Targeted amplification-based whole genome sequencing of <i>Monkeypox virus</i> in clinical specimens
Bibliographic record
Abstract
ABSTRACT The 2022 mpox outbreak has led to more than 91,000 cases in 115 countries. Whole genome sequencing (WGS) has been at the forefront of surveillance and outbreak investigations for different pathogens of public health significance. Many institutions performing WGS on Monkeypox virus (MPXV) use a resource-intensive metagenomic approach. Here we present a targeted amplification method for WGS of MPXV from clinical specimens. We designed 43 pairs of primers (amplicons ~5 kb) with PrimalScheme to span the ~200 kb viral genome and then added 12 additional primers to optimize amplification. We extracted nucleic acid from clinical specimens and amplified the two primer pools. All libraries were sequenced on the MiniSeq platform. Resulting reads were filtered by quality and then mapped to a MPXV reference genome. Consensus sequences were generated for phylogenetic analysis. A total of 91 specimens with a real-time-PCR cycle threshold (Ct) values ≤27.9 were sequenced using our targeted amplification protocol. The sequenced MPXV genomes were of high quality with mean genome coverage of 99.56% (95% CI 99.32-99.80%), mean depth 1,395× (95% CI 1275–1515), and mean mapping quality of 52.87 (95% CI 52.1–53.6) and allowed for greater multiplexing of samples relative to metagenomics. The MPXV genomes belong to 8 of the 13 clades observed during the 2022 global mpox outbreak. Targeted amplification enrichment provides high coverage, throughput, and short turnaround times. It is an efficient low-cost method for MPXV WGS and can benefit public health surveillance and outbreak management. IMPORTANCE We present a protocol to efficiently sequence genomes of the MPXV-causing mpox. This enables researchers and public health agencies to acquire high-quality genomic data using a rapid and cost-effective approach. Genomic data can be used to conduct surveillance and investigate mpox outbreaks. We present 91 mpox genomes that show the diversity of the 2022 mpox outbreak in Ontario, Canada.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".