A comprehensive DNA barcode reference library for the macroinvertebrates of Scottish seagrass beds using Oxford Nanopore Flongle Flowcells
Bibliographic record
Abstract
DNA Barcoding using Sanger sequencing is a popular technique for identifying species on a per specimen basis. However for larger projects, sequencing individual voucher specimens can be time and resource intensive and moreover is associated with high levels of sequencing failure and contamination. Oxford Nanopore Sequencing Technology (ONT) has emerged as a scalable alternative, capable of generating hundreds of DNA barcodes simultaneously using the portable, benchtop MinION sequencing device. In this study we aim to compare and contrast the sequencing outcomes of Oxford Nanopore R10 Flongle flowcells verses Sanger sequencing for DNA barcoding and produce a DNA barcode reference library. We demonstrate that DNA barcodes generated using ONT outperform those produced by Sanger sequencing in terms of recovery and sequence quality with lower rates of contamination. We then produced DNA barcodes for 146 seagrass associated marine invertebrate OTUs collected from four seagrass beds in Scotland, targeting COI and 18S V4 regions. Using both markers, we show the number of recovered OTUs was higher than if each marker was used in isolation and make use of degenerate and group-specific primer pairs to improve recovery. Furthermore we demonstrate how mapping ONT reads to pre-existing DNA barcodes can be used to reduce ambiguous basecalls and improve recovery of sequences from contaminated specimens. Overall this study informs prospective users intending to carry out multimarker DNA barcode projects using Oxford Nanopore Sequencing. Furthermore, we generated the first DNA barcode reference library for seagrass beds in Scotland to support future biomonitoring of these priority habitats.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".