Literature DB >> 28000336

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons.

Hong-Tai Cao1,2,3, Travis E Gibson1, Amir Bashan1,4, Yang-Yu Liu1,5.   

Abstract

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.
© 2016 WILEY Periodicals, Inc.

Entities:  

Keywords:  dynamics inference; ecological modeling; human microbiome; temporal metagenomics

Mesh:

Year:  2016        PMID: 28000336     DOI: 10.1002/bies.201600188

Source DB:  PubMed          Journal:  Bioessays        ISSN: 0265-9247            Impact factor:   4.345


  16 in total

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Review 4.  Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface.

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6.  Mapping the ecological networks of microbial communities.

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Journal:  Bioinformatics       Date:  2021-07-12       Impact factor: 6.937

9.  IMPARO: inferring microbial interactions through parameter optimisation.

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Journal:  BMC Mol Cell Biol       Date:  2020-08-19

10.  Compositional Lotka-Volterra describes microbial dynamics in the simplex.

Authors:  Tyler A Joseph; Liat Shenhav; Joao B Xavier; Eran Halperin; Itsik Pe'er
Journal:  PLoS Comput Biol       Date:  2020-05-29       Impact factor: 4.779

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