bio-sashimi-plots
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/SUPPA2/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 differe
npx skills add BioTender-max/awesome-bio-agent-skills --skill sashimi-plots --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 rMAT
- Version Compatibility
- Tool Selection Matrix
- Decision Tree by Goal
- ggsashimi for Publication Overlays
- Batch Plotting from rMATS Hits
- rmats2sashimiplot
- MAJIQ-VOILA Interactive HTML
- leafviz Shiny App
- Jutils for Tool-Agnostic Output
- pyGenomeTracks for Multi-Track Figures
- Reading Sashimi Plots (Interpretation Guide)
- Per-Tool Failure Modes
- ggsashimi: Off-Strand Junction Artifacts
- rmats2sashimiplot: Wrong Coordinate Convention
rmats2sashimiplot \ MAJIQ V3 (June 2025+) uses Zarr-format splicegraph (V2's .sql is deprecated) voila view -p 5000 -j 8 build/splicegraph.zarr psi_output/sample.psi.voila -o voila_psi_html voila view -p 5000 -j 8 build/splicegraph.zarr deltapsi_output/group1_group2.deltapsi.voila -o voila_dpsi_html prepare_results.R \ leafcutter_perind_numers.counts.gz \ ds_results_cluster_significance.txt \ ds_results_effect_sizes.txt \ annotation_codes jutils convert -t rmats -i SE.MATS.JC.txt -o rmats_jutils.tsv
What does the bio-sashimi-plots skill do?
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/SUPPA2/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 differe
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill sashimi-plots --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.