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

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dc.contributor.author Almeida-Silva, Fabricio
dc.contributor.author Zhao, Tao
dc.contributor.author Ullrich, Kristian K.
dc.contributor.author Schranz, M. Eric
dc.contributor.author Van de Peer, Yves
dc.date.accessioned 2023-03-07T09:05:06Z
dc.date.available 2023-03-07T09:05:06Z
dc.date.issued 2023-01
dc.description AVAILABILITY 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.abstract 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_US
dc.description.department Biochemistry en_US
dc.description.department Genetics en_US
dc.description.department Microbiology and Plant Pathology en_US
dc.description.librarian hj2023 en_US
dc.description.sponsorship The 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.uri http://bioinformatics.oxfordjournals.org en_US
dc.identifier.citation Fabricio 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.issn 1367-4811 (online)
dc.identifier.other 10.1093/bioinformatics/btac806
dc.identifier.uri https://repository.up.ac.za/handle/2263/90001
dc.language.iso en en_US
dc.publisher Oxford University Press en_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.subject syntenet en_US
dc.subject Synteny networks en_US
dc.subject Whole genome sequencing (WGS) en_US
dc.subject Data preprocessing en_US
dc.subject Microsynteny-based phylogeny inference en_US
dc.subject Phylogenomic profiling en_US
dc.subject Synteny detection en_US
dc.subject Network inference en_US
dc.subject Network clustering en_US
dc.title syntenet : an R/Bioconductor package for the inference and analysis of synteny networks en_US
dc.type Article en_US


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