Optimization of 16S amplicon analysis using mock communities: implications for estimating community diversity

Diversity of complex microbial communities can be rapidly assessed by community amplicon sequencing of marker genes (e.g., 16S), often yielding many thousands of DNA sequences per sample. However, analysis of community amplicon sequencing data requires multiple computational steps which affect the outcome of a final data set. Here we use mock communities to describe the effects of parameter adjustments for raw sequence quality filtering, picking operational taxonomic units (OTUs), taxonomic assignment, and OTU table filtering as implemented in QIIME 1.9.1. We demonstrate a workflow optimization based upon this exploration which we also apply to environmental samples. We found that quality filtering of raw data and filtering of OTU tables had large effects on observed OTU diversity. While all taxonomy assigners performed with similar accuracy, an appropriate choice of similarity threshold for defining OTUs depended on the method used for OTU picking. Our “default” analysis in QIIME overestimated mock community diversity by at least a factor of ten, compared to the optimized analysis which correctly characterized the taxonomic composition of the mock communities while still overestimating OTU diversity by about a factor of two. Though observed relative abundances of mock community member taxa were approximately correct, most were still represented by multiple OTUs. Low-frequency OTUs conspecific to constituent mock community taxa were characterized by multiple substitution and indel errors and the presence of a low quality base call resulting in sequence truncation during quality filtering. Low quality base calls were observed at “G” positions most of the time, and were also associated with a preceding “TTT” trinucleotide motif. Environmental diversity estimates were reduced by about 40% from 2508 to 1533 OTUs when comparing output from the default and optimized workflows. We attribute this reduction in observed diversity to the removal of erroneous sequences from the data set. Our results indicate that both strict quality filtering of raw sequencing data and careful filtering of raw OTU tables are important steps for accurate estimation of microbial community diversity.