macwiatrak/bacbench-essential-genes-dna

Dataset for essential genes prediction in bacterial genomes (DNA)

A dataset of 169,408 genes with gene essentiality labels (binary) from 51 bacterial genomes across 37 species.

The gene essentiality labels have been extracted from the Database of Essential Genes and the genome DNA sequences have been extracted from GenBank. Each row contains whole bacterial genome together with annotated gene locations on the genome (start and end). We excluded duplicates and genomes with incomplete genomes.

Labels

The labels for each gene are provided in the dataset in the essential column.

Usage

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

from datasets import load_dataset


ds = load_dataset("macwiatrak/bacbench-essential-genes-dna", split="validation")

# fetch an example
item = ds[0]
# get gene sequences
dna_seq = item['dna_seq']
gene_dna_seqs = [
  item['dna_seq'][int(start - 1):int(end)] for start, end in zip(item["start"], item["end"])
]
# get labels
labels = item["essential"]

Split

We provide 60 / 20 /20 (%) train, validation and test splits respectively as part of the dataset. The split has been performed by randomly.

See github repository for details on how to embed the dataset with DNA and protein language models as well as code to predict gene essentiality. For coding sequence representation of the genome see the macwiatrak/bacbench-essential-genes-protein-sequences dataset.

---

dataset_info: features: - name: genome_name dtype: string - name: essential sequence: string - name: start sequence: int64 - name: end sequence: int64 - name: protein_id sequence: string - name: strand sequence: float64 - name: product sequence: string - name: dna_seq dtype: string - name: index_level_0 dtype: int64 splits: - name: train num_bytes: 120955501 num_examples: 30 - name: validation num_bytes: 45990995 num_examples: 11 - name: test num_bytes: 36170463 num_examples: 10 download_size: 92709156 dataset_size: 203116959 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-*