README.md

metadata

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:

from datasets import load_dataset

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

Load Swiss-Prot reviewed protein entries:

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:

from datasets import load_dataset

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

Stream TrEMBL entries:

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:

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}
}