argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO)
Bibliographic record
Abstract
SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challenging. The comparability of ARG annotation outputs can be improved by mapping gene names and their categories to a common controlled vocabulary such as the Antibiotic Resistance Ontology (ARO). We developed argNorm, a command line tool and Python library, to normalize all detected genes across six ARG annotation tools (eight databases) to the ARO. argNorm also adds information to the outputs using the same ARG categorization so that they are comparable across tools. AVAILABILITY AND IMPLEMENTATION: argNorm is available as an open-source tool at: https://github.com/BigDataBiology/argNorm. It can also be downloaded as a PyPI package and is available on Bioconda and as an nf-core module.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".