Abstract
We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio R package is available at http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.
Highlights
We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data
There are web-based genome browsers, including Ensembl [5], University of California Santa Cruz (UCSC) Genome Browser [6], and GBrowse [7], and several new web-based browsers, like Dalliance, which rely on technologies like HTML5 and Scalable Vector Graphics [8], or Adobe Flash, like DNAnexus [9]
R has some new tools for visualizing genomic data, GenomeGraphs [11] and Gviz [12]
Summary
The ggbio package is an extension for R, a free cross-platform programming environment for statistical analysis and graphics with more than 3, 000 contributed packages. The package depends upon Bioconductor libraries for handling and processing data, including the implementation of the statistics in our extension of the grammar. The visualization methods in ggbio depend heavily on the package ggplot2 [15], which implements the grammar of graphics. We use ggplot as the foundation for ggbio, due to its principled style, intelligent defaults and explicit orientation towards the grammar of graphics model. The Bioconductor packages Rsamtools [36] and GenomicRanges [37] were used to import the BAM files and count reads overlapping exons. The DNA-seq BAM files and VCF files used in Figure 6 were downloaded from the 1000 Genomes Project [27]. Code and data links are available from the documentation section of the ggbio website [40]
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