FEAST: fast expectation-maximization for microbial source tracking

被引：338

作者：

Shenhav, Liat ^{[1
]}

Thompson, Mike ^{[2
]}

Joseph, Tyler A. ^{[3
]}

Briscoe, Leah ^{[2
]}

Furman, Ori ^{[4
]}

Bogumil, David ^{[4
]}

Mizrahi, Itzhak ^{[4
]}

Pe'er, Itsik ^{[3
]}

Halperin, Eran ^{[1
,2
,5
,6
]}

机构：

[1] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA

[2] Univ Calif Los Angeles, Dept Human Genet, Los Angeles, CA 90095 USA

[3] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA

[4] Ben Gurion Univ Negev, Life Sci, Beer Sheva, Israel

[5] Univ Calif Los Angeles, Dept Anesthesiol & Perioperat Med, Los Angeles, CA 90095 USA

[6] Univ Calif Los Angeles, Dept Computat Med, Los Angeles, CA 90095 USA

来源：

NATURE METHODS | 2019年 / 16卷 / 07期

基金：

美国国家科学基金会; 欧洲研究理事会;

关键词：

HUMAN GUT MICROBIOME; OBESITY;

D O I：

10.1038/s41592-019-0431-x

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

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.

引用

页码：627 / +

页数：8

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