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 |
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dc.date.available |
2023-03-07T09:05:06Z |
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dc.date.issued |
2023-01 |
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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) |
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dc.identifier.other |
10.1093/bioinformatics/btac806 |
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dc.identifier.uri |
https://repository.up.ac.za/handle/2263/90001 |
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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 |