)
Proprietary Recursion OS platform for drug discovery
Automated wet lab generating millions of cell experiments per week
Over 50 petabytes of biological and chemical data
NVIDIA BioHive-2 supercomputer for AI model training
Hit identification to IND-enabling studies with improved speed and cost
Potential first-in-class treatments in oncology and rare diseases
Advanced pipeline with phase 1/2/3 clinical trials (REC-4881, REC-3565)
Target identification and validation using machine learning
Generative AI for molecule design and optimization
De-identified patient data integration for precision medicine
Strategic partnerships with pharma and tech leaders
End-to-end drug development capability from preclinical to pivotal studies
Fit-for-purpose dataset generation including ADME and safety profiling
Real-world evidence generation through clinical trials
Continuous feedback loop from experiments into AI models
