lamindb

Track scRNA-seq data lineage by calling ln.track() before processing and saving outputs as artifacts. This captures runs, transforms, and features, ensuring end-to-end reproducibility for biological datasets across complex experimental workflows.

Manage biological data ontologies using the Bionty plugin for standardized annotation and schema validation. It enables cross-domain interoperability and reproducibility by mapping complex experimental metadata to controlled vocabularies across multi-modal datasets.

Integrate biological data management with Nextflow or Snakemake by tracking workflow runs as transforms and saving inputs and outputs as artifacts. This bridges pipeline execution with provenance tracking and cloud storage backends for scalable data analysis.

LaminDB supports querying across multiple registries for spatial transcriptomics data. It provides feature-based search, streaming, and cross-registry traversal to manage and retrieve complex biological datasets efficiently.

You can validate biological dataset schemas against standard ontologies using the Bionty plugin. This enforces standardized annotation and cross-domain interoperability, ensuring complex experimental metadata conforms to controlled vocabulary definitions.

Streaming multi-modal biological data across cloud storage backends requires compatible local or cloud infrastructure configurations. While LaminDB supports streaming and cross-registry traversal, performance depends on the underlying storage backend and network bandwidth.