Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Motivation: PCR is more economical and quicker than Next Generation
Sequencing for detecting target organisms, with primer design being a critical
step. In epidemiology with rapidly mutating viruses, designing effective
primers is challenging. Traditional methods require substantial manual
intervention and struggle to ensure effective primer design across different
strains. For organisms with large, similar genomes like Escherichia coli and
Shigella flexneri, differentiating between species is also difficult but
crucial.
Results: We developed Primer C-VAE, a model based on a Variational
Auto-Encoder framework with Convolutional Neural Networks to identify variants
and generate specific primers. Using SARS-CoV-2, our model classified variants
(alpha, beta, gamma, delta, omicron) with 98% accuracy and generated
variant-specific primers. These primers appeared with >95% frequency in target
variants and <5% in others, showing good performance in in-silico PCR tests.
For Alpha, Delta, and Omicron, our primer pairs produced fragments <200 bp,
suitable for qPCR detection. The model also generated effective primers for
organisms with longer gene sequences like E. coli and S. flexneri.
Conclusion: Primer C-VAE is an interpretable deep learning approach for
developing specific primer pairs for target organisms. This flexible,
semi-automated and reliable tool works regardless of sequence completeness and
length, allowing for qPCR applications and can be applied to organisms with
large and highly similar genomes.
