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FASTQ Quality - Usage Guide

Overview

This skill enables AI agents to help you work with FASTQ quality scores for NGS data analysis using Biopython.

Prerequisites

pip install biopython

Quick Start

Tell your AI agent what you want to do:

  • "Calculate average quality for each read"
  • "Filter reads with mean quality below 25"
  • "Trim low-quality bases from read ends"
  • "Show quality statistics for my FASTQ file"

Example Prompts

Quality Analysis

"Show me the quality distribution of reads.fastq"

Filtering

"Keep only reads with average quality >= 30"

Trimming

"Trim bases from the 3' end where quality drops below 20"

Statistics

"Generate per-position quality profile for the first 50 bases"

What the Agent Will Do

  1. Parse FASTQ records including quality scores
  2. Decode quality encoding (Phred33/64)
  3. Calculate per-base or per-read quality metrics
  4. Report summary statistics

Quality Score Reference

  • Q20 = 99% accuracy (1 error per 100 bases)
  • Q30 = 99.9% accuracy (1 error per 1000 bases)
  • Q40 = 99.99% accuracy (1 error per 10000 bases)

Tips

  • Most modern FASTQ uses Phred+33 encoding (format: 'fastq')
  • For old Illumina data, try 'fastq-illumina' format
  • Process large files as iterators to save memory
  • Quality often drops toward 3' end of reads