PlasEval: a framework for comparing and evaluating plasmid detection tools
Bibliographic record
Abstract
Abstract Plasmids play a major role in the transfer of antimicrobial resistance (AMR) genes among bacteria via horizontal gene transfer. The identification of plasmids in short-read assemblies is a challenging problem and a very active research area. Plasmid binning aims at detecting, in a draft genome assembly, groups (bins) of contigs likely to originate from the same plasmid. Several methods for plasmid binning have been developed recently, such as PlasBin-flow, HyAsP, gplas, MOB-suite, and plasmidSPAdes. This motivates the problem of evaluating the performances of plasmid binning methods, either against a given ground truth or between them. We describe PlasEval, a novel method aimed at comparing the results of plasmid binning tools. PlasEval computes a dissimilarity measure between two sets of plasmid bins, that can originate either from two plasmid binning tools, or from a plasmid binning tool and a ground truth set of plasmid bins. The PlasEval dissimilarity accounts for the contig content of plasmid bins, the length of contigs and is repeat-aware. Moreover, the dissimilarity score computed by PlasEval is broken down into several parts, that allows to understand qualitative differences between the compared sets of plasmid bins. We illustrate the use of PlasEval by benchmarking four recently developed plasmid binning tools – PlasBin-flow, HyAsP, gplas, and MOB-recon – on a data set of 54 E. coli bacterial genomes. PlasEval is freely available at https://github.com/acme92/PlasEval
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".