bio-splicing-quantification

name	bio-splicing-quantification
description	Quantifies alternative splicing events (PSI/percent spliced in) from RNA-seq using SUPPA2 from transcript TPM or rMATS-turbo from BAM files. Calculates inclusion levels for skipped exons, alternative splice sites, mutually exclusive exons, and retained introns. Use when measuring splice site usage or isoform ratios from RNA-seq data.
tool_type	python
primary_tool	SUPPA2

Splicing Quantification

Quantify alternative splicing events as PSI (percent spliced in) values from RNA-seq data.

Event Types

Type	Code	Description
Skipped exon	SE	Exon inclusion/exclusion
Alternative 5' splice site	A5SS	Alternative donor site
Alternative 3' splice site	A3SS	Alternative acceptor site
Mutually exclusive exons	MXE	One of two exons included
Retained intron	RI	Intron retention

Tool Selection

SUPPA2 (transcript TPM-based)

• Input: Transcript TPM from Salmon/kallisto
• Faster, requires transcript quantification
• Better for isoform-level analysis

rMATS-turbo (BAM-based)

• Input: Aligned BAM files
• Junction read counting
• Better for novel junction discovery

SUPPA2 Workflow

import subprocess
import pandas as pd

gtf_file = 'annotation.gtf'
tpm_file = 'transcript_tpm.tsv'
output_prefix = 'events'

# Step 1: Generate splicing events from annotation
subprocess.run([
    'suppa.py', 'generateEvents',
    '-i', gtf_file,
    '-o', output_prefix,
    '-f', 'ioe',  # IOE format for PSI calculation
    '-e', 'SE', 'SS', 'MX', 'RI', 'FL'  # All event types
], check=True)

# Step 2: Calculate PSI values
for event_type in ['SE', 'A5', 'A3', 'MX', 'RI']:
    ioe_file = f'{output_prefix}_{event_type}_strict.ioe'
    subprocess.run([
        'suppa.py', 'psiPerEvent',
        '-i', ioe_file,
        '-e', tpm_file,
        '-o', f'psi_{event_type}'
    ], check=True)

# Load and examine PSI values
psi_se = pd.read_csv('psi_SE.psi', sep='\t', index_col=0)
print(f'Quantified {len(psi_se)} skipped exon events')
print(psi_se.head())

rMATS-turbo Workflow

# rMATS-turbo for BAM-based quantification
rmats.py \
    --b1 condition1_bams.txt \
    --b2 condition2_bams.txt \
    --gtf annotation.gtf \
    -t paired \
    --readLength 150 \
    --nthread 8 \
    --od output_dir \
    --tmp tmp_dir \
    --statoff  # Use for quantification only, no differential testing

import pandas as pd

# Load rMATS output
se_jc = pd.read_csv('output_dir/SE.MATS.JC.txt', sep='\t')

# Calculate average PSI across samples
# IncLevel columns contain PSI values per sample
inc_cols = [c for c in se_jc.columns if c.startswith('IncLevel')]
se_jc['mean_PSI'] = se_jc[inc_cols].mean(axis=1)

# Filter for reliable events (sufficient junction reads)
# Minimum 10-20 junction reads recommended for reliable PSI
se_jc['total_junction_reads'] = se_jc['IJC_SAMPLE_1'] + se_jc['SJC_SAMPLE_1']
reliable_events = se_jc[se_jc['total_junction_reads'] >= 20]
print(f'{len(reliable_events)} events with sufficient coverage')

Quality Thresholds

Metric	Threshold	Rationale
Junction reads	>= 10-20	Minimum for reliable PSI estimation
PSI range	0.1-0.9	Events outside this range are nearly constitutive
Missing values	< 50% samples	High missingness indicates low expression

Output Interpretation

PSI values range from 0 to 1:

• PSI = 1.0: Event fully included (e.g., exon always present)
• PSI = 0.5: Equal inclusion/exclusion
• PSI = 0.0: Event fully excluded (e.g., exon always skipped)

Related Skills

• differential-splicing - Compare PSI between conditions
• rna-quantification/alignment-free-quant - Generate transcript TPM for SUPPA2
• read-alignment/star-alignment - Align reads with junction detection