Alternative splicing

Alternative Splicing

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially splice-aware and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.

When To Use This Skill

• use when the task is differential splicing, isoform switching, or splice-aware QC
• use when aligned RNA-seq reads and transcript annotations are available
• use when the user needs event summaries, PSI-like metrics, or sashimi-style visualization

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• aligned RNA-seq reads
• splice junction summaries
• transcript annotation

Expected Outputs

• event tables
• isoform usage summaries
• sashimi or splice plots

Preferred Tools

• splice-aware quantification tools
• pandas
• matplotlib
• genome track plotting utilities

Starter Pattern

Preferred starting point: splice-aware
Inputs: aligned RNA-seq reads, splice junction summaries, transcript annotation
Outputs: event tables, isoform usage summaries, sashimi or splice plots

Workflow

1. Confirm splice-aware inputs

Verify junction extraction, transcript annotation, and sample group definitions.

2. Choose analysis level

Use event-level methods for exon or junction usage and isoform-level methods for transcript switching.

3. Quantify splicing changes

Compute condition-specific splice usage and test for differential splicing.

4. Inspect representative loci

Plot junction-supported events to verify that statistical hits reflect visible changes.

5. Export interpretable results

Save event IDs, effect estimates, significance values, and plot-ready loci.

Output Artifacts

• Recommended output layout:
  • results/ for final tables and serialized objects
  • figures/ for plots and static visual exports
  • qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• event tables
• isoform usage summaries
• sashimi or splice plots

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Check replicate structure, outlier samples, and whether counts versus normalized values are being mixed.
• Export ranked or contrast-aware tables when downstream enrichment is likely.

Anti-Patterns

• interpreting isoform changes without read support at informative junctions
• mixing event- and transcript-level interpretations without stating which was used
• skipping locus-level review of top hits

Related Skills

• Bulk RNA Expression
• RNA Quantification
• Differential Expression
• Small RNA Seq

Optional Supplements

• pysam