syntenet

The goal of syntenet is to infer synteny networks from whole-genome protein sequence data and analyze them. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform:

Synteny detection using a native implementation of the MCScanX algorithm, a C++ program that has been modified and ported to R with Rcpp. This way, users do not need to install MCScanX beforehand, because syntenet has its own implementation of the same algorithm.
Synteny network inference by treating anchor pairs as edges of a graph;
Network clustering using the Infomap algorithm;
Phylogenomic profiling, which consists in identifying which species contain which clusters. This analysis can reveal highly conserved synteny clusters and taxon-specific ones (e.g., family- and order-specific clusters);
Microsynteny-based phylogeny reconstruction with maximum likelihood, which can be achieved by inferring a phylogeny from a binary matrix of phylogenomic profiles with IQTREE.

Installation instructions

Get the latest stable R release from CRAN. Then install syntenet from Bioconductor using the following code:

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

BiocManager::install("syntenet")

And the development version from GitHub with:

BiocManager::install("almeidasilvaf/syntenet")

Citation

Below is the citation output from using citation('syntenet') in R. Please run this yourself to check for any updates on how to cite syntenet.

print(citation('syntenet'), bibtex = TRUE)
#> 
#> To cite syntenet in publications, use:
#> 
#>   Almeida-Silva, F., Zhao, T., Ullrich, K.K., Schranz, M.E. and Van de
#>   Peer, Y. syntenet: an R/Bioconductor package for the inference and
#>   analysis of synteny networks. Bioinformatics, 39(1), p.btac806.
#>   (2023). https://doi.org/10.1093/bioinformatics/btac806
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {syntenet: an R/Bioconductor package for the inference and analysis of synteny networks},
#>     author = {Fabricio Almeida-Silva and Tao Zhao and Kristian K. Ullrich and M. Eric Schranz and Yves {Van de Peer}},
#>     journal = {Bioinformatics},
#>     year = {2023},
#>     volume = {39},
#>     number = {1},
#>     pages = {btac806},
#>     url = {https://academic.oup.com/bioinformatics/article/39/1/btac806/6947985},
#>     doi = {10.1093/bioinformatics/btac806},
#>   }

Please note that syntenet was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Code of Conduct

Please note that the syntenet project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

Continuous code testing is possible thanks to GitHub actions through usethis, remotes, and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
Code coverage assessment is possible thanks to codecov and covr.
The documentation website is automatically updated thanks to pkgdown.
The code is styled automatically thanks to styler.
The documentation is formatted thanks to devtools and roxygen2.

For more details, check the dev directory.

This package was developed using biocthis.