Introduction¶

LaminDB is an open-source data framework for biology to query, trace, and validate datasets and models at scale. With one API, you get: lakehouse, lineage, feature store, ontologies, bio-registries & formats.

LaminHub is a data collaboration hub built on LaminDB similar to how GitHub is built on git.

💡 Tip: Copy this summary.md into an LLM chat and let AI explain.

Who uses it?

Scientists & engineers in pharma, biotech, and academia, including:

Pfizer – A global BigPharma company with headquarters in the US
Ensocell Therapeutics – A BioTech with offices in Cambridge, UK, and California
DZNE – The National Research Center for Neuro-Degenerative Diseases in Germany
Helmholtz Munich – The National Research Center for Environmental Health in Germany
scverse – An international non-profit for open-source omics data tools
The Global Immunological Swarm Learning Network – Research hospitals at U Bonn, Harvard, MIT, Stanford, ETH Zürich, Charite, Mount Sinai, and others

Quickstart¶

Install the Python package:

pip install lamindb

Query databases¶

You can browse public databases at lamin.ai/explore. To query laminlabs/cellxgene, run:

Py

import lamindb as ln

db = ln.DB("laminlabs/cellxgene")  # a database object for queries
df = db.Artifact.to_dataframe()    # a dataframe listing datasets & models

R

library(laminr)
ln <- import_module("lamindb")

db <- ln$DB("laminlabs/cellxgene")  # a database object for queries
df <- db$Artifact$to_dataframe()    # a dataframe listing datasets & models

To get a specific dataset, run:

Py

artifact = db.Artifact.get("BnMwC3KZz0BuKftR")  # a metadata object for a dataset
artifact.describe()                             # describe the context of the dataset

R

artifact <- db$Artifact$get("BnMwC3KZz0BuKftR")  # a metadata object for a dataset
artifact$describe()                             # describe the context of the dataset

See the output.

Access the content of the dataset via:

Py

local_path = artifact.cache()  # return a local path from a cache
adata = artifact.load()        # load object into memory
accessor = artifact.open()     # return a streaming accessor

R

local_path <- artifact$cache()  # return a local path from a cache
adata <- artifact$load()        # load object into memory
accessor <- artifact$open()     # return a streaming accessor

You can query 14 built-in registries (Artifact, Storage, Feature, Record, etc.) and additional registries via plug-ins (e.g. in bionty, 13 registries for biological entities via Disease, CellType, Tissue, etc. mapping >20 public ontologies), for example:

Py

diseases = db.bionty.Disease.lookup()  # a lookup object to auto-complete diseases
df = db.Artifact.filter(diseases=diseases.alzheimer_disease).to_dataframe()  # filter by fields

R

diseases <- db$bionty$Disease$lookup()  # a lookup object to auto-complete diseases
df <- db$Artifact$filter(diseases = diseases$alzheimer_disease)$to_dataframe()  # filter by fields

Configure your database¶

You can create a LaminDB instance at lamin.ai and invite collaborators. To connect to a remote instance, run:

lamin login
lamin connect account/name

If you prefer to work with a local SQLite database (no login required), run this instead:

lamin init --storage ./quickstart-data --modules bionty

On the terminal and in a Python session, LaminDB will now auto-connect.

CLI¶

To save a file or folder from the command line, run:

lamin save myfile.txt --key examples/myfile.txt

To load the file, run:

lamin load --key examples/myfile.txt

Lake: labeling & queries by fields¶

You can label an artifact by running:

Py

my_label = ln.ULabel(name="My label").save()   # a universal label
project = ln.Project(name="My project").save() # a project label
artifact.ulabels.add(my_label)
artifact.projects.add(project)

R

my_label <- ln$ULabel(name = "My label")$save()   # a universal label
project <- ln$Project(name = "My project")$save() # a project label
artifact$ulabels$add(my_label)
artifact$projects$add(project)

Query for it:

Py

ln.Artifact.filter(ulabels=my_label, projects=project).to_dataframe()

R

ln$Artifact$filter(ulabels = my_label, projects = project)$to_dataframe()

You can also query by the metadata that lamindb automatically collects:

Py

ln.Artifact.filter(run=run).to_dataframe()                # query all artifacts created by a run
ln.Artifact.filter(transform=transform).to_dataframe()    # query all artifacts created by a transform
ln.Artifact.filter(size__gt=1e6).to_dataframe()           # query all artifacts bigger than 1MB

R

ln$Artifact$filter(run = run)$to_dataframe()                # query all artifacts created by a run
ln$Artifact$filter(transform = transform)$to_dataframe()    # query all artifacts created by a transform
ln$Artifact$filter(size__gt = 1e6)$to_dataframe()           # query all artifacts bigger than 1MB

If you want to include more information into the resulting dataframe, pass include.

Py

ln.Artifact.to_dataframe(include=["created_by__name", "storage__root"])  # include fields from related registries

R

ln$Artifact$to_dataframe(include = list("created_by__name", "storage__root"))  # include fields from related registries

Note: The query syntax for DB objects and for your default database is the same.

Lake: rich annotation & queries by features¶

You can annotate datasets and samples with features. Let’s define some:

Py

from datetime import date

ln.Feature(name="gc_content", dtype=float).save()
ln.Feature(name="experiment_note", dtype=str).save()
ln.Feature(name="experiment_date", dtype=date, coerce=True).save()  # accept date strings

R

datetime <- import_module("datetime")
date <- datetime$date

ln$Feature(name = "gc_content", dtype = "float")$save()
ln$Feature(name = "experiment_note", dtype = "str")$save()
ln$Feature(name = "experiment_date", dtype = "date", coerce = TRUE)$save()  # accept date strings

During annotation, feature names and data types are validated against these definitions:

Py

artifact.features.add_values({
    "gc_content": 0.55,
    "experiment_note": "Looks great",
    "experiment_date": "2025-10-24",
})

R

artifact$features$add_values(list(
    gc_content = 0.55,
    experiment_note = "Looks great",
    experiment_date = "2025-10-24"
))

Now that the data is annotated, you can query for it:

Py

ln.Artifact.filter(experiment_date="2025-10-24").to_dataframe()  # query all artifacts annotated with `experiment_date`

R

ln$Artifact$filter(experiment_date = "2025-10-24")$to_dataframe()  # query all artifacts annotated with `experiment_date`

If you want to include the feature values into the dataframe, pass include.

Py

ln.Artifact.to_dataframe(include="features")  # include the feature annotations

R

ln$Artifact$to_dataframe(include = "features")  # include the feature annotations

Lake ♾️ LIMS ♾️ Sheets¶

You can create records for the entities underlying your experiments: samples, perturbations, instruments, etc., for example:

Py

sample = ln.Record(name="Sample", is_type=True).save()  # type sample
ln.Record(name="P53mutant1", type=sample).save()        # sample 1
ln.Record(name="P53mutant2", type=sample).save()        # sample 2

R

sample <- ln$Record(name = "Sample", is_type = TRUE)$save()  # type sample
ln$Record(name = "P53mutant1", type = sample)$save()        # sample 1
ln$Record(name = "P53mutant2", type = sample)$save()        # sample 2

Define the corresponding features and annotate:

Py

ln.Feature(name="design_sample", dtype=sample).save()
artifact.features.add_values({"design_sample": "P53mutant1"})

R

ln$Feature(name = "design_sample", dtype = "sample")$save()
artifact$features$add_values(list(design_sample = "P53mutant1"))

You can query & search the Record registry in the same way as Artifact or Run.

Py

ln.Record.search("p53").to_dataframe()

R

ln$Record$search("p53")$to_dataframe()

You can also create relationships of entities and – if you connect your LaminDB instance to LaminHub – edit them like Excel sheets in a GUI.

Lake: versioning¶

If you change source code or datasets, LaminDB manages their versioning for you. Assume you run a new version of our create-fasta.py script to create a new version of sample.fasta.

Py

import lamindb as ln

ln.track()
open("sample.fasta", "w").write(">seq1\nTGCA\n")  # a new sequence
ln.Artifact("sample.fasta", key="sample.fasta", features={"design_sample": "P53mutant1"}).save()  # annotate with the new sample
ln.finish()

R

library(laminr)
ln <- import_module("lamindb")

ln$track()
writeLines(">seq1\nTGCA\n", "sample.fasta")  # a new sequence
ln$Artifact("sample.fasta", key = "sample.fasta", features = list(design_sample = "P53mutant1"))$save()  # annotate with the new sample
ln$finish()

If you now query by key, you’ll get the latest version of this artifact.

Py

artifact = ln.Artifact.get(key="sample.fasta")  # get artifact by key
artifact.versions.to_dataframe()                # see all versions of that artifact

R

artifact <- ln$Artifact$get(key = "sample.fasta")  # get artifact by key
artifact$versions$to_dataframe()                # see all versions of that artifact

Lakehouse ♾️ feature store¶

Here is how you ingest a DataFrame:

Py

import pandas as pd

df = pd.DataFrame({
    "sequence_str": ["ACGT", "TGCA"],
    "gc_content": [0.55, 0.54],
    "experiment_note": ["Looks great", "Ok"],
    "experiment_date": [date(2025, 10, 24), date(2025, 10, 25)],
})
ln.Artifact.from_dataframe(df, key="my_datasets/sequences.parquet").save()  # no validation

R

pd <- import_module("pandas")

df <- pd$DataFrame(list(
    sequence_str = list("ACGT", "TGCA"),
    gc_content = list(0.55, 0.54),
    experiment_note = list("Looks great", "Ok"),
    experiment_date = list(date(2025L, 10L, 24L), date(2025L, 10L, 25L))
))
ln$Artifact$from_dataframe(df, key = "my_datasets/sequences.parquet")$save()  # no validation

To validate & annotate the content of the dataframe, use a built-in schema:

Py

ln.Feature(name="sequence_str", dtype=str).save()  # define a remaining feature
artifact = ln.Artifact.from_dataframe(
    df,
    key="my_datasets/sequences.parquet",
    schema="valid_features"  # validate columns against features
).save()
artifact.describe()

R

ln$Feature(name = "sequence_str", dtype = "str")$save()  # define a remaining feature
artifact <- ln$Artifact$from_dataframe(
    df,
    key = "my_datasets/sequences.parquet",
    schema = "valid_features"  # validate columns against features
)$save()
artifact$describe()

Now you know which schema the dataset satisfies. You can filter for datasets by schema and then launch distributed queries and batch loading.

Lakehouse beyond tables¶

To validate an AnnData with a built-in schema call:

Py

import anndata as ad
import numpy as np

adata = ad.AnnData(
    X=pd.DataFrame([[1]*10]*21).values,
    obs=pd.DataFrame({'cell_type_by_model': ['T cell', 'B cell', 'NK cell'] * 7}),
    var=pd.DataFrame(index=[f'ENSG{i:011d}' for i in range(10)])
)

artifact = ln.Artifact.from_anndata(
    adata,
    key="my_datasets/scrna.h5ad",
    schema="ensembl_gene_ids_and_valid_features_in_obs"
)
artifact.describe()

R

ad <- import_module("anndata")
np <- import_module("numpy")

adata <- anndata::AnnData(
    X = matrix(1, ncol = 10, nrow = 21),
    obs = pd$DataFrame(list(cell_type_by_model = rep(list('T cell', 'B cell', 'NK cell'), 7))),
    var = pd$DataFrame(index = sprintf("ENSG%010d", 1:10))
)

artifact <- ln$Artifact$from_anndata(
    adata,
    key = "my_datasets/scrna.h5ad",
    schema = "ensembl_gene_ids_and_valid_features_in_obs"
)
artifact$describe()

To validate a spatialdata or any other array-like dataset, you need to construct a Schema. You can do this by composing the schema of a complicated object from simple pandera/pydantic-like schemas: docs.lamin.ai/curate.

Ontologies¶

Plugin bionty gives you >20 of them as SQLRecord registries. This was used to validate the ENSG ids in the adata just before.

Py

import bionty as bt

bt.CellType.import_source()  # import the default ontology
bt.CellType.to_dataframe()   # your extendable cell type ontology in a simple registry

R

bt <- import_module("bionty")

bt$CellType$import_source()  # import the default ontology
bt$CellType$to_dataframe()   # your extendable cell type ontology in a simple registry

Workflow managers¶

LaminDB is not a workflow manager, but it integrates well with existing workflow managers and can subsitute them in some settings.

In github.com/laminlabs/schmidt22 we manage several workflows, scripts, and notebooks to re-construct the project of Schmidt el al. (2022). A phenotypic CRISPRa screening result is integrated with scRNA-seq data. Here is one of the input artifacts:

And here is the lineage of the final result:

You can explore it here.

If you’d like to integrate with Nextflow, Snakemake, or redun, see here: docs.lamin.ai/pipelines