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"""Script for the plant multi-species genomes dataset. This dataset contains the genomes
from 48 different species."""
from typing import List
import datasets
import pandas as pd
from Bio import SeqIO
import os
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """"""
# You can copy an official description
_DESCRIPTION = """\
Dataset made of diverse genomes available on NCBI and coming from 48 different species.
Test and validation are made of 2 species each. The rest of the genomes are used for training.
Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side).The chunks of DNA are cleaned and processed so that
they can only contain the letters A, T, C, G and N.
"""
_HOMEPAGE = "" #"https://www.ncbi.nlm.nih.gov/"
_LICENSE = "" #"https://www.ncbi.nlm.nih.gov/home/about/policies/"
_CHUNK_LENGTHS = [6000,]
def filter_fn(char: str) -> str:
"""
Transforms any letter different from a base nucleotide into an 'N'.
"""
if char in {'A', 'T', 'C', 'G'}:
return char
else:
return 'N'
def clean_sequence(seq: str) -> str:
"""
Process a chunk of DNA to have all letters in upper and restricted to
A, T, C, G and N.
"""
seq = seq.upper()
seq = map(filter_fn, seq)
seq = ''.join(list(seq))
return seq
class PlantMultiSpeciesGenomesConfig(datasets.BuilderConfig):
"""BuilderConfig for the Plant Multi Species Pre-training Dataset."""
def __init__(self, *args, chunk_length: int, overlap: int = 100, **kwargs):
"""BuilderConfig for the multi species genomes.
Args:
chunk_length (:obj:`int`): Chunk length.
overlap: (:obj:`int`): Overlap in base pairs for two consecutive chunks (defaults to 100).
**kwargs: keyword arguments forwarded to super.
"""
num_kbp = int(chunk_length/1000)
super().__init__(
*args,
name=f'{num_kbp}kbp',
**kwargs,
)
self.chunk_length = chunk_length
self.overlap = overlap
class PlantMultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
"""Genomes from 48 species, filtered and split into chunks of consecutive
nucleotides. 2 genomes are taken for test, 2 for validation and 44
for training."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIG_CLASS = PlantMultiSpeciesGenomesConfig
BUILDER_CONFIGS = [PlantMultiSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS]
DEFAULT_CONFIG_NAME = "6kbp"
def _info(self):
features = datasets.Features(
{
"sequence": datasets.Value("string"),
"description": datasets.Value("string"),
"start_pos": datasets.Value("int32"),
"end_pos": datasets.Value("int32"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
filepaths_txt = dl_manager.download_and_extract('plant_genome_file_names.txt')
with open(filepaths_txt) as f:
filepaths = [os.path.join("plant_genomes",filepath.rstrip()) for filepath in f]
test_paths = filepaths[-2:] # 2 genomes for test set
validation_paths = filepaths[-4:-2] # 2 genomes for validation set
train_paths = filepaths[:-4] # 44 genomes for training
train_downloaded_files = dl_manager.download_and_extract(train_paths)
test_downloaded_files = dl_manager.download_and_extract(test_paths)
validation_downloaded_files = dl_manager.download_and_extract(validation_paths)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_downloaded_files, "chunk_length": self.config.chunk_length}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": validation_downloaded_files, "chunk_length": self.config.chunk_length}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": test_downloaded_files, "chunk_length": self.config.chunk_length}),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, files, chunk_length):
key = 0
for file in files:
with open(file, 'rt') as f:
fasta_sequences = SeqIO.parse(f, 'fasta')
for record in fasta_sequences:
# parse descriptions in the fasta file
sequence, description = str(record.seq), record.description
# clean chromosome sequence
sequence = clean_sequence(sequence)
seq_length = len(sequence)
# split into chunks
num_chunks = (seq_length - 2 * self.config.overlap) // chunk_length
if num_chunks < 1:
continue
sequence = sequence[:(chunk_length * num_chunks + 2 * self.config.overlap)]
seq_length = len(sequence)
for i in range(num_chunks):
# get chunk
start_pos = i * chunk_length
end_pos = min(seq_length, (i+1) * chunk_length + 2 * self.config.overlap)
chunk_sequence = sequence[start_pos:end_pos]
# yield chunk
yield key, {
'sequence': chunk_sequence,
'description': description,
'start_pos': start_pos,
'end_pos': end_pos,
}
key += 1
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