Strain profiling and epidemiology of bacterial species from metagenomic sequencing.
Strain profiling and epidemiology of bacterial species from metagenomic sequencing.
复制标题
DOI：
10.1038/s41467-017-02209-5
复制
发表时间：
2017-12-22
影响因子：
16.6
通讯作者：
Donati C
中科院分区：
文献类型：
--
作者：
Albanese D;Donati C
微生物群落通常由同一物种的多种菌株的复杂混合物组成，其特征在于广泛的基因组和表型变异性。能够识别、量化和分类样品中存在的不同菌株的计算方法对于充分利用宏基因组测序在微生物生态学中的潜力是必不可少的，其应用范围从传染病的流行病学到微生物定殖动力学的表征。在这里，我们提出了一种计算方法，使用现有的基因组数据，从宏基因组测序重建复杂的菌株谱，量化不同菌株的丰度，并根据物种的种群结构对其进行编目。我们在合成数据集上验证了该方法，并将其应用于表征真实的样品中几种重要细菌物种的菌株分布，展示了其应用如何为微生物群的结构和复杂性提供新的见解。微生物群通常是多种共存物种和菌株的复杂混合物，具有高水平的表型和基因组变异性。在这里，Albanese和Donati开发了StrainEst，用于估计混合宏基因组样本中共存菌株的数量和身份及其相对丰度。
Microbial communities are often composed by complex mixtures of multiple strains of the same species, characterized by a wide genomic and phenotypic variability. Computational methods able to identify, quantify and classify the different strains present in a sample are essential to fully exploit the potential of metagenomic sequencing in microbial ecology, with applications that range from the epidemiology of infectious diseases to the characterization of the dynamics of microbial colonization. Here we present a computational approach that uses the available genomic data to reconstruct complex strain profiles from metagenomic sequencing, quantifying the abundances of the different strains and cataloging them according to the population structure of the species. We validate the method on synthetic data sets and apply it to the characterization of the strain distribution of several important bacterial species in real samples, showing how its application provides novel insights on the structure and complexity of the microbiota. Microbiota is often a complex mixture of multiple coexisting species and strains with high level of phenotypic and genomic variability. Here, Albanese and Donati develop StrainEst for estimating the number and identity of coexisting strains and their relative abundances in mixed metagenomic samples.
登录
查看更多内容
影响因子：
46.9
作者：
Luo C;Knight R;Siljander H;Knip M;Xavier RJ;Gevers D
通讯作者：
Gevers D
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
DOI：
10.1073/pnas.0406993102
发表时间：
2005-02-08
影响因子：
11.1
作者：
Fraser, C;Hanage, WP;Spratt, BG
通讯作者：
Spratt, BG
影响因子：
5.9
作者：
FAITH, DP
通讯作者：
FAITH, DP
影响因子：
7
作者：
Nayfach S;Rodriguez-Mueller B;Garud N;Pollard KS
通讯作者：
Pollard KS