Biology
Document Type
Article
Abstract
Metagenomic sequencing has greatly enhanced the discovery of viral genomic sequences; however, it remains challenging to identify the host(s) of these new viruses. We developed VirHostMatcher-Net, a flexible, network-based, Markov random field framework for predicting virus–prokaryote interactions using multiple, integrated features: CRISPR sequences and alignment-free similarity measures (s2∗ and WIsH). Evaluation of this method on a benchmark set of 1462 known virus–prokaryote pairs yielded host prediction accuracy of 59% and 86% at the genus and phylum levels, representing 16–27% and 6–10% improvement, respectively, over previous single-feature prediction approaches. We applied our host prediction tool to crAssphage, a human gut phage, and two metagenomic virus datasets: marine viruses and viral contigs recovered from globally distributed, diverse habitats. Host predictions were frequently consistent with those of previous studies, but more importantly, this new tool made many more confident predictions than previous tools, up to nearly 3-fold more (n > 27 000), greatly expanding the diversity of known virus–host interactions.
Publication Title
NAR Genomics and Bioinformatics
Publication Date
6-1-2020
Volume
2
Issue
2
ISSN
2631-9268
DOI
10.1093/nargab/lqaa044
Keywords
bacteriophage, clustered regularly interspaced short palindromic repeat; gastrointestinal tract; habitat; human; Markov random field, metagenomics, nonhuman, phylum, prediction, prokaryote, virus cell interaction
Repository Citation
Wang, Weili; Ren, Jie; Tang, Kujin; Dart, Emily; Ignacio-Espinoza, Julio Cesar; Fuhrman, Jed A.; Braun, Jonathan; Sun, Fengzhu; and Ahlgren, Nathan A., "A network-based integrated framework for predicting virus–prokaryote interactions" (2020). Biology. 56.
https://commons.clarku.edu/faculty_biology/56
Cross Post Location
Student Publications
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright Conditions
Wang, W., Ren, J., Tang, K., Dart, E., Ignacio-Espinoza, J. C., Fuhrman, J. A., ... & Ahlgren, N. A. (2020). A network-based integrated framework for predicting virus–prokaryote interactions. NAR genomics and bioinformatics, 2(2), lqaa044 https://doi.org/10.1093/nargab/lqaa044
