Identification of plasmids in avian-associated Escherichia coli using nanopore and illumina sequencing
Bibliographic record
Abstract
Abstract Background Avian pathogenic Escherichia coli (APEC) are the causative agents of colibacillosis in chickens, a disease which has significant economic impact on the poultry industry. Large plasmids detected in APEC are known to contribute to strain diversity for pathogenicity and antimicrobial resistance, but there could be other plasmids that are missed in standard analysis. In this study, we determined the impact of sequencing and assembly factors for the detection of plasmids in an E. coli whole genome sequencing project. Results Hybrid assembly (Illumina and Nanopore) combined with plasmid DNA extractions allowed for detection of the greatest number of plasmids in E. coli, as detected by MOB-suite software. In total, 79 plasmids were identified in 19 E. coli isolates. Hybrid assemblies were robust and consistent in quality regardless of sequencing kit used or if long reads were filtered or not. In contrast, long read only assemblies were more variable and influenced by sequencing and assembly parameters. Plasmid DNA extractions allowed for the detection of physically smaller plasmids, but when averaged over 19 isolates did not significantly change the overall number of plasmids detected. Conclusions Hybrid assembly can be reliably used to detect plasmids in E. coli, especially if researchers are focused on large plasmids containing antimicrobial resistance genes and virulence factors. If the goal is comprehensive detection of all plasmids, particularly if smaller sized vectors are desired for biotechnology applications, the addition of plasmid DNA extractions to hybrid assemblies is prudent. Long read sequencing is sufficient to detect many plasmids in E. coli, however, it is more prone to errors when expanded to analyze a large number of isolates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".