Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments.
Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments.
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DOI：
10.1093/gigascience/giy054
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发表时间：
2018-05-01
期刊：
影响因子：
9.2
通讯作者：
Finn RD
中科院分区：
文献类型：
--
作者：
Almeida A;Mitchell AL;Tarkowska A;Finn RD
核糖体RNA(RRNA)序列的分类图谱已成为推断复杂微生物生态系统组成的公认标准。微生物生态定量分析(QIIME)和MOTHUR是用于这一目的的最广泛的分类分析工具，MAPseq和QIIME 2是最近发布的两个替代工具。然而，这四个主要工具之间没有进行独立和直接的比较。在这里，我们使用合成模拟数据集比较了MAPseq、Mothur、QIIME和QIIME 2的默认分类器，这些数据集包括在人类肠道、海洋和土壤环境中发现的一些最丰富的属。当与不同的参考数据库和16S rRNA基因的可变亚区配对时，我们评估了它们的准确性。我们发现，QIIME 2在属和科的水平上提供了最好的回忆和F-分数，以及最低的观测样本和模拟样本之间的距离估计。然而，MAPseq显示出最高的精确度，误呼率一直保持在2%。值得注意的是，QIIME 2是计算成本最高的工具，其CPU时间和内存使用量分别是MAPseq的2倍和30倍。使用Silva数据库通常比使用Greengenes产生更高的召回率，而不同16S rRNA可变亚区的分配结果在用同一管道分析的样本之间差异高达40%。我们的结果支持使用QIIME 2或MAPseq来优化16S rRNA基因图谱，我们建议在两者之间进行选择应该基于所需的召回率、精确度和/或计算性能。
Taxonomic 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. We 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. Our 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.
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影响因子：
14.9
作者：
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影响因子：
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