GenVarLoader

GenVarLoader provides a fast, memory efficient data loader for training sequence models on genetic variation. For example, this can be used to train a DNA language model on human genetic variation (e.g. Nucleotide Transformer).

Features

• Respects memory budget
• Supports insertions and deletions
• Scales to 100,000s of individuals
• Fast!
• Extensible to new file formats (drop a feature request!)
• Coming soon: re-aligning tracks (e.g. expression, chromatin accessibility) to genetic variation (e.g. BigRNA)

Installation

pip install genvarloader

A PyTorch dependency is not included since it requires special instructions.

An optional dependency is TensorStore(version >=0.1.50) for writing genotypes as a Zarr store and using TensorStore for I/O. This dramatically speeds up dataloading performance when training a model on genetic variation, for which approximately uniform random sampling across the genome is required. Standard bioinformatics variant formats like VCF, BCF, and PGEN unfortunately do not have a data layout conducive for this. TensorStore is not included as a dependency due a dependency conflict that, within the scope of GenVarLoader, does not cause any issues. GenVarLoader is developed with Poetry and I am waiting for the ability to override/ignore sub-dependencies to include TensorStore as an explicit dependency.

Quick Start

import genvarloader as gvl

ref_fasta = 'reference.fasta'
variants = 'variants.pgen' # highly recommended to convert VCFs to PGEN
regions_of_interest = 'regions.bed'

Create readers for each file providing sequence data:

ref = gvl.Fasta(name='ref', path=ref_fasta, pad='N')
var = gvl.Pgen(variants)
varseq = gvl.FastaVariants(name='varseq', reference=ref, variants=var)

Put them together and get a torch.DataLoader:

gvloader = gvl.GVL(
    readers=varseq,
    bed=regions_of_interest,
    fixed_length=1000,
    batch_size=16,
    max_memory_gb=8,
    batch_dims=['sample', 'ploid'],
    shuffle=True,
)

dataloader = gvloader.torch_dataloader()

And now you're ready to use the dataloader however you need to:

# implement your training loop
for batch in dataloader:
    ...