LaminDB — Biological Data Management

LaminDB — Biological Data Management

Overview

LaminDB is an open-source data framework for biology that makes data queryable, traceable, and FAIR (Findable, Accessible, Interoperable, Reusable). It combines data lakehouse architecture, lineage tracking, biological ontology validation, and a unified Python API for managing biological datasets from raw files to annotated, curated artifacts.

When to Use

• Managing and versioning biological datasets (scRNA-seq, spatial, flow cytometry, multi-modal)
• Tracking computational lineage (which code produced which data)
• Validating and curating data against biological ontologies (cell types, genes, tissues, diseases)
• Building queryable data lakehouses across multiple experiments
• Ensuring reproducibility with automatic environment and provenance capture
• Integrating with workflow managers (Nextflow, Snakemake) or MLOps (W&B, MLflow)
• Standardizing metadata with ontology-based annotation (Bionty)
• For single-cell analysis pipelines (clustering, DE), use scanpy instead
• For ontology lookups only without data management, use bionty directly

Prerequisites

pip install lamindb
# With extras for specific data types
pip install 'lamindb[bionty,zarr,fcs]'

Setup: Requires instance initialization before use:

lamin login
lamin init --storage ./my-data --name my-project
# Or with cloud storage:
# lamin init --storage s3://my-bucket --name my-project --db postgresql://...

Instance types: Local SQLite (development), Cloud + SQLite (small teams), Cloud + PostgreSQL (production).

Quick Start

import lamindb as ln