# Bio Splicing Qc

> Assesses RNA-seq data quality for splicing analysis including junction saturation curves, splice site strength scoring, and junction coverage metrics using RSeQC. Use when evaluating data suitability for splicing analysis or troubleshooting low event detection.

- Skill: `tools-only/bio-splicing-qc-2` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/bio-splicing-qc-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/bio-splicing-qc-2/raw
- Safety review: pending (external: skill-scanner WARNING, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/bio-splicing-qc-2

---


# Splicing Quality Control

Assess RNA-seq data quality specifically for alternative splicing analysis.

## Junction Saturation Analysis

```bash
# RSeQC junction saturation (check sequencing depth)
# Note: -s flag removed in RSeQC v3.0
junction_saturation.py \
    -i sample.bam \
    -r annotation.bed \
    -o sample_junc_sat
```

```python
import subprocess
import matplotlib.pyplot as plt
import pandas as pd

# Run junction saturation for multiple samples
samples = ['sample1.bam', 'sample2.bam', 'sample3.bam']

for sample in samples:
    subprocess.run([
        'junction_saturation.py',
        '-i', sample,
        '-r', 'annotation.bed',
        '-o', sample.replace('.bam', '_junc_sat')
    ], check=True)

# Parse results and check for plateau
# Plateau indicates sufficient depth for splicing analysis
# If curves still rising, may need more sequencing depth
```

## Junction Annotation

```bash
# Classify junctions as known, partial novel, or complete novel
junction_annotation.py \
    -i sample.bam \
    -r annotation.bed \
    -o sample_junc_annot
```

```python
import pandas as pd

# Analyze junction annotation results
junc_stats = pd.read_csv('sample_junc_annot.junction.xls', sep='\t')

# Calculate junction type proportions
total = junc_stats['total_splicing_events'].sum()
known = junc_stats[junc_stats['annotation'] == 'known']['total_splicing_events'].sum()
novel = total - known

print(f'Known junctions: {known/total:.1%}')
print(f'Novel junctions: {novel/total:.1%}')

# High novel junction rate may indicate:
# - Incomplete annotation
# - Mapping artifacts
# - Interesting biology (cancer, tissue-specific)
```

## Splice Site Strength Scoring

```python
# MaxEntScan scoring via maxentpy
# 5'ss (donor): typical score 8-10 bits
# 3'ss (acceptor): typical score 8-12 bits

from maxentpy import maxent
from maxentpy.maxent import score5, score3

# Score 5' splice site (9bp: 3 exon + 6 intron)
donor_seq = 'CAGGTAAGT'  # Consensus: CAG|GTAAGT
score_5ss = score5(donor_seq)
print(f"5'ss score: {score_5ss:.2f}")

# Score 3' splice site (23bp: 20 intron + 3 exon)
acceptor_seq = 'TTTTTTTTTTTTTTTTTTTTCAG'
score_3ss = score3(acceptor_seq)
print(f"3'ss score: {score_3ss:.2f}")

# Weak splice sites (score < 5) may indicate:
# - Alternative/cryptic splice sites
# - Annotation errors
# - Regulatory splice sites
```

## Junction Read Coverage

```python
import pysam
import pandas as pd

def count_junction_reads(bam_path, min_overhang=8):
    '''Count junction-spanning reads per splice site.'''
    bam = pysam.AlignmentFile(bam_path, 'rb')
    junction_counts = {}

    for read in bam.fetch():
        if read.is_unmapped:
            continue

        # Check CIGAR for splice junctions (N operation)
        ref_pos = read.reference_start
        for op, length in read.cigartuples:
            if op == 3:  # N = splice junction
                junction = (read.reference_name, ref_pos, ref_pos + length)
                junction_counts[junction] = junction_counts.get(junction, 0) + 1
            if op in [0, 2, 3]:  # M, D, N consume reference
                ref_pos += length

    bam.close()
    return junction_counts

# Analyze coverage distribution
junctions = count_junction_reads('sample.bam')
counts = list(junctions.values())

print(f'Total junctions: {len(junctions)}')
print(f'Junctions >= 10 reads: {sum(1 for c in counts if c >= 10)}')
print(f'Junctions >= 20 reads: {sum(1 for c in counts if c >= 20)}')
```

## Quality Thresholds

| Metric | Good | Acceptable | Poor |
|--------|------|------------|------|
| Junction saturation | Plateau reached | Near plateau | Still rising |
| Known junctions | > 80% | > 60% | < 60% |
| Junctions >= 10 reads | > 50% | > 30% | < 30% |
| 5'ss score | > 8 | > 5 | < 5 |
| 3'ss score | > 8 | > 5 | < 5 |

## Troubleshooting Low Detection

| Issue | Possible Causes | Solutions |
|-------|-----------------|-----------|
| Few junctions | Low depth, short reads | More sequencing, longer reads |
| Low saturation | Insufficient depth | Increase sequencing |
| Many novel junctions | Annotation gaps | Update annotation, check organism |
| Weak splice sites | Cryptic splicing | Validate experimentally |

## Related Skills

- splicing-quantification - Quantify after QC passes
- read-alignment/star-alignment - Alignment quality affects junctions
- read-qc/quality-reports - General sequencing QC