GoEnrich: creating high quality genomic DNA resources from limited voucher specimen tissues or museum specimens of at-risk species for conservation-friendly use in the validation of environmental DNA assays
Bibliographic record
Abstract
Environmental DNA (eDNA) methods are crucial for monitoring populations, particularly rare and cryptic species. For confident eDNA application, rigorous assay validation is required including specificity testing with genomic DNA (gDNA). However, this critical step is often difficult to achieve as obtaining fresh tissue samples from at-risk species can be difficult, highly limited, or impossible. Natural history museum collections could serve as a valuable and ethical voucher specimen resource for eDNA assay validation. The present study demonstrates the effectiveness of whole genome amplification (WGA) in providing enough gDNA to assemble high quality mitogenomes from which robust targeted eDNA assays can be designed. Using fresh and historical museum tissue samples from six species spanning fish, birds, and mammals, we successfully developed a WGA method with an average yield of 380 to 1,268 ng gDNA per 20 µL reaction. This gDNA was used for whole genome shotgun sequencing and subsequent assembly of high quality mitogenomes using mtGrasp . These mitogenomes were then used to develop six new robust, targeted quantitative real time polymerase chain reaction-based eDNA assays and 200 ng WGA-enriched yielded satisfactory C q values and near 100% detection frequencies for all assays tested. This approach offers a cost-effective and non-invasive alternative, streamlining eDNA research processes and aiding in conservation efforts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".