What is LaminDB used for in Claude Code?

It helps manage, query, and validate biological datasets like single-cell genomics while maintaining data lineage and computational provenance.

Can I use it with workflow managers like Nextflow?

Absolutely, it includes patterns and integration examples for tracking pipeline execution and outputs from Nextflow, Snakemake, and Redun.

Does this skill support biological ontologies?

Yes, it integrates with Bionty to provide standardized terms for genes, proteins, cell types, tissues, diseases, and more.

How does it help with FAIR data principles?

LaminDB automates metadata capture, versioning, and ontology mapping to make research data Findable, Accessible, Interoperable, and Reusable.

Is it compatible with machine learning pipelines?

Yes, it integrates with MLOps tools like Weights & Biases and MLflow to link models directly to the data artifacts used for training.

LaminDB Biological Data Management

Name: LaminDB Biological Data Management
Author: ricable

byricable

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데이터 과학 및 ML

Manages biological datasets with full lineage tracking, ontology-based validation, and FAIR compliance.

소개

LaminDB is a specialized skill for managing complex biological data within a queryable lakehouse architecture. It streamlines the creation of reproducible research by providing automated data lineage, versioning for code and data, and seamless integration with biological ontologies through Bionty. This skill is essential for researchers and bioinformaticians who need to ensure their datasets are Findable, Accessible, Interoperable, and Reusable (FAIR) while maintaining strict computational provenance across single-cell, spatial, and multi-modal workflows.

주요 기능

1 GitHub stars
Seamless integration with workflow managers like Nextflow and MLOps platforms
Biological ontology integration for standardized annotation (genes, cell types, diseases)
Unified query interface for multi-modal biological datasets and lakehouses
Automated data lineage and provenance tracking for computational workflows
Data validation and curation using flexible or strict schemas

사용 사례

Validating and annotating single-cell RNA-seq datasets with standardized cell type ontologies
Building a searchable data lakehouse for cross-study biological data exploration
Ensuring computational reproducibility in ML-driven drug discovery pipelines

소개

주요 기능

1 GitHub stars
Seamless integration with workflow managers like Nextflow and MLOps platforms
Biological ontology integration for standardized annotation (genes, cell types, diseases)
Unified query interface for multi-modal biological datasets and lakehouses
Automated data lineage and provenance tracking for computational workflows
Data validation and curation using flexible or strict schemas

사용 사례

Validating and annotating single-cell RNA-seq datasets with standardized cell type ontologies
Building a searchable data lakehouse for cross-study biological data exploration
Ensuring computational reproducibility in ML-driven drug discovery pipelines