Create plant-multi-species-genomes.py

plant-multi-species-genomes.py

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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
+
+
+# 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 = [line.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