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使用基于精选核心基因的细菌数据库改进宏基因组分类分析揭示了该属中未被识别的物种。

Improved Metagenomic Taxonomic Profiling Using a Curated Core Gene-Based Bacterial Database Reveals Unrecognized Species in the Genus .

作者信息

Chalita Mauricio, Ha Sung-Min, Kim Yeong Ouk, Oh Hyun-Seok, Yoon Seok-Hwan, Chun Jongsik

机构信息

Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.

ChunLab Inc., Seoul 06725, Korea.

出版信息

Pathogens. 2020 Mar 10;9(3):204. doi: 10.3390/pathogens9030204.

Abstract

Shotgun metagenomics is of great importance in order to understand the composition of the microbial community associated with a sample and the potential impact it may exert on its host. For clinical metagenomics, one of the initial challenges is the accurate identification of a pathogen of interest and ability to single out that pathogen within a complex community of microorganisms. However, in absence of an accurate identification of those microorganisms, any kind of conclusion or diagnosis based on misidentification may lead to erroneous conclusions, especially when comparing distinct groups of individuals. When comparing a shotgun metagenomic sample against a reference genome sequence database, the classification itself is dependent on the contents of the database. Focusing on the genus , we built four synthetic metagenomic samples and demonstrated that shotgun taxonomic profiling using the bacterial core genes as the reference database performed better in both taxonomic profiling and relative abundance prediction than that based on the marker gene reference database included in MetaPhlAn2. Additionally, by classifying sputum samples of patients suffering from chronic obstructive pulmonary disease, we showed that adding genomes of genomospecies to a reference database offers higher taxonomic resolution for taxonomic profiling. Finally, we show how our genomospecies database is able to identify correctly a clinical stool sample from a patient with a streptococcal infection, proving that genomospecies provide better taxonomic coverage for metagenomic analyses.

摘要

为了了解与样本相关的微生物群落组成及其对宿主可能产生的潜在影响,鸟枪法宏基因组学至关重要。对于临床宏基因组学而言,最初的挑战之一是准确识别感兴趣的病原体,并能够在复杂的微生物群落中挑选出该病原体。然而,如果不能准确识别这些微生物,基于错误识别得出的任何结论或诊断都可能导致错误的结果,尤其是在比较不同个体组时。当将鸟枪法宏基因组样本与参考基因组序列数据库进行比较时,分类本身取决于数据库的内容。我们聚焦于属水平,构建了四个合成宏基因组样本,并证明使用细菌核心基因作为参考数据库的鸟枪法分类分析在分类分析和相对丰度预测方面比基于MetaPhlAn2中包含的标记基因参考数据库的分析表现更好。此外,通过对慢性阻塞性肺疾病患者的痰液样本进行分类,我们表明在参考数据库中添加基因组种的基因组可为分类分析提供更高的分类分辨率。最后,我们展示了我们的基因组种数据库如何能够正确识别一名链球菌感染患者的临床粪便样本,证明基因组种为宏基因组分析提供了更好的分类覆盖。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f4/7157611/11cb2724c62a/pathogens-09-00204-g001.jpg

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