bio-splicing-quantification

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 05d02f463ef0

Summary

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SKILL.md

Version Compatibility

Reference examples tested with: kallisto 0.50+, 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.

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

Goal: Calculate PSI values for all splicing event types from transcript-level quantification.

Approach: Generate event definitions from GTF annotation, then compute per-event PSI from transcript TPM using SUPPA2.

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 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')

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

bio-splicing-quantification

Summary

SKILL.md

Version Compatibility

Splicing Quantification

Event Types

Tool Selection

SUPPA2 (transcript TPM-based)

rMATS-turbo (BAM-based)

SUPPA2 Workflow

rMATS-turbo Workflow

Quality Thresholds

Output Interpretation

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