lamindb

Biological data lineage is tracked using artifact, run, and transform models to capture versioning and provenance across datasets. This framework records transformations end-to-end, ensuring scRNA-seq and proteomics workflows remain fully traceable and reproducible.

scRNA-seq data is annotated with standard cell type ontologies by applying schema validation and ontology-backed curation workflows. This process integrates controlled vocabularies for genes, tissues, and cell types directly into your data lakehouse for queryable biological metadata.

Biological data management integrates seamlessly with workflow and ML platforms like Nextflow, MLflow, W&B, and Snakemake. This connectivity captures pipeline runs and tracks machine learning transforms alongside your biological artifacts without changing your existing tools.

Biomedical datasets are made FAIR and queryable by centralizing them in a data lakehouse with artifact versioning and ontology annotations. This approach enforces schema validation and tracks data lineage, ensuring records are findable, accessible, interoperable, and reusable.

LaminDB supports multi-user and cloud environments for biological data by allowing you to initialize instances across local and cloud storage. This setup maintains consistent schema validation and cross-system integrations for distributed teams analyzing large-scale datasets.

Tracked proteomics and scRNA-seq artifacts are visualized through seamless integration with Vitessce. This integration connects your queryable, ontology-annotated biological records directly to interactive visualization tools for exploratory analysis.