Using in silico analysis to investigate the false positive potential of qPCR systems for potato disease diagnosis
Bibliographic record
Abstract
Abstract Potato (Solanum tuberosum) is one of the most important global crops facing threats from different diseases. Rapid and accurate diagnosis is essential to control disease development and spread. Quantitative real-time PCR (qPCR) has been widely used in potato disease diagnosis. In this study, we evaluated the specificity of 19 probe-based and four SYBR Green-based qPCR protocols for 17 potato diseases using in silico analysis. Primers and probes of those protocols were subjected to BLASTn analysis against the nucleotide collection (nr/nt) database and the whole-genome shotgun contigs (wgs) database of NCBI for the presence of primer/probe sequences in non-target species. Results showed that 12 of 23 qPCR protocols were not specific to the target pathogens. A qPCR experiment indicated that even nine single nucleotide polymorphisms (SNPs) are present on the sequences of the primer/probe binding sites between the potato silver scurf pathogen Helminthosporium solani and its close-related species H. velutinum, the primers/probe specific to the former could amplify signals from the latter. These findings highlight the need for additional methods to enhance the diagnostic accuracy and new sequencing technologies such as next generation sequencing could provide useful information to develop specific diagnostic protocols for these pathogens.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".