Canadian Centre for Computational Genomics, McGill University and Genome Quebec Innovation Centre, Montréal, QC, H3A 0G1, Canada.
Department of Human Genetics, McGill University, Montreal, H3A 1B1, Canada.
Environ Microbiol. 2019 Jul;21(7):2440-2468. doi: 10.1111/1462-2920.14632. Epub 2019 May 21.
Analysis of 16S ribosomal RNA (rRNA) gene amplification data for microbial barcoding can be inaccurate across complex environmental samples. A method, ANCHOR, is presented and designed for improved species-level microbial identification using paired-end sequences directly, multiple high-complexity samples and multiple reference databases. A standard operating procedure (SOP) is reported alongside benchmarking against artificial, single sample and replicated mock data sets. The method is then directly tested using a real-world data set from surface swabs of the International Space Station (ISS). Simple mock community analysis identified 100% of the expected species and 99% of expected gene copy variants (100% identical). A replicated mock community revealed similar or better numbers of expected species than MetaAmp, DADA2, Mothur and QIIME1. Analysis of the ISS microbiome identified 714 putative unique species/strains and differential abundance analysis distinguished significant differences between the Destiny module (U.S. laboratory) and Harmony module (sleeping quarters). Harmony was remarkably dominated by human gastrointestinal tract bacteria, similar to enclosed environments on earth; however, Destiny module bacteria also derived from nonhuman microbiome carriers present on the ISS, the laboratory's research animals. ANCHOR can help substantially improve sequence resolution of 16S rRNA gene amplification data within biologically replicated environmental experiments and integrated multidatabase annotation enhances interpretation of complex, nonreference microbiomes.
对微生物条形码的 16S 核糖体 RNA(rRNA)基因扩增数据的分析在复杂的环境样本中可能不准确。本文提出并设计了一种方法,即 ANCHOR,用于直接使用配对末端序列、多个高复杂度样本和多个参考数据库,改进基于物种水平的微生物鉴定。本文还报告了一个标准操作程序(SOP),并与人工、单个样本和复制模拟数据集进行了基准测试。然后,该方法直接使用国际空间站(ISS)表面拭子的真实数据集进行了测试。简单的模拟群落分析可识别出 100%预期的物种和 99%预期的基因拷贝变体(完全相同)。对重复模拟群落的分析显示,与 MetaAmp、DADA2、Mothur 和 QIIME1 相比,预期物种的数量相似或更多。对 ISS 微生物组的分析确定了 714 个可能的独特物种/菌株,差异丰度分析区分了 Destiny 模块(美国实验室)和 Harmony 模块(睡眠区)之间的显著差异。Harmony 模块显著被人类胃肠道细菌主导,与地球上的封闭环境相似;然而,Harmony 模块的细菌也来源于存在于 ISS 上的非人类微生物组载体,即实验室的研究动物。ANCHOR 可以大大提高生物复制环境实验中 16S rRNA 基因扩增数据的序列分辨率,并且整合多数据库注释增强了对复杂、非参考微生物组的解释。