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.

# !pip install 'lamindb[jupyter,bionty]'
!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")
    """
    name, id = key.rsplit(" (", 1)
    id = id.rstrip(")")
    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

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PublicOntology
Entity: Pathway
Organism: all
Source: go, 2024-06-17
#terms: 47856

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)

Register gene symbols

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

all_genes = bt.Gene.standardize(list({g for genes in go_bp.values() for g in genes}))
gene_records = bt.Gene.from_values(all_genes, bt.Gene.symbol)
ln.save(gene_records);

! found 56 synonyms in Bionty (output truncated): [np.str_('C10ORF71'), np.str_('C2ORF69'), np.str_('C17ORF97'), np.str_('USP41'), np.str_('ADGRF2'), np.str_('SOGA1'), np.str_('C1ORF112'), np.str_('C9ORF78'), np.str_('SPATA5L1'), np.str_('FAM172A'), '...']
  please add corresponding Gene records via (output truncated): `.from_values([np.str_('C10ORF71'), np.str_('C2ORF69'), np.str_('C17ORF97'), np.str_('USP41'), np.str_('ADGRF2'), np.str_('SOGA1'), np.str_('C1ORF112'), np.str_('C9ORF78'), np.str_('SPATA5L1'), np.str_('FAM172A'), '...'])`

! ambiguous validation in Bionty for 1104 records: 'SLC12A7', 'PHF1', 'TAS2R31', 'MEGF11', 'MLXIP', 'NOC4L', 'PPIAL4F', 'CES1P1', 'IGHA1', 'CRHR1', 'CDHR5', 'KRT12', 'FBXL6', 'LILRA3', 'PLSCR3', 'ASMT', 'BTG3', 'TAS2R60', 'PYCR3', 'RPS3A', ...

! did not create Gene records for 38 non-validated symbols: 'AFD1', 'AZF1', 'CCL3L1', 'CCL4L1', 'DGS2', 'DUX3', 'DUX5', 'FOXL3-OT1', 'IGL', 'LOC100653049', 'LOC102723475', 'LOC102723996', 'LOC102724159', 'LOC107984156', 'LOC112268384', 'LOC122319436', 'LOC122513141', 'LOC122539214', 'LOC344967', 'MDRV', ...

Manually register the 37 non-validated symbols:

inspect_result = bt.Gene.inspect(all_genes, bt.Gene.symbol)

nonval_genes = []
for g in inspect_result.non_validated:
    nonval_genes.append(bt.Gene(symbol=g))

ln.save(nonval_genes)

! received 14696 unique terms, 1 empty/duplicated term is ignored

! 38 unique terms (0.30%) are not validated for symbol: 'MTRNR2L9', 'LOC122319436', 'MDRV', 'TRL-AAG2-3', 'LOC112268384', 'AZF1', 'MTRNR2L8', 'TRA', 'LOC102723475', 'MTRNR2L1', ...

   couldn't validate 38 terms: 'LOC122539214', 'FOXL3-OT1', 'MTRNR2L13', 'MDRV', 'MTRNR2L10', 'TAS2R33', 'LOC102723475', 'MTRNR2L11', 'DUX5', 'AZF1', 'DGS2', 'MTRNR2L9', 'MTRNR2L8', 'LOC107984156', 'CCL3L1', 'LOC102723996', 'LOC112268384', 'CCL4L1', 'IGL', 'TAS2R36', ...

→  if you are sure, create new records via Gene() and save to your registry

! record with similar symbol exists! did you mean to load it?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
6365	217N4Hgazwqu	JAZF1	None	ENSG00000153814	221895	protein_coding	TIP27|ZNF802|DKFZP761K2222	JAZF zinc finger 1	11	1	None	2024-12-20 15:05:02.945537+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
300	4aXWpkO7oC2T	TRAPPC8	None	ENSG00000153339	22878	protein_coding	GSG1|KIAA1012|HST2706|TRS85	trafficking protein particle complex subunit 8	11	1	None	2024-12-20 15:05:02.655671+00:00	1
455	OH1EkHtdQLRz	TRAPPC10	None	ENSG00000160218	7109	protein_coding	TMEM1|EHOC-1|TRS130	trafficking protein particle complex subunit 10	11	1	None	2024-12-20 15:05:02.662288+00:00	1
1169	2xRvI0Oh2TCT	TRA2A	None	ENSG00000164548	29896	protein_coding	HTRA-2-ALPHA|AWMS1	transformer 2 alpha homolog	11	1	None	2024-12-20 15:05:02.694737+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
13	4oEC1FHbExIX	TAS2R31	None	ENSG00000256436	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640496+00:00	1
14	4e6Rg8UcUnf5	TAS2R31	None	ENSG00000263097	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640522+00:00	1
15	1ELqD18jbeeq	TAS2R31	None	ENSG00000282256	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640550+00:00	1

! record with similar symbol exists! did you mean to load it?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
5734	2Z2LBAj16utl	TRAFD1	None	ENSG00000135148	10906	protein_coding	FLN29	TRAF-type zinc finger domain containing 1	11	1	None	2024-12-20 15:05:02.914376+00:00	1

! record with similar symbol exists! did you mean to load it?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
13498	19ZTVOfazMYF	SEPTIN14	None	ENSG00000154997	346288	protein_coding	SEPT14|FLJ44060	septin 14	11	1	None	2024-12-20 15:05:03.466370+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
8223	F36vWNagki41	CCL3	None	ENSG00000274221	6348	protein_coding	LD78|G0S19-1|SCYA3|LD78ALPHA|SCI|MIP-1-ALPHA	C-C motif chemokine ligand 3	11	1	None	2024-12-20 15:05:03.039588+00:00	1
8224	EBoWJxh70guC	CCL3	None	ENSG00000277632	6348	protein_coding	LD78|G0S19-1|SCYA3|LD78ALPHA|SCI|MIP-1-ALPHA	C-C motif chemokine ligand 3	11	1	None	2024-12-20 15:05:03.039611+00:00	1
8225	6XmKAksx3Nn4	CCL3	None	ENSG00000278567	6348	protein_coding	LD78|G0S19-1|SCYA3|LD78ALPHA|SCI|MIP-1-ALPHA	C-C motif chemokine ligand 3	11	1	None	2024-12-20 15:05:03.039634+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
5801	TfqHfw8FjZxq	CCL4	None	ENSG00000275302	6351	protein_coding	MIP-1-BETA|SCYA4|AT744.1|LAG1|ACT-2	C-C motif chemokine ligand 4	11	1	None	2024-12-20 15:05:02.917677+00:00	1
5802	2MLaX8EEZ9sQ	CCL4	None	ENSG00000275824	6351	protein_coding	MIP-1-BETA|SCYA4|AT744.1|LAG1|ACT-2	C-C motif chemokine ligand 4	11	1	None	2024-12-20 15:05:02.917700+00:00	1
5803	7P74Aze5sMxb	CCL4	None	ENSG00000277943	6351	protein_coding	MIP-1-BETA|SCYA4|AT744.1|LAG1|ACT-2	C-C motif chemokine ligand 4	11	1	None	2024-12-20 15:05:02.917723+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
2713	7TBpxi4zzmMB	IGLC3	None	ENSG00000211679		IG_C_gene	IGLC	immunoglobulin lambda constant 3 (Kern-Oz+ mar...	11	1	None	2024-12-20 15:05:02.767475+00:00	1
8748	10LbfkUokHgx	IGLC1	None	ENSG00000211675		IG_C_gene	IGLC	immunoglobulin lambda constant 1	11	1	None	2024-12-20 15:05:03.067440+00:00	1
11200	2qFUCnrrxPlc	IGLC7	None	ENSG00000211685		IG_C_gene		immunoglobulin lambda constant 7	11	1	None	2024-12-20 15:05:03.344408+00:00	1

! records with similar symbols exist! did you mean to load one of them?

	uid	symbol	stable_id	ensembl_gene_id	ncbi_gene_ids	biotype	synonyms	description	source_id	organism_id	run_id	created_at	created_by_id
id
13	4oEC1FHbExIX	TAS2R31	None	ENSG00000256436	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640496+00:00	1
14	4e6Rg8UcUnf5	TAS2R31	None	ENSG00000263097	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640522+00:00	1
15	1ELqD18jbeeq	TAS2R31	None	ENSG00000282256	259290	protein_coding	TAS2R44|T2R31|T2R53	taste 2 receptor member 31	11	1	None	2024-12-20 15:05:02.640550+00:00	1

Link pathway to genes

Now that we are tracking all pathways and genes records, we can link both of them to make the pathways even more queryable.

symbols_gene_records = {record.symbol: record for record in gene_records}

for pathway_record in pathway_records:
    pathway_genes = go_bp_parsed.get(pathway_record.ontology_id)[1]
    pathway_genes_records = [symbols_gene_records.get(gene) for gene in pathway_genes]
    pathway_record.genes.set(pathway_genes_records)

Now genes are linked to pathways:

pathway_record.genes.list("symbol")

['CARD18', 'CST7', 'XIAP', 'CAST', 'CARD8']

pathway_record.genes.list("ensembl_gene_id")

['ENSG00000255501',
 'ENSG00000077984',
 'ENSG00000101966',
 'ENSG00000153113',
 'ENSG00000105483']

Move on to the next analysis: Standardize metadata on-the-fly