加州大学洛杉矶分校Eran Halperin小组在研究中取得进展，该研究使微生物源追踪快速期望最大化，相关论文发表在2019年7月出版的《自然—方法学》杂志上。

研究介绍了快速期望最大化微生物源跟踪(FEAST)，这是一个随时可用的可拓展框架，可以同时及时估计数千个潜在的微生物源环境的贡献，从而帮助解开复杂微生物群落的起源(https://github.com/cozygene/FEAST)。从FEAST中获得的信息可以为量化污染、跟踪微生物群落的形成以及区分和表征与细菌相关的健康状况提供参考。

据了解，分析微生物组数据的组成结构的一个主要挑战是确定其潜在的来源。

附：英文原文

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

Author: Liat Shenhav, Mike Thompson, Tyler A. Joseph, Leah Briscoe, Ori Furman, David Bogumil, Itzhak Mizrahi, Itsik Peer, Eran Halperin

Issue&Volume: Volume 16 Issue 7, July 2019

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.

DOI: 10.1038/s41592-019-0431-x

Source:https://www.nature.com/articles/s41592-019-0431-x