Almeida-Silva, FabricioZhao, TaoUllrich, Kristian K.Schranz, M. EricVan de Peer, Yves2023-03-072023-03-072023-01Fabricio Almeida-Silva, Tao Zhao, Kristian K Ullrich, M Eric Schranz, Yves Van de Peer, syntenet: an R/Bioconductor package for the inference and analysis of synteny networks, Bioinformatics, Volume 39, Issue 1, January 2023, btac806, https://doi.org/10.1093/bioinformatics/btac806.1367-4811 (online)10.1093/bioinformatics/btac806https://repository.up.ac.za/handle/2263/90001AVAILABILITY AND IMPLEMENTATION: syntenet is available on Bioconductor (https://bioconductor.org/packages/syntenet), and the source code is available on a GitHub repository (https://github.com/almeidasilvaf/syntenet).Interpreting and visualizing synteny relationships across several genomes is a challenging task. We previously proposed a network-based approach for better visualization and interpretation of large-scale microsynteny analyses. Here, we present syntenet, an R package to infer and analyze synteny networks from whole-genome protein sequence data. The package offers a simple and complete framework, including data preprocessing, synteny detection and network inference, network clustering and phylogenomic profiling, and microsynteny-based phylogeny inference. Graphical functions are also available to create publication-ready plots. Synteny networks inferred with syntenet can highlight taxon-specific gene clusters that likely contributed to the evolution of important traits, and microsynteny-based phylogenies can help resolve phylogenetic relationships under debate.en© The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).syntenetSynteny networksWhole genome sequencing (WGS)Data preprocessingMicrosynteny-based phylogeny inferencePhylogenomic profilingSynteny detectionNetwork inferenceNetwork clusteringArticle