Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing

TL;DR

This paper introduces an unsupervised, reference-free deep learning framework using VQ-VAE for detecting viral variants in wastewater sequencing data, overcoming noise and coverage challenges.

Contribution

It presents a novel VQ-VAE-based method with masked pretraining and contrastive learning for robust, discrete genomic pattern representation without needing reference genomes or labels.

Findings

Achieved 99.52% token-level accuracy on SARS-CoV-2 wastewater data.

Enhanced variant clustering with contrastive fine-tuning, improving Silhouette scores by up to 42%.

Demonstrated scalable, interpretable genomic surveillance applicable to public health.

Abstract

Wastewater-based genomic surveillance has emerged as a powerful tool for population-level viral monitoring, offering comprehensive insights into circulating viral variants across entire communities. However, this approach faces significant computational challenges stemming from high sequencing noise, low viral coverage, fragmented reads, and the complete absence of labeled variant annotations. Traditional reference-based variant calling pipelines struggle with novel mutations and require extensive computational resources. We present a comprehensive framework for unsupervised viral variant detection using Vector-Quantized Variational Autoencoders (VQ-VAE) that learns discrete codebooks of genomic patterns from k-mer tokenized sequences without requiring reference genomes or variant labels. Our approach extends the base VQ-VAE architecture with masked reconstruction pretraining for robustness to missing data and contrastive learning for highly discriminative embeddings. Evaluated on SARS-CoV-2 wastewater sequencing data comprising approximately 100,000 reads, our VQ-VAE achieves 99.52% mean token-level accuracy and 56.33% exact sequence match rate while maintaining 19.73% codebook utilization (101 of 512 codes active), demonstrating efficient discrete representation learning. Contrastive fine-tuning with different projection dimensions yields substantial clustering improvements: 64-dimensional embeddings achieve +35% Silhouette score improvement (0.31 to 0.42), while 128-dimensional embeddings achieve +42% improvement (0.31 to 0.44), clearly demonstrating the impact of embedding dimensionality on variant discrimination capability. Our reference-free framework provides a scalable, interpretable approach to genomic surveillance with direct applications to public health monitoring.