Manage biological registries

This guide shows how to manage metadata for basic biological entities based on plugin bionty.

# !pip install 'lamindb[bionty]'
!lamin init --storage ./test-registries --schema bionty

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→ connected lamindb: testuser1/test-registries

import lamindb as ln
import bionty as bt

→ connected lamindb: testuser1/test-registries

Seed registries with public ontologies

Let’s first populate our CellType registry with the configured public ontology (Cell Ontology):

# check configured public ontology
bt.Source.filter(entity="bionty.CellType", currently_used=True).one()

Source(uid='1Lhf', entity='bionty.CellType', organism='all', name='cl', version='2024-05-15 00:00:00 ', in_db=False, currently_used=True, description='Cell Ontology', url='http://purl.obolibrary.org/obo/cl/releases/2024-05-15/cl.owl', md5='8a8638a9e79567935793e5007704c650', source_website='https://obophenotype.github.io/cell-ontology', created_by_id=1, created_at=2024-10-18 15:56:17 UTC)

# populate the database with the public ontology
bt.CellType.import_from_source()

This is now your in-house CellType registry:

# all public cell types are now available in LaminDB
bt.CellType.df()

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	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_at	created_by_id
id
2931	6GaGU793	subpial interlaminar astrocyte	CL:4042011	None	None	An Interlaminar Astrocyte Type Whose Soma Is P...	32	None	2024-10-18 15:56:21.730281+00:00	1
2930	3afgdSa3	pial interlaminar astrocyte	CL:4042010	None	None	An Interlaminar Astrocyte Whose Soma Is Part O...	32	None	2024-10-18 15:56:21.730231+00:00	1
2929	n7ezKRlq	interlaminar astrocyte	CL:4042009	None	None	An Astrocyte Type That Presents Radial Protrus...	32	None	2024-10-18 15:56:21.730179+00:00	1
2928	6OXayYqP	fibrous astrocyte	CL:4042008	None	None	A Cell Type Located In The First Layer Of The ...	32	None	2024-10-18 15:56:21.730126+00:00	1
2927	1mO1QVeh	protoplasmic astrocyte	CL:4042007	None	None	An Astrocyte With Highly Branched Protrusions,...	32	None	2024-10-18 15:56:21.730075+00:00	1
...	...	...	...	...	...	...	...	...	...	...
2836	42PLafOv	L2/3 intratelencephalic projecting glutamaterg...	CL:4030059	None	None	A Transcriptomically Distinct Intratelencephal...	32	None	2024-10-18 15:56:21.722835+00:00	1
2835	16Y7GlBk	TCR-positive macrophage	CL:4030058	None	T cell receptor positive macrophage|TCR+ macro...	A Macrophage That Expresses The T Cell Recepto...	32	None	2024-10-18 15:56:21.722784+00:00	1
2834	4wmW6vv9	eccentric medium spiny neuron	CL:4030057	None	eccentric spiny projection neuron	A Medium Spiny Neuron That Exhibits Transcript...	32	None	2024-10-18 15:56:21.722733+00:00	1
2833	7mYcRDsu	umbrella cell of urothelium	CL:4030056	None	facet cell of urothelium|superficial cell of u...	A Urothelial Cell That Is Terminally Different...	32	None	2024-10-18 15:56:21.722682+00:00	1
2832	1Qd9Bmkj	intermediate cell of urothelium	CL:4030055	None	urothelial intermediate cell	A Urothelial Cell That Is Part Of The Regenera...	32	None	2024-10-18 15:56:21.722631+00:00	1

100 rows × 10 columns

# similarly, let's populate the Gene registry with human and mouse genes
bt.Gene.import_from_source(organism="human")
bt.Gene.import_from_source(organism="mouse")

Access records in in-house registries

Search key words:

bt.CellType.search("gamma-delta T").df().head(2)

	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_at	created_by_id
id
780	1HuNn2EP	gamma-delta T cell	CL:0000798	None	gammadelta T cell|gamma-delta T-cell|gamma-del...	A T Cell That Expresses A Gamma-Delta T Cell R...	32	None	2024-10-18 15:56:21.480833+00:00	1
781	70lHcCNw	immature gamma-delta T cell	CL:0000799	None	immature gamma-delta T lymphocyte|immature gam...	A Gamma-Delta T Cell That Has An Immature Phen...	32	None	2024-10-18 15:56:21.480884+00:00	1

Or look up with auto-complete:

cell_types = bt.CellType.lookup()
hsc_record = cell_types.hematopoietic_stem_cell
hsc_record

CellType(uid='2U8xapxu', name='hematopoietic stem cell', ontology_id='CL:0000037', synonyms='blood forming stem cell|hemopoietic stem cell', description='A Stem Cell From Which All Cells Of The Lymphoid And Myeloid Lineages Develop, Including Blood Cells And Cells Of The Immune System. Hematopoietic Stem Cells Lack Cell Markers Of Effector Cells (Lin-Negative). Lin-Negative Is Defined By Lacking One Or More Of The Following Cell Surface Markers: Cd2, Cd3 Epsilon, Cd4, Cd5 ,Cd8 Alpha Chain, Cd11B, Cd14, Cd19, Cd20, Cd56, Ly6G, Ter119.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)

Filter by fields and relationships:

gdt_cell = bt.CellType.get(ontology_id="CL:0000798", created_by__handle="testuser1")
gdt_cell

CellType(uid='1HuNn2EP', name='gamma-delta T cell', ontology_id='CL:0000798', synonyms='gammadelta T cell|gamma-delta T-cell|gamma-delta T-lymphocyte|gamma-delta T lymphocyte', description='A T Cell That Expresses A Gamma-Delta T Cell Receptor Complex.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)

View the ontological hierarchy:

gdt_cell.view_parents()  # pass with_children=True to also view children

Or access the parents and children directly:

gdt_cell.parents.df()

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	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_at	created_by_id
id
83	22LvKd01	T cell	CL:0000084	None	T lymphocyte|T-lymphocyte|T-cell	A Type Of Lymphocyte Whose Defining Characteri...	32	None	2024-10-18 15:56:21.424915+00:00	1

gdt_cell.children.df()

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	uid	name	ontology_id	abbr	synonyms	description	source_id	run_id	created_at	created_by_id
id
781	70lHcCNw	immature gamma-delta T cell	CL:0000799	None	immature gamma-delta T lymphocyte|immature gam...	A Gamma-Delta T Cell That Has An Immature Phen...	32	None	2024-10-18 15:56:21.480884+00:00	1
782	3W6NKGpW	mature gamma-delta T cell	CL:0000800	None	mature gamma-delta T-cell|mature gamma-delta T...	A Gamma-Delta T Cell That Has A Mature Phenoty...	32	None	2024-10-18 15:56:21.480934+00:00	1
1465	26icgrTr	gamma-delta thymocyte	CL:0002405	None	gammadelta thymocyte|gd thymocyte	A Post-Natal Thymocyte Expressing Components O...	32	None	2024-10-18 15:56:21.613676+00:00	1

You can construct custom hierarchies of records:

# register a new cell type
my_celltype = bt.CellType(name="my new T-cell subtype").save()
# specify "gamma-delta T cell" as a parent
my_celltype.parents.add(gdt_cell)

# visualize hierarchy
gdt_cell.view_parents(distance=2, with_children=True)

Create records from values

When accessing datasets, one often encounters bulk references to entities that might be corrupted or standardized using different standardization schemes.

Let’s consider an example based on an AnnData object, in the cell_type annotations of this AnnData object, we find 4 references to cell types:

adata = ln.core.datasets.anndata_with_obs()
adata.obs.cell_type.value_counts()

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cell_type
T cell                     10
hematopoietic stem cell    10
hepatocyte                 10
my new cell type           10
Name: count, dtype: int64

We’d like to load the corresponding records in our in-house registry to annotate a dataset.

To this end, you’ll typically use from_values, which will both validate & retrieve records that match the values.

cell_types = bt.CellType.from_values(adata.obs.cell_type)
cell_types

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! did not create CellType record for 1 non-validated name: 'my new cell type'

[CellType(uid='22LvKd01', name='T cell', ontology_id='CL:0000084', synonyms='T lymphocyte|T-lymphocyte|T-cell', description='A Type Of Lymphocyte Whose Defining Characteristic Is The Expression Of A T Cell Receptor Complex.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC),
 CellType(uid='2U8xapxu', name='hematopoietic stem cell', ontology_id='CL:0000037', synonyms='blood forming stem cell|hemopoietic stem cell', description='A Stem Cell From Which All Cells Of The Lymphoid And Myeloid Lineages Develop, Including Blood Cells And Cells Of The Immune System. Hematopoietic Stem Cells Lack Cell Markers Of Effector Cells (Lin-Negative). Lin-Negative Is Defined By Lacking One Or More Of The Following Cell Surface Markers: Cd2, Cd3 Epsilon, Cd4, Cd5 ,Cd8 Alpha Chain, Cd11B, Cd14, Cd19, Cd20, Cd56, Ly6G, Ter119.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC),
 CellType(uid='7hggmgo1', name='hepatocyte', ontology_id='CL:0000182', description='The Main Structural Component Of The Liver. They Are Specialized Epithelial Cells That Are Organized Into Interconnected Plates Called Lobules. Majority Of Cell Population Of Liver, Polygonal In Shape, Arranged In Plates Or Trabeculae Between Sinusoids; May Have Single Nucleus Or Binucleated.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)]

Logging informed us that 3 cell types were validated. Since we loaded these records at the same time, we could readily use them to annotate a dataset.

What happened under-the-hood?

.from_values() performs the following look ups:

1. If registry records match the values, load these records

2. If values match synonyms of registry records, load these records

3. If no record in the registry matches, attempt to load records from a public ontology

4. Same as 3. but based on synonyms

No records will be returned if all 4 look ups are unsuccessful.

Sometimes, it’s useful to treat validated records differently from non-validated records. Here is a way:

original_values = ["gut", "gut2"]
inspector = bt.Tissue.inspect(original_values)
records_from_validated_values = bt.Tissue.from_values(inspector.validated)

Alternatively, we can retrieve records based on ontology ids:

adata.obs.cell_type_id.unique().tolist()

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['CL:0000084', 'CL:0000037', 'CL:0000182', '']

bt.CellType.from_values(adata.obs.cell_type_id, field=bt.CellType.ontology_id)

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[CellType(uid='2U8xapxu', name='hematopoietic stem cell', ontology_id='CL:0000037', synonyms='blood forming stem cell|hemopoietic stem cell', description='A Stem Cell From Which All Cells Of The Lymphoid And Myeloid Lineages Develop, Including Blood Cells And Cells Of The Immune System. Hematopoietic Stem Cells Lack Cell Markers Of Effector Cells (Lin-Negative). Lin-Negative Is Defined By Lacking One Or More Of The Following Cell Surface Markers: Cd2, Cd3 Epsilon, Cd4, Cd5 ,Cd8 Alpha Chain, Cd11B, Cd14, Cd19, Cd20, Cd56, Ly6G, Ter119.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC),
 CellType(uid='22LvKd01', name='T cell', ontology_id='CL:0000084', synonyms='T lymphocyte|T-lymphocyte|T-cell', description='A Type Of Lymphocyte Whose Defining Characteristic Is The Expression Of A T Cell Receptor Complex.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC),
 CellType(uid='7hggmgo1', name='hepatocyte', ontology_id='CL:0000182', description='The Main Structural Component Of The Liver. They Are Specialized Epithelial Cells That Are Organized Into Interconnected Plates Called Lobules. Majority Of Cell Population Of Liver, Polygonal In Shape, Arranged In Plates Or Trabeculae Between Sinusoids; May Have Single Nucleus Or Binucleated.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)]

Validate & standardize

Simple validation of an iterable of values works like so:

bt.CellType.validate(["fat cell", "blood forming stem cell"])

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! 2 unique terms (100.00%) are not validated for name: fat cell, blood forming stem cell

array([False, False])

Because these values don’t comply with the registry, they’re not validated!

You can easily convert these values to validated standardized names based on synonyms like so:

bt.CellType.standardize(["fat cell", "blood forming stem cell"])

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['adipocyte', 'hematopoietic stem cell']

Alternatively, you can use .from_values(), which will only ever return validated records and automatically standardize under-the-hood:

bt.CellType.from_values(["fat cell", "blood forming stem cell"])

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[CellType(uid='wdLgwUXo', name='adipocyte', ontology_id='CL:0000136', synonyms='fat cell|adipose cell', description='A Fat-Storing Cell Found Mostly In The Abdominal Cavity And Subcutaneous Tissue Of Mammals. Fat Is Usually Stored In The Form Of Triglycerides.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC),
 CellType(uid='2U8xapxu', name='hematopoietic stem cell', ontology_id='CL:0000037', synonyms='blood forming stem cell|hemopoietic stem cell', description='A Stem Cell From Which All Cells Of The Lymphoid And Myeloid Lineages Develop, Including Blood Cells And Cells Of The Immune System. Hematopoietic Stem Cells Lack Cell Markers Of Effector Cells (Lin-Negative). Lin-Negative Is Defined By Lacking One Or More Of The Following Cell Surface Markers: Cd2, Cd3 Epsilon, Cd4, Cd5 ,Cd8 Alpha Chain, Cd11B, Cd14, Cd19, Cd20, Cd56, Ly6G, Ter119.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)]

If you are now sure what to do, use .inspect() to get instructions:

bt.CellType.inspect(["fat cell", "blood forming stem cell"]);

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! 2 unique terms (100.00%) are not validated for name: fat cell, blood forming stem cell

   detected 2 unique terms with synonyms: fat cell, blood forming stem cell

→  standardize terms via .standardize()

We can also add new synonyms to a record like so:

hsc_record.add_synonym("HSC")

And when we encounter this synonym as a value, it will now be standardized using synonyms-lookup, and mapped on the correct registry record:

bt.CellType.standardize(["HSC"])

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['hematopoietic stem cell']

A special synonym is .abbr (short for abbreviation), which has its own field and can be assigned via:

hsc_record.set_abbr("HSC")

You can create a lookup object from the .abbr field:

cell_types = bt.CellType.lookup("abbr")
hsc = cell_types.hsc
hsc

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CellType(uid='2U8xapxu', name='hematopoietic stem cell', ontology_id='CL:0000037', abbr='HSC', synonyms='hemopoietic stem cell|HSC|blood forming stem cell', description='A Stem Cell From Which All Cells Of The Lymphoid And Myeloid Lineages Develop, Including Blood Cells And Cells Of The Immune System. Hematopoietic Stem Cells Lack Cell Markers Of Effector Cells (Lin-Negative). Lin-Negative Is Defined By Lacking One Or More Of The Following Cell Surface Markers: Cd2, Cd3 Epsilon, Cd4, Cd5 ,Cd8 Alpha Chain, Cd11B, Cd14, Cd19, Cd20, Cd56, Ly6G, Ter119.', created_by_id=1, source_id=32, created_at=2024-10-18 15:56:21 UTC)

The same workflow works for all of bionty’s registries.

Manage registries across organisms

Several registries are organism-aware (has a .organism field), for instance, Gene.

In this case, API calls that interact with multi-organism registries require an organism argument when there’s ambiguity.

For instance, when validating gene symbols:

bt.Gene.validate(["TCF7", "ABC1"], organism="human")

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! 1 unique term (50.00%) is not validated for symbol: ABC1

array([ True, False])

In contrary, working with Ensembl Gene IDs doesn’t require passing organism, as there’s no ambiguity:

bt.Gene.validate(["ENSG00000000419", "ENSMUSG00002076988"], field=bt.Gene.ensembl_gene_id)

array([ True,  True])

When working with the same organism throughout your analysis/workflow, you can omit the organism argument by configuring it globally:

bt.settings.organism = "mouse"
bt.Gene.from_source(symbol="Ap5b1")

Gene(uid='3b8mHb0MRal4', symbol='Ap5b1', ensembl_gene_id='ENSMUSG00000049562', ncbi_gene_ids='381201', biotype='protein_coding', synonyms='Gm962', description='adaptor-related protein complex 5, beta 1 subunit ', created_by_id=1, source_id=15, organism_id=2, created_at=2024-10-18 15:56:56 UTC)

Track underlying ontology source versions

Under-the-hood, source ontology versions are automatically tracked for each registry:

bt.Source.filter(currently_used=True).df()

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	uid	entity	organism	name	version	in_db	currently_used	description	url	md5	source_website	dataframe_artifact_id	run_id	created_at	created_by_id
id
1	33TU	bionty.Organism	vertebrates	ensembl	release-112	False	True	Ensembl	https://ftp.ensembl.org/pub/release-112/specie...	0ec37e77f4bc2d0b0b47c6c62b9f122d	https://www.ensembl.org	None	None	2024-10-18 15:56:17.405869+00:00	1
6	6bbV	bionty.Organism	bacteria	ensembl	release-57	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/bacte...	ee28510ed5586ea7ab4495717c96efc8	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406258+00:00	1
7	6s9n	bionty.Organism	fungi	ensembl	release-57	False	True	Ensembl	http://ftp.ensemblgenomes.org/pub/fungi/releas...	dbcde58f4396ab8b2480f7fe9f83df8a	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406322+00:00	1
8	2PmT	bionty.Organism	metazoa	ensembl	release-57	False	True	Ensembl	http://ftp.ensemblgenomes.org/pub/metazoa/rele...	424636a574fec078a61cbdddb05f9132	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406386+00:00	1
9	7GPH	bionty.Organism	plants	ensembl	release-57	False	True	Ensembl	https://ftp.ensemblgenomes.ebi.ac.uk/pub/plant...	eadaa1f3e527e4c3940c90c7fa5c8bf4	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406450+00:00	1
10	4tsk	bionty.Organism	all	ncbitaxon	2023-06-20	False	True	NCBItaxon Ontology	s3://bionty-assets/df_all__ncbitaxon__2023-06-...	00d97ba65627f1cd65636d2df22ea76c	https://github.com/obophenotype/ncbitaxon	None	None	2024-10-18 15:56:17.406515+00:00	1
11	4UGN	bionty.Gene	human	ensembl	release-112	False	True	Ensembl	s3://bionty-assets/df_human__ensembl__release-...	4ccda4d88720a326737376c534e8446b	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406580+00:00	1
15	4r4f	bionty.Gene	mouse	ensembl	release-112	False	True	Ensembl	s3://bionty-assets/df_mouse__ensembl__release-...	519cf7b8acc3c948274f66f3155a3210	https://www.ensembl.org	None	None	2024-10-18 15:56:17.406835+00:00	1
19	4RPA	bionty.Gene	saccharomyces cerevisiae	ensembl	release-112	False	True	Ensembl	s3://bionty-assets/df_saccharomyces cerevisiae...	11775126b101233525a0a9e2dd64edae	https://www.ensembl.org	None	None	2024-10-18 15:56:17.407091+00:00	1
22	3EYy	bionty.Protein	human	uniprot	2024-03	False	True	Uniprot	s3://bionty-assets/df_human__uniprot__2024-03_...	b5b9e7645065b4b3187114f07e3f402f	https://www.uniprot.org	None	None	2024-10-18 15:56:17.407435+00:00	1
25	01RW	bionty.Protein	mouse	uniprot	2024-03	False	True	Uniprot	s3://bionty-assets/df_mouse__uniprot__2024-03_...	b1b6a196eb853088d36198d8e3749ec4	https://www.uniprot.org	None	None	2024-10-18 15:56:17.407826+00:00	1
28	3kDh	bionty.CellMarker	human	cellmarker	2.0	False	True	CellMarker	s3://bionty-assets/human_cellmarker_2.0_CellMa...	d565d4a542a5c7e7a06255975358e4f4	http://bio-bigdata.hrbmu.edu.cn/CellMarker	None	None	2024-10-18 15:56:17.408036+00:00	1
29	7bV5	bionty.CellMarker	mouse	cellmarker	2.0	False	True	CellMarker	s3://bionty-assets/mouse_cellmarker_2.0_CellMa...	189586732c63be949e40dfa6a3636105	http://bio-bigdata.hrbmu.edu.cn/CellMarker	None	None	2024-10-18 15:56:17.408100+00:00	1
30	6LyR	bionty.CellLine	all	clo	2022-03-21	False	True	Cell Line Ontology	https://data.bioontology.org/ontologies/CLO/su...	ea58a1010b7e745702a8397a526b3a33	https://bioportal.bioontology.org/ontologies/CLO	None	None	2024-10-18 15:56:17.408164+00:00	1
32	1Lhf	bionty.CellType	all	cl	2024-05-15	True	True	Cell Ontology	http://purl.obolibrary.org/obo/cl/releases/202...	8a8638a9e79567935793e5007704c650	https://obophenotype.github.io/cell-ontology	None	None	2024-10-18 15:56:17.408292+00:00	1
40	MUtA	bionty.Tissue	all	uberon	2024-08-07	False	True	Uberon multi-species anatomy ontology	http://purl.obolibrary.org/obo/uberon/releases...		http://obophenotype.github.io/uberon	None	None	2024-10-18 15:56:17.408800+00:00	1
49	2L2r	bionty.Disease	all	mondo	2024-06-04	False	True	Mondo Disease Ontology	http://purl.obolibrary.org/obo/mondo/releases/...	c47e8edb894c01f2511dfe0751fbc428	https://mondo.monarchinitiative.org	None	None	2024-10-18 15:56:17.409397+00:00	1
57	4ksw	bionty.Disease	human	doid	2024-05-29	False	True	Human Disease Ontology	http://purl.obolibrary.org/obo/doid/releases/2...	bbefd72247d638edfcd31ec699947407	https://disease-ontology.org	None	None	2024-10-18 15:56:17.409914+00:00	1
65	2a1H	bionty.ExperimentalFactor	all	efo	3.70.0	False	True	The Experimental Factor Ontology	http://www.ebi.ac.uk/efo/releases/v3.70.0/efo.owl		https://bioportal.bioontology.org/ontologies/EFO	None	None	2024-10-18 15:56:17.413493+00:00	1
72	48fB	bionty.Phenotype	human	hp	2024-04-26	False	True	Human Phenotype Ontology	https://github.com/obophenotype/human-phenotyp...	e0f2e534eb2ad44a4d45573ef27b508f	https://hpo.jax.org	None	None	2024-10-18 15:56:17.413928+00:00	1
77	4t7Q	bionty.Phenotype	mammalian	mp	2024-06-18	False	True	Mammalian Phenotype Ontology	https://github.com/mgijax/mammalian-phenotype-...	795d8378fe48ec13b41d01a86dd1c86c	https://github.com/mgijax/mammalian-phenotype-...	None	None	2024-10-18 15:56:17.414232+00:00	1
80	sqPX	bionty.Phenotype	zebrafish	zp	2024-04-18	False	True	Zebrafish Phenotype Ontology	https://github.com/obophenotype/zebrafish-phen...	2231ebaa95becf8ff34a33c95a8d4350	https://github.com/obophenotype/zebrafish-phen...	None	None	2024-10-18 15:56:17.414415+00:00	1
84	6S4q	bionty.Phenotype	all	pato	2024-03-28	False	True	Phenotype And Trait Ontology	http://purl.obolibrary.org/obo/pato/releases/2...	6b1eaacd3d453b34375ce2e31c16328a	https://github.com/pato-ontology/pato	None	None	2024-10-18 15:56:17.414671+00:00	1
86	7Ent	bionty.Pathway	all	go	2024-06-17	False	True	Gene Ontology	https://data.bioontology.org/ontologies/GO/sub...	7fa7ade5e3e26eab3959a7e4bc89ad4f	http://geneontology.org	None	None	2024-10-18 15:56:17.414795+00:00	1
91	3rm9	BFXPipeline	all	lamin	1.0.0	False	True	Bioinformatics Pipeline	s3://bionty-assets/df_all__lamin__1.0.0__BFXpi...		https://lamin.ai	None	None	2024-10-18 15:56:17.415110+00:00	1
92	ugaI	Drug	all	dron	2024-08-05	False	True	Drug Ontology	https://data.bioontology.org/ontologies/DRON/s...		https://bioportal.bioontology.org/ontologies/DRON	None	None	2024-10-18 15:56:17.415171+00:00	1
96	1GbF	bionty.DevelopmentalStage	human	hsapdv	2024-05-28	False	True	Human Developmental Stages	https://github.com/obophenotype/developmental-...		https://github.com/obophenotype/developmental-...	None	None	2024-10-18 15:56:17.415418+00:00	1
98	10va	bionty.DevelopmentalStage	mouse	mmusdv	2024-05-28	False	True	Mouse Developmental Stages	https://github.com/obophenotype/developmental-...		https://github.com/obophenotype/developmental-...	None	None	2024-10-18 15:56:17.415540+00:00	1
100	MJRq	bionty.Ethnicity	human	hancestro	3.0	False	True	Human Ancestry Ontology	https://github.com/EBISPOT/hancestro/raw/3.0/h...	76dd9efda9c2abd4bc32fc57c0b755dd	https://github.com/EBISPOT/hancestro	None	None	2024-10-18 15:56:17.415664+00:00	1
101	5JnV	BioSample	all	ncbi	2023-09	False	True	NCBI BioSample attributes	s3://bionty-assets/df_all__ncbi__2023-09__BioS...	918db9bd1734b97c596c67d9654a4126	https://www.ncbi.nlm.nih.gov/biosample/docs/at...	None	None	2024-10-18 15:56:17.415730+00:00	1

Each record is linked to a versioned public source (if it was created from public):

hepatocyte = bt.CellType.get(name="hepatocyte")
hepatocyte.source

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Source(uid='1Lhf', entity='bionty.CellType', organism='all', name='cl', version='2024-05-15 00:00:00 ', in_db=True, currently_used=True, description='Cell Ontology', url='http://purl.obolibrary.org/obo/cl/releases/2024-05-15/cl.owl', md5='8a8638a9e79567935793e5007704c650', source_website='https://obophenotype.github.io/cell-ontology', created_by_id=1, created_at=2024-10-18 15:56:17 UTC)

Create records from specific source

By default, new records are imported or created from the "currently_used" public sources which are configured during the instance initialization, e.g.:

bt.Source.filter(entity="bionty.Phenotype", currently_used=True).df()

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	uid	entity	organism	name	version	in_db	currently_used	description	url	md5	source_website	dataframe_artifact_id	run_id	created_at	created_by_id
id
72	48fB	bionty.Phenotype	human	hp	2024-04-26	False	True	Human Phenotype Ontology	https://github.com/obophenotype/human-phenotyp...	e0f2e534eb2ad44a4d45573ef27b508f	https://hpo.jax.org	None	None	2024-10-18 15:56:17.413928+00:00	1
77	4t7Q	bionty.Phenotype	mammalian	mp	2024-06-18	False	True	Mammalian Phenotype Ontology	https://github.com/mgijax/mammalian-phenotype-...	795d8378fe48ec13b41d01a86dd1c86c	https://github.com/mgijax/mammalian-phenotype-...	None	None	2024-10-18 15:56:17.414232+00:00	1
80	sqPX	bionty.Phenotype	zebrafish	zp	2024-04-18	False	True	Zebrafish Phenotype Ontology	https://github.com/obophenotype/zebrafish-phen...	2231ebaa95becf8ff34a33c95a8d4350	https://github.com/obophenotype/zebrafish-phen...	None	None	2024-10-18 15:56:17.414415+00:00	1
84	6S4q	bionty.Phenotype	all	pato	2024-03-28	False	True	Phenotype And Trait Ontology	http://purl.obolibrary.org/obo/pato/releases/2...	6b1eaacd3d453b34375ce2e31c16328a	https://github.com/pato-ontology/pato	None	None	2024-10-18 15:56:17.414671+00:00	1

Sometimes, the default source doesn’t contain the ontology term you are looking for.

You can then specify to create a record from a non-default source. For instance, instead of using untyped labels for iris organisms as Tutorial: Features & labels, we can use the ncbitaxon ontology:

source = bt.Source.get(entity="bionty.Organism", name="ncbitaxon")
source

Source(uid='4tsk', entity='bionty.Organism', organism='all', name='ncbitaxon', version='2023-06-20 00:00:00 ', in_db=False, currently_used=True, description='NCBItaxon Ontology', url='s3://bionty-assets/df_all__ncbitaxon__2023-06-20__Organism.parquet', md5='00d97ba65627f1cd65636d2df22ea76c', source_website='https://github.com/obophenotype/ncbitaxon', created_by_id=1, created_at=2024-10-18 15:56:17 UTC)

# validate against the NCBI Taxonomy
bt.Organism.validate(["iris setosa", "iris versicolor", "iris virginica"], source=source)

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! Your Organism registry is empty, consider populating it first!
   → use `.import_from_source()` to import records from a source, e.g. a public ontology

array([False, False, False])

records = bt.Organism.from_values(
    ["iris setosa", "iris versicolor", "iris virginica"], source=source
)

# since we didn't seed the Organism registry with the NCBITaxon public ontology
# we need to save the records to the database
ln.save(records)

# now we can query a iris organism and view its parents and children
iris = bt.Organism.get(name="iris")
iris.view_parents(with_children=True)

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# clean up test instance
!lamin delete --force test-registries

• deleting instance testuser1/test-registries