Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation

PeerJ

article

Akbar Adjie Pratama¹  Benjamin Bolduc¹  Ahmed A. Zayed¹  Zhi-Ping Zhong¹  Jiarong Guo¹  Dean R. Vik¹  Maria Consuelo Gazitúa⁴  James M. Wainaina¹  Simon Roux⁶  Matthew B. Sullivan¹ 

[1] Department of Microbiology, Ohio State University;Center of Microbiome Science, Ohio State University;Byrd Polar and Climate Research Center, Ohio State University;Viromica Consulting;Infectious Diseases Institute at The Ohio State University, Ohio State University;DOE Joint Genome Institute, Lawrence Berkeley National Laboratory;Environmental and Geodetic Engineering, Ohio State University, Department of Civil

关键词: Benchmarks;    Standard operating procedure;    Viruses;    Viromics;    Ecology;   

DOI  :  10.7717/peerj.11447

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundViruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs).ResultsThe in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k-mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k-mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes.ConclusionTogether, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.

【 授权许可】

CC BY