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dc.contributor.author | Almeida-Silva, Fabricio![]() |
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dc.contributor.author | Zhao, Tao![]() |
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dc.contributor.author | Ullrich, Kristian K.![]() |
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dc.contributor.author | Schranz, M. Eric![]() |
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dc.contributor.author | Van de Peer, Yves![]() |
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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 |