STENSL: Microbial Source Tracking with ENvironment SeLection.
STENSL: Microbial Source Tracking with ENvironment SeLection.
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DOI：
10.1128/msystems.00995-21
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发表时间：
2022-10-26
期刊：
影响因子：
6.4
通讯作者：
中科院分区：
文献类型：
--
作者：
微生物来源追踪分析已经成为表征复杂微生物群落特性的一种广泛使用的技术。然而，这一分析目前仅限于在特定研究中抽样的源环境。为了扩大研究范围，使之能够利用大型数据库和储存库，如地球微生物组项目，探索来源环境，需要一个来源选择程序。当所考虑的潜在污染源数量很多时，这种程序将能够区分造成危害的环境和造成危害的环境。在这里，我们介绍了STENSL(微生物来源跟踪与环境选择)，这是一种机器学习方法，通过执行无监督来源选择和实现对潜在来源环境的稀疏识别来扩展常见的微生物来源跟踪分析。通过将稀疏性引入到潜在源环境的估计中，STENSL提高了真实源贡献的准确性，同时显著降低了非贡献源引入的噪声。因此，我们预计，来源选择将加强微生物来源跟踪分析，从而能够从公开可用的储存库中探索多个来源环境，同时保持统计推断的高准确性。重要微生物来源追踪是描述复杂微生物群落特性的有力工具。然而，这一分析目前仅限于在特定研究中抽样的源环境。在许多应用中，显然需要考虑在研究之外的大量微生物环境中进行来源选择。为此，我们开发了STENSL(微生物来源跟踪与环境选择)，这是一种稀疏性的期望最大化算法，能够在大量潜在的微生物环境中识别贡献源。随着地球微生物组项目等微生物组数据库前所未有的扩大，记录了来自50多种分类环境的20多万个样本，STENSL在进行自动化来源勘探和选择方面迈出了第一步。STENSL在识别贡献源和未知源方面明显更准确，即使在考虑数百个潜在源环境时也是如此，在这些环境中，最先进的微生物源跟踪方法会增加相当大的误差。
Microbial source tracking analysis has emerged as a widespread technique for characterizing the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In order to expand the scope beyond one single study and allow the exploration of source environments using large databases and repositories, such as the Earth Microbiome Project, a source selection procedure is required. Such a procedure will allow differentiating between contributing environments and nuisance ones when the number of potential sources considered is high. Here, we introduce STENSL (microbial Source Tracking with ENvironment SeLection), a machine learning method that extends common microbial source tracking analysis by performing an unsupervised source selection and enabling sparse identification of latent source environments. By incorporating sparsity into the estimation of potential source environments, STENSL improves the accuracy of true source contribution, while significantly reducing the noise introduced by noncontributing ones. We therefore anticipate that source selection will augment microbial source tracking analyses, enabling exploration of multiple source environments from publicly available repositories while maintaining high accuracy of the statistical inference. IMPORTANCE Microbial source tracking is a powerful tool to characterize the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In many applications there is a clear need to consider source selection over a large array of microbial environments, external to the study. To this end, we developed STENSL (microbial Source Tracking with ENvironment SeLection), an expectation-maximization algorithm with sparsity that enables the identification of contributing sources among a large set of potential microbial environments. With the unprecedented expansion of microbiome data repositories such as the Earth Microbiome Project, recording over 200,000 samples from more than 50 types of categorized environments, STENSL takes the first steps in performing automated source exploration and selection. STENSL is significantly more accurate in identifying the contributing sources as well as the unknown source, even when considering hundreds of potential source environments, settings in which state-of-the-art microbial source tracking methods add considerable error.
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影响因子：
48
作者：
Shenhav L;Thompson M;Joseph TA;Briscoe L;Furman O;Bogumil D;Mizrahi I;Pe'er I;Halperin E
通讯作者：
Halperin E
影响因子：
3.7
作者：
Teaf, Christopher M.;Flores, David;Harwood, Valerie J.
通讯作者：
Harwood, Valerie J.
影响因子：
3.7
作者：
Wu CH;Sercu B;Van de Werfhorst LC;Wong J;DeSantis TZ;Brodie EL;Hazen TC;Holden PA;Andersen GL
通讯作者：
Andersen GL
DOI：
10.1126/science.1254529
发表时间：
2014-08-29
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Lax S;Smith DP;Hampton-Marcell J;Owens SM;Handley KM;Scott NM;Gibbons SM;Larsen P;Shogan BD;Weiss S;Metcalf JL;Ursell LK;Vázquez-Baeza Y;Van Treuren W;Hasan NA;Gibson MK;Colwell R;Dantas G;Knight R;Gilbert JA
通讯作者：
Gilbert JA
影响因子：
--
作者：
Tong, Maomeng;Jacobs, Jonathan P;McHardy, Ian H;Braun, Jonathan
通讯作者：
Braun, Jonathan