RAMBO: Resolving Amplicons in Mixed Samples for Accurate DNA Barcoding with Oxford Nanopore
Bibliographic record
Abstract
Abstract DNA barcoding, the use of short genetic markers to identify and differentiate species, is a foundational tool for ecological and taxonomic research. The method has been scaled rapidly with next-generation sequencing technologies enabling the processing of thousands of specimens in parallel. Nanopore sequencing not only offers a flexible, low cost alternative to other platforms but produces full-length reads in real time and can be used in remote settings. However, its comparatively high error rate complicates downstream processing, particularly when PCR amplifies multiple templates from a single specimen, reflecting pseudogenes, paralogs, or contaminants. We present a novel pipeline for DNA barcoding that resolves mixed sequence signals from Nanopore reads using unsupervised clustering and staged consensus generation, without relying on curated reference databases, taxonomic priors, or error models. While existing methods to curate Nanopore sequence data assume a single dominant amplicon per sample or require deep sequence divergence among amplicons, our pipeline can distinguish variants differing by as little as 0.15 percent. It combines column-weighted encodings, UMAP projection, and HDBSCAN clustering, followed by conservative consensus refinement. The pipeline was benchmarked and validated using datasets with known composition, including high-fidelity PacBio sequences. The results show that Nanopore barcoding, when paired with appropriate analysis, can recover biologically meaningful variation even in technically complex samples. The pipeline is particularly suited for specimens where divergent templates are co-amplified, including mitochondrial pseudogenes or multicopy nuclear regions like ITS. As such, it provides a generalizable framework for high-resolution Nanopore analysis of complex amplicon mixtures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".