Sashimi Plots

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/alternative-splicing/sashimi-plotsThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors…

---
name: bio-sashimi-plots
description: Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.
tool_type: python
primary_tool: ggsashimi
---

## Version Compatibility

Reference examples tested with: ggsashimi 1.1+, rmats2sashimiplot 3.0+, MAJIQ 3.0+, leafcutter 0.2.9+, pyGenomeTracks 3.8+, ggplot2 3.5+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Sashimi Plot Visualization

Visualize RNA-seq coverage tracks with splice junction arcs labeled by read count. Sashimi plots originated with MISO (Katz 2010 *Nat Methods*); modern tools differ in input handling, group aggregation logic, and customization. Tool choice is not interchangeable — some tools work only with specific upstream output formats.

## Tool Selection Matrix

| Tool | Best for | Input | Strengths | Fails when |
|------|----------|-------|-----------|------------|
| ggsashimi | Publication-quality grouped overlays from any BAM | BAMs + region | `--overlay` aggregates samples within a group; clean PDFs | No native rMATS/MAJIQ integration; need to extract coords manually |
| rmats2sashimiplot | One-line plot from rMATS output | rMATS event file + BAMs | No manual coord extraction | rMATS-specific; doesn't handle leafcutter or MAJIQ |
| MAJIQ-VOILA | Interactive LSV browsing with posterior PSI distributions | MAJIQ build + psi/deltapsi | Splice-graph topology; LSV-aware; posterior violins | Static figures; non-academic license |
| leafviz | Cluster-level interactive browsing with NMD annotation | leafcutter differential output | Filter table + sashimi-like plots; NMD-aware | leafcutter-specific |
| Jutils | Unified output across rMATS, leafcutter, MntJULiP, MAJIQ | Tool-specific differential output | Heatmaps, Venn, sashimi tool-agnostically | Output less polished than ggsashimi |
| pyGenomeTracks | Multi-track publication figures (RNA-seq + ChIP/ATAC) | BigWig + BED + GTF | Combine RNA with chromatin tracks | Not splicing-specific; configure tracks manually |
| IGV (interactive) | Quick ad-hoc inspection | BAM + region | Scrollable, instant | Not for publication figures |
| MISO sashimi | Historical | MISO output | Original sashimi format | MISO unmaintained; no longer recommended |

## Decision Tree by Goal

| Goal | Recommended tool |
|------|-------------------|
| Validate a specific rMATS hit | rmats2sashimiplot (one-line) or ggsashimi (custom) |
| Validate a leafcutter cluster | leafviz (interactive) or ggsashimi with cluster coordinates |
| Validate a MAJIQ LSV (complex topology) | MAJIQ-VOILA (only tool that shows full LSV graph) |
| Publication-quality two-condition comparison | ggsashimi `-O 3 -A mean_j` for grouped overlay |
| Multi-track figure (RNA-seq + H3K4me3 + ATAC) | pyGenomeTracks |
| Quick ad-hoc browsing during development | IGV sashimi |
| Tool-agnostic batch heatmap of significant events | Jutils |
| Interactive cohort-level filtering of leafcutter results | leafviz Shiny |

## ggsashimi for Publication Overlays

**Goal:** Generate publication-quality sashimi plot for a region with samples grouped by condition and per-sample tracks aggregated.

**Approach:** Define samples + groups + colors in a TSV (no header), then call ggsashimi with coordinates, GTF, and visual flags.

```python
import subprocess
import pandas as pd

# ggsashimi input: col1 = sample id, col2 = BAM path, col3 = group (for -O/-C overlay/color)
groups = pd.DataFrame({
    'sample_id': ['ctrl1', 'ctrl2', 'ctrl3', 'trt1', 'trt2', 'trt3'],
    'bam': ['ctrl1.bam', 'ctrl2.bam', 'ctrl3.bam', 'trt1.bam', 'trt2.bam', 'trt3.bam'],
    'group': ['Control', 'Control', 'Control', 'Treatment', 'Treatment', 'Treatment']
})
groups.to_csv('sashimi_groups.tsv', sep='\t', index=False, header=False)

subprocess.run([
    'ggsashimi.py',
    '-b', 'sashimi_groups.tsv',
    '-c', 'chr17:43094000-43125000',
    '-o', 'BRCA1_sashimi',
    '-M', '10',
    '--alpha', '0.25',
    '--height', '3',
    '--width', '10',
    '--shrink',
    '--fix-y-scale',
    '--ann-height', '4',
    '-g', 'gencode_v45.gtf',
    '--base-size', '14',
    '-O', '3',
    '-A', 'mean_j',
    '-F', 'pdf'
], check=True)
```

Key ggsashimi flags (Garrido-Martin 2018 *PLoS Comput Biol*):
- `--overlay 3` (or `-O 3`): aggregate multiple samples within a group into a single overlay track with summary statistics — its signature feature
- `-A mean_j`: junction aggregation method (`mean`, `median`, `mean_j` accounts for sample-wise normalization); use `mean_j` for biological replicates
- `--shrink`: rescale long introns (>2x flanking exons) for compact display
- `--fix-y-scale`: identical y-axis across groups (essential for visual comparison)
- `--alpha 0.25`: transparency for per-sample coverage in overlay mode
- `-M 10`: minimum junction reads to display (lower = noisier; 5-10 typical; raise to 20+ for crowded plots)
- `--ann-height`: gene annotation track height
- `-F pdf`: output format (pdf, png, svg, eps)

## Batch Plotting from rMATS Hits

**Goal:** Auto-generate sashimi plots for all significant rMATS differential events.

**Approach:** Pa

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pandas

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Sashimi Plots

• github.com/GPTomics/bioSkills/tree/main/alternative-splicing/sas — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology