We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. Compared to the existing versions of programs generating sashimi plots, it uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. ggsashimi is freely available at https: //github. com/guigolab/ggsashimi. It is implemented in python, and internally generates R code for plotting.
Alternative splicing is the process through which different combinations of exons of the same gene are selected to produce a variety of mature coding and non-coding transcripts [1]. The genome-wide landscape of alternative splicing can be easily profiled by RNA sequencing (RNA-seq) and tens of thousands of different RNA-seq experiments are now publicly available. While visualization of RNA-seq data is crucial for exploratory data analysis, visualization of splicing events is currently not dynamically integrated in common genome browsers, and stand-alone software are annotation-dependent. Visualizing splicing events is particularly challenging because such events usually occur between two regions, known as splice sites, that are not contiguous on the genome sequence, and can be as distant as tens or even hundreds of kilobases in linear space. The representation of a splicing event implies drawing a connective element that illustrates the presence of a splice junction between two splice sites. The sashimi plot [2] is a very effective and established diagram which combines the information of read coverage along a gene –a signal track– with curves connecting splice sites supported by RNA-seq data. A tool for drawing sashimi plots was initially developed as part of the MISO suite [3], a software that quantifies and compares alternative splicing from RNA-seq experiments. Current popular implementations include a stand-alone utility to create sashimi plots specifically for MISO-indexed splicing events [2] and a built-in available within the Integrative Genomics Viewer, IGV [4]. Thus, the former relies on a proper compatible annotation of the event, while the latter requires IGV installation and the time-consuming uploading of voluminous alignment files. Moreover, both of them represent splicing events for each RNA-seq experiment on a separate line, which hinders the comparison of more than a dozen samples.
ggsashimi presents several unique features that distinguish it from its predecessors and make it a useful tool especially for large-scale projects:
To illustrate how ggsashimi performs and to compare it with existing implementations, we obtained a set of 12 RNA-seq samples from the ENCODE project [6], publicly available at www. encodeproject. org. Samples were classified into three cell type groups: endothelial, epithelial and mesenchymal. We focused on a single cassette exon (chr10: 27044584-27044670) with different levels of inclusion across the three cell type groups (mesenchymal > epithelial > endothelial). For comparison purposes, the genomic region containing the selected splicing event was represented both using ggsashimi and the sashimi-plot built-in available within the IGV Browser (Fig 1). In the case of ggsashimi, aggregation of samples belonging to the same group (through the --overlay option) and shrinkage of intron lengths were applied (see Features), enhancing the visualization of the event.
Although the sashimi representation for splicing events is one of the standards for splicing visualization, current implementations present several limitations that narrow substantially its application. We believe that our implementation solves many of the current issues, especially we eliminated the need for specialized annotation formats and we support summarized views for hundreds of samples. Since ggsashimi uses the most popular file formats and has very few dependencies, it can be easily integrated in any splicing analysis pipeline, and can facilitate the interrogation of alternative splicing in large-scale RNA sequencing projects, such as ENCODE [6] and GTEx [7]. ggsashimi is freely available at https: //github. com/guigolab/ggsashimi. Further extensions of ggsashimi include incorporating spread metrics to accompany mean and median aggregating methods, allowing the user to select which type of reads to plot (e. g. uniquely mapped) or optionally showing only the aggregated coverage.
Efficient visualization of splicing events from RNA sequencing data is key for the computational analysis of alternative splicing. This process, that results in a single gene giving rise to multiple transcripts, is usually illustrated through sashimi plots: a representation of the read coverage and the support of each splicing junction in the region of interest. However, available tools for this purpose present several limitations that significantly hinder their applicability. Among them, dependence on event annotation, visualization difficulties with moderate sample sizes and long introns, use of non-standard file formats or inefficient implementations. With ggsashimi, we comprehensively overcome these flaws and provide the user with a fast, stand-alone application that generates publication-ready sashimi plots. Furthermore, ggsashimi supports the display of aggregated experiments, a crucial feature in order to explore alternative splicing in the era of large RNA sequencing projects.
sequencing techniques
alternative splicing
genome analysis
genome annotation
molecular biology techniques
rna sequencing
research and analysis methods
genomic libraries
genome complexity
computer and information sciences
gene expression
comparative genomics
molecular biology
biochemistry
rna
rna processing
data visualization
nucleic acids
genetics
biology and life sciences
genomics
computational biology
introns
ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization