bio-splicing-qc

name	bio-splicing-qc
description	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.
tool_type	python
primary_tool	RSeQC

Version Compatibility

Reference examples tested with: matplotlib 3.8+, pandas 2.2+, pysam 0.22+

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 Quality Control

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

Junction Saturation Analysis

Goal: Determine whether sequencing depth is sufficient for comprehensive splicing detection.

Approach: Run RSeQC junction saturation on BAM files and check whether the junction discovery curve reaches a plateau.

"Assess RNA-seq quality for splicing analysis" -> Evaluate junction saturation, junction novelty rate, splice site strength, and read coverage.

• Python/CLI: junction_saturation.py, junction_annotation.py (RSeQC)
• Python: maxentpy for splice site scoring, pysam for junction read counting

# 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

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

Goal: Classify observed junctions as known, partially novel, or completely novel relative to annotation.

Approach: Run RSeQC junction annotation and compute the ratio of known to novel junctions as a data quality indicator.

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

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

Goal: Score donor and acceptor splice sites to identify weak or cryptic splice sites.

Approach: Use MaxEntScan (via maxentpy) to compute information-theoretic scores for 5' and 3' splice site sequences.

# 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

Goal: Profile the distribution of junction-spanning read counts across all splice sites in a BAM file.

Approach: Parse CIGAR strings for N operations (splice junctions) using pysam and tally reads per junction.

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: ')

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