Mining bacterial NGS data vastly expands the complete genomes of temperate phages
Bibliographic record
Abstract
Temperate phages (active prophages induced from bacteria) help control pathogenicity, modulate community structure, and maintain gut homeostasis. Complete phage genome sequences are indispensable for understanding phage biology. Traditional plaque techniques are inapplicable to temperate phages due to their lysogenicity, curbing their identification and characterization. Existing bioinformatics tools for prophage prediction usually fail to detect accurate and complete temperate phage genomes. This study proposes a novel computational temperate phage detection method (TemPhD) mining both the integrated active prophages and their spontaneously induced forms (temperate phages) from next-generation sequencing raw data. Applying the method to the available dataset resulted in 192 326 complete temperate phage genomes with different host species, expanding the existing number of complete temperate phage genomes by more than 100-fold. The wet-lab experiments demonstrated that TemPhD can accurately determine the complete genome sequences of the temperate phages, with exact flanking sites, outperforming other state-of-the-art prophage prediction methods. Our analysis indicates that temperate phages are likely to function in the microbial evolution by (i) cross-infecting different bacterial host species; (ii) transferring antibiotic resistance and virulence genes and (iii) interacting with hosts through restriction-modification and CRISPR/anti-CRISPR systems. This work provides a comprehensively complete temperate phage genome database and relevant information, which can serve as a valuable resource for phage research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".