bio-sashimi-plots

ggsashimi Usage

Goal: Generate sashimi plots showing read coverage and junction counts for a genomic region.

Approach: Define sample groupings in a TSV file, then run ggsashimi with genomic coordinates and annotation.

"Visualize a splicing event" -> Plot RNA-seq coverage tracks with splice junction arcs grouped by condition.

• Python/CLI: ggsashimi.py (ggsashimi)
• CLI: rmats2sashimiplot (rMATS-specific)

import subprocess
import pandas as pd

## 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

Goal: Automatically generate sashimi plots for all significant differential splicing events.

Approach: Load rMATS results, filter for significant events, extract flanking coordinates, and iterate ggsashimi over each event.

import subprocess
import pandas as pd