Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation
Background 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. T...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-06-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/11447.pdf |
| _version_ | 1797418489366970368 |
|---|---|
| author | Akbar Adjie Pratama Benjamin Bolduc Ahmed A. Zayed Zhi-Ping Zhong Jiarong Guo Dean R. Vik Maria Consuelo Gazitúa James M. Wainaina Simon Roux Matthew B. Sullivan |
| author_facet | Akbar Adjie Pratama Benjamin Bolduc Ahmed A. Zayed Zhi-Ping Zhong Jiarong Guo Dean R. Vik Maria Consuelo Gazitúa James M. Wainaina Simon Roux Matthew B. Sullivan |
| author_sort | Akbar Adjie Pratama |
| collection | DOAJ |
| description | Background 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). Results 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. 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. Conclusion 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. |
| first_indexed | 2024-03-09T06:33:24Z |
| format | Article |
| id | doaj.art-cf9912e52ab448e0a4015084874e068b |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:33:24Z |
| publishDate | 2021-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-cf9912e52ab448e0a4015084874e068b2023-12-03T11:02:12ZengPeerJ Inc.PeerJ2167-83592021-06-019e1144710.7717/peerj.11447Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curationAkbar Adjie Pratama0Benjamin Bolduc1Ahmed A. Zayed2Zhi-Ping Zhong3Jiarong Guo4Dean R. Vik5Maria Consuelo Gazitúa6James M. Wainaina7Simon Roux8Matthew B. Sullivan9Department of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaViromica Consulting, Santiago, ChileDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of AmericaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of AmericaBackground 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). Results 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. 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. Conclusion 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.https://peerj.com/articles/11447.pdfBenchmarksStandard operating procedureVirusesViromicsEcology |
| spellingShingle | Akbar Adjie Pratama Benjamin Bolduc Ahmed A. Zayed Zhi-Ping Zhong Jiarong Guo Dean R. Vik Maria Consuelo Gazitúa James M. Wainaina Simon Roux Matthew B. Sullivan Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation PeerJ Benchmarks Standard operating procedure Viruses Viromics Ecology |
| title | Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation |
| title_full | Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation |
| title_fullStr | Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation |
| title_full_unstemmed | Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation |
| title_short | Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation |
| title_sort | expanding standards in viromics in silico evaluation of dsdna viral genome identification classification and auxiliary metabolic gene curation |
| topic | Benchmarks Standard operating procedure Viruses Viromics Ecology |
| url | https://peerj.com/articles/11447.pdf |
| work_keys_str_mv | AT akbaradjiepratama expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT benjaminbolduc expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT ahmedazayed expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT zhipingzhong expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT jiarongguo expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT deanrvik expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT mariaconsuelogazitua expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT jamesmwainaina expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT simonroux expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration AT matthewbsullivan expandingstandardsinviromicsinsilicoevaluationofdsdnaviralgenomeidentificationclassificationandauxiliarymetabolicgenecuration |