Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.

LaminDB Overview LaminDB is an open source, lineage native lakehouse for biology. It makes datasets and models queryable, traceable, validated, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) while storing data in open formats across local filesystems, S3, GCS, Hugging Face, SQLite, and Postgres. Core Value Proposition: Queryability : Search and filter artifacts, records, runs, features, schemas, and collections Traceability : Track inputs, outputs, parameters, source code, and environments for notebooks, scripts, functions, and pipelines Validation : Curate DataFrame, AnnData, SpatialData, TileDB SOMA, Parquet, Zarr, and other biological formats with schemas FAIR Compliance : Standardize annotations with Bionty backed ontologies and custom registries Change management : Organize work with projects, branches, spaces, collections, and saved notes or plans 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, functions, shell scripts, and 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, Hugging Face, Lightning, 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. 1. Core Concepts and Data Lineage Core entities: Artifacts : Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.) Records & ULabels : Experimental entities, typed records, and simple labels Collections : Versioned, immutable sets of artifacts Runs & Transforms : Computational lineage tracking (what code produced what data) Features : Typed metadata fields for annotation and querying Projects, Branches & Spaces : Project grouping, change management, and access boundaries Key workflows: Create and version artifacts from files or Python objects Track notebook/script execution with ln.track() and ln.finish() Track function workflows with @ln.flow() and @ln.step() Annotate artifacts with records, ulabels, projects, and 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. 2. 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, including expression style queries with Feature objects Cross registry traversal with double underscore syntax Full text search across registries Advanced logical queries with ln.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. 3. Annotation and Validation Curation process: 1. Validation : Confirm datasets match desired schemas 2. Standardization : Fix typos, map synonyms to canonical terms 3. 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 , AnnDataCurator , SpatialDataCurator , or TiledbsomaExperimentCurator 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. 4. 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. 5. Integrations Workflow managers: Nextflow: Track pipeline processes and outputs Snakemake: Integrate into Snakemake rules Redun: Combine with Redun task tracking Lightning: Persist checkpoints and training metadata MLOps platforms: Weights & Biases: Link experiments with data artifacts MLflow: Track models and experiments Hugging Face: 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. 6. Setup and Deployment Installation: Current stable baseline: lamindb==2.5.1 (released 2026 06 01; Python =3.10, <=3.14) Basic: uv pip install 'lamindb==2.5.1' With extras: uv pip install 'lamindb[gcp,zarr v2,fcs]==2.5.1' Minimal namespace only: uv pip install 'lamindb core==2.5.1' Bionty module: included in the LaminDB docs and available as uv pip install 'bionty==2.4.0' Optional modules: pin reviewed releases for wetlab or clinical schema modules rather than installing floating latest versions 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 Named environment variables for credentials and connection URLs 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. Safety and Security Defaults When helping with LaminDB setup or integrations: Never display, log, or transmit actual API keys, cloud credentials, database passwords, or full connection strings that include secrets. Prefer IAM roles, workload identity, secret managers, or named environment variables such as LAMIN DB URL , AWS ACCESS KEY ID , AWS SECRET ACCESS KEY , and GOOGLE APPLICATION CREDENTIALS ; only check whether a named variable is present, not its value. Before saving content from REST APIs, external databases, or user provided files, validate and sanitize it with an explicit schema or curator. For reproducible installs, pin package versions or use a lock file. Floating installs are acceptable only when the user explicitly wants the latest upstream release. Common Use Case Workflows Use Case 1: Single Cell RNA seq Analysis with Ontology Validation Use Case 2: Building a Queryable Data Lakehouse Use Case 3: ML Pipeline with W&B Integration Use Case 4: Nextflow Pipeline Integration For native Nextflow projects, prefer the nf lamin plugin and current nextflow.config patterns when available; use inline Python tracking for small or custom pipeline steps. Getting Started Checklist To start using LaminDB effectively: 1. Installation & Setup ( references/setup deployment.md ) Install pinned LaminDB and required extras Authenticate with lamin login Initialize instance with lamin init storage ... 2. Learn Core Concepts ( references/core concepts.md ) Understand Artifacts, Records, Runs, Transforms Practice creating and retrieving artifacts Implement ln.track() / ln.finish() or @ln.flow() / @ln.step() in workflows 3. Master Querying ( references/data management.md ) Practice filtering and searching registries Learn feature based queries and expression style filters Experiment with streaming large files 4. Set Up Validation ( references/annotation validation.md ) Define features relevant to research domain Create schemas for data types Practice curation workflows 5. Integrate Ontologies ( references/ontologies.md ) Import relevant biological ontologies (genes, cell types, etc.) Validate existing annotations Standardize metadata with ontology terms 6. 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: 1. Track everything : Use ln.track() at the start of every analysis for automatic lineage capture 2. Validate early : Define schemas and validate data before extensive analysis 3. Use ontologies : Leverage public biological ontologies for standardized annotations 4. Organize with keys : Structure artifact keys hierarchically (e.g., project/experiment/batch/file.h5ad ) 5. Query metadata first : Filter and search before loading large files 6. Version, don't duplicate : Use built in versioning instead of creating new keys for modifications 7. Annotate with features : Define typed features and use artifact.features.set values() for queryable metadata 8. Document thoroughly : Add descriptions to artifacts, schemas, and transforms 9. Leverage lineage : Use view lineage() to understand data provenance 10. Start local, scale cloud : Develop locally with SQLite, deploy to cloud with PostgreSQL Reference Files This skill includes comprehe