A workflow for accurate metabarcoding using nanopore MinION sequencing
Bibliographic record
Abstract
Abstract Metabarcoding has become a common approach to the rapid identification of the species composition in a mixed sample. The majority of studies use established short-read high-throughput sequencing platforms. The Oxford Nanopore MinION™, a portable sequencing platform, represents a low-cost alternative allowing researchers to generate sequence data in the field. However, a major drawback is the high raw read error rate that can range from 10% to 22%. To test if the MinION™ represents a viable alternative to other sequencing platforms we used rolling circle amplification (RCA) to generate full-length consensus DNA barcodes (658bp of cytochrome oxidase I - COI) for a bulk mock sample of 50 aquatic invertebrate species. By applying two different laboratory protocols, we generated two MinION™ runs that were used to build consensus sequences. We also developed a novel Python pipeline, ASHURE, for processing, consensus building, clustering, and taxonomic assignment of the resulting reads. We were able to show that it is possible to reduce error rates to a median accuracy of up to 99.3% for long RCA fragments (>45 barcodes). Our pipeline successfully identified all 50 species in the mock community and exhibited comparable sensitivity and accuracy to MiSeq. The use of RCA was integral for increasing consensus accuracy, but it was also the most time-consuming step during the laboratory workflow and most RCA reads were skewed towards a shorter read length range with a median RCA fragment length of up to 1262bp. Our study demonstrates that Nanopore sequencing can be used for metabarcoding but we recommend the exploration of other isothermal amplification procedures to improve consensus length.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".