import gzip import os import shutil import urllib from pathlib import Path from typing import List from tqdm import tqdm from ast import literal_eval import re import datasets import numpy as np import pandas as pd from datasets import DatasetInfo from pyfaidx import Fasta from abc import ABC, abstractmethod from Bio.Seq import Seq from Bio import SeqIO import pysam """ -------------------------------------------------------------------------------------------- Reference Genome URLS: ------------------------------------------------------------------------------------------- """ H38_REFERENCE_GENOME_URL = ( "https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/" "hg38.fa.gz" ) H19_REFERENCE_GENOME_URL = ( "https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/" "hg19.fa.gz" ) """ -------------------------------------------------------------------------------------------- Task Specific Handlers: ------------------------------------------------------------------------------------------- """ class GenomicLRATaskHandler(ABC): """ Abstract method for the Genomic LRA task handlers. """ @abstractmethod def __init__(self, **kwargs): pass @abstractmethod def get_info(self, description: str) -> DatasetInfo: """ Returns the DatasetInfo for the task """ pass def split_generators( self, dl_manager, cache_dir_root ) -> List[datasets.SplitGenerator]: """ Downloads required files using dl_manager and separates them by split. """ return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"handler": self, "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"handler": self, "split": "test"} ), ] @abstractmethod def generate_examples(self, split): """ A generator that yields examples for the specified split. """ pass @staticmethod def hook(t): last_b = [0] def inner(b=1, bsize=1, tsize=None): """ b : int, optional Number of blocks just transferred [default: 1]. bsize : int, optional Size of each block (in tqdm units) [default: 1]. tsize : int, optional Total size (in tqdm units). If [default: None] remains unchanged. """ if tsize is not None: t.total = tsize t.update((b - last_b[0]) * bsize) last_b[0] = b return inner def download_and_extract_gz(self, file_url, cache_dir_root): """ Downloads and extracts a gz file into the given cache directory. Returns the full file path of the extracted gz file. Args: file_url: url of the gz file to be downloaded and extracted. cache_dir_root: Directory to extract file into. """ file_fname = Path(file_url).stem file_complete_path = os.path.join(cache_dir_root, "downloads", file_fname) if not os.path.exists(file_complete_path): if not os.path.exists(file_complete_path + ".gz"): with tqdm( unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=file_url.split("/")[-1], ) as t: urllib.request.urlretrieve( file_url, file_complete_path + ".gz", reporthook=self.hook(t) ) with gzip.open(file_complete_path + ".gz", "rb") as file_in: with open(file_complete_path, "wb") as file_out: shutil.copyfileobj(file_in, file_out) return file_complete_path class CagePredictionHandler(GenomicLRATaskHandler): """ Handler for the CAGE prediction task. """ NUM_TRAIN = 33891 NUM_TEST = 1922 NUM_VALID = 2195 DEFAULT_LENGTH = 114688 # 896 x 128bp TARGET_SHAPE = ( 896, 50, ) # 50 is a subset of CAGE tracks from the original enformer dataset NPZ_SPLIT = 1000 # number of files per npz file. NUM_BP_PER_BIN = 128 # number of base pairs per bin in labels def __init__(self, sequence_length=DEFAULT_LENGTH, **kwargs): """ Creates a new handler for the CAGE task. Args: sequence_length: allows for increasing sequence context. Sequence length must be a multiple of 128 to align with binned labels. Note: increasing sequence length may decrease the number of usable samples. """ self.reference_genome = None self.coordinate_csv_file = None self.target_files_by_split = {} assert (sequence_length // 128) % 2 == 0, ( f"Requested sequence length must be an even multuple of 128 to align with the binned labels." ) self.sequence_length = sequence_length if self.sequence_length < self.DEFAULT_LENGTH: self.TARGET_SHAPE = (self.sequence_length//128,50) def get_info(self, description: str) -> DatasetInfo: """ Returns the DatasetInfo for the CAGE dataset. Each example includes a genomic sequence and a 2D array of labels """ features = datasets.Features( { # DNA sequence "sequence": datasets.Value("string"), # array of sequence length x num_labels "labels": datasets.Array2D(shape=self.TARGET_SHAPE, dtype="float32"), # chromosome number "chromosome":datasets.Value(dtype="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, ) def split_generators(self, dl_manager, cache_dir_root): """ Separates files by split and stores filenames in instance variables. The CAGE dataset requires reference genome, coordinate csv file,and npy files to be saved. """ # Manually download the reference genome since there are difficulties when # streaming the dataset reference_genome_file = self.download_and_extract_gz( H38_REFERENCE_GENOME_URL, cache_dir_root ) self.reference_genome = Fasta(reference_genome_file, one_based_attributes=False) self.coordinate_csv_file = dl_manager.download_and_extract( "cage_prediction/sequences_coordinates.csv" ) train_file_dict = {} for train_key, train_file in self.generate_npz_filenames( "train", self.NUM_TRAIN, folder="cage_prediction/targets_subset" ): train_file_dict[train_key] = dl_manager.download(train_file) test_file_dict = {} for test_key, test_file in self.generate_npz_filenames( "test", self.NUM_TEST, folder="cage_prediction/targets_subset" ): test_file_dict[test_key] = dl_manager.download(test_file) valid_file_dict = {} for valid_key, valid_file in self.generate_npz_filenames( "valid", self.NUM_VALID, folder="cage_prediction/targets_subset" ): valid_file_dict[valid_key] = dl_manager.download(valid_file) # convert file list to a dict keyed by target number self.target_files_by_split["train"] = train_file_dict self.target_files_by_split["test"] = test_file_dict self.target_files_by_split["validation"] = valid_file_dict return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"handler": self, "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"handler": self, "split": "validation"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"handler": self, "split": "test"} ), ] def generate_examples(self, split): """ A generator which produces examples for the given split, each with a sequence and the corresponding labels. The sequences are padded to the correct sequence length and standardized before returning. """ target_files = self.target_files_by_split[split] sequence_length = self.sequence_length key = 0 coordinates_dataframe = pd.read_csv(self.coordinate_csv_file) filtered = coordinates_dataframe[coordinates_dataframe["split"] == split] for sequential_idx, row in filtered.iterrows(): start, stop = int(row["start"]) - 1, int( row["stop"]) - 1 # -1 since vcf coords are 1-based chromosome = row['chrom'] padded_sequence = pad_sequence( chromosome=self.reference_genome[chromosome], start=start, sequence_length=sequence_length, end=stop, ) # floor npy_idx to the nearest 1000 npz_file = np.load( target_files[int((row["npy_idx"] // self.NPZ_SPLIT) * self.NPZ_SPLIT)] ) if ( split == "validation" ): # npy files are keyed by ["train", "test", "valid"] split = "valid" targets = npz_file[f"target-{split}-{row['npy_idx']}.npy"][0] # select 0 since there is extra dimension # subset the targets if sequence length is smaller than 114688 ( # DEFAULT_LENGTH) if self.sequence_length < self.DEFAULT_LENGTH: idx_diff = (self.DEFAULT_LENGTH - self.sequence_length) // 2 // 128 targets = targets[idx_diff:-idx_diff] if padded_sequence: yield key, { "labels": targets, "sequence": standardize_sequence(padded_sequence), "chromosome": re.sub("chr","",chromosome) } key += 1 @staticmethod def generate_npz_filenames(split, total, folder, npz_size=NPZ_SPLIT): """ Generates a list of filenames for the npz files stored in the dataset. Yields a tuple of floored multiple of 1000, filename Args: split: split to generate filenames for. Must be in ['train', 'test', 'valid'] due to the naming of the files. total: total number of npy targets for given split folder: folder where data is stored. npz_size: number of npy files per npz. Defaults to 1000 because this is the number currently used in the dataset. """ for i in range(total // npz_size): yield i * npz_size, f"{folder}/targets-{split}-{i * npz_size}-{i * npz_size + (npz_size - 1)}.npz" if total % npz_size != 0: yield ( npz_size * (total // npz_size), f"{folder}/targets-{split}-" f"{npz_size * (total // npz_size)}-" f"{npz_size * (total // npz_size) + (total % npz_size - 1)}.npz", ) class BulkRnaExpressionHandler(GenomicLRATaskHandler): """ Handler for the Bulk RNA Expression task. """ DEFAULT_LENGTH = 114688 def __init__(self, sequence_length=DEFAULT_LENGTH, **kwargs): """ Creates a new handler for the Bulk RNA Expression Prediction Task. Args: sequence_length: Length of the sequence around the TSS_CAGE start site """ self.reference_genome = None self.coordinate_csv_file = None self.labels_csv_file = None self.sequence_length = sequence_length def get_info(self, description: str) -> DatasetInfo: """ Returns the DatasetInfo for the Bulk RNA Expression dataset. Each example includes a genomic sequence and a list of label values. """ features = datasets.Features( { # DNA sequence "sequence": datasets.Value("string"), # list of expression values in each tissue "labels": datasets.Sequence(datasets.Value("float32")), # chromosome number "chromosome":datasets.Value(dtype="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, ) def split_generators(self, dl_manager, cache_dir_root): """ Separates files by split and stores filenames in instance variables. The Bulk RNA Expression dataset requires the reference hg19 genome, coordinate csv file,and label csv file to be saved. """ # Manually download the reference genome since there are difficulties when streaming reference_genome_file = self.download_and_extract_gz( H19_REFERENCE_GENOME_URL, cache_dir_root ) self.reference_genome = Fasta(reference_genome_file, one_based_attributes=False) self.coordinate_csv_file = dl_manager.download_and_extract( "bulk_rna_expression/gene_coordinates.csv" ) self.labels_csv_file = dl_manager.download_and_extract( "bulk_rna_expression/rna_expression_values.csv" ) return super().split_generators(dl_manager, cache_dir_root) def generate_examples(self, split): """ A generator which produces examples for the given split, each with a sequence and the corresponding labels. The sequences are padded to the correct sequence length and standardized before returning. """ coordinates_df = pd.read_csv(self.coordinate_csv_file) labels_df = pd.read_csv(self.labels_csv_file) coordinates_split_df = coordinates_df[coordinates_df["split"] == split] key = 0 for idx, coordinates_row in coordinates_split_df.iterrows(): start = coordinates_row[ "CAGE_representative_TSS"] - 1 # -1 since vcf coords are 1-based chromosome = coordinates_row["chrom"] labels_row = labels_df.loc[idx].values padded_sequence = pad_sequence( chromosome=self.reference_genome[chromosome], start=start, sequence_length=self.sequence_length, negative_strand=coordinates_row["strand"] == "-", ) if padded_sequence: yield key, { "labels": labels_row, "sequence": standardize_sequence(padded_sequence), "chromosome":re.sub("chr","",chromosome) } key += 1 class VariantEffectPredictionHandler(GenomicLRATaskHandler): """ Handler for the Variant Effect Prediction task. """ DEFAULT_LENGTH = 114688 def __init__(self, sequence_length=DEFAULT_LENGTH, **kwargs): """ Creates a new handler for the Variant Effect Prediction Task. Args: sequence_length: Length of the sequence to pad around the SNP position """ self.reference_genome = None self.sequence_length = sequence_length def get_info(self, description: str) -> DatasetInfo: """ Returns the DatasetInfo for the Variant Effect Prediction dataset. Each example includes a genomic sequence with the reference allele as well as the genomic sequence with the alternative allele, and a binary label. """ features = datasets.Features( { # DNA sequence "ref_forward_sequence": datasets.Value("string"), "alt_forward_sequence": datasets.Value("string"), # binary label "label": datasets.Value(dtype="int8"), # tissue type "tissue": datasets.Value(dtype="string"), # chromosome number "chromosome": datasets.Value(dtype="string"), # distance to nearest tss "distance_to_nearest_tss":datasets.Value(dtype="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, ) def split_generators(self, dl_manager, cache_dir_root): """ Separates files by split and stores filenames in instance variables. The variant effect prediction dataset requires the reference hg38 genome and coordinates_labels_csv_file to be saved. """ # Manually download the reference genome since there are difficulties # when streaming reference_genome_file = self.download_and_extract_gz( H38_REFERENCE_GENOME_URL, cache_dir_root ) self.reference_genome = Fasta(reference_genome_file, one_based_attributes=False) self.coordinates_labels_csv_file = dl_manager.download_and_extract( f"variant_effect_gene_expression/All_Tissues.csv" ) return super().split_generators(dl_manager, cache_dir_root) def generate_examples(self, split): """ A generator which produces examples each with ref/alt allele and corresponding binary label. The sequences are extended to the desired sequence length and standardized before returning. """ coordinates_df = pd.read_csv(self.coordinates_labels_csv_file) coordinates_split_df = coordinates_df[coordinates_df["split"] == split] key = 0 for idx, row in coordinates_split_df.iterrows(): start = row["POS"] - 1 # sub 1 to create idx since vcf coords are 1-based alt_allele = row["ALT"] label = row["label"] tissue = row['tissue'] chromosome = row["CHROM"] distance = int(row["distance_to_nearest_TSS"]) # get reference forward sequence ref_forward = pad_sequence( chromosome=self.reference_genome[chromosome], start=start, sequence_length=self.sequence_length, negative_strand=False, ) # only if a valid sequence returned if ref_forward: # Mutate sequence with the alt allele at the SNP position, which is always # centered in the string returned from pad_sequence alt_forward = list(ref_forward) alt_forward[self.sequence_length // 2] = alt_allele alt_forward = "".join(alt_forward) yield key, { "label": label, "tissue": tissue, "chromosome": re.sub("chr", "", chromosome), "ref_forward_sequence": standardize_sequence(ref_forward), "alt_forward_sequence": standardize_sequence(alt_forward), "distance_to_nearest_tss": distance } key += 1 """ -------------------------------------------------------------------------------------------- Dataset loader: ------------------------------------------------------------------------------------------- """ _DESCRIPTION = """ Dataset for benchmark of genomic deep learning models. """ _TASK_HANDLERS = { "cage_prediction": CagePredictionHandler, "bulk_rna_expression": BulkRnaExpressionHandler, "variant_effect_gene_expression": VariantEffectPredictionHandler, } # define dataset configs class GenomicsLRAConfig(datasets.BuilderConfig): """ BuilderConfig. """ def __init__(self, *args, task_name: str, **kwargs): # type: ignore """BuilderConfig for the location tasks dataset. Args: **kwargs: keyword arguments forwarded to super. """ super().__init__() self.handler = _TASK_HANDLERS[task_name](task_name=task_name,**kwargs) # DatasetBuilder class GenomicsLRATasks(datasets.GeneratorBasedBuilder): """ Tasks to annotate human genome. """ VERSION = datasets.Version("1.1.0") BUILDER_CONFIG_CLASS = GenomicsLRAConfig def _info(self) -> DatasetInfo: return self.config.handler.get_info(description=_DESCRIPTION) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: """ Downloads data files and organizes it into train/test/val splits """ return self.config.handler.split_generators(dl_manager, self._cache_dir_root) def _generate_examples(self, handler, split): """ Read data files and create examples(yield) Args: handler: The handler for the current task split: A string in ['train', 'test', 'valid'] """ yield from handler.generate_examples(split) """ -------------------------------------------------------------------------------------------- Global Utils: ------------------------------------------------------------------------------------------- """ def standardize_sequence(sequence: str): """ Standardizes the sequence by replacing all unknown characters with N and converting to all uppercase. Args: sequence: genomic sequence to standardize """ pattern = "[^ATCG]" # all characters to upper case sequence = sequence.upper() # replace all characters that are not A,T,C,G with N sequence = re.sub(pattern, "N", sequence) return sequence def pad_sequence(chromosome, start, sequence_length, end=None, negative_strand=False): """ Extends a given sequence to length sequence_length. If padding to the given length is outside the gene, returns None. Args: chromosome: Chromosome from pyfaidx extracted Fasta. start: Start index of original sequence. sequence_length: Desired sequence length. If sequence length is odd, the remainder is added to the end of the sequence. end: End index of original sequence. If no end is specified, it creates a centered sequence around the start index. negative_strand: If negative_strand, returns the reverse compliment of the sequence """ if end: pad = (sequence_length - (end - start)) // 2 start = start - pad end = end + pad + (sequence_length % 2) else: pad = sequence_length // 2 end = start + pad + (sequence_length % 2) start = start - pad if start < 0 or end >= len(chromosome): return if negative_strand: return chromosome[start:end].reverse.complement.seq return chromosome[start:end].seq