Pangebin: improving plasmid binning in bacterial isolates using pangenome-assembly graphs
Bibliographic record
Abstract
Abstract Short-read genome assemblies typically consist of many contigs of variable lengths and their putative connections represented as an assembly graph. Assembly graphs produced by different tools from the same data may differ significantly, posing a challenge to tools for downstream processing tasks. One such task is plasmid binning, that is identifying plasmids in sequenced bacterial isolates, which is crucial for monitoring the spread of antimicrobial resistance. When plasmid binning tools are applied to assembly graphs produced by different tools, they may exhibit different performance, and choosing the best results a priori can be difficult. To address the above issue, we propose the use of a pangenome graph, built from assembly graphs produced by assembling short reads of the same sample with different assemblers. The resulting pangenome-assembly graph highlights similarities between contigs from different assemblies while retaining information on contigs that appear only in one of the input assemblies. We then used the PlasBin-flow plasmid binning tool customized to take into account pangenome information to identify plasmid bins. The results for pangenome-assemblies built by Unicycler and Skesa show an increase in accuracy measures compared to the mean results obtained on single assemblies, leading to an overall more accurate prediction than a blind choice of assemblers. The source code of the pipeline is available at https://github.com/AlgoLab/pangebin along with the dataset used in this study.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".