Computational requirements and speed of Karp, Kallisto, SINTAX, UCLUST, USEARCH61, SortMeRNA, and the Wang <i>et al.</i> (2007) Naive Bayes using Mothur.

All programs were run using 12 multi-threaded cores except Mothur. Mothur’s memory requirements scale with the number of cores used, and in order to keep memory <16GB we limited it to 4 cores. The values for UCLUST and USEARCH give the time to assign taxonomy, generally with these methods reads are clustered before taxonomy is assigned and the value in parenthesis gives the time to first cluster and then assign taxonomy. The results for the method 16S Classifier are not shown: it was fast and required a few minutes at most; however its memory usage scaled dramatically with the number of reads. To keep memory usage < 128GB samples needed to be split into several smaller samples and then reassembled. Additionally, it could not be run in parallel, so a meaningful comparison against the other methods of speed and memory requirements was not possible.除Mothur外，所有程序均基于12个多线程核心运行。Mothur的内存需求随所使用的核心数增长，为将内存占用控制在16GB以内，我们将其核心数限制为4核。UCLUST与USEARCH的对应数值为分类学分配耗时；此类方法通常会先对测序读段（reads）进行聚类，再执行分类学分配，括号内的数值则为先完成聚类后进行分类学分配的总耗时。16S分类器（16S Classifier）的结果未予展示：该方法运行速度较快，最多仅需数分钟；但其内存占用随测序读段数量急剧增加。为将内存占用控制在128GB以内，需将样本拆分为多个小子集后再重新合并处理。此外，该方法无法并行运行，因此无法对其与其他方法的速度及内存占用开展有效对比。