Gene ontologies#
lamindb provides access to the following public gene ontologies through lnschema-bionty:
Here we show how to access and search gene ontologies to standardize new data.
Setup#
import lamindb_setup
lamindb_setup.init(storage="./test-gene", schema="bionty")
2023-12-22 11:23:10,799:INFO - HTTP Request: GET https://hub.lamin.ai/rest/v1/instance?select=%2A%2C%20account%21inner%21fk_instance_account_id_account%28%2A%29&account.handle=eq.testuser1&name=eq.test-gene "HTTP/1.1 200 OK"
2023-12-22 11:23:11,036:INFO - HTTP Request: GET https://hub.lamin.ai/rest/v1/account?select=%2A&handle=eq.testuser1 "HTTP/1.1 200 OK"
2023-12-22 11:23:11,138:INFO - HTTP Request: GET https://hub.lamin.ai/rest/v1/instance?select=%2A&account_id=eq.29cff183-c34d-445f-b6cf-31fb3b566158&name=eq.test-gene "HTTP/1.1 200 OK"
✅ saved: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2023-12-22 11:23:17 UTC)
✅ saved: Storage(uid='81mfUU3k', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-gene', type='local', updated_at=2023-12-22 11:23:17 UTC, created_by_id=1)
💡 loaded instance: testuser1/test-gene
💡 did not register local instance on hub
# !lamin init --storage ./test-gene --schema bionty
import lnschema_bionty as lb
import pandas as pd
# adds an entry "human" into an empty instance
lb.settings.organism = "human"
Bionty objects#
Let us create a public knowledge accessor with bionty(), which chooses a default public knowledge source from BiontySource. It’s a Bionty object, which you can think about as a less-capable registry:
gene_bt = lb.Gene.bionty(organism="human")
gene_bt
Gene
Organism: human
Source: ensembl, release-110
#terms: 75719
📖 Gene.df(): ontology reference table
🔎 Gene.lookup(): autocompletion of terms
🎯 Gene.search(): free text search of terms
✅ Gene.validate(): strictly validate values
🧐 Gene.inspect(): full inspection of values
👽 Gene.standardize(): convert to standardized names
🪜 Gene.diff(): difference between two versions
🔗 Gene.ontology: Pronto.Ontology object
As for registries, you can export the ontology as a DataFrame:
df = gene_bt.df()
df.head()
| ensembl_gene_id | symbol | ncbi_gene_id | biotype | description | synonyms | |
|---|---|---|---|---|---|---|
| 0 | ENSG00000000003 | TSPAN6 | 7105 | protein_coding | tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858] | T245|TSPAN-6|TM4SF6 |
| 1 | ENSG00000000005 | TNMD | 64102 | protein_coding | tenomodulin [Source:HGNC Symbol;Acc:HGNC:17757] | TEM|CHM1L|BRICD4|MYODULIN|TENDIN |
| 2 | ENSG00000000419 | DPM1 | 8813 | protein_coding | dolichyl-phosphate mannosyltransferase subunit... | MPDS|CDGIE |
| 3 | ENSG00000000457 | SCYL3 | 57147 | protein_coding | SCY1 like pseudokinase 3 [Source:HGNC Symbol;A... | PACE-1|PACE1 |
| 4 | ENSG00000000460 | C1orf112 | 55732 | protein_coding | chromosome 1 open reading frame 112 [Source:HG... | APOLO1|FLIP|FLJ10706 |
Unlike registries, you can also export it as a Pronto object via gene_bt.ontology.
Look up terms#
As for registries, terms can be looked up with auto-complete:
lookup = gene_bt.lookup()
The . accessor provides normalized terms (lower case, only contains alphanumeric characters and underscores):
lookup.tcf7
Gene(ensembl_gene_id='ENSG00000081059', symbol='TCF7', ncbi_gene_id='6932', biotype='protein_coding', description='transcription factor 7 [Source:HGNC Symbol;Acc:HGNC:11639]', synonyms='TCF-1')
To look up the exact original strings, convert the lookup object to dict and use the [] accessor:
lookup_dict = lookup.dict()
lookup_dict["TCF7"]
Gene(ensembl_gene_id='ENSG00000081059', symbol='TCF7', ncbi_gene_id='6932', biotype='protein_coding', description='transcription factor 7 [Source:HGNC Symbol;Acc:HGNC:11639]', synonyms='TCF-1')
By default, the name field is used to generate lookup keys. You can specify another field to look up:
lookup = gene_bt.lookup(gene_bt.ncbi_gene_id)
If multiple entries are matched, they are returned as a list:
lookup.bt_100126572
Gene(ensembl_gene_id='ENSG00000203733', symbol='GJE1', ncbi_gene_id='100126572', biotype='protein_coding', description='gap junction protein epsilon 1 [Source:HGNC Symbol;Acc:HGNC:33251]', synonyms='CX23')
Search terms#
Search behaves in the same way as it does for registries:
gene_bt = lb.Gene.bionty()
gene_bt.search("TP53").head(3)
| ensembl_gene_id | ncbi_gene_id | biotype | description | synonyms | __ratio__ | |
|---|---|---|---|---|---|---|
| symbol | ||||||
| TP53 | ENSG00000141510 | 7157 | protein_coding | tumor protein p53 [Source:HGNC Symbol;Acc:HGNC... | LFS1|P53 | 100.0 |
| TP53TG3GP | ENSG00000261274 | None | unprocessed_pseudogene | TP53 target 3 family member G, pseudogene [Sou... | 90.0 | |
| TP53TG1 | ENSG00000182165 | None | lncRNA | TP53 target 1 [Source:HGNC Symbol;Acc:HGNC:17026] | TP53AP1|H_RG012D21.9|LINC00096|TP53LC12 | 90.0 |
By default, search also covers synonyms:
gene_bt.search("PDL1").head(3)
| ensembl_gene_id | ncbi_gene_id | biotype | description | synonyms | __ratio__ | |
|---|---|---|---|---|---|---|
| symbol | ||||||
| CD274 | ENSG00000120217 | 29126 | protein_coding | CD274 molecule [Source:HGNC Symbol;Acc:HGNC:17... | B7-H1|B7H1|PDL1|PD-L1|PDCD1LG1|B7-H | 100.0 |
| GAPDHP73 | ENSG00000226540 | None | processed_pseudogene | glyceraldehyde-3-phosphate dehydrogenase pseud... | GAPDL19|GAPDHL19 | 90.0 |
| IGKV1-16 | ENSG00000282282 | None | IG_V_gene | immunoglobulin kappa variable 1-16 [Source:HGN... | IGKV116|L1 | 90.0 |
You can turn this off synonym by passing synonyms_field=None:
gene_bt.search("PDL1", synonyms_field=None).head(3)
| ensembl_gene_id | ncbi_gene_id | biotype | description | synonyms | __ratio__ | |
|---|---|---|---|---|---|---|
| symbol | ||||||
| SPDL1 | ENSG00000040275 | 54908 | protein_coding | spindle apparatus coiled-coil protein 1 [Sourc... | HSPINDLY|FLJ20364|CCDC99 | 88.888889 |
| PKD2L1 | ENSG00000107593 | 9033 | protein_coding | polycystin 2 like 1, transient receptor potent... | TRPP3|PCL|PKDL|PKD2L | 80.000000 |
| PKD1L1 | ENSG00000158683 | 168507 | protein_coding | polycystin 1 like 1, transient receptor potent... | PRO19563 | 80.000000 |
Search another field (default is .name):
gene_bt.search("tumor protein p53", field=gene_bt.description).head()
| ensembl_gene_id | symbol | ncbi_gene_id | biotype | synonyms | __ratio__ | |
|---|---|---|---|---|---|---|
| description | ||||||
| tyrosinase related protein 1 [Source:HGNC Symbol;Acc:HGNC:12450] | ENSG00000107165 | TYRP1 | 7306 | protein_coding | B-PROTEIN|CAS2|CATB|TYRP|OCA3|TRP|GP75 | 61.538462 |
| novel protein | ENSG00000258674 | None | None | protein_coding | 60.000000 | |
| novel protein | ENSG00000289012 | None | None | lncRNA | 60.000000 | |
| novel protein | ENSG00000289653 | None | None | protein_coding | 60.000000 | |
| novel protein | ENSG00000289565 | None | None | protein_coding | 60.000000 |
Standardize gene identifiers#
Let us generate a DataFrame that stores a number of gene identifiers, some of which corrupted:
data = {
"gene symbol": ["A1CF", "A1BG", "FANCD1", "corrupted"],
"ncbi id": ["29974", "1", "5133", "corrupted"],
"ensembl_gene_id": [
"ENSG00000148584",
"ENSG00000121410",
"ENSG00000188389",
"ENSGcorrupted",
],
}
df_orig = pd.DataFrame(data).set_index("ensembl_gene_id")
df_orig
| gene symbol | ncbi id | |
|---|---|---|
| ensembl_gene_id | ||
| ENSG00000148584 | A1CF | 29974 |
| ENSG00000121410 | A1BG | 1 |
| ENSG00000188389 | FANCD1 | 5133 |
| ENSGcorrupted | corrupted | corrupted |
First we can check whether any of our values are validated against the ontology reference:
validated = gene_bt.validate(df_orig.index, gene_bt.ensembl_gene_id)
df_orig.index[~validated]
❗ 1 term (25.00%) is not validated: ENSGcorrupted
Index(['ENSGcorrupted'], dtype='object', name='ensembl_gene_id')
Next, we validate which symbols are mappable against the ontology:
# based on NCBI gene ID
gene_bt.validate(df_orig["ncbi id"], gene_bt.ncbi_gene_id)
❗ 1 term (25.00%) is not validated: corrupted
array([ True, True, True, False])
# based on Gene symbols
validated_symbols = gene_bt.validate(df_orig["gene symbol"], gene_bt.symbol)
df_orig["gene symbol"][~validated_symbols]
❗ 2 terms (50.00%) are not validated: FANCD1, corrupted
ensembl_gene_id
ENSG00000188389 FANCD1
ENSGcorrupted corrupted
Name: gene symbol, dtype: object
Here, 2 of the gene symbols are not validated. Inspect why:
gene_bt.inspect(df_orig["gene symbol"], gene_bt.symbol);
❗ 2 terms (50.00%) are not validated for symbol: FANCD1, corrupted
detected 1 terms with synonym: FANCD1
→ standardize terms via .standardize()
Logging suggests to use .standardize():
mapped_symbol_synonyms = gene_bt.standardize(df_orig["gene symbol"])
mapped_symbol_synonyms
['A1CF', 'A1BG', 'BRCA2', 'corrupted']
Optionally, you can return a mapper in the form of {synonym1: standardized_name1, ...}:
gene_bt.standardize(df_orig["gene symbol"], return_mapper=True)
{'FANCD1': 'BRCA2'}
We can use the standardized symbols as the new standardized index:
df_curated = df_orig.reset_index()
df_curated.index = mapped_symbol_synonyms
df_curated
| ensembl_gene_id | gene symbol | ncbi id | |
|---|---|---|---|
| A1CF | ENSG00000148584 | A1CF | 29974 |
| A1BG | ENSG00000121410 | A1BG | 1 |
| BRCA2 | ENSG00000188389 | FANCD1 | 5133 |
| corrupted | ENSGcorrupted | corrupted | corrupted |
You can convert identifiers by passing return_field to standardize():
gene_bt.standardize(
df_curated.index,
field=gene_bt.symbol,
return_field=gene_bt.ensembl_gene_id,
)
['ENSG00000148584', 'ENSG00000121410', 'ENSG00000139618', 'corrupted']
And return mappable identifiers as a dict:
gene_bt.standardize(
df_curated.index,
field=gene_bt.symbol,
return_field=gene_bt.ensembl_gene_id,
return_mapper=True,
)
{'A1BG': 'ENSG00000121410',
'BRCA2': 'ENSG00000139618',
'A1CF': 'ENSG00000148584'}
Ontology source versions#
For any given entity, we can choose from a number of versions:
lb.BiontySource.filter(entity="Gene").df()
| uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 9 | o36k | Gene | human | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_human__ensembl__release-... | 832f3947e83664588d419608a469b528 | https://www.ensembl.org | 2023-12-22 11:23:17.151087+00:00 | 1 |
| 10 | mIAU | Gene | human | False | ensembl | Ensembl | release-109 | s3://bionty-assets/human_ensembl_release-109_G... | 72da9968c74e96d136a489a6102a4546 | https://www.ensembl.org | 2023-12-22 11:23:17.151120+00:00 | 1 |
| 11 | VTEw | Gene | mouse | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_mouse__ensembl__release-... | fa4ce130f2929aefd7ac3bc8eaf0c4de | https://www.ensembl.org | 2023-12-22 11:23:17.151154+00:00 | 1 |
| 12 | bN62 | Gene | mouse | False | ensembl | Ensembl | release-109 | s3://bionty-assets/mouse_ensembl_release-109_G... | 08a1165061151b270b985317322bd2ed | https://www.ensembl.org | 2023-12-22 11:23:17.151187+00:00 | 1 |
| 13 | Uhnp | Gene | saccharomyces cerevisiae | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_saccharomyces cerevisiae... | 2e59495a3e87ea6575e408697dd73459 | https://www.ensembl.org | 2023-12-22 11:23:17.151219+00:00 | 1 |
When instantiating a Bionty object, we can choose a source or version:
bionty_source = lb.BiontySource.filter(
source="ensembl", version="release-110", organism="human"
).one()
gene_bt = lb.Gene.bionty(bionty_source=bionty_source)
gene_bt
Gene
Organism: human
Source: ensembl, release-110
#terms: 75719
📖 Gene.df(): ontology reference table
🔎 Gene.lookup(): autocompletion of terms
🎯 Gene.search(): free text search of terms
✅ Gene.validate(): strictly validate values
🧐 Gene.inspect(): full inspection of values
👽 Gene.standardize(): convert to standardized names
🪜 Gene.diff(): difference between two versions
🔗 Gene.ontology: Pronto.Ontology object
The currently used ontologies can be displayed using:
lb.BiontySource.filter(currently_used=True).df()
| uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 1 | zvGR | Organism | vertebrates | True | ensembl | Ensembl | release-110 | https://ftp.ensembl.org/pub/release-110/specie... | f3faf95648d3a2b50fd3625456739706 | https://www.ensembl.org | 2023-12-22 11:23:17.150789+00:00 | 1 |
| 4 | TE9h | Organism | bacteria | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/bacte... | ee28510ed5586ea7ab4495717c96efc8 | https://www.ensembl.org | 2023-12-22 11:23:17.150917+00:00 | 1 |
| 5 | OZIG | Organism | fungi | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/fungi/releas... | dbcde58f4396ab8b2480f7fe9f83df8a | https://www.ensembl.org | 2023-12-22 11:23:17.150951+00:00 | 1 |
| 6 | W07m | Organism | metazoa | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/metazoa/rele... | 424636a574fec078a61cbdddb05f9132 | https://www.ensembl.org | 2023-12-22 11:23:17.150985+00:00 | 1 |
| 7 | AVh3 | Organism | plants | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/plant... | eadaa1f3e527e4c3940c90c7fa5c8bf4 | https://www.ensembl.org | 2023-12-22 11:23:17.151019+00:00 | 1 |
| 8 | MdBu | Organism | all | True | ncbitaxon | NCBItaxon Ontology | 2023-06-20 | s3://bionty-assets/df_all__ncbitaxon__2023-06-... | 00d97ba65627f1cd65636d2df22ea76c | https://github.com/obophenotype/ncbitaxon | 2023-12-22 11:23:17.151052+00:00 | 1 |
| 9 | o36k | Gene | human | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_human__ensembl__release-... | 832f3947e83664588d419608a469b528 | https://www.ensembl.org | 2023-12-22 11:23:17.151087+00:00 | 1 |
| 11 | VTEw | Gene | mouse | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_mouse__ensembl__release-... | fa4ce130f2929aefd7ac3bc8eaf0c4de | https://www.ensembl.org | 2023-12-22 11:23:17.151154+00:00 | 1 |
| 13 | Uhnp | Gene | saccharomyces cerevisiae | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_saccharomyces cerevisiae... | 2e59495a3e87ea6575e408697dd73459 | https://www.ensembl.org | 2023-12-22 11:23:17.151219+00:00 | 1 |
| 14 | 000Q | Protein | human | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_human__uniprot__2023-03_... | 1c46e85c6faf5eff3de5b4e1e4edc4d3 | https://www.uniprot.org | 2023-12-22 11:23:17.151253+00:00 | 1 |
| 16 | tD7O | Protein | mouse | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_mouse__uniprot__2023-03_... | 9d5e9a8225011d3218e10f9bbb96a46c | https://www.uniprot.org | 2023-12-22 11:23:17.151319+00:00 | 1 |
| 18 | vqWI | CellMarker | human | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/human_cellmarker_2.0_CellMa... | d565d4a542a5c7e7a06255975358e4f4 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 11:23:17.151386+00:00 | 1 |
| 19 | ypPK | CellMarker | mouse | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/mouse_cellmarker_2.0_CellMa... | 189586732c63be949e40dfa6a3636105 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 11:23:17.151419+00:00 | 1 |
| 20 | 2Zjk | CellLine | all | True | clo | Cell Line Ontology | 2022-03-21 | https://data.bioontology.org/ontologies/CLO/su... | ea58a1010b7e745702a8397a526b3a33 | https://bioportal.bioontology.org/ontologies/CLO | 2023-12-22 11:23:17.151452+00:00 | 1 |
| 21 | 4shh | CellType | all | True | cl | Cell Ontology | 2023-08-24 | http://purl.obolibrary.org/obo/cl/releases/202... | 46e7dd89421f1255cf0191eca1548f73 | https://obophenotype.github.io/cell-ontology | 2023-12-22 11:23:17.151485+00:00 | 1 |
| 25 | LmWQ | Tissue | all | True | uberon | Uberon multi-species anatomy ontology | 2023-09-05 | http://purl.obolibrary.org/obo/uberon/releases... | abcee3ede566d1311d758b853ccdf5aa | http://obophenotype.github.io/uberon | 2023-12-22 11:23:17.151616+00:00 | 1 |
| 29 | zMWv | Disease | all | True | mondo | Mondo Disease Ontology | 2023-08-02 | http://purl.obolibrary.org/obo/mondo/releases/... | 7f33767422042eec29f08b501fc851db | https://mondo.monarchinitiative.org | 2023-12-22 11:23:17.151748+00:00 | 1 |
| 33 | cxPr | Disease | human | True | doid | Human Disease Ontology | 2023-03-31 | http://purl.obolibrary.org/obo/doid/releases/2... | 64f083a1e47867c307c8eae308afc3bb | https://disease-ontology.org | 2023-12-22 11:23:17.151880+00:00 | 1 |
| 35 | 2wto | ExperimentalFactor | all | True | efo | The Experimental Factor Ontology | 3.57.0 | http://www.ebi.ac.uk/efo/releases/v3.57.0/efo.owl | 2ecafc69b3aba7bdb31ad99438505c05 | https://bioportal.bioontology.org/ontologies/EFO | 2023-12-22 11:23:17.151946+00:00 | 1 |
| 37 | 3SSF | Phenotype | human | True | hp | Human Phenotype Ontology | 2023-06-17 | https://github.com/obophenotype/human-phenotyp... | 65e8d96bc81deb893163927063b10c06 | https://hpo.jax.org | 2023-12-22 11:23:17.152012+00:00 | 1 |
| 40 | nwdt | Phenotype | mammalian | True | mp | Mammalian Phenotype Ontology | 2023-05-31 | https://github.com/mgijax/mammalian-phenotype-... | be89052cf6d9c0b6197038fe347ef293 | https://github.com/mgijax/mammalian-phenotype-... | 2023-12-22 11:23:17.152110+00:00 | 1 |
| 41 | zAfB | Phenotype | zebrafish | True | zp | Zebrafish Phenotype Ontology | 2022-12-17 | https://github.com/obophenotype/zebrafish-phen... | 03430b567bf153216c0fa4c3440b3b24 | https://github.com/obophenotype/zebrafish-phen... | 2023-12-22 11:23:17.152143+00:00 | 1 |
| 43 | p1co | Phenotype | all | True | pato | Phenotype And Trait Ontology | 2023-05-18 | http://purl.obolibrary.org/obo/pato/releases/2... | bd472f4971492109493d4ad8a779a8dd | https://github.com/pato-ontology/pato | 2023-12-22 11:23:17.152209+00:00 | 1 |
| 44 | h0rU | Pathway | all | True | go | Gene Ontology | 2023-05-10 | https://data.bioontology.org/ontologies/GO/sub... | e9845499eadaef2418f464cd7e9ac92e | http://geneontology.org | 2023-12-22 11:23:17.152242+00:00 | 1 |
| 46 | fxHJ | BFXPipeline | all | True | lamin | Bioinformatics Pipeline | 1.0.0 | s3://bionty-assets/bfxpipelines.json | a7eff57a256994692fba46e0199ffc94 | https://lamin.ai | 2023-12-22 11:23:17.152309+00:00 | 1 |
| 47 | chfO | Drug | all | True | dron | Drug Ontology | 2023-03-10 | https://data.bioontology.org/ontologies/DRON/s... | 75e86011158fae76bb46d96662a33ba3 | https://bioportal.bioontology.org/ontologies/DRON | 2023-12-22 11:23:17.152342+00:00 | 1 |
| 48 | 7JhT | DevelopmentalStage | human | True | hsapdv | Human Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/HSAPDV/11... | 52181d59df84578ed69214a5cb614036 | https://github.com/obophenotype/developmental-... | 2023-12-22 11:23:17.152375+00:00 | 1 |
| 49 | JIKv | DevelopmentalStage | mouse | True | mmusdv | Mouse Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/MMUSDV/9/... | 5bef72395d853c7f65450e6c2a1fc653 | https://github.com/obophenotype/developmental-... | 2023-12-22 11:23:17.152408+00:00 | 1 |
| 50 | clid | Ethnicity | human | True | hancestro | Human Ancestry Ontology | 3.0 | https://github.com/EBISPOT/hancestro/raw/3.0/h... | 76dd9efda9c2abd4bc32fc57c0b755dd | https://github.com/EBISPOT/hancestro | 2023-12-22 11:23:17.152441+00:00 | 1 |
| 51 | rsbG | BioSample | all | True | ncbi | NCBI BioSample attributes | 2023-09 | s3://bionty-assets/df_all__ncbi__2023-09__BioS... | 918db9bd1734b97c596c67d9654a4126 | https://www.ncbi.nlm.nih.gov/biosample/docs/at... | 2023-12-22 11:23:17.152474+00:00 | 1 |
Show code cell content
!lamin delete --force test-gene
!rm -r test-gene
💡 deleting instance testuser1/test-gene
✅ deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-gene.env
✅ instance cache deleted
✅ deleted '.lndb' sqlite file
❗ consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-gene