DeepViFi

Abstract

We consider the problem of identifying viral reads in human host genome data. We pose the problem as open-set classification as reads can originate from unknown sources such as bacterial and fungal genomes. Sequence-matching methods have low sensitivity in recognizing viral reads when the viral family is highly diverged. Hidden Markov models have higher sensitivity but require domain-specific training and are difficult to repurpose for identifying different viral families. Supervised learning methods can be trained with little domain-specific knowledge but have reduced sensitivity in open-set scenarios. We present DeepViFi, a transformer-based pipeline, to detect viral reads in short-read whole genome sequence data. At 90% precision, DeepViFi achieves 90% recall compared to 15% for other deep learning methods. DeepViFi provides a semi-supervised framework to learn representations of viral families without domain-specific knowledge, and rapidly and accurately identify target sequences in open-set settings.