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

Author:

Almeida-Silva Fabricio12ORCID,Zhao Tao3,Ullrich Kristian K4,Schranz M Eric5ORCID,Van de Peer Yves1267

Affiliation:

1. Department of Plant Biotechnology and Bioinformatics, Ghent University , 9052 Ghent, Belgium

2. VIB Center for Plant Systems Biology, VIB , 9052 Ghent, Belgium

3. State Key Laboratory of Crop Stress Biology for Arid Areas/Shaanxi Key Laboratory of Apple, College of Horticulture, Northwest A&F University , Yangling 712100, China

4. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology , Ploen 24306, Germany

5. Biosystematics Group, Wageningen University and Research , Wageningen 6708, The Netherlands

6. Department of Biochemistry, Genetics and Microbiology, Centre for Microbial Ecology and Genomics, University of Pretoria , Pretoria 0028, South Africa

7. College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University , Nanjing 210095, China

Abstract

Abstract Summary 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. 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). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Union’s Horizon 2020 Research and Innovation Program

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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