Abstract
Utility of a recently developed long-read pipeline, Emu, was assessed using an expectation-maximization algorithm for accurate read classification. We compared it to conventional short- and long-read pipelines, using well-characterized mock bacterial samples. Our findings highlight the necessity of appropriate data-processing for taxonomic descriptions, expanding our understanding of the precise microbiome.
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