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Sashimi plots: Quantitative visualization of RNA sequencing read alignments
Yarden Katz, Eric T. Wang, Jacob Silterra, Schraga Schwartz, Bang Wong, Jill P. Mesirov, Edoardo M. Airoldi, Christopher B. Burge
TL;DR
RNA-Seq makes alternative splicing widespread, but ambiguous read assignments and conventional browser displays hinder quantitative comparison of isoforms and exon usage. Sashimi plots combine annotated gene models with aligned reads in static and IGV-based visualizations, and demonstrate differential exon usage between wild-type and Muscleblind1-depleted heart samples. The approach supports rapid exploratory screening and customized publication-quality figures, while junction-count encoding remains an area for future improvement.
Problem
Ambiguous read compatibility with multiple isoforms makes isoform abundance estimation and cross-sample visualization of differential exon usage challenging.
Method
Sashimi plots combine BAM read alignments and gene-model annotations to display exon read densities, junction-count arcs, and optional MISO isoform estimates through static or IGV implementations.
Results
In a four-sample example, the middle exon’s Percent Spliced In was approximately 77% in wild-type hearts and 25% after Muscleblind1 depletion.
Takeaways & Limitations
Static plots support publication-quality figures, while IGV-Sashimi enables rapid screening and quantification of differentially spliced genomic regions.
Takeaways & Limitations
The current implementation represents junction-read counts with arc width, and alternative encodings remain future work.
Abstract
from arXiv · showhide
We introduce Sashimi plots, a quantitative multi-sample visualization of mRNA sequencing reads aligned to gene annotations. Sashimi plots are made using alignments (stored in the SAM/BAM format) and gene model annotations (in GFF format), which can be custom-made by the user or obtained from databases such as Ensembl or UCSC. We describe two implementations of Sashimi plots: (1) a stand-alone command line implementation aimed at making customizable publication quality figures, and (2) an implementation built into the Integrated Genome Viewer (IGV) browser, which enables rapid and dynamic creation of Sashimi plots for any genomic region of interest, suitable for exploratory analysis of alternatively spliced regions of the transcriptome. Isoform expression estimates outputted by the MISO program can be optionally plotted along with Sashimi plots. Sashimi plots can be used to quickly screen differentially spliced exons along genomic regions of interest and can be used in publication quality figures. The Sashimi plot software and documentation is available from: http://genes.mit.edu/burgelab/miso/docs/sashimi.html
1 Background
RNA-Seq studies show widespread alternative splicing and isoform-specific expression, but assigning read abundance to individual isoforms and comparing them across samples is difficult. Sashimi plots address this by quantitatively summarizing aligned reads across multiple samples.
- Most human genes produce alternatively spliced mRNAs, whose isoform expression profiles can vary across tissues and organisms.
- Because most sequenced reads are compatible with multiple isoforms, estimating distinct isoform abundances is statistically challenging.
- Sashimi plots quantitatively summarize RNA-Seq read alignments for simultaneous display across multiple samples.
2 Sashimi plot visualizations
Sashimi plots combine annotated gene models with aligned RNA-Seq reads to quantify exon coverage and splice-junction usage. They support both customizable static figures and dynamic exploration through IGV.
- Features and inputs: Sashimi plots use gene annotations and read alignments to summarize genomic coverage and splice-junction reads quantitatively.Exonic reads become mapped-read-scaled per-base densities in RPKM units, while junction reads become arcs whose widths reflect junction-read counts.
- Implementations: The command-line implementation produces static, highly customizable, publication-quality figures.An external settings file controls colors, scales, fonts, and figure size.
- Implementations: IGV-Sashimi enables dynamic, on-the-fly plots for genomic regions of interest within the IGV browser.MISO isoform-abundance estimates can optionally be added to the plots.
3 Interpreting Sashimi plots
Sashimi plots make exon-level splicing differences visible through read densities, junction counts, and optional isoform estimates. Static and IGV-based implementations support both detailed figures and rapid regional screening.
- Across four samples, the middle exon was mostly included in wild-type hearts but mostly excluded after Muscleblind1 depletion.MISO estimates matched this pattern: Percent Spliced In was approximately 77% in wild-type samples and 25% in knockout samples.
- IGV-Sashimi generates plots directly from a browser-selected region, with boundaries adjustable through zooming.The plots display raw junction-read counts above each junction arc and can include user-provided annotations alongside RefSeq transcripts.
- The two Sashimi plot variants support rapid screening and quantification of differential exon regulation, followed by customized publication plotting.IGV-Sashimi is used for exploratory identification, while the static implementation formats selected regions for publication.
- Implementation: The command-line implementation uses Python and matplotlib, while IGV-Sashimi is implemented in Java; both are open source and cross-platform.Both versions are available through publicly hosted Git repositories and support Unix-based, Windows, and Mac OS X systems.
- Implementation: Installation instructions and the Sashimi plot manual are available on the project website.
4 Conclusions and future work
Sashimi plots provide quantitative, comparative visualization of RNA-Seq reads across samples for detecting differential exon and isoform splicing. Static plots target publication figures, while IGV-Sashimi supports rapid exploratory analysis, with future work focused on richer junction-count encodings.
- Conclusions: Sashimi plots quantitatively compare RNA-Seq reads across samples to help detect differentially spliced exons and isoforms.
- Conclusions: Static Sashimi plots support publication-quality figures, whereas IGV-Sashimi supports rapid exploration of genomic regions.
- Related work: Existing genome browsers can display exon read densities but represent junction reads individually, limiting quantitative comparison and becoming unwieldy at high coverage.
- Future work: Future work could replace arc width with alternative representations such as circle diameter, area, or color shade for junction-read counts.
Annotation
Sashimi plots support region-focused visualization through static figures and dynamic IGV Browser views. The implementations enable customizable publication-quality output and rapid exploration of genomic regions.
- The static implementation visualizes regions of interest in publication-quality figures.
- Users can customize plot features through a configuration file.
- The IGV Browser implementation provides dynamic, on-the-fly visualization with zooming and integrated genome tracks.
- Figure 2 illustrates an example Sashimi plot for an alternatively spliced exon.