Quantitative Assessment of Nucleocytoplasmic Large DNA Virus and Host Interactions Predicted by Co-occurrence Analyses. Meng, L., Endo, H., Blanc-Mathieu, R., Chaffron, S., Hernandez-Velazquez, R., Kaneko, H., & Ogata, H. mSphere, 2021. Meng, Lingjie Endo, Hisashi Blanc-Mathieu, Romain Chaffron, Samuel Hernandez-Velazquez, Rodrigo Kaneko, Hiroto Ogata, Hiroyuki eng mSphere. 2021 Apr 21;6(2). pii: 6/2/e01298-20. doi: 10.1128/mSphere.01298-20.![Quantitative Assessment of Nucleocytoplasmic Large DNA Virus and Host Interactions Predicted by Co-occurrence Analyses [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Nucleocytoplasmic large DNA viruses (NCLDVs) are highly diverse and abundant in marine environments. However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. Our results underpin the usage of co-occurrence approaches for the metagenomic exploration of the ecology of this diverse group of viruses.
@article{RN250,
author = {Meng, L. and Endo, H. and Blanc-Mathieu, R. and Chaffron, S. and Hernandez-Velazquez, R. and Kaneko, H. and Ogata, H.},
title = {Quantitative Assessment of Nucleocytoplasmic Large DNA Virus and Host Interactions Predicted by Co-occurrence Analyses},
journal = {mSphere},
volume = {6},
number = {2},
note = {Meng, Lingjie
Endo, Hisashi
Blanc-Mathieu, Romain
Chaffron, Samuel
Hernandez-Velazquez, Rodrigo
Kaneko, Hiroto
Ogata, Hiroyuki
eng
mSphere. 2021 Apr 21;6(2). pii: 6/2/e01298-20. doi: 10.1128/mSphere.01298-20.},
abstract = {Nucleocytoplasmic large DNA viruses (NCLDVs) are highly diverse and abundant in marine environments. However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. Our results underpin the usage of co-occurrence approaches for the metagenomic exploration of the ecology of this diverse group of viruses.},
keywords = {Ncldv
Tara Oceans
assessment
co-occurrence
host prediction},
ISSN = {2379-5042 (Electronic)
2379-5042 (Linking)},
DOI = {10.1128/mSphere.01298-20},
url = {https://www.ncbi.nlm.nih.gov/pubmed/33883262},
year = {2021},
type = {Journal Article}
}
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However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. 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However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. 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