MultiQC — Multi-Sample QC Report Aggregator

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.