File size: 7,310 Bytes
198549f 724e064 8425135 198549f 2945b99 8425135 198549f e47472b 198549f 4f59a0c 898a3ad 198549f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script
# contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script for the multi-species genomes dataset. This dataset contains the genomes
from 850 different species."""
from typing import List
import datasets
import pandas as pd
from Bio import SeqIO
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{o2016reference,
title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation},
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},
journal={Nucleic acids research},
volume={44},
number={D1},
pages={D733--D745},
year={2016},
publisher={Oxford University Press}
}
"""
# You can copy an official description
_DESCRIPTION = """\
Dataset made of diverse genomes available on NCBI and coming from ~850 different species.
Test and validation are made of 50 species each. The rest of the genomes are used for training.
Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). Similarly,
the "12kbp"configuration yields chunks of 12.2kbp. 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, 12000]
_OVERLAP = 100
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 MultiSpeciesGenomesConfig(datasets.BuilderConfig):
"""BuilderConfig for The Human Reference Genome."""
def __init__(self, *args, chunk_length: int, **kwargs):
"""BuilderConfig for the multi species genomes.
Args:
chunk_length (:obj:`int`): Chunk length.
**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
class MultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
"""Genomes from 850 species, filtered and split into chunks of consecutive
nucleotides. 50 genomes are taken for test, 50 for validation and 800
for training."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIG_CLASS = MultiSpeciesGenomesConfig
BUILDER_CONFIGS = [MultiSpeciesGenomesConfig(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"),
"fasta_url": datasets.Value("string")
}
)
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]:
urls_file = dl_manager.download_and_extract('urls.csv')
urls_df = pd.read_csv(urls_file)
urls = list(urls_df['URL'])
test_urls = urls[-50:] # 50 genomes for test set
validation_urls = urls[-100:-50] # 50 genomes for validation set
train_urls = urls[:-100] # 800 genomes for training
train_downloaded_files = dl_manager.download_and_extract(train_urls)
test_downloaded_files = dl_manager.download_and_extract(test_urls)
validation_downloaded_files = dl_manager.download_and_extract(validation_urls)
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 * _OVERLAP) // chunk_length
if num_chunks < 1:
continue
sequence = sequence[:(chunk_length * num_chunks + 2 * _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 * _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,
'fasta_url': file.split('::')[-1]
}
key += 1
|