Enhancing DNA metabarcoding performance and applicability with bait capture enrichment and DNA from conservative ethanol
Bibliographic record
Abstract
Metabarcoding is often presented as an alternative identification tool to compensate for coarse taxonomic resolution and misidentification encountered with traditional morphological approaches. However, metabarcoding comes with two major impediments which slow down its adoption. First, the picking and destruction of organisms for DNA extraction are time and cost consuming and do not allow organism conservation for further evaluations. Second, current metabarcoding protocols include a PCR enrichment step which induces errors in the estimation of species diversity and relative biomasses. In this study, we first evaluated the capacity of capture enrichment to replace PCR enrichment using controlled freshwater macrozoobenthos mock communities. Then, we tested if DNA extracted from the fixative ethanol (etDNA) of the same mock communities can be used as an alternative to DNA extracted from pools of whole organisms (bulk DNA). We show that capture enrichment provides more reliable and accurate representation of species occurrences and relative biomasses in comparison with PCR enrichment for bulk DNA. While etDNA does not permit to estimate relative biomasses, etDNA and bulk DNA provide equivalent species detection rates. Thanks to its robustness to mismatches, capture enrichment is already an efficient alternative to PCR enrichment for metabarcoding and, if coupled to etDNA, is a time-saver option in studies where presence information only is sufficient.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".