Environmental DNA Barcode Sequence Capture: Targeted, PCR-free Sequence Capture for Biodiversity Analysis from Bulk Environmental Samples
Bibliographic record
Abstract
ABSTRACT Environmental DNA analysis using PCR amplified marker genes has been a key application of high-throughput sequencing (HTS). However, PCR bias is a major drawback to gain accurate qualitative and quantitative biodiversity data. We developed a PCR-free approach using enrichment baits for species-specific mitochondrial cytochrome c oxidase 1(COI) DNA barcodes. The sequence capture was tested on species-rich bulk terrestrial and aquatic benthic samples. Hybridization capture recovered an average of 6 and 4.7 more arthropod orders than amplicon sequencing for terrestrial and benthic samples, respectively. For the terrestrial sample, the four most abundant arthropod orders comprised 94.0% of the sample biomass. These same four orders comprised 95.5% and 97.5% of the COI sequences recovered by amplification and capture, respectively. Hybridization capture recovered three arthropod orders that were detected by biomass analysis, but not by amplicon sequencing and two other insect orders that were not detected by either biomass or amplicon methods. These results indicate the advantage of using sequence capture for a more accurate analysis of biodiversity in bulk environmental samples. The protocol can be easily customized to other DNA barcode markers or gene regions of interest for a wide range of taxa or for a specific target group.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".