fastqc

Use when you need to perform quality control analysis on high-throughput sequencing data (fastq, bam, sam, or fast5 files) to identify potential problems before downstream analysis.

SKILL.md

GitHub

fastqc

Quick Start

  • Command: fastqc [options] seqfile1 seqfile2 .. seqfileN
  • Local executable: /home/vimalinx/miniforge3/envs/bio/bin/fastqc
  • Version: 0.12.1
  • Full reference: See references/help.md

When To Use This Tool

  • Inspect raw or cleaned sequencing reads before downstream analysis.
  • Generate per-sample QC reports for FASTQ, BAM, SAM, or FAST5 inputs.
  • Use it before trimming and again after trimming if you want a before/after QC comparison.
  • Pair it with multiqc when handling many samples.

Common Patterns

# 1) QC two FASTQ files
fastqc -o fastqc_out sample_R1.fastq.gz sample_R2.fastq.gz
# 2) Run several files with multiple threads
fastqc -t 8 -o fastqc_out *.fastq.gz
# 3) Keep only zipped output package
fastqc --noextract -o fastqc_out sample.fastq.gz

Recommended Workflow

  1. Run FastQC on representative raw reads before trimming.
  2. Inspect the HTML report for adapter content, per-base quality, sequence duplication, and overrepresented sequences.
  3. If trimming or filtering is applied, rerun FastQC on cleaned reads.
  4. Aggregate all sample reports with multiqc for cohort-level review.

Guardrails

  • The output directory must already exist; FastQC does not create it for you.
  • Each thread needs roughly a few hundred MB of RAM; oversubscribing threads is counterproductive.
  • --nogroup can produce huge plots and unstable behavior on long reads.
  • A warning flag in FastQC is not automatically a fatal problem; interpret modules in context of assay type and library prep.