Ribo Seq Agent Skill

Ribo Seq

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially ribo-seq 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 ribosome profiling, P-site aware preprocessing, periodicity checks, ORF detection, and translation efficiency analysis.

When To Use This Skill

• use when the task is ribosome profiling or translation efficiency analysis
• use when matched RNA-seq is available for translation-vs-expression comparisons
• use when periodicity and P-site validation are necessary before biological claims

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

• ribo-seq reads
• optional matched RNA-seq
• transcript annotation

Expected Outputs

• periodicity metrics
• ORF candidates
• translation efficiency summaries

Preferred Tools

• ribo-seq preprocessing utilities
• pandas
• matplotlib

Starter Pattern

Preferred starting point: ribo-seq
Inputs: ribo-seq reads, optional matched RNA-seq, transcript annotation
Outputs: periodicity metrics, ORF candidates, translation efficiency summaries

Workflow

1. Validate read quality and offsets

Establish read-length distributions and P-site offsets before counting footprints.

2. Check periodicity

Confirm expected triplet periodicity and frame enrichment.

3. Quantify translation signal

Compute footprint abundance at transcript or ORF level and compare with matched RNA when available.

4. Identify translated features

Report canonical or novel ORFs with explicit evidence criteria.

5. Export clear diagnostics

Save periodicity plots, TE tables, and prioritized translated features.

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:
• periodicity metrics
• ORF candidates
• translation efficiency summaries

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

• making translation claims without periodicity evidence
• comparing unmatched RNA and ribo data as if they were paired
• ignoring read-length-specific behavior

Related Skills

• Bulk RNA Expression
• RNA Quantification
• Differential Expression
• Alternative Splicing

Optional Supplements

• None required for the first pass.