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RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms
Author(s) -
Zhaodong Hao,
Dekang Lv,
Ying Ge,
Jisen Shi,
Dolf Weijers,
Guangchuang Yu,
Jinhui Chen
Publication year - 2020
Publication title -
peerj computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.251
Subject(s) - scalable vector graphics , genome , visualization , genome browser , computer science , synteny , reference genome , sequence assembly , data visualization , graphics , computational biology , genomics , biology , data mining , world wide web , genetics , gene , computer graphics (images) , gene expression , transcriptome
Background Owing to the rapid advances in DNA sequencing technologies, whole genome from more and more species are becoming available at increasing pace. For whole-genome analysis, idiograms provide a very popular, intuitive and effective way to map and visualize the genome-wide information, such as GC content, gene and repeat density, DNA methylation distribution, genomic synteny, etc. However, most available software programs and web servers are available only for a few model species, such as human, mouse and fly, or have limited application scenarios. As more and more non-model species are sequenced with chromosome-level assembly being available, tools that can generate idiograms for a broad range of species and be capable of visualizing more data types are needed to help better understanding fundamental genome characteristics. Results The R package RIdeogram allows users to build high-quality idiograms of any species of interest. It can map continuous and discrete genome-wide data on the idiograms and visualize them in a heat map and track labels, respectively. Conclusion The visualization of genome-wide data mapping and comparison allow users to quickly establish a clear impression of the chromosomal distribution pattern, thus making RIdeogram a useful tool for any researchers working with omics.

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