bio-sashimi-plots

name	bio-sashimi-plots
description	Creates sashimi plots showing RNA-seq read coverage and splice junction counts using ggsashimi or rmats2sashimiplot. Visualizes differential splicing events with grouped samples and junction read support. Use when visualizing specific splicing events or validating differential splicing results.
tool_type	python
primary_tool	ggsashimi

Sashimi Plot Visualization

Create sashimi plots to visualize splicing events with read coverage and junction counts.

ggsashimi Usage

import subprocess
import pandas as pd

# Create sample grouping file (TSV: path, group, color)
groups = pd.DataFrame({
    'bam': ['sample1.bam', 'sample2.bam', 'sample3.bam', 'sample4.bam'],
    'group': ['control', 'control', 'treatment', 'treatment'],
    'color': ['#1f77b4', '#1f77b4', '#ff7f0e', '#ff7f0e']
})
groups.to_csv('sashimi_groups.tsv', sep='\t', index=False, header=False)

# Basic sashimi plot for a region
subprocess.run([
    'ggsashimi.py',
    '-b', 'sashimi_groups.tsv',
    '-c', 'chr1:1000000-1010000',  # Genomic coordinates
    '-o', 'sashimi_output',
    '-M', '10',  # Minimum junction reads to show
    '--alpha', '0.25',  # Coverage transparency
    '--height', '3',
    '--width', '8',
    '-g', 'annotation.gtf'
], check=True)

Batch Plotting Significant Events

import subprocess
import pandas as pd

# Load differential splicing results
diff_results = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')
significant = diff_results[
    (diff_results['FDR'] < 0.05) &
    (diff_results['IncLevelDifference'].abs() > 0.1)
]

# Generate plots for top events
for idx, event in significant.head(20).iterrows():
    chrom = event['chr']
    # Extend region around the exon
    start = event['upstreamES'] - 500
    end = event['downstreamEE'] + 500
    region = f'{chrom}:{start}-{end}'
    gene = event['geneSymbol']

    subprocess.run([
        'ggsashimi.py',
        '-b', 'sashimi_groups.tsv',
        '-c', region,
        '-o', f'sashimi_plots/{gene}_{chrom}_{start}',
        '-M', '5',
        '--shrink',  # Shrink introns for better visualization
        '-g', 'annotation.gtf',
        '--fix-y-scale'  # Same y-axis across groups
    ], check=True)

rmats2sashimiplot

# For rMATS output specifically
rmats2sashimiplot \
    --b1 sample1.bam,sample2.bam \
    --b2 sample3.bam,sample4.bam \
    -t SE \
    -e rmats_output/SE.MATS.JC.txt \
    --l1 Control \
    --l2 Treatment \
    -o sashimi_rmats \
    --exon_s 1 \
    --intron_s 5

Customization Options

# Advanced ggsashimi options
subprocess.run([
    'ggsashimi.py',
    '-b', 'sashimi_groups.tsv',
    '-c', 'chr1:1000000-1010000',
    '-o', 'custom_sashimi',
    '-g', 'annotation.gtf',

    # Visual options
    '-M', '10',           # Min junction reads
    '--alpha', '0.25',    # Coverage alpha
    '--height', '3',      # Plot height per track
    '--width', '10',      # Plot width
    '--base-size', '14',  # Font size

    # Layout options
    '--shrink',           # Shrink introns
    '--fix-y-scale',      # Same y-axis
    '-A', 'mean',         # Aggregate: mean, median, or none

    # Annotation options
    '--gtf-filter', 'protein_coding',  # Filter GTF features

    # Output format
    '-F', 'pdf'           # pdf, png, svg, eps
], check=True)

Best Practices

Tip	Rationale
Use --shrink for large introns	Keeps exons visible
Set --fix-y-scale for comparisons	Fair visual comparison
Aggregate replicates with -A mean	Reduces clutter
Limit to 3-4 groups	More groups become hard to read
Include flanking exons	Show full splicing context

Troubleshooting

Issue	Solution
No junctions shown	Lower -M threshold
Plot too crowded	Use --shrink, reduce samples
Annotation missing	Check GTF format, gene name field
Memory issues	Plot smaller regions

Related Skills

• differential-splicing - Identify events to plot
• splicing-quantification - Context for PSI values
• data-visualization/ggplot2-fundamentals - Further customization