Systematic evaluation of the influence of Taq enzyme choice and amplification conditions on targeted environmental DNA assay performance and detection in field samples
Bibliographic record
Abstract
Abstract Targeted environmental DNA (eDNA) studies mainly rely on quantitative real‐time polymerase chain reaction (qPCR) to amplify extremely low concentrations of DNA present in environmental samples. Understanding factors that influence targeted eDNA assay performance and detection in field samples, such as Taq DNA polymerase enzyme type and thermocycle protocol, is critical for the interpretation of qPCR results. We completed a systematic performance evaluation of five distinct targeted eDNA assays (eANFI6, eFISH1, eANBO5, eGLIN1, and eLICA3 targeting sablefish, general fish, Boreal toad, wood turtle, and the American bullfrog) by subjecting the same samples to analysis by five different Taq enzyme reagent mixes (Immolase, Environmental Master Mix (EMM), Amplitaq, QIAcuity, and QuantiNova) and the commonly used 2‐step (95°C, 60°C) and 3‐step (95°C, 64°C, 72°C) thermocycle protocols. We evaluated assay performance using a standardized dilution series of synthetic dsDNA target sequences and calculated limits of detection (LOD) and quantification (LOQ) and 95% confidence intervals for each combination of Taq enzyme and thermocycle protocol. All assays performed within acceptable performance criteria as defined by the Canadian national standard for targeted eDNA assays. Based on data generated by synthetic dsDNA fragments, the eDNA assays performed comparably regardless of the enzyme reagent mix and protocol used, except for eANFI6 and eGLIN1 using EMM and the 3‐step protocol, where no amplification was observed. On freshwater field samples, EMM and Immolase performed best. On marine field samples, Immolase, EMM, Qiacuity, and QuantiNova performed equally well, although EMM failed to amplify some samples. The work reveals that an enzyme reaction mix or thermocycle protocol can affect the result of an eDNA assay, but the appropriate choice also depends on the nature of the field sample. It is therefore imperative that these are considered when selecting appropriate reaction conditions and that they are clearly reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".