FEAST: fast expectation-maximization for microbial source tracking

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...

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Published inNature methods Vol. 16; no. 7; pp. 627 - 632
Main Authors Shenhav, Liat, Thompson, Mike, Joseph, Tyler A., Briscoe, Leah, Furman, Ori, Bogumil, David, Mizrahi, Itzhak, Pe’er, Itsik, Halperin, Eran
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.07.2019
Nature Publishing Group
Subjects
631/114
631/158
631/326
Accuracy
Adult
Bacteria - isolation & purification
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Communities
Computer science
Datasets
Gastrointestinal Microbiome
Humans
Infant
Intensive Care Units
Life Sciences
Markov analysis
Maximization
Methods
Microbial activity
Microbial source tracking
Microbiomes
Microbiota
Microorganisms
Optimization
Origins
Proteomics
Tracking
United States
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Summary: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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L.S. and E.H. conceived the statistical model. L.S. designed the algorithm and software, and performed computational experiments. L.S., M.T., T.A.J. and L.B. wrote the manuscript. O.F. and D.B. contributed to writing the manuscript. T.A.J. and M.T. contributed to algorithm design. M.T. and L.B contributed to the computational experiments. I.M., I.P. and E.H. supervised the project.
Author contributions
ISSN:1548-7091
1548-7105
DOI:10.1038/s41592-019-0431-x