README.md

metadata

pretty_name: Gene Ontology Terms
license: cc-by-4.0
tags:
  - biology
  - ontology
  - gene-ontology
  - go
  - obo
  - parquet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet

Gene Ontology Terms

The Gene Ontology is a structured knowledgebase and controlled vocabulary for describing gene product functions, biological processes, and cellular components across organisms.

This dataset contains a viewer-friendly Parquet table derived from the Gene Ontology OBO files in this repository. Each row is one [Term] stanza from go.obo, with repeated OBO fields stored as list columns. The original source files are preserved in the repository:

• go.obo
• go-basic.obo

in_go_basic marks whether a term is also present in go-basic.obo. Relationship typedef metadata from go.obo is available as metadata/typedefs.parquet.

Splits

The split is deterministic by GO identifier: sha256(go_id) % 10. Bucket 0 is test; buckets 1 through 9 are train.

Split	Rows
train	43,522
test	4,769
total	48,291

Dataset Statistics

Field	Value
GO release	releases/2026-03-25
Terms	48,291
Typedefs	11
Terms in go-basic.obo	48,291
Active terms	38,560
Obsolete terms	9,731

Namespace	Rows
biological_process	30,857
molecular_function	12,839
cellular_component	4,595

Usage

Install the Hugging Face Datasets library:

pip install datasets

Load all splits:

from datasets import load_dataset

ds = load_dataset("LiteFold/GO")
print(ds)

row = ds["train"][0]
print(row["go_id"], row["name"], row["namespace"])

Load one split:

from datasets import load_dataset

train = load_dataset("LiteFold/GO", split="train")
test = load_dataset("LiteFold/GO", split="test")

Stream rows without downloading the full table first:

from datasets import load_dataset

stream = load_dataset("LiteFold/GO", split="train", streaming=True)
for row in stream.take(5):
    print(row["go_id"], row["name"])

Filter active biological process terms:

from datasets import load_dataset

ds = load_dataset("LiteFold/GO", split="train")
active_bp = ds.filter(
    lambda row: row["namespace"] == "biological_process" and not row["is_obsolete"]
)
print(active_bp[0])

Load typedef metadata directly:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="LiteFold/GO",
    repo_type="dataset",
    filename="metadata/typedefs.parquet",
)
typedefs = pd.read_parquet(path)
print(typedefs[["id", "name"]].head())

Columns

Column	Description
go_id	GO identifier, such as GO:0008150.
go_numeric_id	Numeric portion of go_id.
name	Term name.
namespace	GO namespace: biological_process, molecular_function, or cellular_component.
definition	Parsed OBO definition text.
definition_xrefs	Cross-references attached to the definition.
comment	OBO comment text, when present.
synonyms	Parsed synonym strings.
synonym_scopes	Scope for each synonym, such as EXACT, BROAD, NARROW, or RELATED.
alt_ids	Alternate GO identifiers.
subsets	GO subsets or slims containing the term.
xrefs	Term cross-references.
is_a_ids	Direct is_a parent GO identifiers.
relationship_edges	Raw relationship edges with comments removed.
relationship_types	Relationship predicates, such as part_of or regulates.
relationship_target_ids	GO identifiers targeted by relationship edges.
parent_ids	Combined unique is_a_ids and relationship_target_ids.
intersection_of	Parsed intersection_of entries.
union_of	Parsed union_of entries.
disjoint_from	Parsed disjoint_from entries.
replaced_by	Replacement IDs for obsolete terms.
consider	Suggested replacement IDs for obsolete terms.
property_values	Raw OBO property values.
created_by	Creator metadata, when present.
creation_date	Creation date metadata, when present.
is_obsolete	Whether the term is obsolete.
in_go_basic	Whether the same GO ID appears in go-basic.obo.
split_bucket	Deterministic split bucket from sha256(go_id) % 10.

Citation

@article{geneontology2023,
  title   = {The Gene Ontology knowledgebase in 2023},
  author  = {{The Gene Ontology Consortium}},
  journal = {Genetics},
  volume  = {224},
  number  = {1},
  year    = {2023},
  doi     = {10.1093/genetics/iyad031}
}