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Cost-effective Method for Microbial Source Tracking Using Specific Human and Animal Viruses
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FEAST: fast expectation-maximization for microbial source tracking.

Liat Shenhav1, Mike Thompson2, Tyler A Joseph3

  • 1Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.

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|June 12, 2019
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Summary
This summary is machine-generated.

Identifying the origins of microbiome data is challenging. Fast Expectation-Maximization Microbial Source Tracking (FEAST) is a new framework that efficiently estimates contributions from thousands of potential sources to understand microbial community origins.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing the compositional structure of microbiome data is crucial for understanding microbial ecology.
  • Identifying the origins of microbial communities remains a significant analytical challenge.

Purpose of the Study:

  • To introduce a novel, scalable framework for microbial source tracking.
  • To enable the simultaneous estimation of contributions from numerous potential source environments.

Main Methods:

  • Development and application of the Fast Expectation-Maximization Microbial Source Tracking (FEAST) framework.
  • Utilizing an expectation-maximization algorithm for efficient source attribution.

Main Results:

  • FEAST provides a timely and scalable solution for analyzing microbiome data origins.
  • The framework can handle thousands of potential source environments simultaneously.
  • Demonstrated ability to unravel the origins of complex microbial communities.

Conclusions:

  • FEAST offers valuable insights into quantifying contamination in microbiome samples.
  • The tool aids in tracking the formation and development of microbial communities.
  • FEAST can assist in distinguishing and characterizing bacteria-related health conditions.