epigenetic_marks_pham2005.py

# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


_CITATION = """\
    @article{pham2005qualitatively,
  title={Qualitatively predicting acetylation and methylation areas in dna sequences},
  author={Pham, Tho Hoan and Tran, Dang Hung and Ho, Tu Bao and Satou, Kenji and Valiente, Gabriel},
  journal={Genome Informatics},
  volume={16},
  number={2},
  pages={3--11},
  year={2005},
  publisher={Japanese Society for Bioinformatics}
}
"""

_LICENSE = ""

_DESCRIPTION = """\
    This contains datasets of histone occupancy, acetylation, and methylation by ChiP-Chip protocol in vivo from Pham et al., as retrieved from https://www.jaist.ac.jp/~tran/nucleosome/members.htm in January 2023.
"""

_HOMEPAGE = "https://www.jaist.ac.jp/~tran/nucleosome/"

FILES = {
    "h3": [
        "data/processed/h3/fold_0.csv",
        "data/processed/h3/fold_1.csv",
        "data/processed/h3/fold_2.csv",
    ],
    "h3k14ac": [
        "data/processed/h3k14ac/fold_0.csv",
        "data/processed/h3k14ac/fold_1.csv",
        "data/processed/h3k14ac/fold_2.csv",
    ],
    "h3k36me3": [
        "data/processed/h3k36me3/fold_0.csv",
        "data/processed/h3k36me3/fold_1.csv",
        "data/processed/h3k36me3/fold_2.csv",
    ],
    "h3k4me1": [
        "data/processed/h3k4me1/fold_0.csv",
        "data/processed/h3k4me1/fold_1.csv",
        "data/processed/h3k4me1/fold_2.csv",
    ],
    "h3k4me2": [
        "data/processed/h3k4me2/fold_0.csv",
        "data/processed/h3k4me2/fold_1.csv",
        "data/processed/h3k4me2/fold_2.csv",
    ],
    "h3k79me3": [
        "data/processed/h3k79me3/fold_0.csv",
        "data/processed/h3k79me3/fold_1.csv",
        "data/processed/h3k79me3/fold_2.csv",
    ],
    "h3k9ac": [
        "data/processed/h3k9ac/fold_0.csv",
        "data/processed/h3k9ac/fold_1.csv",
        "data/processed/h3k9ac/fold_2.csv",
    ],
    "h4": [
        "data/processed/h4/fold_0.csv",
        "data/processed/h4/fold_1.csv",
        "data/processed/h4/fold_2.csv",
    ],
}


class EpigeneticMarks(datasets.GeneratorBasedBuilder):
    """
    This contains datasets of histone occupancy, acetylation, and methylation by ChiP-Chip protocol in vivo from Pham et al., as retrieved from https://www.jaist.ac.jp/~tran/nucleosome/members.htm in January 2023.
    """

    VERSION = datasets.Version("0.0.1")

    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="h3",
            version=VERSION,
            description="H3 occupancy",
        ),
        datasets.BuilderConfig(
            name="h4",
            version=VERSION,
            description="H4 occupancy",
        ),
        datasets.BuilderConfig(
            name="h3k9ac",
            version=VERSION,
            description="H3K9 acetylation relative to H3",
        ),
        datasets.BuilderConfig(
            name="h3k14ac",
            version=VERSION,
            description="H3K14 acetylation relative to H3",
        ),
        # datasets.BuilderConfig(
        #     name="h4ac",
        #     version=VERSION,
        #     description="H4 acetylation relative to H3",
        # ),
        datasets.BuilderConfig(
            name="h3k4me1",
            version=VERSION,
            description="H3K4 monomethylation relative to H3",
        ),
        datasets.BuilderConfig(
            name="h3k4me2",
            version=VERSION,
            description="H3K4 dimethylation relative to H3",
        ),
        # datasets.BuilderConfig(
        #     name="h3k4me3",
        #     version=VERSION,
        #     description="H3K4 trimethylation relative to H3",
        # ),
        datasets.BuilderConfig(
            name="h3k36me3",
            version=VERSION,
            description="H3K36 trimethylation relative to H3",
        ),
        datasets.BuilderConfig(
            name="h3k79me3",
            version=VERSION,
            description="H3K79 trimethylation relative to H3",
        ),
    ]
    DEFAULT_CONFIG_NAME = "h3"

    def _info(self):
        features = datasets.Features(
            {
                "description": datasets.Value("string"),
                "sequence": datasets.Value("string"),
                "label": datasets.Value("int32"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("sequence", "label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        dataset = self.config.name

        downloaded_files = dl_manager.download(FILES[dataset])

        return [
            datasets.SplitGenerator(
                name=datasets.NamedSplit("fold0"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files[0],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.NamedSplit("fold1"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files[1],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.NamedSplit("fold2"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files[2],
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.DictReader(f)
            for key, data in enumerate(reader):
                yield key, {
                    "description": data["description"],
                    "sequence": data["sequence"],
                    "label": data["label"],
                }