Density based clustering and error correction of metabarcodes in Nanopore sequencing
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Nanopore sequencing can enable field-based research, but few studies demonstrate its suitability for metabarcoding and analysis of environmental DNA. We show metabarcodes in a bulk sample of 50 different aquatic invertebrate species can be identified with Nanopore Sequencing, and error corrected to accuracy comparable to MiSeq (up to 99.3% match against reference) .
Our python bioinformatics pipeline generates consensus reads from concatemers, performs OTU clustering with OPTICS, and enables exploration of error profiles and species composition. Concatemer generation, error correction, and density based clustering enabled high fidelity identification and reconstruction of species barcodes de novo