Short-Read Whole-Genome Sequencing for Laboratory-Based Surveillance of Bordetella pertussis
Bibliographic record
Abstract
ABSTRACT Bordetella pertussis is a Gram-negative bacterium that causes respiratory infections in humans. Ongoing molecular surveillance of B. pertussis acellular vaccine (aP) antigens is critical for understanding the interaction between evolutionary pressures, disease pathogenesis, and vaccine effectiveness. Methods currently used to characterize aP components are relatively labor-intensive and low throughput. To address this challenge, we sought to derive aP antigen genotypes from minimally processed short-read whole-genome sequencing data generated from 40 clinical B. pertussis isolates and analyzed using the SRST2 bioinformatic package. SRST2 was able to identify aP antigen genotypes for all antigens with the exception of pertactin, possibly due to low read coverage in GC-rich low-complexity regions of variation. Two main genotypes were observed in addition to a singular third genotype that contained an 84-bp deletion that was identified by SRST2 despite the issues in allele calling. This method has the potential to generate large pools of B. pertussis molecular data that can be linked to clinical and epidemiological information to facilitate research of vaccine effectiveness and disease severity in the context of emerging vaccine antigen-deficient strains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".