Scholarlink

<p>Microbial maximum growth rates vary widely across species and are key parameters for ecosystem modeling. Measuring these rates is challenging, but genomic features like codon usage statistics provide useful signals for predicting growth rates for as-yet uncultivated organisms. Here we present Phydon, a framework for genome-based maximum growth rate prediction that combines codon statistics and phylogenetic information to enhance the precision of maximum growth rate estimates, especially when a close relative with a known growth rate is available. We use Phydon to construct a large and taxonomically broad database of temperature-corrected growth rate estimates for 111,349 microbial species. The results reveal a bimodal distribution of maximum growth rates, resolving distinct groups of fast and slow growers. Our work provides insight into the predictive power of taxonomic information versus mechanistic, gene-based inference.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improved maximum growth rate prediction from microbial genomes by integrating phylogenetic information

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月7日

查看全文
  • Liang Xu,
  • Emily Zakem,
  • JL Weissman

摘要

Microbial maximum growth rates vary widely across species and are key parameters for ecosystem modeling. Measuring these rates is challenging, but genomic features like codon usage statistics provide useful signals for predicting growth rates for as-yet uncultivated organisms. Here we present Phydon, a framework for genome-based maximum growth rate prediction that combines codon statistics and phylogenetic information to enhance the precision of maximum growth rate estimates, especially when a close relative with a known growth rate is available. We use Phydon to construct a large and taxonomically broad database of temperature-corrected growth rate estimates for 111,349 microbial species. The results reveal a bimodal distribution of maximum growth rates, resolving distinct groups of fast and slow growers. Our work provides insight into the predictive power of taxonomic information versus mechanistic, gene-based inference.