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Abstract:
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Despite a rapidly-improving accessibility of sequencing resources in microbiome research, amplicon sequencing remains the first and most-common metagenomics method applied to new biospecimens, especially due to its low cost and greater sensitivity to rare microbes. A pervasive misunderstanding of amplicon sequencing data in the microbiome literature is that (1) amplicon sequences must be processed through an ad hoc "OTU" clustering, and that (2) the resolution limit is a sequence similarity radius of 3%. I will demonstrate that neither of these presumptions are true. There is enough information in current Illumina-derived sequence data to achieve de novo single-nucleotide resolution in practice. Several algorithms now approach this, especially DADA2, which also exhibits best-in-class accuracy and computational scaling to large modern datasets with thousands of subjects. In addition to contrasting DADA2's probabilistic model with the approach of other available methods, I will also discuss the implications for study design and translational value of (relatively) inexpensive sub-species and intra-genomic resolution, as well as the still unsolved analytics challenges that it reveals.
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