Assessing the biogeography of marine giant viruses in four oceanic transects
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Abstract
Viruses of the phylum Nucleocytoviricota are ubiquitous in ocean waters and play important roles in shaping the dynamics of marine ecosystems. In this study, we leveraged the bioGEOTRACES metagenomic dataset collected across the Atlantic and Pacific Oceans to investigate the biogeography of these viruses in marine environments. We identified 330 viral genomes, including 212 in the order Imitervirales and 54 in the order Algavirales. We found that most viruses appeared to be prevalent in shallow waters (<150 m), and that viruses of the Mesomimiviridae (Imitervirales) and Prasinoviridae (Algavirales) are by far the most abundant and diverse groups in our survey. Five mesomimiviruses and one prasinovirus are particularly widespread in oligotrophic waters; annotation of these genomes revealed common stress response systems, photosynthesis-associated genes, and oxidative stress modulation genes that may be key to their broad distribution in the pelagic ocean. We identified a latitudinal pattern in viral diversity in one cruise that traversed the North and South Atlantic Ocean, with viral diversity peaking at high latitudes of the northern hemisphere. Community analyses revealed three distinct Nucleocytoviricota communities across latitudes, categorized by latitudinal distance towards the equator. Our results contribute to the understanding of the biogeography of these viruses in marine systems.
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--phred33
This is fairly stringent phred score trimming. I'm curious why you used such a high threshold. I'd also be curious to see if dropping the phred score to ~10 increased viral genome recall in these samples
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Metagenome data set
Although this would be a separate effort from what you have reported here, I think it could be fascinating to expand this type of inquiry beyond the GEOTRACES data set. Using a tool like sourmash branchwater, you could use the viral genome database you describe above and search for metagenomes on the sequence read archive that contain those genomes. It could be a very nice complement to see what sort of global diversity/biogeography there is for these types of viruses. https://www.biorxiv.org/content/10.1101/2022.11.02.514947v1
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Subsampling reads and calculating diversity
I'm curious if you've ever explored alpha diversity estimation metrics that don't require subsampling. The StatDivLab out of UW has developed some really lovely software for doing this type of thing. DivNet in particular might work as a drop in replacement to subsampling, allowing the full amount of data captured in each sequencing run to be used while accounting for unequal sequencing depth: https://github.com/adw96/DivNet
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--phred33
This is fairly stringent phred score trimming. I'm curious why you used such a high threshold. I'd also be curious to see if dropping the phred score to ~10 increased viral genome recall in these samples
-
Metagenome data set
Although this would be a separate effort from what you have reported here, I think it could be fascinating to expand this type of inquiry beyond the GEOTRACES data set. Using a tool like sourmash branchwater, you could use the viral genome database you describe above and search for metagenomes on the sequence read archive that contain those genomes. It could be a very nice complement to see what sort of global diversity/biogeography there is for these types of viruses. https://www.biorxiv.org/content/10.1101/2022.11.02.514947v1
-
Subsampling reads and calculating diversity
I'm curious if you've ever explored alpha diversity estimation metrics that don't require subsampling. The StatDivLab out of UW has developed some really lovely software for doing this type of thing. DivNet in particular might work as a drop in replacement to subsampling, allowing the full amount of data captured in each sequencing run to be used while accounting for unequal sequencing depth: https://github.com/adw96/DivNet
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