Benchmarking Community-Wide Estimates of Growth Potential from Metagenomes Using Codon Usage Statistics

Trait inference from mixed-species assemblages is a central problem in microbial ecology. Frequently, sequencing information from an environment is available, but phenotypic measurements from individual community members are not. With the increasing availability of molecular data for microbial communities, bioinformatic approaches that map metagenome to (meta)phenotype are needed. Recently, we developed a tool, gRodon, that enables the prediction of the maximum growth rate of an organism from genomic data on the basis of codon usage patterns. Our work and that of other groups suggest that such predictors can be applied to mixed-species communities in order to derive estimates of the average community-wide maximum growth rate. Here, we present an improved maximum growth rate predictor designed for metagenomes that corrects a persistent GC bias in the original gRodon model for metagenomic prediction. We benchmark this predictor with simulated metagenomic data sets to show that it has superior performance on mixed-species communities relative to earlier models. We go on to provide guidance on data preprocessing and show that calling genes from assembled contigs rather than directly from reads dramatically improves performance. Finally, we apply our predictor to large-scale metagenomic data sets from marine and human microbiomes to illustrate how community-wide growth prediction can be a powerful approach for hypothesis generation. Altogether, we provide an updated tool with clear guidelines for users about the uses and pitfalls of metagenomic prediction of the average community-wide maximal growth rate. Microbes dominate nearly every known habitat, and therefore tools to survey the structure and function of natural microbial communities are much needed. Metagenomics, in which the DNA content of an entire community of organisms is sequenced all at once, allows us to probe the genetic diversity contained in a habitat. Yet, mapping metagenomic information to the actual traits of community members is a difficult and largely unsolved problem. Here, we present and validate a tool that allows users to predict the average maximum growth rate of a microbial community directly from metagenomic data. Maximum growth rate is a fundamental characteristic of microbial species that can give us a great deal of insight into their ecological role, and by applying our community-level predictor to large-scale metagenomic data sets from marine and human-associated microbiomes, we