/multiqc-qc-reports

name: "multiqc-qc-reports"
description: "Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting."
license: "GPL-3.0"

MultiQC — Multi-Sample QC Report Aggregator

Overview

MultiQC automatically searches directories for QC log files from 150+ bioinformatics tools and aggregates statistics across all samples into a single interactive HTML report. It parses outputs from FastQC, samtools flagstat, STAR, HISAT2, Trim Galore, Salmon, Kallisto, featureCounts, Picard, GATK, and many more — eliminating the need to manually review per-sample QC files. Reports include interactive bar plots, scatter plots, heatmaps, and tables with configurable warnings and pass/fail thresholds.

When to Use

• Reviewing QC metrics across 10+ samples at once after FastQC, alignment, or quantification
• Final QC checkpoint before differential expression or variant analysis
• Sharing QC summaries with collaborators or including in publications
• Identifying batch effects, outlier samples, or failed sequencing runs
• Combining QC from multi-step pipelines (trimming → alignment → quantification) into one view
• Use FastQC directly instead for initial single-sample QC exploration
• For custom QC metrics not from standard tools, use Python/R directly; MultiQC parses tool outputs only

Prerequisites

• **Python packages**: `multiqc`
• **Input requirements**: Output files from bioinformatics tools (FastQC `.zip`, samtools `.flagstat`, STAR `Log.final.out`, etc.) — MultiQC finds them automatically
• **Environment**: Python 3.8+

> **Check before installing**: The tool may already be available in the current environment (e.g., inside a `pixi` / `conda` env). Run `command -v multiqc` first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via `pixi run multiqc` rather than bare `multiqc`.

pip install multiqc

# Verify
multiqc --version
# MultiQC v1.25.0

# With conda (recommended for bioinformatics)
conda install -c bioconda multiqc

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: searchScope
    kind: required
    source: data
    ask: "Which directories should be scanned, and are there old or partial runs in them that must be excluded?"
    default: "the pipeline output root, with no exclusions"

  - id: D2
    param: sampleNameCleaning
    kind: required
    source: data
    ask: "Do the discovered sample names collapse correctly, or do suffixes from different tools split one sample into several rows?"
    default: "built-in extension stripping; verify the resulting sample list"

  - id: D3
    param: moduleFilter
    kind: optional
    source: user
    ask: "Report on every tool found, or only some?"
    default: "all modules detected"

  - id: D4
    param: exportData
    kind: optional_conditional
    source: upstream
    ask: "Should the parsed metrics be written as flat tables for a later programmatic QC gate?"
    default: "HTML report only"

MultiQC aggregates rather than analyses, so most of its parameters are presentation. The two that are not are D1 and D2: a stale directory in scope silently adds samples that were never part of this run, and wrong name cleaning reports one sample as several. Both produce a clean-looking report.

Workflow

Step 1: Generate Tool-Specific QC Files

MultiQC aggregates existing output — first run your QC tools.

# FastQC on all FASTQ files
mkdir -p qc/fastqc
fastqc data/*.fastq.gz -o qc/fastqc/ -t 8

# samtools flagstat on all BAM files
for bam in results/*.bam; do
    samtools flagstat $bam > qc/$(basename $bam .bam).flagstat
done
echo "QC files generated: $(ls qc/ | wc -l)"

Step 2: Run MultiQC on a Directory

MultiQC recursively scans for recognized QC files.

# Basic run: scan current directory recursively
multiqc .

# Specify output directory and report name
multiqc . -o reports/ -n project_qc_report

# Scan specific subdirectories only
multiqc qc/fastqc/ results/star/ logs/trimming/ -o reports/

# Output: reports/project_qc_report.html
echo "Report: reports/project_qc_report.html"

Step 3: Configure Report Behavior

Use `multiqc_config.yaml` to set custom thresholds, sample naming, and module order.

# multiqc_config.yaml — place in working directory
title: "RNA-seq QC Report — Project X"
subtitle: "Analysis date: 2026-02"
intro_text: "Quality control summary for all 48 samples."

# Sample name cleaning: remove path prefixes and suffixes
fn_clean_exts:
  - ".fastq.gz"
  - "_R1"
  - ".sorted"

# Thresholds for pass/warn/fail coloring
general_stats_addcols:
  FastQC:
    pct_duplication:
      max: 40
      warn: 30

# Module run order
module_order:
  - fastqc
  - trimgalore
  - star
  - featurecounts
  - samtools

# Run with config file
multiqc . --config multiqc_config.yaml -o reports/

Step 4: Use MultiQC Modules and Filters

Control which tools and samples are included.

# Run only specific modules
multiqc . --module fastqc --module samtools

# Exclude specific modules
multiqc . --exclude fastqc

# Include only files matching a pattern
multiqc . --filename "*.flagstat" --filename "*_fastqc.zip"

# Ignore specific directories or files
multiqc . --ignore "tmp/" --ignore "*.bam"

# Add sample name regex substitution
multiqc . --replace-names "sample_" ""

Step 5: Export Data for Downstream Analysis

Extract machine-readable statistics from the MultiQC report.

# Export data tables (CSV, JSON, YAML, TSV)
multiqc . -o reports/ --data-format json
# Generates: reports/multiqc_data/multiqc_data.json

# Export flat CSV tables per tool
multiqc . -o reports/ --export