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Create aggregate bioinformatics analysis reports across many samples and tools

Project description

  MultiQC MultiQC  

Aggregate bioinformatics results across many samples into a single report

Find documentation and example reports at

PyPI Version Bioconda Version DOI

MultiQC is a tool to create a single report with interactive plots for multiple bioinformatics analyses across many samples.

Reports are generated by scanning given directories for recognised log files. These are parsed and a single HTML report is generated summarising the statistics for all logs found. MultiQC reports can describe multiple analysis steps and large numbers of samples within a single plot, and multiple analysis tools making it ideal for routine fast quality control.

A very large number of Bioinformatics tools are supported by MultiQC. Please see the MultiQC website for a complete list. MultiQC can also easily parse data from custom scripts, if correctly formatted / configured - a feature called Custom Content.

More modules are being written all the time. Please suggest any ideas as a new issue (please include example log files).


You can install MultiQC from PyPI using pip as follows:

pip install multiqc

Alternatively, you can install using Conda from Bioconda (set up your channels first):

conda install multiqc

If you would like the development version from GitHub instead, you can install it with pip:

pip install --upgrade --force-reinstall git+

MultiQC is also available via Docker and Singularity images, Galaxy wrappers, and many more software distribution systems. See the documentation for details.


Once installed, you can use MultiQC by navigating to your analysis directory (or a parent directory) and running the tool:

multiqc .

That's it! MultiQC will scan the specified directory (. is the current dir) and produce a report detailing whatever it finds.

cd test_data/data/modules/fastqc/v0.10.1 && multiqc .

The report is created in multiqc_report.html by default. Tab-delimited data files are also created in multiqc_data/, containing extra information. These can be easily inspected using Excel (use --data-format to get yaml or json instead).

For more detailed instructions, run multiqc -h or see the documentation.


Please consider citing MultiQC if you use it in your analysis.

MultiQC: Summarize analysis results for multiple tools and samples in a single report.
Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
Bioinformatics (2016)
doi: 10.1093/bioinformatics/btw354
PMID: 27312411

 author = {Ewels, Philip and Magnusson, Måns and Lundin, Sverker and Käller, Max},
 title = {MultiQC: summarize analysis results for multiple tools and samples in a single report},
 journal = {Bioinformatics},
 volume = {32},
 number = {19},
 pages = {3047},
 year = {2016},
 doi = {10.1093/bioinformatics/btw354},
 URL = { +},
 eprint = {/oup/backfile/Content_public/Journal/bioinformatics/32/19/10.1093_bioinformatics_btw354/3/btw354.pdf}

Contributions & Support

Contributions and suggestions for new features are welcome, as are bug reports! Please create a new issue for any of these, including example reports where possible. Pull-requests for fixes and additions are very welcome. Please see the contributing notes for more information about how the process works.

MultiQC has extensive documentation describing how to write new modules, plugins and templates.

If in doubt, feel free to get in touch with the author directly: @ewels (


MultiQC is developed and maintained by Phil Ewels (@ewels) at Seqera Labs. It was originally written at the National Genomics Infrastructure, part of SciLifeLab in Sweden.

A huge thank you to all code contributors - there are a lot of you! See the Contributors Graph for details.

MultiQC is released under the GPL v3 or later licence.

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