Differential Splicing

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/differential-splicingThe 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: Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on…

---
name: bio-differential-splicing
description: Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
tool_type: mixed
primary_tool: rMATS-turbo
---

## Version Compatibility

Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- 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.

# Differential Splicing

Detect splicing changes between conditions. Tool choice is a decision about **statistical model**, **annotation dependence**, and **calibration regime** under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias.

## Statistical Model Taxonomy

| Tool | Model | Test statistic | Min reps per group | Calibration regime | Fails when |
|------|-------|-----------------|---------------------|---------------------|------------|
| rMATS-turbo | Binomial counts with hierarchical PSI variance | LRT on \|ΔPSI\| > `cutoff` (default 0.0001) | n>=3 | Well-calibrated at n>=3 with adequate junction reads | Junction read imbalance; very low coverage; uncorrected for confounders |
| leafcutter | Dirichlet-multinomial GLM at cluster level | LRT on group factor | n>=2 (n>=3 preferred) | Strong at n>=3; novel-junction-friendly | Undersampled clusters (DM dispersion unstable); cluster topology arbitrariness |
| MAJIQ deltapsi | Beta-binomial bootstrap -> posterior over PSI per LSV | P(\|ΔPSI\| > T) threshold (T=0.2) | n>=3 | Replicate-structured n=3 vs n=3 | Cohorts where between-sample variability dominates between-group |
| MAJIQ HET | Same model, heterogeneity-aware | Per-LSV permutation-based test | n>=10 | n>=10 vs n>=10 cohort designs | Tightly-controlled small replicate experiments |
| SUPPA2 (empirical) | Empirical null from between-replicate ΔPSI | ECDF on \|ΔPSI\| conditioned on TPM | n>=4 | n>=4 vs n>=4 with paired-end deep sequencing | n<=3 vs n<=3 (sparse null collapses) |
| SUPPA2 (classical) | Wilcoxon rank-sum on PSI distributions | Wilcoxon p-value | n>=2 | Small samples; non-parametric backup | Cassette events with tight PSI distributions |
| Shiba (2025) | Beta-binomial with explicit junction-imbalance correction | LRT | n>=2 | n=2-3 vs n=2-3 | Established benchmarks limited (new tool) |
| LeafcutterMD | Dirichlet-multinomial outlier mode | Per-sample p-value | n=1 vs cohort >=20 | Single-patient vs cohort | Too few controls (<20) |
| FRASER 2.0 | Beta-binomial autoencoder on Intron Jaccard Index | Per-sample p-value with delta cutoff | n=1 vs cohort >=20 | n>=20 control cohort, single-patient query | See `outlier-splicing-detection` for this regime |

The first decision is which **regime** the design falls into: between-group with replicates, heterogeneous cohort, or single-sample-vs-cohort. Within each regime, tool choice is much smaller (1-2 options).

Comprehensive 2023-2026 benchmarks: Olofsson 2023 *Biochem Biophys Res Commun*; Tran 2025 *WIREs RNA*; Kubota 2025 *NAR*. Methodology evolves — verify benchmarks and tool docs before reporting. Default 2026 recommendation: run **two complementary tools** (rMATS + leafcutter) and require concordance for high-confidence calls.

## Decision Tree by Experimental Design

| Scenario | Recommended tool | Why | Threshold |
|----------|------------------|-----|-----------|
| Standard n=3 vs n=3, GENCODE-annotated | rMATS-turbo + leafcutter (concordance) | Two algorithmic families; concordant hits = high-confidence | FDR<0.05, \|ΔPSI\|>0.10 |
| n=2 vs n=2 small pilot | Shiba | Junction-imbalance correction matters most at low coverage | FDR<0.10, \|ΔPSI\|>0.10 |
| n=10+ vs n=10+ heterogeneous (clinical, GTEx-style) | MAJIQ V3 HET | HET designed for between-sample heterogeneity | P(\|ΔPSI\|>0.2)>0.95 |
| Single rare-disease patient vs panel of n>=20 | FRASER 2.0 (see outlier-splicing-detection) | Outlier detection statistical model is fundamentally different | padj<0.05, \|delta-jaccard\|>=0.1 |
| Time-course / multi-condition design | Custom DEXSeq or limma on PSI matrix | rMATS/leafcutter primarily 2-group | FDR<0.05 on time:group interaction |
| Paired tumor-normal | rMATS with `--paired-stats` | Paired test reduces inter-patient variance | FDR<0.05, paired \|ΔPSI\|>0.10 |
| Cancer with spliceosomal mutation (SF3B1, U2AF1) | leafcutter or MAJIQ denovo | Cryptic events not in annotation | FDR<0.05; check 3'ss shifts in IGV |
| TDP-43 loss / ALS post-mortem | leafcutter denovo | Cryptic exons not in annotation | FDR<0.05; expect UNC13A, STMN2 |
| Non-model organism without GENCODE-grade annotation | leafcutter | Annotation-free | FDR<0.05, \|ΔPSI\|>0.10 |
| Long-read available | rMATS-long, FLAIR diffSplice | See long-read-splicing | Tool-specific |

## rMATS-turbo Differential Analysis

**Goal:** Detect statistically significant differential splicing between two groups from BAMs.

**Approach:** Run rMATS-turbo without `--statoff`, then filter by FDR + ΔPSI + per-replicate coverage.

```bash
rmats.py \
    --b1 condition1_bams

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 suppa pandas numpy

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 Differential Splicing

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

Spotted something wrong? Report a correction · Methodology