RAMBO: Resolving Amplicons in Mixed Samples for Accurate DNA Barcoding with Oxford Nanopore

DNA barcoding, the use of short genetic markers to identify and differentiate species, is a foundational tool in ecological and taxonomic research. The method has scaled rapidly with next-generation sequencing technologies, enabling the processing of thousands of specimens in parallel. Nanopore sequencing offers a flexible, low-cost alternative to other platforms, producing full-length reads in real time and supporting applications in remote settings. However, its comparatively high error rate complicates downstream processing, particularly when PCR co-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. Unlike existing approaches to curate Nanopore sequence data that assume a single dominant amplicon per sample or require large sequence divergence among amplicons, our method distinguishes variants differing by 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 tools, can recover biologically meaningful variation even in technically complex samples. The pipeline is particularly suited for specimens where divergent templates are often 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 data.