Genome Annotation of Novel K1 Subcluster Mycobacteriophage Blizzard
Bibliographic record
Abstract
The evolution of antimicrobial resistant pathogens constitutes a significant global public health threat. Combined with the lack of incentive for pharmaceutical companies to invest in developing new antibiotics, it is clear alternative treatments are needed. Bacteriophages present one possible avenue as they harness the diversity and specificity of a microorganism that has coevolved with bacteria. However, little is known about these bacterial viruses. The SEA-PHAGES program was designed to identify and characterize novel bacteriophages and their associated gene functions. Herein, we report the genome annotation of one such novel phage: Mycobacteriophage Blizzard (GenBank accession number MW712733). Blizzard’s gene content was functionally annotated using bioinformatic tools including DNA Master, Phamerator, and NCBI BLAST, to call start sites as well as predict gene function. Overall, 96 genes were identified, including a tRNA and a translational frameshift, using highly similar reference phages BEEST, Belladonna, and CREW. From the 96 genes identified, 46 were functionally annotated. The remaining 50 genes have unknown functions due to the lack of significant matches in the databases. Our results demonstrate a novel annotated phage, whose genome serves to expand the understanding of phage biology and potential implications as alternative treatment to antibiotics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".