High-Performance Genome Annotation for a Safer and Faster-Developing Phage Therapy
Bibliographic record
Abstract
Phage therapy, which uses phages to decrease bacterial load in an ecosystem, introduces a multitude of gene copies (bacterial and phage) into said ecosystem. While it is widely accepted that phages have a significant impact on ecology, the mechanisms underlying their impact are not well understood. It is therefore paramount to understand what is released in the said ecosystem, to avoid alterations with difficult-to-predict-but potentially huge-consequences. An in-depth annotation of therapeutic phage genomes is therefore essential. Currently, the average published phage genome has only 20-30% functionally annotated genes, which represents a hurdle to overcome to deliver safe phage therapy, for both patients and the environment. This study aims to compare the effectiveness of manual versus automated phage genome annotation methods. Twenty-seven phage genomes were annotated using SEA-PHAGE and Rime Bioinformatics protocols. The structural (gene calling) and functional annotation results were compared. The results suggest that during the structural annotation step, the SEA-PHAGE method was able to identify an average of 1.5 more genes per phage (typically a frameshift gene) and 5.3 gene start sites per phage. Despite this difference, the impact on functional annotation appeared to be limited: on average, 1.2 genes per phage had erroneous functions, caused by the structural annotation. Rime Bioinformatics' tool (rTOOLS, v2) performed better at assigning functions, especially where the SEA-PHAGE methods assigned hypothetical proteins: 7.0 genes per phage had a better functional annotation on average, compared to SEA PHAGE's 1.7. The method comparison detailed in this article indicate that (1) manual structural annotation is marginally superior to rTOOLS automated structural annotation; (2) rTOOLS automated functional annotation is superior to manual functional annotation. Previously, the only way to obtain a high-quality annotation was by using manual protocols, such as SEA-PHAGES. In the relatively new field of phage therapy, which requires support to advance, manual work can be problematic due to its high cost. Rime Bioinformatics' rTOOLS software allows for time and money to be saved by providing high-quality genome annotations that are comparable to manual results, enabling a safer and faster-developing phage therapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".