可扩展性
微生物群
计算机科学
最大化
数据科学
源跟踪
计算生物学
跟踪(教育)
生化工程
数据挖掘
生物
生物信息学
万维网
数据库
数学优化
数学
工程类
心理学
教育学
作者
Liat Shenhav,Michael Thompson,Tyler Joseph,Leah Briscoe,Ori Furman,David Bogumil,Itzhak Mizrahi,Itsik Pe’er,Eran Halperin
出处
期刊:Nature Methods
[Springer Nature]
日期:2019-06-10
卷期号:16 (7): 627-632
被引量:353
标识
DOI:10.1038/s41592-019-0431-x
摘要
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. FEAST provides a computationally efficient tool to estimate the contribution of microbial sources to a target microbial community, as demonstrated for a variety of complex environmental samples.
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