macwiatrak/bacbench-ppi-stringdb-protein-sequences

Dataset for protein-protein interaction prediction across bacteria (Protein sequences)

A dataset of 10,533 bacterial genomes across 6,956 species with protein-protein interaction (PPI) scores for each genome.

The genome protein sequences and PPI scores have been extracted from STRING DB. Each row contains a set of protein sequences from a genome, ordered by their location on the chromosome and plasmids and a set of associated PPI scores. The PPI scores have been extracted using the combined score from STRING DB.

The interaction between two proteins is represented by a triple: [prot1_index, prot2_index, score]. Where to get a probability score, you must divide the score by 1000 (i.e. if the score is 721 then to get a true score do 721/1000=0.721). The index of a protein refers to the index of the protein in the protein_sequences column of the row. See example below in Usage

Usage

We recommend loading the dataset in a streaming mode to prevent memory errors.

from datasets import load_dataset


ds = load_dataset("macwiatrak/bacbench-ppi-stringdb-protein-sequences", split="validation", streaming=True)
item = next(iter(ds))

# fetch protein sequences from a genome (list of strings)
prot_seqs = item["protein_sequences"]
# fetch PPI triples labels (i.e. [prot1_index, prot2_index, score])
ppi_triples = item["triples_combined_score"]

# get protein seqs and label for one pair of proteins
prot1 = prot_seqs[ppi_triples[0][0]]
prot2 = prot_seqs[ppi_triples[0][1]]
score = ppi_triples[0][2] / 1000

# we recommend binarizing the labels based on the threshold of 0.6
binary_ppi_triples = [
  (prot1_index, prot2_index, int((score / 1000) >= 0.6)) for prot1_index, prot2_index, score in ppi_triples
]

Split

We provide train, validation and test splits with proportions of 70 / 10 / 20 (%) respectively as part of the dataset. The split was performed randomly at genome level.

See github repository for details on how to embed the dataset with DNA and protein language models as well as code to predict antibiotic resistance from sequence.

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dataset_info: features: - name: taxid dtype: int64 - name: genome_name dtype: string - name: protein_sequences sequence: string - name: protein_ids sequence: string - name: triples_combined_score sequence: sequence: int64 - name: index_level_0 dtype: int64 splits: - name: train num_bytes: 169097180197 num_examples: 7354 - name: validation num_bytes: 27541966357 num_examples: 1091 - name: test num_bytes: 50704862883 num_examples: 2088 download_size: 53853247305 dataset_size: 247344009437 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-*