Delete plant-multi-species-genomes.py

plant-multi-species-genomes.py

@@ -1,173 +0,0 @@
-
-"""Script for the plant multi-species genomes dataset. This dataset contains the genomes
-from 48 different species."""
-
-from typing import List
-import datasets
-from Bio import SeqIO
-import os
-
-
-# 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 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