Datasets:

LiteFold
/

GOA

pretty_name: Gene Ontology Annotation UniProt Sample
license: other
tags:
  - biology
  - gene-ontology
  - goa
  - uniprot
  - protein-annotation
  - gaf
  - gpa
  - parquet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet

Gene Ontology Annotation UniProt

GOA provides high-quality, evidence-coded Gene Ontology annotations for UniProtKB proteins, RNAs, and protein complexes.

This dataset contains the original GOA UniProt source files plus a viewer-friendly Parquet sample/index table. The source goa_uniprot_all.gaf.gz and goa_uniprot_all.gpa.gz files are very large, so the default Dataset Viewer table contains the first 50,000 parsed annotation rows from each source file, along with a source-file manifest in metadata/source_files.parquet. Use the original compressed files for complete GOA coverage. Use the default Parquet table for quick inspection, schema discovery, examples, and Dataset Viewer previews.

Splits

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

Split	Rows
train	89,958
test	10,042
total	100,000

Source Files

File	Size
`goa_uniprot_all.gaf.gz`	15,387,303,487 bytes
`goa_uniprot_all.gpa.gz`	9,462,421,263 bytes

Usage

pip install datasets

Load the viewer table:

from datasets import load_dataset

ds = load_dataset("LiteFold/GOA")
print(ds)
print(ds["train"][0])

Load one split:

from datasets import load_dataset

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

Stream rows:

from datasets import load_dataset

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

Filter the sample for molecular-function GAF rows:

from datasets import load_dataset

ds = load_dataset("LiteFold/GOA", split="train")
mf = ds.filter(lambda row: row["source_format"] == "GAF" and row["aspect"] == "F")
print(mf[0])

Download the source manifest:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="LiteFold/GOA",
    repo_type="dataset",
    filename="metadata/source_files.parquet",
)
source_files = pd.read_parquet(path)
print(source_files)

Download the full raw files when needed:

from huggingface_hub import hf_hub_download

gaf_path = hf_hub_download(
    repo_id="LiteFold/GOA",
    repo_type="dataset",
    filename="goa_uniprot_all.gaf.gz",
)

Columns

Column	Description
`annotation_id`	Stable SHA-256 ID for the sampled annotation row.
`source_file`	Source file: GAF or GPA.
`source_format`	Parsed source format, `GAF` or `GPA`.
`source_row_number`	Row number within the source annotation stream.
`db`	Source database.
`db_object_id`	Annotated object identifier.
`db_object_symbol`	GAF object symbol, when available.
`qualifier`	Raw qualifier field.
`qualifiers`	Qualifier field split on `
`go_id`	GO identifier.
`db_references`	References split on `
`evidence_code`	GO or ECO evidence code.
`with_from`	With/from field split on `
`aspect`	GAF aspect: `F`, `P`, or `C`; missing for GPA rows.
`db_object_name`	GAF object name, when available.
`db_object_synonyms`	GAF synonyms split on `
`db_object_type`	GAF object type, when available.
`taxon_ids`	GAF taxon IDs split on `
`interacting_taxon_id`	GPA interacting taxon ID, when available.
`date`	Annotation date.
`assigned_by`	Annotation provider.
`annotation_extension`	Annotation extension field.
`gene_product_form_id`	Gene product form identifier.
`split_bucket`	Deterministic split bucket from `sha256(annotation_id) % 10`.

Citaton

@article{huntley2015goa,
  title   = {The {GOA} database: Gene Ontology annotation updates for 2015},
  author  = {Huntley, Rachael P. and Sawford, Tony and Mutowo-Meullenet, Prudence and Shypitsyna, Aleksandra and Bonilla, Carlos and Martin, Maria Jesus and O'Donovan, Claire},
  journal = {Nucleic Acids Research},
  volume  = {43},
  number  = {D1},
  pages   = {D1057--D1063},
  year    = {2015},
  doi     = {10.1093/nar/gku1113}
}