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

Abstract

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,more » 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.« less

Authors:
 [1];  [1];  [1];  [2];  [1];  [1];  [3];  [4];  [5];  [1]
+ Show Author Affiliations
  1. The Ohio State Univ., Columbus, OH (United States); The Ohio State Univ., Columbus, OH (United States). Center of Microbiome Science
  2. 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
  3. Viromica Consulting, Santiago (Chile)
  4. 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.
  5. USDOE Joint Genome Institute (JGI), Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); Gordon and Betty Moore Foundation
OSTI Identifier:
1813767
Grant/Contract Number:  
AC02-05CH11231; OCE1829831; ABI1758974; 3790
Resource Type:
Accepted Manuscript
Journal Name:
PeerJ
Additional Journal Information:
Journal Volume: 9; Journal ID: ISSN 2167-8359
Publisher:
PeerJ Inc.
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; bioinformatics; ecology; microbiology; virology; benchmarks; standard operating procedure; viruses; viromics

Citation Formats

Pratama, Akbar Adjie, Bolduc, Benjamin, Zayed, Ahmed A., Zhong, Zhi-Ping, Guo, Jiarong, Vik, Dean R., Gazitúa, Maria Consuelo, Wainaina, James M., Roux, Simon, and Sullivan, Matthew B. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. United States: N. p., 2021. Web. doi:10.7717/peerj.11447.
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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. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. United States. https://doi.org/10.7717/peerj.11447
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Pratama, Akbar Adjie, Bolduc, Benjamin, Zayed, Ahmed A., Zhong, Zhi-Ping, Guo, Jiarong, Vik, Dean R., Gazitúa, Maria Consuelo, Wainaina, James M., Roux, Simon, and Sullivan, Matthew B. Mon . "Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation". United States. https://doi.org/10.7717/peerj.11447. https://www.osti.gov/servlets/purl/1813767.
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@article{osti_1813767,
title = {Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation},
author = {Pratama, Akbar Adjie and Bolduc, Benjamin and Zayed, Ahmed A. and Zhong, Zhi-Ping and Guo, Jiarong and Vik, Dean R. and Gazitúa, Maria Consuelo and Wainaina, James M. and Roux, Simon and Sullivan, Matthew B.},
abstractNote = {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.},
doi = {10.7717/peerj.11447},
journal = {PeerJ},
number = ,
volume = 9,
place = {United States},
year = {Mon Jun 14 00:00:00 EDT 2021},
month = {Mon Jun 14 00:00:00 EDT 2021}
}
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