Development and validation of probe-based multiplex real-time PCR assays for the rapid and accurate detection of freshwater fish species
Bibliographic record
Abstract
Reliable species identification methods are important for industrial environmental monitoring programs. Probe based real-time quantitative polymerase chain reaction (qPCR) provides an accurate, cost-effective and high-throughput method for species identification. Here we present the development and validation of species-specific primers and probes for the cytochrome c oxidase (COI) gene for the identification of eight ecologically and economically important freshwater fish species: lake whitefish (Coregonus clupeaformis), yellow perch (Perca flavescens), rainbow smelt (Osmerus mordax), brook trout (Salvelinus fontinalis), smallmouth bass (Micropterus dolomieu), round whitefish (Prosopium cylindraceum), spottail shiner (Notropis hudsonius) and deepwater sculpin (Myoxocephalus thompsonii). In order to identify novel primer-probe sets with maximum species-specificity, two separate primer-probe design criteria were employed. Highest ranked primer-probe sets from both methods were assayed to identify sequences that demonstrated highest specificity. Specificity was determined using control species from same genus and non-target species from different genus. Selected primer-probe sets were optimized for annealing temperature and primer-probe concentrations to identify minimum reagent parameters. The selected primer-probe sets were highly sensitive, with DNA concentrations as low as 1 ng adequate for positive species identification. A decoder algorithm was developed based on the cumulative qPCR results that allowed for full automation of species identification. Blinded experiments revealed that the combination of the species-specific primer/probes sets with the automated species decoder resulted in target species identification with 100% accuracy. We also conducted a cost/time comparison analysis between the qPCR assays established in this study with other species identification methods. The qPCR technique was the most cost-effective and least time consuming method of species identification. In summary, probe-based multiplex qPCR assays provide a rapid and accurate method for freshwater fish species identification, and the methodology established in this study can be utilized for various other species identification initiatives.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".