Interpretable detection of novel human viruses from genome sequencing data.

ABSTRACT: Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangl

SUBMITTER: Bartoszewicz JM 

PROVIDER: S-EPMC7849996 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature