Epitranscriptomics

Epitranscriptomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially peak-calling 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 RNA modification analysis such as m6A peak calling, differential modification, and transcript-level visualization.

When To Use This Skill

• use when the task is MeRIP-seq, direct RNA modification analysis, or differential RNA modification
• use when enriched IP and input comparisons need to be modeled carefully
• use when the user needs modification-aware plots and transcript-level context

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

• modification-enriched reads
• input reads
• transcript annotations

Expected Outputs

• modification peaks
• differential modification results
• transcript-level plots

Preferred Tools

• peak-calling tools
• pandas
• matplotlib

Starter Pattern

Preferred starting point: peak-calling
Inputs: modification-enriched reads, input reads, transcript annotations
Outputs: modification peaks, differential modification results, transcript-level plots

Workflow

1. Validate assay design

Confirm IP and input matching, replicate availability, and transcript annotation consistency.

2. Call modification features

Detect modification-enriched regions with assay-aware models.

3. Compare conditions

Test differential modification while separating abundance changes from modification-specific changes where possible.

4. Visualize representative transcripts

Plot peaks or signal tracks over transcripts to support interpretation.

5. Export clearly labeled outputs

Separate modification results from standard expression results in all tables and plots.

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:
• modification peaks
• differential modification results
• transcript-level 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 assay-specific QC such as enrichment quality, coverage behavior, or replicate consistency.
• Verify genome build, interval coordinates, and annotation compatibility.

Anti-Patterns

• equating expression shifts with modification shifts
• calling differential modification without matched inputs or replicates where possible
• overstating transcript-level resolution when the assay is region-based

Related Skills

• ATAC Seq
• ChIP Seq
• Methylation Analysis
• Hi-C And 3D Genomics

Optional Supplements

• None required for the first pass.