RIdeogram : drawing SVG graphics to visualize and map genome-wide data on the idiograms

• Published in: PeerJ. Computer science Vol. 6; p. e251
• Main Authors: Hao, Zhaodong, Lv, Dekang, Ge, Ying, Shi, Jisen, Weijers, Dolf, Yu, Guangchuang, Chen, Jinhui
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 20.01.2020; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Animal behavior; Annotations; Binding sites; Bioinformatics; Chromosome; Chromosomes; Data Science; Data visualization; Deoxyribonucleic acid; DNA; DNA methylation; DNA sequencing; Gene expression; Gene sequencing; Genes; Genome; Genomes; Genomics; Graphics; Idiogram; Language; Mapping; Methylation; MicroRNAs; Pictographs; R package; Servers; Servers (Computers); Software; Technology; Visual Analytics; Visualization; Visualization (Computer); Chromosome; Genome; Data visualization; Idiogram; R package
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.251

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. The R package 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. The visualization of genome-wide data mapping and comparison allow users to quickly establish a clear impression of the chromosomal distribution pattern, thus making a useful tool for any researchers working with omics.