Jupyter Notebook

Gene Ontology (GO)#

Pathways represent interconnected molecular networks of signaling cascades that govern critical cellular processes. They provide understandings cellular behavior mechanisms, insights of disease progression and treatment responses. In an R&D organization, managing pathways across different datasets are crucial for gaining insights of potential therapeutic targets and intervention strategies.

In this notebook we manage a pathway registry based on “2023 GO Biological Process” ontology. We’ll walk you through the steps of registering pathways and link them to genes.

In the following Standardize metadata on-the-fly notebook, we’ll demonstrate how to perform a pathway enrichment analysis and track the dataset with LaminDB.

Setup#

!lamin init --storage ./use-cases-registries --schema bionty
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💡 connected lamindb: testuser1/use-cases-registries
import lamindb as ln
import bionty as bt
import gseapy as gp

bt.settings.organism = "human"  # globally set organism
💡 connected lamindb: testuser1/use-cases-registries

Fetch GO pathways annotated with human genes using Enrichr#

First we fetch the “GO_Biological_Process_2023” pathways for humans using GSEApy which wraps GSEA and Enrichr.

go_bp = gp.get_library(name="GO_Biological_Process_2023", organism="Human")
print(f"Number of pathways {len(go_bp)}")
Number of pathways 5406
go_bp["ATF6-mediated Unfolded Protein Response (GO:0036500)"]
['MBTPS1', 'MBTPS2', 'XBP1', 'ATF6B', 'DDIT3', 'CREBZF']

Parse out the ontology_id from keys, convert into the format of {ontology_id: (name, genes)}

def parse_ontology_id_from_keys(key):
    """Parse out the ontology id.

    "ATF6-mediated Unfolded Protein Response (GO:0036500)" -> ("GO:0036500", "ATF6-mediated Unfolded Protein Response")
    """
    id = key.split(" ")[-1].replace("(", "").replace(")", "")
    name = key.replace(f" ({id})", "")
    return (id, name)
go_bp_parsed = {}

for key, genes in go_bp.items():
    id, name = parse_ontology_id_from_keys(key)
    go_bp_parsed[id] = (name, genes)
go_bp_parsed["GO:0036500"]
('ATF6-mediated Unfolded Protein Response',
 ['MBTPS1', 'MBTPS2', 'XBP1', 'ATF6B', 'DDIT3', 'CREBZF'])

Register pathway ontology in LaminDB#

bionty = bt.Pathway.public()
bionty
PublicOntology
Entity: Pathway
Organism: all
Source: go, 2023-05-10
#terms: 47514

📖 .df(): ontology reference table
🔎 .lookup(): autocompletion of terms
🎯 .search(): free text search of terms
✅ .validate(): strictly validate values
🧐 .inspect(): full inspection of values
👽 .standardize(): convert to standardized names
🪜 .diff(): difference between two versions
🔗 .to_pronto(): Pronto.Ontology object

Next, we register all the pathways and genes in LaminDB to finally link pathways to genes.

Register pathway terms#

To register the pathways we make use of .from_values to directly parse the annotated GO pathway ontology IDs into LaminDB.

pathway_records = bt.Pathway.from_values(go_bp_parsed.keys(), bt.Pathway.ontology_id)
ln.save(pathway_records, parents=False)  # not recursing through parents

Register gene symbols#

Similarly, we use .from_values for all Pathway associated genes to register them with LaminDB.

all_genes = {g for genes in go_bp.values() for g in genes}
gene_records = bt.Gene.from_values(all_genes, bt.Gene.symbol)
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❗ ambiguous validation in Bionty for 1082 records: 'CHRFAM7A', 'SLC6A3', 'PRAMEF9', 'NKRF', 'IRF9', 'ZNF430', 'IGHG2', 'GOLGA8M', 'TRIM26', 'MEF2D', 'CEP20', 'MAGEA12', 'HNRNPH1', 'PCDHB9', 'CTAGE4', 'OR5T2', 'HLA-DMB', 'SLC9A3', 'LRTM2', 'KIFC1', ...
did not create Gene records for 37 non-validated symbols: 'MDRV', 'MTRNR2L12', 'TAS2R33', 'TRL-AAG2-3', 'MTRNR2L2', 'MTRNR2L3', 'DUX3', 'LOC344967', 'LOC122319436', 'LOC102724159', 'MTRNR2L4', 'TRA', 'IGL', 'MTRNR2L1', 'AZF1', 'LOC107984156', 'MTRNR2L11', 'MTRNR2L7', 'TAS2R36', 'LOC102723475', ...
gene_records[:3]
[Gene(uid='3kUJOYSJA4EZ', symbol='ADGRD1', ensembl_gene_id='ENSG00000111452', ncbi_gene_ids='283383', biotype='protein_coding', description='adhesion G protein-coupled receptor D1 ', synonyms='GPR133|PGR25|DKFZP434B1272', organism_id=1, public_source_id=9, created_by_id=1),
 Gene(uid='6utXc2hJN6Vr', symbol='COX6B1', ensembl_gene_id='ENSG00000126267', ncbi_gene_ids='1340', biotype='protein_coding', description='cytochrome c oxidase subunit 6B1 ', synonyms='COX6B|COXG', organism_id=1, public_source_id=9, created_by_id=1),
 Gene(uid='3sGJRwGQz9G4', symbol='RALGAPB', ensembl_gene_id='ENSG00000170471', ncbi_gene_ids='57148', biotype='protein_coding', description='Ral GTPase activating protein non-catalytic subunit beta ', synonyms='RALGAPBETA|DKFZP781M2411|KIAA1219', organism_id=1, public_source_id=9, created_by_id=1)]
ln.save(gene_records);