Combining genomic sequencing methods to explore viral diversity and reveal potential virus-host interactions
Bibliographic record
Abstract
Viral diversity and virus-host interactions in oxygen-starved regions of the ocean, also known as oxygen minimum zones (OMZs), remain relatively unexplored. Microbial community metabolism in OMZs alters nutrient and energy flow through marine food webs, resulting in biological nitrogen loss and greenhouse gas production. Thus, viruses infecting OMZ microbes have the potential to modulate community metabolism with resulting feedback on ecosystem function. Here, we describe viral communities inhabiting oxic surface (10 m) and oxygen-starved basin (200 m) waters of Saanich Inlet, a seasonally anoxic fjord on the coast of Vancouver Island, British Columbia using viral metagenomics and complete viral fosmid sequencing on samples collected between April 2007 and April 2010. Of 6459 open reading frames (ORFs) predicted across all 34 viral fosmids, 77.6% (n = 5010) had no homology to reference viral genomes. These fosmids recruited a higher proportion of viral metagenomic sequences from Saanich Inlet than from nearby northeastern subarctic Pacific Ocean (Line P) waters, indicating differences in the viral communities between coastal and open ocean locations. While functional annotations of fosmid ORFs were limited, recruitment to NCBI's non-redundant "nr" database and publicly available single-cell genomes identified putative viruses infecting marine thaumarchaeal and SUP05 proteobacteria to provide potential host linkages with relevance to coupled biogeochemical cycling processes in OMZ waters. Taken together, these results highlight the power of coupled analyses of multiple sequence data types, such as viral metagenomic and fosmid sequence data with prokaryotic single cell genomes, to chart viral diversity, elucidate genomic and ecological contexts for previously unclassifiable viral sequences, and identify novel host interactions in natural and engineered ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".