JL will be hiring a postdoc and recruiting PhD students starting Fall 2024 - please reach out (jackie.weissman@stonybrook.edu) if you are interested in working on projects like this one!
Do you have growth rates matched to genomes for cultured isolates? Email JL (jackie.weissman@stonybrook.edu) and she will be happy to incorporate that data into the next version of gRodon in development! They are always on the lookout for more data, and always happy to have more collaborators on board.
gRodon is an R package to estimate maximal growth rates of prokaryotes and microbial eukaryotes (new in v2) 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/gRodon2")
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:
library(gRodon)
library(Biostrings)
# 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.gz',
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)
You can find a detailed tutorial (vignette) on how to use the gRodon package here.
Using gRodon with docker
We have compiled two docker images for gRodon v1.0.0 (no eukaryotes, no metagenome_v2 mode) and v2.0.0, respectively, to ease the installation process. You can pull the preferred version to your local computer and run it like this:
# pull the image
# shengwei/grodon:latest for gRodon v1.0.0
$ docker pull shengwei/grodon2:latest
# start an interactive container
$ docker run -ti --rm shengwei/grodon2:latestNow you're inside of the docker container, let's start an R session
$ root@5218b31cd695:/mnt# RNow you're inside of the 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.
Susheel Busi has setup a conda environment to run gRodon v1.0.0 (no eukaryotes, no metagenome_v2 mode) with some helper scripts that made be useful to some users. The gRodon developers take no responsibility for the functioning of this code though and all questions should be submitted directly to the author.
If you find gRodon is useful to your study, please cite us!
For prokaryotic prediction: the gRodon paper:
JL Weissman, Shengwei Hou, Jed A. Fuhrman. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proceedings of the National Academy of Sciences 2021, 118 (12) e2016810118; DOI: 10.1073/pnas.2016810118
@article {Weissmane2016810118,
author = {Weissman, JL and Hou, Shengwei and Fuhrman, Jed A.},
title = {Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns},
volume = {118},
number = {12},
elocation-id = {e2016810118},
year = {2021},
doi = {10.1073/pnas.2016810118},
publisher = {National Academy of Sciences},
issn = {0027-8424},
URL = {https://www.pnas.org/content/118/12/e2016810118},
eprint = {https://www.pnas.org/content/118/12/e2016810118.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}
For eukaryotic prediction: the gRodon2 paper:
JL Weissman, Edward-Robert O Dimbo, Arianna I Krinos, Christopher Neely, Yuniba Yagues, Delaney Nolin, Shengwei Hou, Sarah Laperriere, David A Caron, Benjamin L Tully, Harriet Alexander, Jed A Fuhrman. Estimating the maximal growth rates of eukaryotic microbes from cultures and metagenomes via codon usage patterns. bioRxiv 2021.10.15.464604; DOI: https://doi.org/10.1101/2021.10.15.464604
@article {Weissman2021.10.15.464604,
author = {Weissman, JL and Dimbo, Edward-Robert O and Krinos, Arianna I and Neely, Christopher and Yagues, Yuniba and Nolin, Delaney and Hou, Shengwei and Laperriere, Sarah and Caron, David A and Tully, Benjamin L and Alexander, Harriet and Fuhrman, Jed A},
title = {Estimating the maximal growth rates of eukaryotic microbes from cultures and metagenomes via codon usage patterns},
elocation-id = {2021.10.15.464604},
year = {2021},
doi = {10.1101/2021.10.15.464604},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2021/10/16/2021.10.15.464604},
eprint = {https://www.biorxiv.org/content/early/2021/10/16/2021.10.15.464604.full.pdf},
journal = {bioRxiv}
}
For metagenomic prediction: the Metagenome Mode v2 paper:
JL Weissman, Marie Peras, Tyler P Barnum, Jed A Fuhrman. Benchmarking community-wide estimates of growth potential from metagenomes using codon usage statistics. mSystems 2022, 7 (5) e00745-22; DOI: https://doi.org/10.1128/msystems.00745-22
@article {Weissman2022.04.12.488109,
author = {Weissman, JL and Peras, Marie and Barnum, Tyler P. and Fuhrman, Jed A.},
title = {Benchmarking Community-Wide Estimates of Growth Potential from Metagenomes Using Codon Usage Statistics},
journal = {mSystems},
volume = {7},
number = {5},
pages = {e00745-22},
year = {2022},
doi = {10.1128/msystems.00745-22},
URL = {https://journals.asm.org/doi/abs/10.1128/msystems.00745-22},
eprint = {https://journals.asm.org/doi/pdf/10.1128/msystems.00745-22}
}
Elek A, Kuzman M, Vlahovicek K (2020). coRdon: Codon Usage Analysis and Prediction of Gene Expressivity. R package version 1.8.0, https://github.com/BioinfoHR/coRdon
Pagès H, Aboyoun P, Gentleman R, DebRoy S (2020). Biostrings: Efficient manipulation of biological strings. R package version 2.58.0, https://bioconductor.org/packages/Biostrings.
Henrik Bengtsson (2021). matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). R package version 0.58.0. https://CRAN.R-project.org/package=matrixStats
As well as the original paper describing the MILC statistic:
Supek, Fran, and Kristian Vlahovicek. “Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity.” BMC bioinformatics vol. 6 182. 19 Jul. 2005, doi:10.1186/1471-2105-6-182