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.