README.md

---
license: cc-by-4.0
pretty_name: UniProtKB Processed
size_categories:
  - 100M<n<1B
task_categories:
  - feature-extraction
language:
  - en
tags:
  - biology
  - proteins
  - uniprot
  - uniprotkb
  - swiss-prot
  - trembl
  - protein-sequences
  - bioinformatics
  - train-validation-test-split
  - jsonl
configs:
  - config_name: default
    data_files:
      - split: train
        path:
          - data/train-*.jsonl.gz
      - split: test
        path:
          - data/test-*.jsonl.gz
  - config_name: sprot
    data_files:
      - split: train
        path:
          - tables/source_set=sprot/split=train/*.jsonl.gz
      - split: validation
        path:
          - tables/source_set=sprot/split=validation/*.jsonl.gz
      - split: test
        path:
          - tables/source_set=sprot/split=test/*.jsonl.gz
  - config_name: sprot_varsplic
    data_files:
      - split: train
        path:
          - tables/source_set=sprot_varsplic/split=train/*.jsonl.gz
      - split: validation
        path:
          - tables/source_set=sprot_varsplic/split=validation/*.jsonl.gz
      - split: test
        path:
          - tables/source_set=sprot_varsplic/split=test/*.jsonl.gz
  - config_name: trembl
    data_files:
      - split: train
        path:
          - tables/source_set=trembl/split=train/*.jsonl.gz
      - split: validation
        path:
          - tables/source_set=trembl/split=validation/*.jsonl.gz
      - split: test
        path:
          - tables/source_set=trembl/split=test/*.jsonl.gz
---

# UniProtKB Processed

The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe ongoing changes to our production pipeline to limit the sequences available in UniProtKB to high-quality, non-redundant reference proteomes. We continue to manually curate the scientific literature to add the latest functional data and use machine learning techniques. We also encourage community curation to ensure key publications are not missed. We provide an update on the automatic annotation methods used by UniProtKB to predict information for unreviewed entries describing unstudied proteins. Finally, updates to the UniProt website are described, including a new tab linking protein to genomic information. In recognition of its value to the scientific community, the UniProt database has been awarded Global Core Biodata Resource status.

## Dataset Summary

| Source set | Description | Protein records |
|---|---|---:|
| `sprot` | Swiss-Prot reviewed canonical proteins | 574,627 |
| `sprot_varsplic` | Swiss-Prot alternative isoform sequences | 41,333 |
| `trembl` | TrEMBL unreviewed proteins | 202,556,314 |
| **Total** |  | **203,172,274** |

Additional source totals:

| Metric | Value |
|---|---:|
| Total residues | 75,747,523,712 |
| Sequence shards | 205 |
| Protein-entry table shards | 615 |
| Default index rows | 830 |
| Sequence shard bytes | 46,504,287,641 |
| Metadata records bytes | 74,373,082,266 |
| Protein-entry table bytes | 18,549,213,567 |

## Default Index Splits

The default Dataset Viewer index is split deterministically by `sha256(file_id) % 10`: bucket `0` is `test`, and buckets `1` through `9` are `train`.

| Split | Rows |
|---|---:|
| `train` | 733 |
| `test` | 97 |

## Protein-Entry Splits

The full protein-entry tables use deterministic exact-sequence hash splits. Exact duplicate amino-acid sequences are kept in the same split.

| Split | Protein records |
|---|---:|
| `train` | 162,548,965 |
| `validation` | 20,308,533 |
| `test` | 20,314,776 |

These are exact-sequence splits, not homology-cluster splits. For strict homology-aware model evaluation, create an additional split using UniRef, MMseqs, or another sequence-clustering method.

## Loading With `datasets`

Load the default file/table index:

```python
from datasets import load_dataset

index = load_dataset("LiteFold/UniProtKB")
print(index)
print(index["train"][0])
```

Load Swiss-Prot reviewed protein entries:

```python
from datasets import load_dataset

sprot = load_dataset("LiteFold/UniProtKB", "sprot")
train = sprot["train"]
valid = sprot["validation"]
test = sprot["test"]
```

Load Swiss-Prot alternative isoform entries:

```python
from datasets import load_dataset

isoforms = load_dataset("LiteFold/UniProtKB", "sprot_varsplic")
```

Stream TrEMBL entries:

```python
from datasets import load_dataset

rows = load_dataset("LiteFold/UniProtKB", "trembl", split="train", streaming=True)
for row in rows:
    print(row["accession"], row["protein_name"])
    break
```

Use the default index to discover table shards:

```python
from datasets import load_dataset

index = load_dataset("LiteFold/UniProtKB", split="train")
trembl_train_shards = index.filter(
    lambda row: row["role"] == "protein_entry_table_shard"
    and row["source_set"] == "trembl"
    and row["table_split"] == "train"
)
print(trembl_train_shards[0]["path"])
```

## Default Columns

| Column | Type | Description |
|---|---|---|
| `file_id` | string | Stable file identifier, currently the repository path. |
| `repo_id` | string | Hugging Face dataset repository id. |
| `source_sha` | string | Source repository commit used to build the index. |
| `dataset_id` | string | Source dataset id from `_MANIFEST.json`. |
| `source_set` | string | `sprot`, `sprot_varsplic`, `trembl`, or empty for repository-level files. |
| `source_slug` | string | Source file slug used in the original manifests. |
| `source_file` | string | Original source file path. |
| `path` | string | Path in this Hugging Face repository. |
| `role` | string | File role such as `protein_entry_table_shard`, `sequence_shard`, or `metadata_records`. |
| `table_split` | string | Protein-entry split for table shards. |
| `shard_index` | int64 | Parsed shard index when present, otherwise `-1`. |
| `size_bytes` | int64 | File size in bytes. |
| `compression` | string | Compression format when applicable. |
| `records_in_source` | int64 | Protein records in the source set, otherwise `-1`. |
| `residues_in_source` | int64 | Residues in the source set, otherwise `-1`. |
| `shards_in_source` | int64 | Number of sequence shards in the source set, otherwise `-1`. |
| `records_in_table_split` | int64 | Protein records in that source set and split, otherwise `-1`. |
| `records_total` | int64 | Total protein records across UniProtKB. |
| `residues_total` | int64 | Total residues across UniProtKB. |
| `total_sequence_shards` | int64 | Total sequence shards. |
| `is_sequence_shard` | bool | Whether the row points to a FASTA sequence shard. |
| `is_table_shard` | bool | Whether the row points to a parsed protein-entry table shard. |
| `is_metadata_records` | bool | Whether the row points to metadata records. |
| `download_pattern` | string | Glob or exact path that can be used for file downloads. |
| `access_note` | string | Short note describing how to load the row's data. |
| `split_bucket` | int64 | Deterministic bucket used for the default train/test split. |

## Files

- `data/*.jsonl.gz`: default file/table index for Dataset Viewer.
- `tables/source_set=*/split=*/*.jsonl.gz`: full parsed protein-entry tables.
- `sequences/*/*.fasta.zst`: compressed source sequence shards.
- `metadata/*.records.jsonl`: source metadata records.
- `_MANIFEST.json`: source sequence manifest.
- `_POSTPROCESS_MANIFEST.json`: table-generation manifest.
- `dataset_summary.json`: summary of the default index build.
- `scripts/prepare_uniprotkb_dataset.py`: script used to generate the default index.

## License

CC BY 4.0.

## Citation

```
@article{uniprot2025,
  title     = {{UniProt}: the {Universal Protein Knowledgebase} in 2025},
  author    = {{The UniProt Consortium}},
  journal   = {Nucleic Acids Research},
  volume    = {53},
  number    = {D1},
  pages     = {D609--D617},
  year      = {2025},
  publisher = {Oxford University Press},
  doi       = {10.1093/nar/gkae1010}
}
```