Metagenomic characterization of viral communities in corals: mining biological signal from methodological noise
Bibliographic record
Abstract
Reef-building corals form close associations with organisms from all three domains of life and therefore have many potential viral hosts. Yet knowledge of viral communities associated with corals is barely explored. This complexity presents a number of challenges in terms of the metagenomic assessments of coral viral communities and requires specialized methods for purification and amplification of viral nucleic acids, as well as virome annotation. In this minireview, we conduct a meta-analysis of the limited number of existing coral virome studies, as well as available coral transcriptome and metagenome data, to identify trends and potential complications inherent in different methods. The analysis shows that the method used for viral nucleic acid isolation drastically affects the observed viral assemblage and interpretation of the results. Further, the small number of viral reference genomes available, coupled with short sequence read lengths might cause errors in virus identification. Despite these limitations and potential biases, the data show that viral communities associated with corals are diverse, with double- and single-stranded DNA and RNA viruses. The identified viruses are dominated by double-stranded DNA-tailed bacteriophages, but there are also viruses that infect eukaryote hosts, likely the endosymbiotic dinoflagellates, Symbiodinium spp., host coral and other eukaryotes in close association.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".