FEAST: fast expectation-maximization for microbial source tracking

Liat Shenhav; Mike Thompson; Tyler A. Joseph; Leah Briscoe; Ori Furman; David Bogumil; Itzhak Mizrahi; Itsik Pe’er; Eran Halperin

doi:10.1038/s41592-019-0431-x

FEAST: fast expectation-maximization for microbial source tracking

Liat Shenhav, Mike Thompson, Tyler A. Joseph, Leah Briscoe, Ori Furman, David Bogumil, Itzhak Mizrahi, Itsik Pe’er, Eran Halperin

Department of Life Sciences

Research output: Contribution to journal › Article › peer-review

276 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	627-632
Number of pages	6
Journal	Nature Methods
Volume	16
Issue number	7
DOIs	https://doi.org/10.1038/s41592-019-0431-x
State	Published - 1 Jul 2019

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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title = "FEAST: fast expectation-maximization for microbial source tracking",

abstract = "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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AU - Shenhav, Liat

AU - Thompson, Mike

AU - Joseph, Tyler A.

AU - Briscoe, Leah

AU - Furman, Ori

AU - Bogumil, David

AU - Mizrahi, Itzhak

AU - Pe’er, Itsik

AU - Halperin, Eran

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AB - 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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FEAST: fast expectation-maximization for microbial source tracking

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