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 TM , 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 whether the MinION TM represents a viable alternative to other sequencing platforms, we used rolling circle amplification (RCA) to generate full‐length consensus DNA barcodes for a bulk mock sample of 50 aquatic invertebrate species with at least 15% genetic distance to each other. By applying two different laboratory protocols, we generated two MinION TM runs that were used to build error‐corrected consensus sequences. A newly developed Python pipeline, ASHURE, was used for data processing, consensus building, clustering and taxonomic assignment of the resulting reads. Our pipeline achieved median accuracies of up to 99.3% for long concatemeric reads (>45 barcodes) and successfully identified all 50 species in the mock community. The use of RCA was integral for increasing consensus accuracy but was also the most time‐consuming step of the laboratory workflow. Most concatemeric reads were skewed towards a shorter read length range with a median read length of up to 1,262 bp. Our study demonstrates that Nanopore sequencing can be used for metabarcoding, but exploration of other isothermal amplification procedures to improve consensus accuracy is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".