Biology
Document Type
Article
Abstract
RESULTS: VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder's potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients.
Publication Title
Microbiome
Publication Date
7-6-2017
Volume
5
Issue
1
First Page
69
ISSN
2049-2618
DOI
10.1186/s40168-017-0283-5
Keywords
human gut, k-mer, liver cirrhosis, metagenome, virus
Repository Citation
Ren, Jie; Ahlgren, Nathan A.; Lu, Yang Young; Fuhrman, Jed A.; and Sun, Fengzhu, "VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data" (2017). Biology. 65.
https://commons.clarku.edu/faculty_biology/65
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Conditions
Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A., & Sun, F. (2017). VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome, 5, 1-20. https://doi.org/10.1186/s40168-017-0283-5
