KTH Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, 17165 Solna, Sweden BILS, Bioinformatics Infrastructure for Life Sciences, Vetenskapsrådet, SwedenDepartments of Ecology and Genetics/Limnology Ecology and Genetics/Population Biology, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden.
Environ Microbiol Rep. 2012 Jun;4(3):367-72. doi: 10.1111/j.1758-2229.2012.00345.x. Epub 2012 Apr 23.
The vastness of microbial diversity implies that an almost infinite number of individuals needs to be identified to accurately describe such communities. Practical and economical constraints may therefore prevent appropriate study designs. However, for many questions in ecology it is not essential to know the actual diversity but rather the trends among samples thereof. It is, hence, important to know to what depth microbial communities need to be sampled to accurately measure trends in diversity. We used three data sets of freshwater and sediment bacteria, where diversity was explored using 454 pyrosequencing. Each data set contained 6-15 communities from which 15 000-20 000 16S rRNA gene sequences each were obtained. These data sets were subsampled repeatedly to 10 different depths down to 200 sequences per community. Diversity estimates varied with sequencing depth, yet, trends in diversity among samples were less sensitive. We found that 1000 denoised sequences per sample explained to 90% the trends in β-diversity (Bray-Curtis index) among samples observed for 15 000-20 000 sequences. Similarly, 5000 denoised sequences were sufficient to describe trends in α-diversity (Shannon index) with the same accuracy. Further, 5000 denoised sequences captured to more than 80% the trends in Chao1 richness and Pielou's evenness.
微生物多样性的广泛性意味着需要识别几乎无限数量的个体,才能准确描述这些群落。因此,实际和经济上的限制可能会阻止适当的研究设计。然而,对于生态学中的许多问题,知道实际的多样性并不一定是必要的,而了解样本多样性的趋势则更为重要。因此,了解微生物群落需要采样到多深才能准确测量多样性的趋势非常重要。我们使用了三个淡水和沉积物细菌数据集,其中使用 454 焦磷酸测序法探索了多样性。每个数据集包含 6-15 个群落,每个群落获得了 15000-20000 条 16S rRNA 基因序列。这些数据集被重复取样至 10 个不同的深度,每个群落的序列数降至 200 条。多样性估计随测序深度而变化,但样本间多样性的趋势则不太敏感。我们发现,对于 15000-20000 条序列观察到的 1000 条去噪序列/样本可以解释 90%的β多样性(Bray-Curtis 指数)趋势。同样,5000 条去噪序列足以以相同的准确性描述α多样性(香农指数)的趋势。此外,5000 条去噪序列捕获了 Chao1 丰富度和皮埃洛均匀度趋势的 80%以上。