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FASTQ Quality Analysis

from operon60

Analyze, filter, and trim FASTQ sequence read quality scores using Biopython to ensure high-quality genomic data.

triggers:filter FASTQ readstrim low-quality basesFASTQ quality scoresPhred quality profileanalyze read quality

GitHub SKILL.md

What it does\nThis skill enables an agent to process and refine raw sequencing data by manipulating Phred quality scores. It provides the logic to identify low-quality reads, perform precision trimming, and generate quality profiles for genomic datasets.\n\n## When to use it\nUse this skill when tasked with cleaning raw FASTQ files, filtering reads by a minimum mean quality threshold, or performing 3' trimming to remove end-of-read degradation.\n\n## What's included\n- Instructions: Comprehensive patterns for calculating average quality, implementing sliding-window trims, and detecting quality encoding (Sanger vs Solexa).\n- Reference: Detailed Phred score accuracy tables and FASTQ format variant mappings.\n\n## Compatible agents\nDesigned for agents with Python execution capabilities (e.g., Codex, Claude Code, Gemini CLI) that can utilize the BioPython library.

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Repository: operon
Stars: 60

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FASTQ Quality Analysis