Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ , USA ; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY , USA.
State Key Laboratory of Organ Failure Prevention, and Department of Environmental Health, School of Public Health and Tropical Medicine, Southern Medical University , Guangzhou, Guangdong , China.
PeerJ. 2014 Aug 21;2:e545. doi: 10.7717/peerj.545. eCollection 2014.
We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to "classic" open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, "classic" open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of "classic" open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by "classic" open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME's uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME's OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.
我们提出了一种性能优化的算法，即亚采样开放式参考 OTU 挑选，用于将下一代测序平台上生成的标记基因（例如 16S rRNA）序列分配给微生物群落分析的操作分类单元 (OTUs)。与从头聚类 OTU 挑选（聚类可以很大程度上并行进行，减少运行时间）和封闭式参考 OTU 挑选（所有读取的序列都进行聚类，而不仅仅是那些与高相似度的参考数据库序列匹配的序列）相比，该算法具有优势。由于与“经典”开放式参考 OTU 聚类相比，我们的算法可以更多地并行运行，因此它使开放式参考 OTU 聚类在大规模扩增子序列数据集上变得可行（尽管在较小的数据集上，“经典”开放式参考 OTU 聚类通常更快）。我们通过将其应用于地球微生物组计划 (EMP) 中测序的前 15000 个样本（13 亿个 V4 16S rRNA 扩增子）来说明这一点。据我们所知，这是迄今为止进行的最大的 OTU 挑选运行，我们估计我们的新算法的运行时间不到“经典”开放式参考 OTU 挑选所需时间的 1/5。我们通过在三个经过充分研究的数据集上进行比较，表明亚采样开放式参考 OTU 挑选产生的结果与“经典”开放式参考 OTU 挑选产生的结果高度相关。该算法的实现提供在流行的 QIIME 软件包中，该软件包使用 uclust 进行读取聚类。所有分析均使用 QIIME 的 uclust 包装器进行，尽管我们提供了详细信息（借助我们在 GitHub 存储库中的开源代码），这些信息将允许在不依赖于 QIIME 的情况下独立实现亚采样开放式参考 OTU 挑选（例如，在编译编程语言中，运行时间应该进一步缩短）。我们的分析应该适用于这些 OTU 挑选算法的其他实现。最后，我们比较了 QIIME 的 OTU 挑选工作流程中的参数设置，并针对这些自由参数提出了设置建议，以优化运行时间而不降低结果的质量。这些优化的参数可以大大缩短 QIIME 中基于 uclust 的 OTU 挑选的运行时间。
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