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智科眼: 双语看懂新科研
首页 中国TOP论文 Nature Science JACS AngewChem PRL AdvMater Lancet Cell EnglMed JAMA
FEAST: fast expectation-maximization for microbial source tracking
FEAST:快速期望最大化 for 微生物源追踪
Liat Shenhav; Mike Thompson; Tyler A. Joseph; Leah Briscoe; Ori Furman; David Bogumil; Itzhak Mizrahi; Itsik Pe’er; Eran Halperin
  • Nat Methods vol: issue: (2019) [全文下载] 扫码分享
  • 影响因子: 26.9 点击(275) 收藏(0) 评分(0)
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  • 生化研究方法
  • 分析微生物组数据的组成结构的一个主要挑战是确定其潜在的起源。在这里,我们引入快速期望最大化微生物源跟踪(FEAST),这是一个随时可用的可扩展框架,可以同时估计数千个潜在源环境的贡献,从而帮助解开复杂微生物群落的起源( https://github.com/cozygene/FEAST)。从FEAST获得的信息可以提供对污染量化,跟踪发展中微生物群落形成以及区分和表征细菌相关健康状况的见解。
  • A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities (https://github.com/cozygene/FEAST). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.
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