Strain/species identification in metagenomes using genome-specific markers.

Strain/species identification in metagenomes using genome-specific markers.
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DOI:
10.1093/nar/gku138
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发表时间:
2014-04
影响因子:
14.9
通讯作者:
Zhou J
Zhou J
中科院分区:
生物学2区
文献类型:
--
作者:
Tu Q;He Z;Zhou J

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鸟枪法宏基因组测序已成为一种快速、廉价和高通量的技术,用于表征复杂环境和人体部位的微生物群落。然而,在菌株/物种水平上准确鉴定微生物仍然极具挑战性。我们提出了一种新的k-mer为基础的方法,称为GSMer,确定基因组特异性标记(GSM)从目前测序的微生物基因组,然后用于菌株/物种水平的鉴定宏基因组。利用已测序的5390个微生物基因组,分别鉴定出4088株和2005种(4933株)的8 770 321个50-mer菌株特异性和11 736 360个种特异性GSMs。首先针对模拟社区宏基因组、最近测序的基因组和来自不同身体部位的真实的宏基因组对GSM进行评估,这表明所鉴定的GSM对其靶基因组具有特异性。针对具有不同覆盖度的合成宏基因组的灵敏度评估表明,每个菌株50个GSM足以鉴定具有≥0.25×覆盖度的大多数微生物菌株,并且数据库中10%的选定GSM应被检测到以获得可信的阳性调用。应用GSM从相应的胃肠道宏基因组中分别鉴定出45种和74种与2型糖尿病患者和肥胖/瘦个体显著相关的微生物菌株/物种。我们的结果与以前的研究一致,但提供了应变水平的信息。该方法可以直接应用于从原始宏基因组中识别微生物菌株/物种,而无需复杂的数据预处理。
Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.
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