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

Fabricio De Almeida Silva (UGent) , Tao Zhao, Kristian K Ullrich, M Eric Schranz and Yves Van de Peer (UGent)
(2023) BIOINFORMATICS. 39(1).
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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.
Keywords
Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability, DUPLICATIONS, EVOLUTION

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Citation

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MLA
De Almeida Silva, Fabricio, et al. “Syntenet : An R/Bioconductor Package for the Inference and Analysis of Synteny Networks.” BIOINFORMATICS, edited by Pier Luigi Martelli, vol. 39, no. 1, 2023, doi:10.1093/bioinformatics/btac806.
APA
De Almeida Silva, F., Zhao, T., Ullrich, K. K., Schranz, M. E., & Van de Peer, Y. (2023). syntenet : an R/Bioconductor package for the inference and analysis of synteny networks. BIOINFORMATICS, 39(1). https://doi.org/10.1093/bioinformatics/btac806
Chicago author-date
De Almeida Silva, Fabricio, Tao Zhao, Kristian K Ullrich, M Eric Schranz, and Yves Van de Peer. 2023. “Syntenet : An R/Bioconductor Package for the Inference and Analysis of Synteny Networks.” Edited by Pier Luigi Martelli. BIOINFORMATICS 39 (1). https://doi.org/10.1093/bioinformatics/btac806.
Chicago author-date (all authors)
De Almeida Silva, Fabricio, Tao Zhao, Kristian K Ullrich, M Eric Schranz, and Yves Van de Peer. 2023. “Syntenet : An R/Bioconductor Package for the Inference and Analysis of Synteny Networks.” Ed by. Pier Luigi Martelli. BIOINFORMATICS 39 (1). doi:10.1093/bioinformatics/btac806.
Vancouver
1.
De Almeida Silva F, Zhao T, Ullrich KK, Schranz ME, Van de Peer Y. syntenet : an R/Bioconductor package for the inference and analysis of synteny networks. Martelli PL, editor. BIOINFORMATICS. 2023;39(1).
IEEE
[1]
F. De Almeida Silva, T. Zhao, K. K. Ullrich, M. E. Schranz, and Y. Van de Peer, “syntenet : an R/Bioconductor package for the inference and analysis of synteny networks,” BIOINFORMATICS, vol. 39, no. 1, 2023.
@article{01GP3AHHZ8VR3BTBCPA6FEXGM0,
  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.}},
  articleno    = {{btac806}},
  author       = {{De Almeida Silva, Fabricio and Zhao, Tao and Ullrich, Kristian K and Schranz, M Eric and Van de Peer, Yves}},
  editor       = {{Martelli, Pier Luigi}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability,DUPLICATIONS,EVOLUTION}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{3}},
  title        = {{syntenet : an R/Bioconductor package for the inference and analysis of synteny networks}},
  url          = {{http://doi.org/10.1093/bioinformatics/btac806}},
  volume       = {{39}},
  year         = {{2023}},
}
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