syntenet : an R/Bioconductor package for the inference and analysis of synteny networks

dc.contributor.authorAlmeida-Silva, Fabricio
dc.contributor.authorZhao, Tao
dc.contributor.authorUllrich, Kristian K.
dc.contributor.authorSchranz, M. Eric
dc.contributor.authorVan de Peer, Yves
dc.date.accessioned2023-03-07T09:05:06Z
dc.date.available2023-03-07T09:05:06Z
dc.date.issued2023-01
dc.descriptionAVAILABILITY 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).en_US
dc.description.abstractInterpreting 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_US
dc.description.departmentBiochemistryen_US
dc.description.departmentGeneticsen_US
dc.description.departmentMicrobiology and Plant Pathologyen_US
dc.description.librarianhj2023en_US
dc.description.sponsorshipThe European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program; Ghent University; the Max Planck Society and the Chinese Universities Scientific Fund.en_US
dc.description.urihttp://bioinformatics.oxfordjournals.orgen_US
dc.identifier.citationFabricio 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.en_US
dc.identifier.issn1367-4811 (online)
dc.identifier.other10.1093/bioinformatics/btac806
dc.identifier.urihttps://repository.up.ac.za/handle/2263/90001
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.rights© 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/).en_US
dc.subjectsynteneten_US
dc.subjectSynteny networksen_US
dc.subjectWhole genome sequencing (WGS)en_US
dc.subjectData preprocessingen_US
dc.subjectMicrosynteny-based phylogeny inferenceen_US
dc.subjectPhylogenomic profilingen_US
dc.subjectSynteny detectionen_US
dc.subjectNetwork inferenceen_US
dc.subjectNetwork clusteringen_US
dc.titlesyntenet : an R/Bioconductor package for the inference and analysis of synteny networksen_US
dc.typeArticleen_US

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