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README.md

@@ -1,3 +1,71 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+## Overview
+This dataset consists of reference genomes for 10 species:
+
+| Species                  | Assembly Accession                                  |
+|--------------------------|-----------------------------------------------------|
+| *Arabidopsis thaliana*   | `GCF_000001735.4_TAIR10.1`                          |
+| *Caenorhabditis elegans* | `GCF_000002985.6_WBcel235`                          |
+| *Danio rerio*            | `GCF_000002035.6_GRCz11`                            |
+| *Drosophila melanogaster*| `GCF_000001215.4_Release_6_plus_ISO1_MT`            |
+| *Felis catus*            | `GCF_018350175.1_F.catus_Fca126_mat1.0`             |
+| *Gallus gallus*          | `GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b`       |
+| *Gorilla gorilla*        | `GCF_029281585.2_NHGRI_mGorGor1-v2.0_pri`           |
+| *Homo sapiens*           | `GCF_000001405.40_GRCh38.p14`                       |
+| *Mus musculus*           | `GCF_000001635.27_GRCm39`                           |
+| *Salmo trutta*           | `GCF_901001165.1_fSalTru1.1`                        |
+
+Each item in the dataset contains the following fields:
+```
+"sequence": datasets.Value("string"),
+"species_label": datasets.ClassLabel()  # See below
+"description": datasets.Value("string"),
+"start_pos": datasets.Value("int32"),
+"end_pos": datasets.Value("int32"),
+"fasta_url": datasets.Value("string")
+```
+
+The class labels are as follows:
+| Class                       | Label |
+|-----------------------------|-------|
+| `'Homo_sapiens'`            |     0 |
+| `'Mus_musculus'`            |     1 |
+| `'Drosophila_melanogaster'` |     2 |
+| `'Danio_rerio'`             |     3 |
+| `'Caenorhabditis_elegans'`  |     4 |
+| `'Gallus_gallus'`           |     5 |
+| `'Gorilla_gorilla'`         |     6 |
+| `'Felis_catus'`             |     7 |
+| `'Salmo_trutta'`            |     8 |
+| `'Arabidopsis_thaliana'`    |     9 |
+
+
+## Usage
+To use this dataset, set the `chunk_length` (length of each sequence, in base-pairs) and the `overlap` (amount each sequence overlaps, in base-pairs).
+The dataset only contains a `train` split.
+We recommend randomly splitting the dataset to create validation/test sets.
+See below for example usage:
+
+```python
+import datasets
+
+max_length = 32_768
+overlap = 0
+
+dataset = datasets.load_dataset(
+  'yairschiff/ten_species',
+  split='train',  # original dataset only has `train` split
+  chunk_length=max_length,
+  overlap=overlap,
+  trust_remote_code=True
+)
+train_validation_splits = dataset.train_test_split(
+  test_size=0.05, seed=42)
+```
+
+
+## Acknowledgments
+Code for dataset processing is derived from https://huggingface.co/datasets/InstaDeepAI/multi_species_genomes.

ten_species.py

@@ -0,0 +1,147 @@
+from typing import List
+import datasets
+import pandas as pd
+from Bio import SeqIO
+
+_CHUNK_LENGTHS = [16384, 32768]
+
+
+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 TenSpeciesGenomesConfig(datasets.BuilderConfig):
+    """BuilderConfig for The Human Reference Genome."""
+
+    def __init__(self, *args, chunk_length: int, overlap: int = 0, **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 0).
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            name=f'{chunk_length}bp',
+            **kwargs,
+        )
+        self.chunk_length = chunk_length
+        self.overlap = overlap
+
+
+class TenSpeciesGenomes(datasets.GeneratorBasedBuilder):
+    """Genomes from 10 species, filtered and split into chunks of consecutive nucleotides.
+
+    Species include:
+        - Homo_sapiens
+        - Mus_musculus
+        - Drosophila_melanogaster
+        - Danio_rerio
+        - Caenorhabditis_elegans
+        - Gallus_gallus
+        - Gorilla_gorilla
+        - Felis_catus
+        - Salmo_trutta
+        - Arabidopsis_thaliana
+    """
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIG_CLASS = TenSpeciesGenomesConfig
+    BUILDER_CONFIGS = [TenSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS]
+    DEFAULT_CONFIG_NAME = "32768bp"
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "sequence": datasets.Value("string"),
+                "species_label": datasets.ClassLabel(
+                    num_classes=10,
+                    names=['Homo_sapiens', 'Mus_musculus', 'Drosophila_melanogaster', 'Danio_rerio',
+                           'Caenorhabditis_elegans', 'Gallus_gallus', 'Gorilla_gorilla', 'Felis_catus',
+                           'Salmo_trutta', 'Arabidopsis_thaliana']),
+                "description": datasets.Value("string"),
+                "start_pos": datasets.Value("int32"),
+                "end_pos": datasets.Value("int32"),
+                "fasta_url": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            # This defines the different columns of the dataset and their types
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+
+        urls_filepath = dl_manager.download_and_extract('ten_species_urls.csv')
+        with open(urls_filepath) as urls_file:
+            all_species = [line.rstrip().split(',')[0] for line in urls_file]
+        with open(urls_filepath) as urls_file:
+            urls = [line.rstrip().split(',')[-1] for line in urls_file]
+        all_species = tuple(all_species)
+        downloaded_files = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"all_species": all_species, "files": downloaded_files,
+                            "chunk_length": self.config.chunk_length, "overlap": self.config.overlap}
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, all_species, files, chunk_length, overlap):
+        key = 0
+        for species, file in zip(all_species, 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,
+                            'species_label': species,
+                            'start_pos': start_pos,
+                            'end_pos': end_pos,
+                            'fasta_url': file.split('::')[-1]
+                        }
+                        key += 1

ten_species_urls.csv

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+Homo_sapiens,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/latest_assembly_versions/GCF_000001405.40_GRCh38.p14/GCF_000001405.40_GRCh38.p14_genomic.fna.gz
+Mus_musculus,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Mus_musculus/latest_assembly_versions/GCF_000001635.27_GRCm39/GCF_000001635.27_GRCm39_genomic.fna.gz
+Drosophila_melanogaster,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/invertebrate/Drosophila_melanogaster/latest_assembly_versions/GCF_000001215.4_Release_6_plus_ISO1_MT/GCF_000001215.4_Release_6_plus_ISO1_MT_genomic.fna.gz
+Danio_rerio,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_other/Danio_rerio/latest_assembly_versions/GCF_049306965.1_GRCz12tu/GCF_049306965.1_GRCz12tu_genomic.fna.gz
+Caenorhabditis_elegans,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/invertebrate/Caenorhabditis_elegans/latest_assembly_versions/GCF_000002985.6_WBcel235/GCF_000002985.6_WBcel235_genomic.fna.gz
+Gallus_gallus,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_other/Gallus_gallus/latest_assembly_versions/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b_genomic.fna.gz
+Gorilla_gorilla,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Gorilla_gorilla/latest_assembly_versions/GCF_029281585.2_NHGRI_mGorGor1-v2.1_pri/GCF_029281585.2_NHGRI_mGorGor1-v2.1_pri_genomic.fna.gz
+Felis_catus,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Felis_catus/latest_assembly_versions/GCF_018350175.1_F.catus_Fca126_mat1.0/GCF_018350175.1_F.catus_Fca126_mat1.0_genomic.fna.gz
+Salmo_trutta,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_other/Salmo_trutta/latest_assembly_versions/GCF_901001165.1_fSalTru1.1/GCF_901001165.1_fSalTru1.1_genomic.fna.gz
+Arabidopsis_thaliana,https://ftp.ncbi.nlm.nih.gov/genomes/refseq/plant/Arabidopsis_thaliana/latest_assembly_versions/GCF_000001735.4_TAIR10.1/GCF_000001735.4_TAIR10.1_genomic.fna.gz