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PyTorch Integration

GCGC implements a that loads data from common bioinformatics file formats via BioPython.

The easiest way to see how this works to see Splice Site example.

Genomic Dataset

The GenomicDataset implements the interface for a PyTorch Dataset.

Assuming you have the FASTAs to read, create a dataset by first creating a TorchSequenceParser object, the alphabet, then the actual Dataset.

from gcgc.alphabet import IUPACProteinEncoding
from import GenomicDataset
from import TorchSequenceParser

length_parser = EncodedSeqLengthParser(conform_to=100)  # Make seq length uniform at 100
parser = TorchSequenceParser(
  encapsulate=False, seq_length_parser=length_parser, sequence_offset=1

alphabet = IUPACProteinEncoding()  # Use an Amino Acid vocab.

files = ['fasta1.fasta', 'fasta2.fasta']
dataset = GenomicDataset.from_paths(files, parser, alphabet=alphabet)
  • GenomicDataset, again, implements the interface.
  • TorchSequenceParser is an object that helps parse the incoming sequences to PyTorch tensors.
  • IUPACProteinEncoding is the protein encoding alphabet, think of it as the vocabulary from NLP.

Now that a Dataset exists, it can be passed to DataLoader.

from import DataLoader
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

for batch in data_loader:

  # seq_tensor is (B, S) tensor where B is the batch size and A is the sequence
  # length, which in this case is 100
  seq_tensor = batch['seq_tensor']

Use with PyTorch LSTM Models

One important gotcha is that for using PyTorch models is that typically it expects the batches to have the sequence length as the first dimension and batch size as the second. Therefore, you may want to transpose the matrix.

# Now has batch first.
seq_tensor = seq_tensor.transpose(0, 1)