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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".