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.

1. Core Concepts and Data Lineage