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
syntenethas 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.
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")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.
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.
- 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.
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