Fig. 2

Computational performance of amplicon sequencing pipelines. 16S rRNA amplicon MiSeq data from A gut-16S, B soil-16S, and C soil-ITS samples were processed to benchmark resource usage of each pipeline, run on the same system under equal conditions (12 cores, max 150 Gb memory). In all pipelines, OTUs/ASVs were classified by similarity comparisons to SILVA 138.1. In LotuS2, Lambda was used to align sequences for all clustering algorithms. Pipeline runs were separated by common steps (pre-processing, sequence clustering, taxonomic classification, and phylogenetic tree construction and/or off-target removal). Because native DADA2 cannot demultiplex reads, we used the average demultiplexing time of QIIME 2 and LotuS2 (LotuS2 demultiplexed, unfiltered reads were provided to DADA2). Since phylogenetic trees based on ITS sequences may lead to erroneous phylogenies [55], we did not include the phylogenetic tree construction step in the analysis of the soil-ITS dataset. LotuS2 runs are labelled with red color. D, E, F Data usage efficiency of each tested pipeline, by comparing the number of sequence clusters (OTUs or ASVs) to retrieved read counts in the final output matrix of each pipeline. Note that mothur results for soil-16S are not shown, because the pipeline rejected all sequences at the default parameters