Genome_database.py

import datasets
from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
from typing import Any, Dict, List, Tuple
import os
import gzip
import re

class GenomeDatasetConfig(datasets.BuilderConfig):
    def __init__(self,*args, num_urls: int, **kwargs):
        super(GenomeDatasetConfig, self).__init__(**kwargs)
        self.num_urls = num_urls

class GenomeDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = GenomeDatasetConfig

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features({
                "DNA_id": datasets.Value("string"),
                "organism": datasets.Value("string"),
                "year": datasets.Value("string"),
                "region_type": datasets.Value("string"),
                "specific_class": datasets.Value("string"),
                "product": datasets.Value("string"),
                "sequence": datasets.Value("string"),
                "gc_content": datasets.Value("float"),
                "translation_code": datasets.Value("string"),
                "start_postion": datasets.Value("int32"),
                "end_position": datasets.Value("int32"),
            })
        )

#    def load_genome_dataset(num_urls=None):
#    builder_kwargs = {}
#    if num_urls is not None:
#        builder_kwargs['config'] = GenomeDatasetConfig(num_urls=num_urls)
#    return load_dataset("GenomeDataset", builder_kwargs=builder_kwargs)
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
      urls_filepath = dl_manager.download_and_extract('urlfile.txt')
      with open(urls_filepath) as urls_file:
        downloaded_files = [line.rstrip() for line in urls_file]

      num_urls = self.config.num_urls or len(downloaded_files)
      downloaded_files = downloaded_files[:num_urls]

      train_files = downloaded_files[:int(len(downloaded_files) * 0.8)] # first 80% for training
      test_files = downloaded_files[int(len(downloaded_files) * 0.8):]  # last 20% for testing

      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN,
              gen_kwargs={"filepaths": train_files}
          ),
          datasets.SplitGenerator(
              name=datasets.Split.TEST,
              gen_kwargs={"filepaths": test_files}
          )
      ]


    def _generate_examples(self, filepaths: List[str]) -> Tuple[str, Dict[str, Any]]:
        split_regex = re.compile('-')
        id_ = 0  
        for filepath in filepaths:
            if filepath.endswith(".seq.gz"):
                with gzip.open(filepath, 'rt') as handle:
                    for record in SeqIO.parse(handle, "genbank"):
                        if 'molecule_type' in record.annotations and record.annotations['molecule_type'] == 'DNA':
                            organism = record.annotations.get('organism', 'unknown')
                            collection_date = record.annotations.get('date', 'unknown')
                            year = split_regex.split(collection_date)[-1] if '-' in collection_date else collection_date
                            for feature in record.features:
                                seq = feature.extract(record.seq)
                                gc_content = gc_fraction(seq)
                                start_position = int(feature.location.start)
                                end_position = int(feature.location.end)
                                if feature.type in ['rRNA', 'tRNA','CDS','tmRNA','mRNA','mat_peptide','sig_peptide','propeptide']:
                                    region_type = 'coding'
                                    product = feature.qualifiers.get('product', ['Unknown'])[0]
                                    if feature.type == 'CDS':
                                        translation = feature.qualifiers.get('translation', ['NA'])[0]
                                        specific_class = 'Protein'
                                    else:
                                        translation = 'NA'
                                        specific_class = feature.type
                                elif feature.type == 'regulatory':
                                    region_type = feature.type
                                    specific_class = feature.qualifiers.get('regulatory_class', ['regulatory'])[0]
                                    translation = 'NA'
                                    product = feature.qualifiers.get('product', ['NA'])[0]
                                elif feature.type == 'gene':
                                    continue
                                else:
                                    if 'product' in feature.qualifiers:
                                        product = feature.qualifiers.get('product')[0]
                                        region_type = 'coding'
                                        specific_class = feature.type
                                    else:
                                        product = 'NA'
                                        region_type = feature.type
                                        specific_class = 'NA'
                                    translation = 'NA'
                                yield str(id_), {
                                    'DNA_id': record.id,
                                    'organism': organism, 
                                    'year': year, 
                                    'region_type': region_type, 
                                    'specific_class': specific_class, 
                                    'product': product, 
                                    'sequence': str(seq), 
                                    'gc_content': gc_content, 
                                    'translation_code': translation,
                                    'start_postion': start_position,
                                    'end_position': end_position
                                }
                                id_+= 1