LaminDB Overview
LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.
Core Value Proposition:
Queryability: Search and filter datasets by metadata, features, and ontology terms Traceability: Automatic lineage tracking from raw data through analysis to results Reproducibility: Version control for data, code, and environment FAIR Compliance: Standardized annotations using biological ontologies When to Use This Skill
Use this skill when:
Managing biological datasets: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data Tracking computational workflows: Notebooks, scripts, pipeline execution (Nextflow, Snakemake, Redun) Curating and validating data: Schema validation, standardization, ontology-based annotation Working with biological ontologies: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty) Building data lakehouses: Unified query interface across multiple datasets Ensuring reproducibility: Automatic versioning, lineage tracking, environment capture Integrating ML pipelines: Connecting with Weights & Biases, MLflow, HuggingFace, scVI-tools Deploying data infrastructure: Setting up local or cloud-based data management systems Collaborating on datasets: Sharing curated, annotated data with standardized metadata Core Capabilities
LaminDB provides six interconnected capability areas, each documented in detail in the references folder.
- Core Concepts and Data Lineage
Core entities:
Artifacts: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.) Records: Experimental entities (samples, perturbations, instruments) Runs & Transforms: Computational lineage tracking (what code produced what data) Features: Typed metadata fields for annotation and querying
Key workflows:
Create and version artifacts from files or Python objects Track notebook/script execution with ln.track() and ln.finish() Annotate artifacts with typed features Visualize data lineage graphs with artifact.view_lineage() Query by provenance (find all outputs from specific code/inputs)
Reference: references/core-concepts.md - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.
- Data Management and Querying
Query capabilities:
Registry exploration and lookup with auto-complete Single record retrieval with get(), one(), one_or_none() Filtering with comparison operators (__gt, __lte, __contains, __startswith) Feature-based queries (query by annotated metadata) Cross-registry traversal with double-underscore syntax Full-text search across registries Advanced logical queries with Q objects (AND, OR, NOT) Streaming large datasets without loading into memory
Key workflows:
Browse artifacts with filters and ordering Query by features, creation date, creator, size, etc. Stream large files in chunks or with array slicing Organize data with hierarchical keys Group artifacts into collections
Reference: references/data-management.md - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.
- Annotation and Validation
Curation process:
Validation: Confirm datasets match desired schemas Standardization: Fix typos, map synonyms to canonical terms Annotation: Link datasets to metadata entities for queryability
Schema types:
Flexible schemas: Validate only known columns, allow additional metadata Minimal required schemas: Specify essential columns, permit extras Strict schemas: Complete control over structure and values
Supported data types:
DataFrames (Parquet, CSV) AnnData (single-cell genomics) MuData (multi-modal) SpatialData (spatial transcriptomics) TileDB-SOMA (scalable arrays)
Key workflows:
Define features and schemas for data validation Use DataFrameCurator or AnnDataCurator for validation Standardize values with .cat.standardize() Map to ontologies with .cat.add_ontology() Save curated artifacts with schema linkage Query validated datasets by features
Reference: references/annotation-validation.md - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.
- Biological Ontologies
Available ontologies (via Bionty):
Genes (Ensembl), Proteins (UniProt) Cell types (CL), Cell lines (CLO) Tissues (Uberon), Diseases (Mondo, DOID) Phenotypes (HPO), Pathways (GO) Experimental factors (EFO), Developmental stages Organisms (NCBItaxon), Drugs (DrugBank)
Key workflows:
Import public ontologies with bt.CellType.import_source() Search ontologies with keyword or exact matching Standardize terms using synonym mapping Explore hierarchical relationships (parents, children, ancestors) Validate data against ontology terms Annotate datasets with ontology records Create custom terms and hierarchies Handle multi-organism contexts (human, mouse, etc.)
Reference: references/ontologies.md - Read this for comprehensive ontology operations, standardization strategies, hierarchy navigation, and annotation workflows.
- Integrations
Workflow managers:
Nextflow: Track pipeline processes and outputs Snakemake: Integrate into Snakemake rules Redun: Combine with Redun task tracking
MLOps platforms:
Weights & Biases: Link experiments with data artifacts MLflow: Track models and experiments HuggingFace: Track model fine-tuning scVI-tools: Single-cell analysis workflows
Storage systems:
Local filesystem, AWS S3, Google Cloud Storage S3-compatible (MinIO, Cloudflare R2) HTTP/HTTPS endpoints (read-only) HuggingFace datasets
Array stores:
TileDB-SOMA (with cellxgene support) DuckDB for SQL queries on Parquet files
Visualization:
Vitessce for interactive spatial/single-cell visualization
Version control:
Git integration for source code tracking
Reference: references/integrations.md - Read this for integration patterns, code examples, and troubleshooting for third-party systems.
- Setup and Deployment
Installation:
Basic: uv pip install lamindb With extras: uv pip install 'lamindb[gcp,zarr,fcs]' Modules: bionty, wetlab, clinical
Instance types:
Local SQLite (development) Cloud storage + SQLite (small teams) Cloud storage + PostgreSQL (production)
Storage options:
Local filesystem AWS S3 with configurable regions and permissions Google Cloud Storage S3-compatible endpoints (MinIO, Cloudflare R2)
Configuration:
Cache management for cloud files Multi-user system configurations Git repository sync Environment variables
Deployment patterns:
Local dev → Cloud production migration Multi-region deployments Shared storage with personal instances
Reference: references/setup-deployment.md - Read this for detailed installation, configuration, storage setup, database management, security best practices, and troubleshooting.
Common Use Case Workflows Use Case 1: Single-Cell RNA-seq Analysis with Ontology Validation import lamindb as ln import bionty as bt import anndata as ad
Start tracking
ln.track(params={"analysis": "scRNA-seq QC and annotation"})
Import cell type ontology
bt.CellType.import_source()
Load data
adata = ad.read_h5ad("raw_counts.h5ad")
Validate and standardize cell types
adata.obs["cell_type"] = bt.CellType.standardize(adata.obs["cell_type"])
Curate with schema
curator = ln.curators.AnnDataCurator(adata, schema) curator.validate() artifact = curator.save_artifact(key="scrna/validated.h5ad")
Link ontology annotations
cell_types = bt.CellType.from_values(adata.obs.cell_type) artifact.feature_sets.add_ontology(cell_types)
ln.finish()
Use Case 2: Building a Queryable Data Lakehouse import lamindb as ln
Register multiple experiments
for i, file in enumerate(data_files): artifact = ln.Artifact.from_anndata( ad.read_h5ad(file), key=f"scrna/batch_{i}.h5ad", description=f"scRNA-seq batch {i}" ).save()
# Annotate with features
artifact.features.add_values({
"batch": i,
"tissue": tissues[i],
"condition": conditions[i]
})
Query across all experiments
immune_datasets = ln.Artifact.filter( key__startswith="scrna/", tissue="PBMC", condition="treated" ).to_dataframe()
Load specific datasets
for artifact in immune_datasets: adata = artifact.load() # Analyze
Use Case 3: ML Pipeline with W&B Integration import lamindb as ln import wandb
Initialize both systems
wandb.init(project="drug-response", name="exp-42") ln.track(params={"model": "random_forest", "n_estimators": 100})
Load training data from LaminDB
train_artifact = ln.Artifact.get(key="datasets/train.parquet") train_data = train_artifact.load()
Train model
model = train_model(train_data)
Log to W&B
wandb.log({"accuracy": 0.95})
Save model in LaminDB with W&B linkage
import joblib joblib.dump(model, "model.pkl") model_artifact = ln.Artifact("model.pkl", key="models/exp-42.pkl").save() model_artifact.features.add_values({"wandb_run_id": wandb.run.id})
ln.finish() wandb.finish()
Use Case 4: Nextflow Pipeline Integration
In Nextflow process script
import lamindb as ln
ln.track()
Load input artifact
input_artifact = ln.Artifact.get(key="raw/batch_${batch_id}.fastq.gz") input_path = input_artifact.cache()
Process (alignment, quantification, etc.)
... Nextflow process logic ...
Save output
output_artifact = ln.Artifact( "counts.csv", key="processed/batch_${batch_id}_counts.csv" ).save()
ln.finish()
Getting Started Checklist
To start using LaminDB effectively:
Installation & Setup (references/setup-deployment.md)
Install LaminDB and required extras Authenticate with lamin login Initialize instance with lamin init --storage ...
Learn Core Concepts (references/core-concepts.md)
Understand Artifacts, Records, Runs, Transforms Practice creating and retrieving artifacts Implement ln.track() and ln.finish() in workflows
Master Querying (references/data-management.md)
Practice filtering and searching registries Learn feature-based queries Experiment with streaming large files
Set Up Validation (references/annotation-validation.md)
Define features relevant to research domain Create schemas for data types Practice curation workflows
Integrate Ontologies (references/ontologies.md)
Import relevant biological ontologies (genes, cell types, etc.) Validate existing annotations Standardize metadata with ontology terms
Connect Tools (references/integrations.md)
Integrate with existing workflow managers Link ML platforms for experiment tracking Configure cloud storage and compute Key Principles
Follow these principles when working with LaminDB:
Track everything: Use ln.track() at the start of every analysis for automatic lineage capture
Validate early: Define schemas and validate data before extensive analysis
Use ontologies: Leverage public biological ontologies for standardized annotations
Organize with keys: Structure artifact keys hierarchically (e.g., project/experiment/batch/file.h5ad)
Query metadata first: Filter and search before loading large files
Version, don't duplicate: Use built-in versioning instead of creating new keys for modifications
Annotate with features: Define typed features for queryable metadata
Document thoroughly: Add descriptions to artifacts, schemas, and transforms
Leverage lineage: Use view_lineage() to understand data provenance
Start local, scale cloud: Develop locally with SQLite, deploy to cloud with PostgreSQL
Reference Files
This skill includes comprehensive reference documentation organized by capability:
references/core-concepts.md - Artifacts, records, runs, transforms, features, versioning, lineage references/data-management.md - Querying, filtering, searching, streaming, organizing data references/annotation-validation.md - Schema design, curation workflows, validation strategies references/ontologies.md - Biological ontology management, standardization, hierarchies references/integrations.md - Workflow managers, MLOps platforms, storage systems, tools references/setup-deployment.md - Installation, configuration, deployment, troubleshooting
Read the relevant reference file(s) based on the specific LaminDB capability needed for the task at hand.
Additional Resources Official Documentation: https://docs.lamin.ai API Reference: https://docs.lamin.ai/api GitHub Repository: https://github.com/laminlabs/lamindb Tutorial: https://docs.lamin.ai/tutorial FAQ: https://docs.lamin.ai/faq