File size: 4,510 Bytes
87cf6fa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e2e3ac
87cf6fa
0e2e3ac
87cf6fa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e2e3ac
87cf6fa
0e2e3ac
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
---
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

```bash
pip install datasets
```

Load the viewer table:

```python
from datasets import load_dataset

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

Load one split:

```python
from datasets import load_dataset

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

Stream rows:

```python
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:

```python
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:

```python
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:

```python
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}
}
```