NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes

Author:

He Weiming12ORCID,Yang Jian3,Jing Yi1,Xu Lian3,Yu Kang2,Fang Xiaodong12

Affiliation:

1. BGI-Sanya, BGI-Shenzhen , Sanya 572025, China

2. BGI-Shenzhen , Shenzhen 518103, China

3. Key Laboratory of Neuroregeneration, Ministry of Education and Jiangsu Province, Co-Innovation Center of Neuroregeneration, Nantong University , Nantong, Jiangsu 226001, China

Abstract

AbstractSummaryLarge-scale comparative genomic studies have provided important insights into species evolution and diversity, but also lead to a great challenge to visualize. Quick catching or presenting key information hidden in the vast amount of genomic data and relationships among multiple genomes requires an efficient visualization tool. However, current tools for such visualization remain inflexible in layout and/or require advanced computation skills, especially for visualization of genome-based synteny. Here, we developed an easy-to-use and flexible layout tool, NGenomeSyn [multiple (N) Genome Synteny], for publication-ready visualization of syntenic relationships of the whole genome or local region and genomic features (e.g. repeats, structural variations, genes) across multiple genomes with a high customization. NGenomeSyn provides an easy way for its users to visualize a large amount of data with a rich layout by simply adjusting options for moving, scaling, and rotation of target genomes. Moreover, NGenomeSyn could be applied on the visualization of relationships on non-genomic data with similar input formats.Availability and implementationNGenomeSyn is freely available at GitHub (https://github.com/hewm2008/NGenomeSyn) and Zenodo (https://doi.org/10.5281/zenodo.7645148).

Funder

Haiyan Yazhou Bay Seed Laboratory and BGI−Sanya

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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