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"""Script for the multi-species genomes dataset. This dataset contains the genomes |
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from 850 different species.""" |
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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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_CITATION = """\ |
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@article{o2016reference, |
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title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation}, |
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author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-White, Brian and Ako-Adjei, Danso and others}, |
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journal={Nucleic acids research}, |
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volume={44}, |
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number={D1}, |
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pages={D733--D745}, |
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year={2016}, |
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publisher={Oxford University Press} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Dataset made of diverse genomes available on NCBI and coming from ~850 different species. |
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Test and validation are made of 50 species each. The rest of the genomes are used for training. |
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Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). Similarly, |
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the "12kbp"configuration yields chunks of 12.2kbp. The chunks of DNA are cleaned and processed so that |
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they can only contain the letters A, T, C, G and N. |
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""" |
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_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/" |
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_LICENSE = "https://www.ncbi.nlm.nih.gov/home/about/policies/" |
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url_df = pd.read_csv('urls.csv') |
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urls = list(url_df['URL']) |
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_TEST_URLS = urls[-50:] |
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_VALIDATION_URLS = urls[-100:-50] |
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_TRAIN_URLS = urls[:-100] |
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_CHUNK_LENGTHS = [6000, 12000] |
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_OVERLAP = 100 |
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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 MultiSpeciesGenomesConfig(datasets.BuilderConfig): |
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"""BuilderConfig for The Human Reference Genome.""" |
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def __init__(self, *args, chunk_length: int, **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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**kwargs: keyword arguments forwarded to super. |
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""" |
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num_kbp = int(chunk_length/1000) |
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super().__init__( |
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*args, |
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name=f'{num_kbp}kbp', |
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**kwargs, |
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) |
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self.chunk_length = chunk_length |
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class MultiSpeciesGenomes(datasets.GeneratorBasedBuilder): |
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"""Genomes from 850 species, filtered and split into chunks of consecutive |
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nucleotides. 50 genomes are taken for test, 50 for validation and 800 |
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for training.""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIG_CLASS = MultiSpeciesGenomesConfig |
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BUILDER_CONFIGS = [MultiSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS] |
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DEFAULT_CONFIG_NAME = "6kbp" |
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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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"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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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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train_downloaded_files = dl_manager.download_and_extract(_TRAIN_URLS) |
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test_downloaded_files = dl_manager.download_and_extract(_TEST_URLS) |
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validation_downloaded_files = dl_manager.download_and_extract(_VALIDATION_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_downloaded_files, "chunk_length": self.config.chunk_length}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": validation_downloaded_files, "chunk_length": self.config.chunk_length}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": test_downloaded_files, "chunk_length": self.config.chunk_length}), |
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] |
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def _generate_examples(self, files, chunk_length): |
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key = 0 |
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for file in 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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'description': description, |
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'start_pos': start_pos, |
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'end_pos': end_pos, |
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'fasta_url': file + '.fna.gz' |
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} |
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key += 1 |
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