DNA extraction free whole genome sequencing of bacteriophage genomes from a single plaque
Bibliographic record
Abstract
Abstract DNA sequencing is at the core of genome characterization, proteomics, and identification of novel organisms. For microorganisms such as bacteriophages, sequencing their DNA can provide key insights into their tropism, infectivity, and virulence. There remains however a critical lack of rapid sequencing techniques with the traditional process of replating and incubating individual plaques, collecting lysate, extracting DNA, preparing the DNA library, and sequencing that is labor intensive. Herein, we demonstrate the use of an adapted Nanopore Rapid PCR Barcoding protocol to sequence the bacteriophage genome directly from individual plaques. This technique provides sequencing genome assemblies with 99.88-100% (mean 99.97%) average nucleotide identity (ANI) scores when compared to the traditional methods involving phage amplification, extraction, and sequencing using Illumina. The optimization of bacteriophage identification by the technique of tagmentation directly to isolated plaques will enable rapid and cost-effective sequencing of novel phages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".