Pathway¶
lamindb provides access to the following public Pathway ontologies through bionty:
Here we show how to access and search Pathway ontologies to standardize new data.
import bionty as bt
import pandas as pd
PublicOntology objects¶
Let us create a public ontology accessor with .public method, which chooses a default public ontology source from Source.
It’s a PublicOntology object, which you can think about as a public registry:
pathways = bt.Pathway.public(organism="all")
pathways
→ connected lamindb: testuser1/test-public-ontologies
PublicOntology
Entity: Pathway
Organism: all
Source: go, 2024-06-17
#terms: 47856
As for registries, you can export the ontology as a DataFrame:
df = pathways.df()
df.head()
| name | definition | synonyms | parents | |
|---|---|---|---|---|
| ontology_id | ||||
| GO:0000001 | mitochondrion inheritance | The Distribution Of Mitochondria, Including Th... | mitochondrial inheritance | [GO:0048311, GO:0048308] |
| GO:0002230 | positive regulation of defense response to vir... | Any Host Process That Results In The Promotion... | upregulation of antiviral response by host|pos... | [GO:0050691] |
| GO:0002231 | detection of oomycetes | The Series Of Events In Which A Stimulus From ... | None | [GO:0098543, GO:0002239] |
| GO:0002232 | leukocyte chemotaxis involved in inflammatory ... | The Movement Of An Immune Cell In Response To ... | None | [GO:0030595, GO:0002523] |
| GO:0002233 | leukocyte chemotaxis involved in immune response | The Movement Of An Immune Cell In Response To ... | immune cell chemotaxis during immune response|... | [GO:0030595, GO:0002522] |
Unlike registries, you can also export it as a Pronto object via public.ontology.
Look up terms¶
As for registries, terms can be looked up with auto-complete:
lookup = pathways.lookup()
The . accessor provides normalized terms (lower case, only contains alphanumeric characters and underscores):
lookup.acetyl_coa_assimilation_pathway
Pathway(ontology_id='GO:0019681', name='acetyl-CoA assimilation pathway', definition='The Pathways By Which Acetyl-Coa Is Processed And Converted Into Alpha-Ketoglutarate (2-Oxoglutarate); Methanogenic Archaea Use These Pathways To Assimilate Acetyl-Coa Into The Cell.', synonyms='acetyl-CoA catabolic process to alpha-ketoglutarate|acetyl-CoA catabolic process to 2-ketoglutarate|acetyl-CoA catabolism to 2-ketoglutarate|acetyl-CoA catabolic process to 2-oxoglutarate|acetyl-CoA catabolism to alpha-oxoglutarate|acetyl-CoA catabolic process to alpha-oxoglutarate|acetyl-CoA catabolism to alpha-ketoglutarate|acetyl-CoA catabolism to 2-oxoglutarate', parents=array(['GO:0046356', 'GO:0006103'], dtype=object))
To look up the exact original strings, convert the lookup object to dict and use the [] accessor:
lookup_dict = lookup.dict()
lookup_dict["acetyl-CoA assimilation pathway"]
Pathway(ontology_id='GO:0019681', name='acetyl-CoA assimilation pathway', definition='The Pathways By Which Acetyl-Coa Is Processed And Converted Into Alpha-Ketoglutarate (2-Oxoglutarate); Methanogenic Archaea Use These Pathways To Assimilate Acetyl-Coa Into The Cell.', synonyms='acetyl-CoA catabolic process to alpha-ketoglutarate|acetyl-CoA catabolic process to 2-ketoglutarate|acetyl-CoA catabolism to 2-ketoglutarate|acetyl-CoA catabolic process to 2-oxoglutarate|acetyl-CoA catabolism to alpha-oxoglutarate|acetyl-CoA catabolic process to alpha-oxoglutarate|acetyl-CoA catabolism to alpha-ketoglutarate|acetyl-CoA catabolism to 2-oxoglutarate', parents=array(['GO:0046356', 'GO:0006103'], dtype=object))
By default, the name field is used to generate lookup keys. You can specify another field to look up:
lookup = pathways.lookup(pathways.ontology_id)
lookup.go_0019681
Pathway(ontology_id='GO:0019681', name='acetyl-CoA assimilation pathway', definition='The Pathways By Which Acetyl-Coa Is Processed And Converted Into Alpha-Ketoglutarate (2-Oxoglutarate); Methanogenic Archaea Use These Pathways To Assimilate Acetyl-Coa Into The Cell.', synonyms='acetyl-CoA catabolic process to alpha-ketoglutarate|acetyl-CoA catabolic process to 2-ketoglutarate|acetyl-CoA catabolism to 2-ketoglutarate|acetyl-CoA catabolic process to 2-oxoglutarate|acetyl-CoA catabolism to alpha-oxoglutarate|acetyl-CoA catabolic process to alpha-oxoglutarate|acetyl-CoA catabolism to alpha-ketoglutarate|acetyl-CoA catabolism to 2-oxoglutarate', parents=array(['GO:0046356', 'GO:0006103'], dtype=object))
Search terms¶
Search behaves in the same way as it does for registries:
pathways.search("acetyl-coa assimilation").head(3)
| name | definition | synonyms | parents | |
|---|---|---|---|---|
| ontology_id | ||||
| GO:0019681 | acetyl-CoA assimilation pathway | The Pathways By Which Acetyl-Coa Is Processed ... | acetyl-CoA catabolic process to alpha-ketoglut... | [GO:0046356, GO:0006103] |
By default, search also covers synonyms and all other fileds containing strings:
pathways.search("acetyl-CoA catabolism").head(3)
| name | definition | synonyms | parents | |
|---|---|---|---|---|
| ontology_id | ||||
| GO:0046356 | acetyl-CoA catabolic process | The Chemical Reactions And Pathways Resulting ... | acetyl-CoA degradation|acetyl-CoA breakdown|ac... | [GO:0034034, GO:0034031, GO:0044273, GO:000608... |
| GO:0019681 | acetyl-CoA assimilation pathway | The Pathways By Which Acetyl-Coa Is Processed ... | acetyl-CoA catabolic process to alpha-ketoglut... | [GO:0046356, GO:0006103] |
Search specific field (by default, search is done on all fields containing strings):
pathways.search(
"chemical reactions and pathways resulting in the breakdown of acetyl-CoA",
field=pathways.definition,
).head()
| name | definition | synonyms | parents | |
|---|---|---|---|---|
| ontology_id | ||||
| GO:0046356 | acetyl-CoA catabolic process | The Chemical Reactions And Pathways Resulting ... | acetyl-CoA degradation|acetyl-CoA breakdown|ac... | [GO:0034034, GO:0034031, GO:0044273, GO:000608... |
Standardize Pathway identifiers¶
Let us generate a DataFrame that stores a number of Pathway identifiers, some of which corrupted:
df_orig = pd.DataFrame(
index=[
"GO:1905210",
"GO:1905211",
"GO:1905212",
"GO:1905208",
"This pathway does not exist",
]
)
df_orig
| GO:1905210 |
|---|
| GO:1905211 |
| GO:1905212 |
| GO:1905208 |
| This pathway does not exist |
We can check whether any of our values are validated against the ontology reference:
validated = pathways.validate(df_orig.index, pathways.name)
df_orig.index[~validated]
! 5 unique terms (100.00%) are not validated: 'GO:1905210', 'GO:1905211', 'GO:1905212', 'GO:1905208', 'This pathway does not exist'
Index(['GO:1905210', 'GO:1905211', 'GO:1905212', 'GO:1905208',
'This pathway does not exist'],
dtype='object')
Ontology source versions¶
For any given entity, we can choose from a number of versions:
bt.Source.filter(entity="bionty.Pathway").df()
# only lists the sources that are currently used
bt.Source.filter(entity="bionty.Pathway", currently_used=True).df()
| uid | entity | organism | name | in_db | currently_used | description | url | md5 | source_website | space_id | dataframe_artifact_id | version | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 25 | 7Ent3V2y | bionty.Pathway | all | go | False | True | Gene Ontology | http://purl.obolibrary.org/obo/go/releases/202... | None | http://geneontology.org | 1 | None | 2024-06-17 | None | 2025-07-14 06:41:44.843000+00:00 | 1 | None | 1 |
When instantiating a Bionty object, we can choose a source or version:
source = bt.Source.filter(
name="go", organism="all"
).first()
pathways= bt.Pathway.public(source=source)
pathways
PublicOntology
Entity: Pathway
Organism: all
Source: go, 2024-06-17
#terms: 47856
The currently used ontologies can be displayed using:
bt.Source.filter(currently_used=True).df()