Datasets:

LiteFold
/

UniProtKB

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

This repository contains two useful views of LiteFold/UniProtKB:

default: a compact JSONL file/table shard index that is easy to browse in the Hugging Face Dataset Viewer.
sprot, sprot_varsplic, and trembl: the full parsed UniProtKB protein-entry tables from the original repository.

The default config does not duplicate all 203M protein rows. It indexes the repository files, table shards, source sets, source sizes, and split-level row counts so the dataset has a stable table preview while the full source-specific tables remain available through named configs.

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

If you use the UniProtKB records, cite UniProt:

The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51(D1):D523-D531, 2023.