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用于检测和追踪的高分辨率微生物组分析

High-Resolution Microbiome Profiling for Detection and Tracking of .

作者信息

Grim Christopher J, Daquigan Ninalynn, Lusk Pfefer Tina S, Ottesen Andrea R, White James R, Jarvis Karen G

机构信息

Center for Food Safety and Applied Nutrition, United States Food and Drug Administration, LaurelMD, United States.

Center for Food Safety and Applied Nutrition, United States Food and Drug Administration, College ParkMD, United States.

出版信息

Front Microbiol. 2017 Aug 18;8:1587. doi: 10.3389/fmicb.2017.01587. eCollection 2017.

Abstract

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for , which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect in the clinical setting, in this case, bloodborne infections.

摘要

16S rRNA群落分析仍然是研究微生物组组成和动态的有用工具,部分原因是下一代测序技术的进步带来了成本的降低。然而,由于16S rRNA基因覆盖不完整,尤其是对于短读长测序平台而言,在物种水平上进行可靠的分类鉴定仍然很困难。对于[具体物种未明确写出]来说尤其如此,它通常是微生物群落中低丰度的成员,并且经常与其他几种密切相关的肠道物种一起被发现。在此,我们报告了Resphera Insight的评估和应用,这是一种用于将16S rRNA序列在物种水平上进行超高分辨率分类归属的算法。该分析流程在从FDA GenomeTrakr生物项目提取的WGS数据集中正确鉴定[具体物种未明确写出]时达到了99.7%的灵敏度,同时对其他[具体物种未明确写出]成员显示出99.9%的特异性。对于低多样性和低复杂性的样本,即冰淇淋,该算法在检测[具体物种未明确写出]时达到了100%的特异性和灵敏度。正如使用香菜和辣椒粉所证明的那样,对于高度复杂和多样的样本,尤其是那些包含密切相关物种的样本,可能需要将检测阈值调整得更高,以考虑错误鉴定的情况。我们还展示了这种方法在临床环境中检测[具体物种未明确写出]的实用性,在这种情况下是血源感染。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e451/5563311/6781388c57aa/fmicb-08-01587-g001.jpg

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