RNA Quantification

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially salmon 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 gene and transcript quantification from RNA-seq reads using alignment-based or alignment-free tools.

When To Use This Skill

• use when the user needs counts or transcript abundances from FASTQ files
• use when the task is featureCounts, salmon, kallisto, or tximport-style quantification
• use when quantification outputs need to be prepared for DE or expression reporting

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

• FASTQ files
• reference genome or transcriptome
• annotation GTF or GFF

Expected Outputs

• gene counts
• transcript abundances
• quantification QC summaries

Preferred Tools

• salmon
• kallisto
• featureCounts
• tximport-style imports
• pandas

Starter Pattern

salmon quant \
  -i transcriptome_index \
  -l A \
  -1 sample_R1.fastq.gz \
  -2 sample_R2.fastq.gz \
  -o quant/sample

Workflow

1. Choose quantification strategy

Prefer alignment-free quantification for speed and transcript-level abundance, and alignment-based counting when genomic alignment is already available.

2. Verify references

Ensure transcriptome, genome, and annotation versions are consistent before quantification.

3. Run quantification

Capture both abundance tables and tool-specific mapping or assignment rates.

4. Aggregate to analysis level

Convert transcript-level outputs to gene-level summaries only when the downstream task calls for it.

5. Prepare outputs

Standardize sample IDs and produce a count or abundance matrix plus QC metadata.

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:
• gene counts
• transcript abundances
• quantification QC 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

• combining references from different releases
• dropping assignment-rate QC when quantification quality is uncertain
• using abundance estimates as counts without tracking the distinction

Related Skills

• Bulk RNA Expression
• Differential Expression
• Alternative Splicing
• Small RNA Seq

Optional Supplements

• pysam