Create multi-model-plant-genome-corpus.py
Browse files
multi-model-plant-genome-corpus.py
ADDED
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from typing import List
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import datasets
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from Bio import SeqIO
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import os
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_CITATION = ""
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_DESCRIPTION = """
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Dataset made of model plants genomes available on NCBI.
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Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). 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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_CHUNK_LENGTHS = [6000,]
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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 PlantMultiSpeciesGenomesConfig(datasets.BuilderConfig):
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"""BuilderConfig for the Plant Multi Species Pre-training Dataset."""
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Copy codedef __init__(self, *args, chunk_length: int, overlap: int = 100, **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 100).
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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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self.overlap = overlap
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class PlantMultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
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"""Genomes from multiple plant species, filtered and split into chunks of consecutive
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nucleotides."""
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Copy codeVERSION = datasets.Version("1.1.0")
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BUILDER_CONFIG_CLASS = PlantMultiSpeciesGenomesConfig
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BUILDER_CONFIGS = [PlantMultiSpeciesGenomesConfig(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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}
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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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# Get the list of genome files in the plant_genomes directory
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genome_dir = "plant_genomes"
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genome_files = [os.path.join(genome_dir, f) for f in os.listdir(genome_dir) if f.endswith(".fna.gz")]
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": genome_files, "chunk_length": self.config.chunk_length})
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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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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# clean chromosome sequence
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sequence = clean_sequence(sequence)
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seq_length = len(sequence)
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# split into chunks
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num_chunks = (seq_length - 2 * self.config.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 * self.config.overlap)]
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seq_length = len(sequence)
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for i in range(num_chunks):
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# get chunk
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start_pos = i * chunk_length
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end_pos = min(seq_length, (i+1) * chunk_length + 2 * self.config.overlap)
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chunk_sequence = sequence[start_pos:end_pos]
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# yield chunk
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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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}
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key += 1</document_content>
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