sashimi-plots

Name: sashimi-plots
Author: GPTomics

1.1kstars185forks

How to install sashimi-plots?

gptomics/bioskills/sashimi-plots

$npx -y skills add GPTomics/bioSkills --skill sashimi-plots

Installs into the current project.

Summary

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 (

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	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+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Sashimi Plot Visualization
13
14	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.
15
16	## Tool Selection Matrix
17
18	\| Tool \| Best for \| Input \| Strengths \| Fails when \|
19	\|------\|----------\|-------\|-----------\|------------\|
20	\| 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 \|
21	\| rmats2sashimiplot \| One-line plot from rMATS output \| rMATS event file + BAMs \| No manual coord extraction \| rMATS-specific; doesn't handle leafcutter or MAJIQ \|
22	\| 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 \|
23	\| leafviz \| Cluster-level interactive browsing with NMD annotation \| leafcutter differential output \| Filter table + sashimi-like plots; NMD-aware \| leafcutter-specific \|
24	\| Jutils \| Unified output across rMATS, leafcutter, MntJULiP, MAJIQ \| Tool-specific differential output \| Heatmaps, Venn, sashimi tool-agnostically \| Output less polished than ggsashimi \|
25	\| pyGenomeTracks \| Multi-track publication figures (RNA-seq + ChIP/ATAC) \| BigWig + BED + GTF \| Combine RNA with chromatin tracks \| Not splicing-specific; configure tracks manually \|
26	\| IGV (interactive) \| Quick ad-hoc inspection \| BAM + region \| Scrollable, instant \| Not for publication figures \|
27	\| MISO sashimi \| Historical \| MISO output \| Original sashimi format \| MISO unmaintained; no longer recommended \|
28
29	## Decision Tree by Goal
30
31	\| Goal \| Recommended tool \|
32	\|------\|-------------------\|
33	\| Validate a specific rMATS hit \| rmats2sashimiplot (one-line) or ggsashimi (custom) \|
34	\| Validate a leafcutter cluster \| leafviz (interactive) or ggsashimi with cluster coordinates \|
35	\| Validate a MAJIQ LSV (complex topology) \| MAJIQ-VOILA (only tool that shows full LSV graph) \|
36	\| Publication-quality two-condition comparison \| ggsashimi `-O 3 -A mean_j` for grouped overlay \|
37	\| Multi-track figure (RNA-seq + H3K4me3 + ATAC) \| pyGenomeTracks \|
38	\| Quick ad-hoc browsing during development \| IGV sashimi \|
39	\| Tool-agnostic batch heatmap of significant events \| Jutils \|
40	\| Interactive cohort-level filtering of leafcutter results \| leafviz Shiny \|
41
42	## ggsashimi for Publication Overlays
43
44	Goal: Generate publication-quality sashimi plot for a region with samples grouped by condition and per-sample tracks aggregated.
45
46	Approach: Define samples + groups + colors in a TSV (no header), then call ggsashimi with coordinates, GTF, and visual flags.
47
48	```python
49	import subprocess
50	import pandas as pd
51
52	# ggsashimi input: col1 = sample id, col2 = BAM path, col3 = group (for -O/-C overlay/color)
53	groups = pd.DataFrame({
54	'sample_id': ['ctrl1', 'ctrl2', 'ctrl3', 'trt1', 'trt2', 'trt3'],
55	'bam': ['ctrl1.bam', 'ctrl2.bam', 'ctrl3.bam', 'trt1.bam', 'trt2.bam', 'trt3.bam'],
56	'group': ['Control', 'Control', 'Control', 'Treatment', 'Treatment', 'Treatment']
57	})
58	groups.to_csv('sashimi_groups.tsv', sep='\t', index=False, header=False)
59
60	subprocess.run([
61	'ggsashimi.py',
62	'-b', 'sashimi_groups.tsv',
63	'-c', 'chr17:43094000-43125000',
64	'-o', 'BRCA1_sashimi',
65	'-M', '10',
66	'--alpha', '0.25',
67	'--height', '3',
68	'--width', '10',
69	'--shrink',
70	'--fix-y-scale',
71	'--ann-height', '4',
72	'-g', 'gencode_v45.gtf',
73	'--base-size', '14',

People who liked this also viewed

6 picks

Type

sashimi-plots

1.1kstars185forks

View on GitHub

How to install sashimi-plots?

gptomics/bioskills/sashimi-plots

$npx -y skills add GPTomics/bioSkills --skill sashimi-plots

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	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+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Sashimi Plot Visualization
13
14	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.
15
16	## Tool Selection Matrix
17
18	\| Tool \| Best for \| Input \| Strengths \| Fails when \|
19	\|------\|----------\|-------\|-----------\|------------\|
20	\| 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 \|
21	\| rmats2sashimiplot \| One-line plot from rMATS output \| rMATS event file + BAMs \| No manual coord extraction \| rMATS-specific; doesn't handle leafcutter or MAJIQ \|
22	\| 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 \|
23	\| leafviz \| Cluster-level interactive browsing with NMD annotation \| leafcutter differential output \| Filter table + sashimi-like plots; NMD-aware \| leafcutter-specific \|
24	\| Jutils \| Unified output across rMATS, leafcutter, MntJULiP, MAJIQ \| Tool-specific differential output \| Heatmaps, Venn, sashimi tool-agnostically \| Output less polished than ggsashimi \|
25	\| pyGenomeTracks \| Multi-track publication figures (RNA-seq + ChIP/ATAC) \| BigWig + BED + GTF \| Combine RNA with chromatin tracks \| Not splicing-specific; configure tracks manually \|
26	\| IGV (interactive) \| Quick ad-hoc inspection \| BAM + region \| Scrollable, instant \| Not for publication figures \|
27	\| MISO sashimi \| Historical \| MISO output \| Original sashimi format \| MISO unmaintained; no longer recommended \|
28
29	## Decision Tree by Goal
30
31	\| Goal \| Recommended tool \|
32	\|------\|-------------------\|
33	\| Validate a specific rMATS hit \| rmats2sashimiplot (one-line) or ggsashimi (custom) \|
34	\| Validate a leafcutter cluster \| leafviz (interactive) or ggsashimi with cluster coordinates \|
35	\| Validate a MAJIQ LSV (complex topology) \| MAJIQ-VOILA (only tool that shows full LSV graph) \|
36	\| Publication-quality two-condition comparison \| ggsashimi `-O 3 -A mean_j` for grouped overlay \|
37	\| Multi-track figure (RNA-seq + H3K4me3 + ATAC) \| pyGenomeTracks \|
38	\| Quick ad-hoc browsing during development \| IGV sashimi \|
39	\| Tool-agnostic batch heatmap of significant events \| Jutils \|
40	\| Interactive cohort-level filtering of leafcutter results \| leafviz Shiny \|
41
42	## ggsashimi for Publication Overlays
43
44	Goal: Generate publication-quality sashimi plot for a region with samples grouped by condition and per-sample tracks aggregated.
45
46	Approach: Define samples + groups + colors in a TSV (no header), then call ggsashimi with coordinates, GTF, and visual flags.
47
48	```python
49	import subprocess
50	import pandas as pd
51
52	# ggsashimi input: col1 = sample id, col2 = BAM path, col3 = group (for -O/-C overlay/color)
53	groups = pd.DataFrame({
54	'sample_id': ['ctrl1', 'ctrl2', 'ctrl3', 'trt1', 'trt2', 'trt3'],
55	'bam': ['ctrl1.bam', 'ctrl2.bam', 'ctrl3.bam', 'trt1.bam', 'trt2.bam', 'trt3.bam'],
56	'group': ['Control', 'Control', 'Control', 'Treatment', 'Treatment', 'Treatment']
57	})
58	groups.to_csv('sashimi_groups.tsv', sep='\t', index=False, header=False)
59
60	subprocess.run([
61	'ggsashimi.py',
62	'-b', 'sashimi_groups.tsv',
63	'-c', 'chr17:43094000-43125000',
64	'-o', 'BRCA1_sashimi',
65	'-M', '10',
66	'--alpha', '0.25',
67	'--height', '3',
68	'--width', '10',
69	'--shrink',
70	'--fix-y-scale',
71	'--ann-height', '4',
72	'-g', 'gencode_v45.gtf',
73	'--base-size', '14',

People who liked this also viewed

6 picks

Type