Long-read metagenomics using PromethION uncovers oral bacteriophages and their interaction with host bacteria
Bibliographic record
Abstract
Bacteriophages (phages), or bacterial viruses, are very diverse and highly abundant worldwide, including as a part of the human microbiomes. Although a few metagenomic studies have focused on oral phages, they relied on short-read sequencing. Here, we conduct a long-read metagenomic study of human saliva using PromethION. Our analyses, which integrate both PromethION and HiSeq data of >30 Gb per sample with low human DNA contamination, identify hundreds of viral contigs; 0-43.8% and 12.5-56.3% of the confidently predicted phages and prophages, respectively, do not cluster with those reported previously. Our analyses demonstrate enhanced scaffolding, and the ability to place a prophage in its host genomic context and enable its taxonomic classification. Our analyses also identify a Streptococcus phage/prophage group and nine jumbo phages/prophages. 86% of the phage/prophage group and 67% of the jumbo phages/prophages contain remote homologs of antimicrobial resistance genes. Pan-genome analysis of the phages/prophages reveals remarkable diversity, identifying 0.3% and 86.4% of the genes as core and singletons, respectively. Furthermore, our study suggests that oral phages present in human saliva are under selective pressure to escape CRISPR immunity. Our study demonstrates the power of long-read metagenomics utilizing PromethION in uncovering bacteriophages and their interaction with host bacteria.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".