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from typing import List |
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import datasets |
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import pandas as pd |
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from Bio import SeqIO |
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_CHUNK_LENGTHS = [16384, 32768] |
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def filter_fn(char: str) -> str: |
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""" |
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Transforms any letter different from a base nucleotide into an 'N'. |
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""" |
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if char in {'A', 'T', 'C', 'G'}: |
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return char |
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else: |
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return 'N' |
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def clean_sequence(seq: str) -> str: |
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""" |
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Process a chunk of DNA to have all letters in upper and restricted to |
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A, T, C, G and N. |
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""" |
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seq = seq.upper() |
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seq = map(filter_fn, seq) |
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seq = ''.join(list(seq)) |
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return seq |
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class TenSpeciesGenomesConfig(datasets.BuilderConfig): |
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"""BuilderConfig for The Human Reference Genome.""" |
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def __init__(self, *args, chunk_length: int, overlap: int = 0, **kwargs): |
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"""BuilderConfig for the multi species genomes. |
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Args: |
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chunk_length (:obj:`int`): Chunk length. |
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overlap: (:obj:`int`): Overlap in base pairs for two consecutive chunks (defaults to 0). |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__( |
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*args, |
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name=f'{chunk_length}bp', |
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**kwargs, |
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) |
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self.chunk_length = chunk_length |
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self.overlap = overlap |
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class TenSpeciesGenomes(datasets.GeneratorBasedBuilder): |
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"""Genomes from 10 species, filtered and split into chunks of consecutive nucleotides. |
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Species include: |
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- Homo_sapiens |
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- Mus_musculus |
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- Drosophila_melanogaster |
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- Danio_rerio |
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- Caenorhabditis_elegans |
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- Gallus_gallus |
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- Gorilla_gorilla |
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- Felis_catus |
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- Salmo_trutta |
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- Arabidopsis_thaliana |
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""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIG_CLASS = TenSpeciesGenomesConfig |
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BUILDER_CONFIGS = [TenSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS] |
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DEFAULT_CONFIG_NAME = "32768bp" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"sequence": datasets.Value("string"), |
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"species_label": datasets.ClassLabel( |
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num_classes=10, |
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names=['Homo_sapiens', 'Mus_musculus', 'Drosophila_melanogaster', 'Danio_rerio', |
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'Caenorhabditis_elegans', 'Gallus_gallus', 'Gorilla_gorilla', 'Felis_catus', |
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'Salmo_trutta', 'Arabidopsis_thaliana']), |
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"description": datasets.Value("string"), |
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"start_pos": datasets.Value("int32"), |
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"end_pos": datasets.Value("int32"), |
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"fasta_url": datasets.Value("string") |
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} |
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) |
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return datasets.DatasetInfo( |
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features=features, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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urls_filepath = dl_manager.download_and_extract('ten_species_urls.csv') |
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with open(urls_filepath) as urls_file: |
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all_species = [line.rstrip().split(',')[0] for line in urls_file] |
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with open(urls_filepath) as urls_file: |
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urls = [line.rstrip().split(',')[-1] for line in urls_file] |
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all_species = tuple(all_species) |
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downloaded_files = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"all_species": all_species, "files": downloaded_files, |
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"chunk_length": self.config.chunk_length, "overlap": self.config.overlap} |
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), |
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] |
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def _generate_examples(self, all_species, files, chunk_length, overlap): |
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key = 0 |
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for species, file in zip(all_species, files): |
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with open(file, 'rt') as f: |
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fasta_sequences = SeqIO.parse(f, 'fasta') |
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for record in fasta_sequences: |
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sequence, description = str(record.seq), record.description |
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sequence = clean_sequence(sequence) |
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seq_length = len(sequence) |
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num_chunks = (seq_length - 2 * overlap) // chunk_length |
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if num_chunks < 1: |
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continue |
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sequence = sequence[:(chunk_length * num_chunks + 2 * overlap)] |
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seq_length = len(sequence) |
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for i in range(num_chunks): |
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start_pos = i * chunk_length |
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end_pos = min(seq_length, (i+1) * chunk_length + 2 * overlap) |
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chunk_sequence = sequence[start_pos:end_pos] |
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yield key, { |
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'sequence': chunk_sequence, |
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'species_label': species, |
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'start_pos': start_pos, |
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'end_pos': end_pos, |
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'fasta_url': file.split('::')[-1] |
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} |
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key += 1 |
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