MultiQC Tutorial


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Overview of MultiQC

MultiQC is a powerful tool designed to aggregate results from various bioinformatics analyses across multiple samples into a single comprehensive report. This tool is particularly useful for bioinformaticians and researchers who deal with large volumes of data generated from high-throughput sequencing experiments. MultiQC automates the process of collecting and summarizing data, which can be a tedious and error-prone task when done manually.

The primary function of MultiQC is to search a specified directory for analysis logs and compile them into an HTML report. This report provides a visual and numerical summary of the data, making it easier to track how the data behaves throughout the analysis pipeline. MultiQC supports a wide range of bioinformatics tools—137 at the time of writing—and is designed to be general-purpose, meaning it can be used with various types of bioinformatics data.

One of the key features of MultiQC is its ability to visualize statistics from multiple samples together, allowing for detailed comparisons that are not possible by examining individual reports. The tool is also extensible and well-documented, making it possible for users to add support for additional tools or customize the reports to fit their specific needs.

MultiQC is supported by a community of developers and users who contribute to its ongoing development. Users can request support for new tools by opening an issue on the MultiQC GitHub repository, and if they provide an example log file, the addition of the new tool is usually a swift process.

The latest release of MultiQC at the time of writing is version 1.19, and it can be installed using pip or conda, making it accessible to a wide range of users with different system configurations.


Installing MultiQC is straightforward and can be done using Python's package manager pip or through the conda package management system. For users who prefer containerization, MultiQC is also available as a Docker image.

To install MultiQC using pip, simply run the following command in your terminal:

pip install multiqc

For those who use conda, the installation command is:

conda install multiqc

It is recommended to check the full installation instructions on the MultiQC website to ensure that all dependencies and requirements are met for a successful installation.

Quick Start

Once MultiQC is installed, running it is as simple as navigating to your analysis directory and executing the multiqc command followed by the directories you wish to search. In its simplest form, you can run MultiQC on the current working directory by using the following command:

multiqc .

This command will prompt MultiQC to search the current directory for analysis logs from supported tools and generate a report summarizing the findings.

Code Examples Of Popular Commands

Here are five popular commands that you can use with MultiQC to get the most out of your data analysis:

  1. Basic MultiQC Report Generation:
    Generate a report for the current directory.

    multiqc .
  2. Specifying Output Directory:
    Generate a report and specify the output directory for the report files.

    multiqc . -o output_directory
  3. Including Specific Modules:
    Run MultiQC and include only specific modules in the report.

    multiqc . --modules fastqc,star
  4. Excluding Specific Modules:
    Run MultiQC and exclude certain modules from the report.

    multiqc . --exclude fastqc
  5. Generating a ZIP Archive:
    Generate a report and also create a ZIP archive for easy sharing.

    multiqc . --zip-data-dir

These commands showcase the flexibility of MultiQC in handling various scenarios that researchers might encounter during their data analysis workflows. The ability to include or exclude specific modules, specify output directories, and create ZIP archives for sharing makes MultiQC a versatile tool in the bioinformatics toolkit.

In conclusion, MultiQC is an indispensable tool for bioinformaticians, streamlining the process of data analysis and reporting. Its ease of installation, simple usage, and extensive support for bioinformatics tools make it a go-to solution for generating comprehensive reports from high-throughput sequencing data.