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

Pratama, Akbar Adjie; Bolduc, Benjamin; Zayed, Ahmed A.; Zhong, Zhi-Ping; Guo, Jiarong; Vik, Dean R.; Gazitúa, Maria Consuelo; Wainaina, James M.; Roux, Simon; Sullivan, Matthew B.

doi:10.7717/peerj.11447

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

Journal Article · Mon Jun 14 00:00:00 EDT 2021 · PeerJ

DOI:https://doi.org/10.7717/peerj.11447· OSTI ID:1813767

Pratama, Akbar Adjie ^[1]; Bolduc, Benjamin ^[1]; Zayed, Ahmed A. ^[1]; Zhong, Zhi-Ping ^[2]; Guo, Jiarong ^[1]; Vik, Dean R. ^[1]; Gazitúa, Maria Consuelo ^[3]; Wainaina, James M. ^[4]; Roux, Simon ^[5]; Sullivan, Matthew B. ^[1]

The Ohio State Univ., Columbus, OH (United States); The Ohio State Univ., Columbus, OH (United States). Center of Microbiome Science
The Ohio State Univ., Columbus, OH (United States); The Ohio State Univ., Columbus, OH (United States). Center of Microbiome Science; The Ohio State Univ., Columbus, OH (United States). Byrd Polar and Climate Research Center
Viromica Consulting, Santiago (Chile)
The Ohio State Univ., Columbus, OH (United States); The Ohio State Univ., Columbus, OH (United States). Center of Microbiome Science; The Ohio State Univ., Columbus, OH (United States). Infectious Diseases Inst.
USDOE Joint Genome Institute (JGI), Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Viruses 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). The 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. Furthermore, 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. Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); Gordon and Betty Moore Foundation

Grant/Contract Number:: AC02-05CH11231; OCE1829831; ABI1758974; 3790

OSTI ID:: 1813767

Journal Information:: PeerJ, Vol. 9; ISSN 2167-8359

Publisher:: PeerJ Inc.Copyright Statement

Country of Publication:: United States

Language:: English

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Journal Article · Tue Feb 06 00:00:00 EST 2024 · Nature Microbiology · OSTI ID:1813767

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VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families

Journal Article · Wed Jan 20 00:00:00 EST 2021 · Bioinformatics · OSTI ID:1813767

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
bioinformatics
ecology
microbiology
virology
benchmarks
standard operating procedure
viruses
viromics

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

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