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Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information
Liang Xu1, Emily Zakem2, J L Weissman3,4
1Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA. lxu@carnegiescience.edu.
Nature Communications
|May 7, 2025
Summary
Microbial maximum growth rates can now be predicted using genomic data with Phydon, a new framework. This reveals distinct fast and slow-growing microbial groups, aiding ecosystem modeling.
Area of Science:
- Microbiology
- Genomics
- Computational Biology
Background:
- Microbial maximum growth rates are crucial for ecosystem modeling but difficult to measure.
- Genomic features, such as codon usage statistics, offer predictive signals for growth rates, even for uncultivated microbes.
Purpose of the Study:
- To introduce Phydon, a novel framework for predicting microbial maximum growth rates using genome-based data.
- To enhance growth rate prediction accuracy by integrating codon statistics and phylogenetic information.
Main Methods:
- Developed the Phydon framework combining codon usage statistics and phylogenetic data for growth rate prediction.
- Constructed a large database of temperature-corrected growth rate estimates for 111,349 microbial species using Phydon.
Main Results:
- Phydon successfully predicts microbial maximum growth rates, particularly when related species with known rates are available.
- The study identified a bimodal distribution of microbial maximum growth rates, distinguishing fast and slow growers.
- Analysis provided insights into the comparative predictive power of taxonomic versus gene-based inference.
Conclusions:
- Phydon offers a robust method for estimating microbial growth rates from genomic data.
- The findings highlight distinct microbial growth strategies and improve our understanding of microbial ecology and evolution.
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