Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

PloS One
Peter E LarsenYang Dai

Abstract

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understan...Continue Reading

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Citations

Sep 18, 2015·GigaScience·Peter E Larsen, Yang Dai
Jul 5, 2016·Frontiers in Plant Science·Pierre-Emmanuel Courty, Daniel Wipf
Feb 17, 2018·Frontiers in Plant Science·Rosemarie WiltonKenneth M Kemner
May 8, 2018·International Journal of Molecular Sciences·Kristian PetersNicole M van Dam

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Software Mentioned

PRMT
PRTT
BLAST
- Blast
e1071
Primer

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