PrimalScheme: open-source community resources for low-cost viral genome sequencing
Bibliographic record
Abstract
Viral genome sequencing using the ARTIC protocol has been a vital tool for understanding the spread of epidemics including Ebola, Zika, COVID-19 and Mpox and has seen widespread adoption due to its low cost and high sensitivity. Here, we describe PrimalScheme, an open-source toolkit and website that allows users to easily design primer schemes for amplicon sequencing of viruses and has generated over 67,000 primer schemes for a global community since 2017. In January 2020, PrimalScheme was used to rapidly generate a primer scheme for SARS-CoV-2, with primer pools distributed to researchers from 44 countries to help scale-up genomic surveillance efforts. Overall, these primers were used to generate an estimated 18M genome sequences and the protocols were viewed online ~250K times. To complement PrimalScheme, we have built PrimalScheme Labs, a scheme repository which allows users to find and share primers schemes as well as establishing a set of data standards. Through improvements to the primer design process, including the use of discrete primer clouds, we have expanded the use of amplicon sequencing to include diverse virus species. We demonstrate the utility of this approach through a high diversity pan-genotype Measles virus (MeV) scheme. We also demonstrate its use on a high sensitivity, short amplicon Monkeypox virus (MPXV) scheme with over 1000 primers, showing high genome recovery on low-titre clinical samples. These developments have implications for sequencing from samples such as wastewater, for genomic surveillance of endemic pathogens and in preparing for future pandemics.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.068 |
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".