A new Python-based package called LabTorch has been released this week, promising to simplify deep learning workflows for researchers in molecular biology. Developed by a cross-institutional team of computational biologists, LabTorch combines PyTorch’s power with domain-specific helpers for biological sequences, structures, and imaging data.
Highlights for Developers
- Native support for DNA, RNA, and protein sequence encoding
- Ready-to-use datasets from UniProt, PDB, and GenBank
- Built-in convolutional and transformer models optimized for 1D and 3D biological inputs
- Lightweight syntax that integrates easily into Jupyter notebooks and HPC clusters
Quick Example
from labtorch.sequences import encode_dna
from labtorch.models import CNN1D
seqs = ["ATGCTAGG", "CGTATCGA"]
X = encode_dna(seqs, max_len=10)
model = CNN1D(input_dim=4, num_classes=2)
output = model(X)
print(output)
Why It Matters
Most deep learning libraries are designed for general-purpose tasks like image or language processing. LabTorch focuses specifically on the needs of wet-lab biologists and computational genomics researchers. It abstracts away the complexity of input encoding and model configuration, enabling faster experimentation and reproducibility.
The toolkit has already seen early adoption in labs working on CRISPR guide RNA prediction, transcription factor binding site modeling, and cell phenotype classification.
Next Steps
LabTorch is currently available via PyPI and GitHub, with planned releases for pretrained models and integration with popular bioinformatics databases.
Sources
https://github.com/labtorch/labtorch
https://pypi.org/project/labtorch/
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