Splicing Qc

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/alternative-splicing/splicing-qcThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction…

---
name: bio-splicing-qc
description: Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.
tool_type: python
primary_tool: RSeQC
---

## Version Compatibility

Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, 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-Specific Quality Control

Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design -> library prep -> alignment strategy -> annotation -> diagnostic metrics. Each layer's failure mode is distinct.

## QC Layer Taxonomy

| Layer | Target | Tool | Fails when |
|-------|--------|------|------------|
| Experimental design | Read length, depth, replicates, library type | Pre-sequencing review | <PE 75nt; n<3 vs n<3; <30M reads/sample |
| Library prep | poly(A) vs rRNA depletion | Pre-sequencing review | poly(A) library used for IR analysis |
| Alignment | STAR 2-pass cohort-style | STAR | 1-pass loses 14% novel junctions; per-sample 2-pass introduces inconsistency |
| Junction discovery | Saturation, novelty | RSeQC `junction_saturation`, `junction_annotation` | Curve still rising = under-sequenced; novel% >40% suggests biology or artifact |
| Strand specificity | Library protocol consistency | RSeQC `infer_experiment` | Wrong `--libType` halves usable junctions |
| Splice site strength | Cryptic vs canonical | MaxEntScan, SpliceAI | Weak splice sites (MaxEnt<5) may indicate cryptic, regulated, or annotation error |
| Junction overhang | Read-junction support quality | pysam CIGAR parsing | Overhang <8nt = high false-positive rate |
| Contamination | rRNA, adapters | fastq_screen | >20% rRNA in "depleted" library = failed depletion |
| Annotation | GENCODE basic vs comprehensive | Annotation choice | Basic for canonical events; comprehensive for DTU |

## Decision Tree by Question

| Question | Recommended QC |
|----------|-----------------|
| Will my planned RNA-seq design support AS analysis? | Pre-sequencing audit: library type, read length, depth, replicates |
| Is my data suitable for cassette exon analysis? | Junction saturation + known/novel ratio + read length |
| Why does my AS analysis call so few events? | Saturation curve, depth, library type, alignment 2-pass |
| Why does my AS analysis call so many novel junctions? | Annotation completeness + novel% + biology check (TDP-43, SF3B1) |
| Are my SpliceAI predictions calibrated for my tissue? | MaxEntScan + SpliceAI concordance for known sites |
| Did STAR 2-pass actually run cohort-style? | Verify SJ.out.tab merging across samples |
| Is intron retention detectable in my data? | Library type (must be rRNA-depleted); strand-specific |
| Are my microexons detectable? | Read length >=100; aligner anchor settings; consider VAST-TOOLS |

## Experimental Design Audit (Before Sequencing)

| Decision | For splicing analysis | Rationale |
|----------|------------------------|-----------|
| **Library prep** | rRNA depletion (Ribo-Zero, RiboCop) | poly(A) selection loses pre-mRNA, nascent transcripts, and detained introns; for IR analysis rRNA depletion is mandatory (convention) |
| **Read length** | PE 100-150 nt (PE 150 strongly preferred) | Junction-spanning reads need >=8 nt overhang on each exon; shorter single-end reads bias junction detection toward shorter exons (convention) |
| **Pairing** | Paired-end | Single-end loses fragment-level disambiguation of junctions |
| **Depth** | 50-100M reads/sample | DGE-grade 30M misses low-PSI events; 100M for low-abundance event discovery |
| **Strandedness** | Stranded library (Illumina TruSeq stranded) | Distinguishes overlapping antisense; some tools double-count unstranded junctions |
| **Replicates** | n>=3 per condition | n=2 vs n=2 has poor calibration in most tools (especially SUPPA2) |
| **Annotation** | GENCODE basic for canonical, comprehensive for DTU/discovery | basic = high-confidence; comprehensive includes putative — affects FDR control |
| **Microexons** | PE 100+ with `--alignSJoverhangMin 8`; VAST-TOOLS | Default aligners miss 3-27nt exons |
| **Long-intron genes (TTN, brain)** | Increased `--alignIntronMax` | Default 1Mb may miss >1Mb introns |

## STAR 2-Pass Alignment

**Goal:** Maximize novel-junction sensitivity for downstream AS analysis.

**Approach:** Run STAR once per sample to discover novel junctions (pass 1), merge novel junctions across cohort, then re-align with the augmented junction set (pass 2). Cohort-style 2-pass beats per-sample basic 2-pass for differential splicing because all samples use the same junction reference.

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Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install rseqc maxentpy pysam pandas matplotlib spliceai

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Splicing Qc

• github.com/GPTomics/bioSkills/tree/main/alternative-splicing/spl — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology