Bulk RNA-seq Alternative Splicing Analysis

Alternative splicing quantification and differential splicing event detection. Accepts pre-computed splicing event tables (e.g. from rMATS or SUPPA2), computes PSI-based statistics, identifies significant differential splicing events, and produces publication-ready visualizations.

CLI Reference

python omicsclaw.py run bulkrna-splicing --demo
python omicsclaw.py run bulkrna-splicing --input <splicing_events.csv> --output <dir>
python bulkrna_splicing.py --input events.csv --output results/ --dpsi-cutoff 0.1 --padj-cutoff 0.05
python bulkrna_splicing.py --demo --output /tmp/splicing_demo

Why This Exists

• Without it: Researchers must manually parse rMATS/SUPPA2 output files, compute delta-PSI statistics, apply multiple-testing correction, and create splicing-specific visualizations across thousands of events.
• With it: A single Python command summarizes splicing events by type, identifies significant differential splicing, and produces volcano plots and event-type distributions ready for publication.
• Why OmicsClaw: Wraps standard alternative splicing analysis into the OmicsClaw reporting framework with consistent output structure, reproducibility scripts, and automatic demo data generation.

Algorithm / Methodology

Splicing Event Types

PSI Quantification

Percent Spliced In (PSI) measures the fraction of transcripts that include a given exon or splice site:

PSI = inclusion_reads / (inclusion_reads + exclusion_reads)

Delta-PSI (dPSI) between conditions:

dPSI = PSI_treatment - PSI_control

Statistical Testing

• Per-event t-test: Welch's t-test on PSI replicates between conditions
• Multiple testing correction: Benjamini-Hochberg FDR
• Significance thresholds: |dPSI| > cutoff AND padj < cutoff

Upstream Tools

This skill operates on pre-computed splicing event tables produced by:

• rMATS (replicate Multivariate Analysis of Transcript Splicing) — detects differential alternative splicing from replicate RNA-seq data
• SUPPA2 — fast quantification of splicing events from transcript-level TPMs

Parameters

--input

--output

--demo

--dpsi-cutoff

0.1

--padj-cutoff

0.05

Workflow

1. Load: Read a splicing events table (CSV with event_id, event_type, gene, psi_ctrl, psi_treat, delta_psi, pvalue, padj columns).
2. Summarize: Count events by type, compute overall statistics.
3. Filter: Identify significant differential splicing events by |dPSI| and adjusted p-value thresholds.
4. Visualize: Generate event type distribution (pie chart), delta-PSI histogram, and splicing volcano plot.
5. Report: Write markdown report, result.json, full and filtered event tables, and a reproducibility script.

Example Queries

• "Analyze alternative splicing events from my rMATS output"
• "Find significant differential splicing between conditions"
• "Show me the distribution of splicing event types"
• "Run splicing analysis with a delta-PSI cutoff of 0.15"

Output Structure

output_directory/
├── report.md
├── result.json
├── figures/
│   ├── event_type_distribution.png
│   ├── dpsi_distribution.png
│   └── volcano_splicing.png
├── tables/
│   ├── splicing_events.csv
│   └── significant_events.csv
└── reproducibility/
    └── commands.sh

Safety

• Local-first: All processing runs locally; no data is uploaded to external services.
• Disclaimer: Every report includes the standard OmicsClaw disclaimer.
• Audit trail: Parameters, thresholds, and input metadata are recorded in result.json.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked when user intent matches alternative splicing analysis keywords.

Chaining partners:

• bulkrna-alignment

  — Upstream: aligned BAM files for rMATS/SUPPA2 input

• bulkrna-de

  — Parallel: gene-level differential expression to complement exon-level splicing

• bulkrna-enrichment

  — Downstream: pathway enrichment of genes with significant splicing changes

Version Compatibility

Reference examples tested with: scipy 1.11+, pandas 2.0+, numpy 1.24+, matplotlib 3.7+

Dependencies

Required: numpy, pandas, scipy, matplotlib

Citations

• rMATS — Shen et al., PNAS 2014
• SUPPA2 — Trincado et al., Genome Research 2018
• Benjamini-Hochberg — Benjamini & Hochberg, JRSSB 1995

Related Skills

• bulkrna-alignment

  — Read alignment upstream

• bulkrna-de

  — Gene-level differential expression

• bulkrna-enrichment

  — Pathway enrichment of affected genes