Please use this url to cite or link to this publication:
hdl.handle.net/1854/LU-01GP3AHHZ8VR3BTBCPA6FEXGM0
- MLA
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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. doi:10.1093/bioinformatics/btac806.
- APA
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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;
- Chicago author-date
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[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, 2023.
- Chicago author-date (all authors)
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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, Oxford University Press (OUP), 2023, doi:10.1093/bioinformatics/btac806.
- Vancouver
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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. doi.org/10.1093/bioinformatics/btac806
- IEEE
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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. doi.org/10.1093/bioinformatics/btac806.
@article{01GP3AHHZ8VR3BTBCPA6FEXGM0, abstract = {{<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>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).</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>}}, 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}}, language = {{eng}}, publisher = {{Oxford University Press (OUP)}}, title = {{syntenet: an R/Bioconductor package for the inference and analysis of synteny networks}}, url = {{http://dx.doi.org/10.1093/bioinformatics/btac806}}, year = {{2023}}, }
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