Small Rna Seq

Small RNA Seq

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially miRge3 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 small RNA and miRNA preprocessing, quantification, differential analysis, and target-oriented interpretation.

When To Use This Skill

• use when the user has miRNA or other small RNA sequencing data
• use when adapter-heavy preprocessing and short-read-specific QC are required
• use when the goal is differential miRNA analysis or target prediction follow-up

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

• small RNA FASTQ files
• adapter sequences
• reference miRNA annotations

Expected Outputs

• small RNA count matrix
• differential miRNA tables
• target candidate summaries

Preferred Tools

• miRge3
• miRDeep2-style workflows
• pandas
• seaborn

Starter Pattern

Preferred starting point: miRge3
Inputs: small RNA FASTQ files, adapter sequences, reference miRNA annotations
Outputs: small RNA count matrix, differential miRNA tables, target candidate summaries

Workflow

1. Handle short inserts carefully

Trim adapters and confirm read-length distributions before quantification.

2. Quantify annotated species

Map or assign reads to miRNAs and other small RNA classes with class-aware counting.

3. Perform count-aware comparisons

Use replicate-aware statistics for differential abundance.

4. Review library composition

Inspect proportions of miRNA, tRNA fragments, rRNA fragments, and other classes.

5. Prepare interpretation outputs

Export mature miRNA results and optional target-prediction inputs.

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:
• small RNA count matrix
• differential miRNA tables
• target candidate 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

• treating adapter-trimmed and untrimmed samples as comparable
• ignoring multi-mapping behavior for short RNAs
• reporting targets without clarifying they are predictions

Related Skills

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

Optional Supplements

• None required for the first pass.