Datasetpublic

LiteFold/STRING

STRING v12.0 STRING is a protein association network database that integrates experimental, computational, text-mined, and curated evidence for functional and physical protein interactions. Configs Config Raw source Description protein_links protein.links.full.v12.0.txt.gz Protein-protein association edges with all STRING evidence channels and combined_score. protein_info protein.info.v12.0.txt.gz Protein identifiers, preferred names, sizes, and… See the full description on the dataset page: https://huggingface.co/datasets/LiteFold/STRING.

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Dataset Card

STRING v12.0

STRING is a protein association network database that integrates experimental, computational, text-mined, and curated evidence for functional and physical protein interactions.

Configs

Config	Raw source	Description
`protein_links`	`protein.links.full.v12.0.txt.gz`	Protein-protein association edges with all STRING evidence channels and `combined_score`.
`protein_info`	`protein.info.v12.0.txt.gz`	Protein identifiers, preferred names, sizes, and annotations.
`protein_aliases`	`protein.aliases.v12.0.txt.gz`	External aliases and identifier sources for STRING proteins.
`protein_sequences`	`protein.sequences.v12.0.fa.gz`	Protein amino-acid sequences parsed from FASTA.
`species`	`species.v12.0.txt`	Organism metadata: taxonomy id, STRING type, compact name, official NCBI name, and domain.

Splits

The post-processing script assigns rows to train, validation, and test with a deterministic CRC32 hash. The default ratios are 98/1/1. Re-running with the same --split-seed gives the same split assignment.

Split keys:

Config	Split key
`protein_links`	`protein1 + protein2`
`protein_info`	`string_protein_id`
`protein_aliases`	`string_protein_id + alias + source`
`protein_sequences`	`string_protein_id`
`species`	`taxon_id`

Usage

Install the client library:

bash

python -m pip install datasets

Load the interaction table in streaming mode:

python

from datasets import load_dataset

links = load_dataset("LiteFold/STRING", "protein_links", split="train", streaming=True)
first_row = next(iter(links))
print(first_row)

Load a smaller metadata table normally:

python

from datasets import load_dataset

proteins = load_dataset("LiteFold/STRING", "protein_info", split="train")
print(proteins[0])

Load local Parquet files generated before upload:

python

from datasets import load_dataset

data_files = {
    "train": "data/protein_links/train-*.parquet",
    "validation": "data/protein_links/validation-*.parquet",
    "test": "data/protein_links/test-*.parquet",
}
links = load_dataset("parquet", data_files=data_files, split="train", streaming=True)

Post-processing

Install conversion dependencies:

bash

python -m pip install -r requirements.txt

Create the full Parquet dataset using 32 worker processes:

bash

python scripts/prepare_hf_dataset.py \
  --raw-dir v12.0 \
  --output-dir data \
  --num-proc 32 \
  --overwrite

Create a quick preview dataset before running the full conversion:

bash

python scripts/prepare_hf_dataset.py \
  --raw-dir v12.0 \
  --output-dir data_preview \
  --tables species,protein_info,protein_links \
  --max-rows-per-table 10000 \
  --num-proc 32 \
  --overwrite

Useful options:

Option	Purpose
`--num-proc 32`	Uses 32 parser workers.
`--rows-per-chunk 100000`	Controls rows parsed per worker task. Lower this if memory is tight.
`--max-in-flight 32`	Bounds queued chunks to avoid unbounded RAM growth. Defaults to `--num-proc`.
`--link-min-combined-score 700`	Optionally keep only higher-confidence links.
`--compression zstd`	Writes compressed Parquet shards.

Validate generated local files:

bash

python scripts/validate_hf_dataset.py --data-dir data --config protein_links --split train --streaming

Validate after upload:

bash

python scripts/validate_hf_dataset.py --repo-id LiteFold/STRING --config protein_links --split train --streaming

Upload

After generating data/, upload the processed files and this dataset card:

bash

huggingface-cli upload LiteFold/STRING README.md README.md --repo-type dataset
huggingface-cli upload LiteFold/STRING data data --repo-type dataset

The repository already tracks *.parquet with Git LFS through .gitattributes.

Citation

Please cite the upstream STRING database:

bibtex

@article{szklarczyk2023string,
  title = {The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest},
  author = {Szklarczyk, Damian and Kirsch, Rebecca and Koutrouli, Mikaela and Nastou, Katerina and Mehryary, Farrokh and Hachilif, Radja and Gable, Annika L. and Fang, Tao and Doncheva, Nadezhda T. and Pyysalo, Sampo and Bork, Peer and Jensen, Lars J. and von Mering, Christian},
  journal = {Nucleic Acids Research},
  volume = {51},
  number = {D1},
  pages = {D638--D646},
  year = {2023},
  doi = {10.1093/nar/gkac1000}
}