gRodon is an R package to estimate maximal growth rates of prokaryotic organisms from genome-wide codon usage statistics. You can find a detailed tutorial (vignette) on how to use the gRodon package here.

You can use gRodon to get maximal growth rate predictions from individual genomes, as well-as bulk community-wide average growth rates from metagenomes.

To run gRodon you will need a fasta file with your coding sequence (ORFs), as well as a list of highly expressed proteins (typically ribosomal proteins). If you would like to run abundance-weighted metagenome mode you will also need mean depth of coverage estimates for each of your ORFs.

The easiest way to install gRodon is with devtools.

devtools::install_github("jlw-ecoevo/gRodon")

gRodon has a few dependencies - namely the Biostrings, coRdon, and matrixStats packages which are bioconductor packages and cannot be installed via CRAN. To install them run the following:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Biostrings")
BiocManager::install("coRdon")
install.packages("matrixStats")

Currently gRodon only has a single function available to users: predictGrowth.

To see the details of how this function works type ?predictGrowth().

A minimal example with data included in the package is:

# Load in example genome (Streptococcus pyogenes M1, downloaded from RefSeq)
# included with gRodon
path_to_genome <- system.file('extdata',
  'GCF_000349925.2_ASM34992v2_cds_from_genomic.fna',
  package = 'gRodon')
genes <- readDNAStringSet(path_to_genome)

# Search pre-existing annotations for ribosomal proteins, which we
# will use as our set of highly expressed genes
highly_expressed <- grepl("ribosomal protein",names(genes),ignore.case = T)

# Run the gRodon growth prediction pipeline
predictGrowth(genes, highly_expressed)

We have compiled a docker image for gRodon to ease the installation process. You can pull it to your local computer and run it like this:

# pull the image
$ docker pull shengwei/grodon:latest

# start an interactive container
$ docker run -ti --rm shengwei/grodon:latest

Now you're inside of docker container, let's start an R session

$ root@5218b31cd695:/mnt# R

Now you're inside of R REPL of the docker container, let's test gRodon:

> library(gRodon)
> library(Biostrings)
> path_to_genome <- system.file('extdata',
  'GCF_000349925.2_ASM34992v2_cds_from_genomic.fna.gz',
  package = 'gRodon')
> genes <- readDNAStringSet(path_to_genome)
> highly_expressed <- grepl("ribosomal protein",names(genes),ignore.case = T)
> predictGrowth(genes, highly_expressed)

To mount your own data volume and run in non-interactive mode, please refer to this example.

If you want to modify and build your own docker image, the source code can be found here.

If you find gRodon is useful to your study, please cite:

Jake L. Weissman, Shengwei Hou, Jed A. Fuhrman. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. bioRxiv 2020.07.25.221176; doi: https://doi.org/10.1101/2020.07.25.221176