Abstract
Background:Viruses, including bacteriophage, are important components of environmental and human associated microbial communities. Viruses can act as extracellular reservoirs of bacterial genes, can mediate microbiome dynamics, and can influence the virulence of clinical pathogens. It is essential, therefore, to have robust sequence analysis methods in place to detect and annotate viral elements within microbial communities. Various targeted metagenomic analysis techniques detect viral sequences, but these methods often exclude large and genome integrated viruses. In this study, we evaluate and compare the ability of nine state-of-the-art bioinformatic tools, including Vibrant, VirSorter, VirSorter2, VirFinder, DeepVirFinder, MetaPhinder, JGI Earth Virome Pipeline, Kraken 2, and VirBrant, to identify viral contiguous sequences (contigs) across simulated metagenomes with different read distributions, taxonomic compositions, and complexities.Results:Of the tools tested in this study, VirSorter achieved the best F1 score while Vibrant had the highest average F1 score at predicting integrated prophages. Though less balanced in its precision and recall, Kraken2 had the highest average precision by a substantial margin. We introduced the machine learning tool, VirBrant, which demonstrated an improvement in average F1 score over tools such as MetaPhinder. The tool utilizes machine learning with both protein compositional and nucleotide features. The addition of nucleotide features improves the precision and recall compared to the protein compositional features alone. Viral identification by all tools was not impacted by underlying read distribution but did improve with contig length. Tool performance was inversely related to taxonomic complexity and varied by the phage host. Rhizobium and Enterococcus phage were identified consistently by the tools; whereas, Neisseria phage were commonly missed in this study.Conclusion:This study benchmarked the performance of nine state-of-the-art bioinformatic tools to identify viral contigs across different simulation conditions. This study explored the ability of the tools to identify integrated prophage elements traditionally excluded from targeted sequencing approaches. Our comprehensive analysis of viral identification tools to assess their performance in a variety of situations provides valuable insights to viral researchers looking to mine viral elements from publicly available metagenomic data.
Searching for signatures across microbial communities: Metagenomic analysis of soil samples from mangrove and other ecosystems
