---
name: bio-differential-splicing
description: Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. 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: STAR 2.7.11+, 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
- 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 differential alternative splicing events between experimental conditions.

## Tool Comparison

| Tool | Input | Approach | Strengths |
|------|-------|----------|-----------|
| rMATS-turbo | BAM | Junction counting | Novel junctions, statistical model |
| SUPPA2 | TPM | Transcript ratios | Speed, isoform-aware |
| leafcutter | BAM | Intron clustering | Novel events, no annotation bias |

## rMATS-turbo Analysis

**Goal:** Detect statistically significant differential splicing events between two conditions from BAM files.

**Approach:** Run rMATS-turbo on condition-grouped BAMs, then filter results by FDR and delta PSI thresholds.

**"Find differential splicing between conditions"** -> Compare junction-level inclusion across sample groups with statistical testing.
- CLI/Python: `rmats.py` + pandas filtering (rMATS-turbo)
- Python/CLI: `suppa.py diffSplice` (SUPPA2, TPM-based)
- R: `leafcutter_ds.R` (leafcutter, annotation-free)

```bash
# Create sample lists (one BAM path per line)
# condition1_bams.txt: /path/to/sample1.bam, /path/to/sample2.bam, ...
# condition2_bams.txt: /path/to/sample3.bam, /path/to/sample4.bam, ...

rmats.py \
    --b1 condition1_bams.txt \
    --b2 condition2_bams.txt \
    --gtf annotation.gtf \
    -t paired \
    --readLength 150 \
    --nthread 8 \
    --od rmats_output \
    --tmp rmats_tmp
```

```python
import pandas as pd

# Load results for skipped exons
se = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')

# Filter significant differential splicing events
# |deltaPSI| > 0.1 (lenient) or > 0.2 (stringent)
# FDR < 0.05
significant = se[
    (se['FDR'] < 0.05) &
    (se['IncLevelDifference'].abs() > 0.1)
].copy()

print(f'{len(significant)} significant SE events')
print(significant[['GeneID', 'geneSymbol', 'IncLevelDifference', 'FDR']].head(10))

# Additional filtering by junction read support
# Require at least 10 reads supporting each junction type
significant = significant[
    (significant['IJC_SAMPLE_1'].str.split(',').apply(lambda x: min(map(int, x))) >= 10) |
    (significant['SJC_SAMPLE_1'].str.split(',').apply(lambda x: min(map(int, x))) >= 10)
]
```

## SUPPA2 Differential Analysis

**Goal:** Identify differential splicing from transcript quantification without alignment.

**Approach:** Compare per-event PSI distributions between conditions using SUPPA2 empirical p-value calculation.

```python
import subprocess

# Requires PSI files from suppa.py psiPerEvent
# TPM file with samples from both conditions

# Run differential splicing
subprocess.run([
    'suppa.py', 'diffSplice',
    '-m', 'empirical',  # Empirical p-value calculation
    '-i', 'events_SE_strict.ioe',
    '-p', 'condition1.psi', 'condition2.psi',
    '-e', 'condition1.tpm', 'condition2.tpm',
    '-o', 'diff_SE'
], check=True)

# Load results
import pandas as pd
diff = pd.read_csv('diff_SE.dpsi', sep='\t', index_col=0)

# SUPPA2 tends to be more stringent
significant = diff[
    (diff['p-value'] < 0.05) &
    (diff['dPSI'].abs() > 0.1)
]
```

## leafcutter Analysis

**Goal:** Detect differential intron usage without relying on transcript annotation.

**Approach:** Extract junctions from BAMs, cluster introns by shared splice sites, then test differential usage between groups.

```r
library(leafcutter)

# Convert BAMs to junction files
# leafcutter_bam_to_junc.sh uses regtools
system('for bam in *.bam; do
    regtools junctions extract -a 8 -m 50 -s 0 $bam -o ${bam%.bam}.junc
done')

# Create junction file list
writeLines(list.files(pattern = '\\.junc$'), 'juncfiles.txt')

# Cluster introns
system('python leafcutter_cluster_regtools.py -j juncfiles.txt -o leafcutter')

# Run differential analysis
groups <- data.frame(
    sample = c('sample1', 'sample2', 'sample3', 'sample4'),
    group = c('control', 'control', 'treatment', 'treatment')
)
write.table(groups, 'groups.txt', sep = '\t', quote = FALSE, row.names = FALSE)

# Differential intron usage
system('leafcutter_ds.R --num_threads 4 leafcutter_perind_numers.counts.gz groups.txt')
```

## Significance Thresholds

| Stringency | deltaPSI | FDR | Use Case |
|------------|----------|-----|----------|
| Lenient | > 0.1 | < 0.05 | Discovery, exploratory |
| Standard | > 0.15 | < 0.05 | Publication |
| Stringent | > 0.2 | < 0.01 | High-confidence set |

## Result Prioritization

**Goal:** Rank differential splicing events by combined statistical and biological significance.

**Approach:** Compute a composite score from FDR and effect size, then select top-scoring events for follow-up.

```python
# Prioritize by effect size and significance
significant['score'] = -np.log10(significant['FDR']) * significant['IncLevelDifference'].abs()
top_events = significant.nlargest(50, 'score')

# Annotate with gene function
# Consider protein domain disruption, NMD sensitivity
```

## Related Skills

- splicing-quantification - Calculate PSI values first
- isoform-switching - Functional consequence analysis
- sashimi-plots - Visualize significant events
- read-alignment/star-alignment - STAR 2-pass alignment required


---
Source attribution: GPTomics
Original project: https://github.com/GPTomics/bioSkills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 FridrichMethod

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

NOTE ON BUNDLED CONTENT

This repository aggregates skills from multiple upstream projects (see the
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# Mega AI Hub resource metadata

Creator / publisher: GPTomics
Original project: https://github.com/GPTomics/bioSkills
Attribution basis: Exact package in biomedical source index and publisher source table

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Scientist, Researcher
Specialties: Specialty not specified
Tools: Suppa2, Leafcutter, Rmats Turbo
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 96c2759d6668545073f8e35024ce8ce87ed5ffa4a3c41fb27a22f9463fedf069
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.