Abstract
BackgroundTaxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. Quantitative Insights Into Microbial Ecology (QIIME) and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean, and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene.FindingsWe show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently <2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost 2 and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline.ConclusionsOur results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision, and/or computational performance required.
Highlights
Title: Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments
It would be nice to see the overall dissimilaritymetrics presented unaggregated by method and biome in a supplemental table
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Summary
Title: Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments. # OverviewIn this paper the authors have sought to evaluate the performance of the 4 main packages and their default classifiers/settings used in the taxonomic profiling of rRNA sequences.They did this using synthetic simulated read sets representative of 3 commonlystudied microbiome environments and investigated the role of locus andreference database selection on classification metrics.This is well done research that will form a useful benchmark for researchers engaged in rRNA taxonomic profiling to help design and conduct their own studies.## General CommentsIt should be emphasised throughout the manuscript that as of January 1st 2018, QIIME1 is deprecated and no longer supported by the developers(https://qiime.wordpress.com/2018/01/03/qiime-2-has-succeeded-qiime-1/).QIIME1 is no longer recommended to be used at all.Secondly, it is probably worth emhpasising that QIIME1, QIIME2 and mothurare very large toolsets with many parts and functions capable of more than just taxonomic assignment. The methods section may benefit from inclusionof this database information.Figure S4: Would be nice to include a key as per Figure 1 instead of needing to crossreference to the tables
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