Aggregate bioinformatics results across many samples into a single report.

Find documentation and example reports at http://multiqc.info

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

MultiQC is written in Python (tested with v2.7, 3.4, 3.5 and 3.6). It is available on the Python Package Index and through conda using Bioconda.

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.

Currently, supported tools include:

Read QC & pre-processing	Aligners / quantifiers	Post-alignment processing	Post-alignment QC
Adapter Removal	Bismark	Bamtools	BUSCO
AfterQC	Bowtie	Bcftools	Conpair
Bcl2fastq	Bowtie 2	GATK	Disambiguate
BioBloom Tools	HiCUP	HOMER	goleft
Cluster Flow	HISAT2	HTSeq	methylQA
Cutadapt	Kallisto	MACS2	Peddy
leeHom	Salmon	Picard	Preseq
FastQC	Slamdunk	Prokka	QoRTs
FastQ Screen	STAR	Samblaster	Qualimap
Flexbar	Tophat	Samtools	QUAST
Jellyfish		SnpEff	RNA-SeQC
Skewer		Subread featureCounts	RSeQC
SortMeRNA		THetA2	VCFTools
Trimmomatic

MultiQC can also easily parse data from custom scripts, if correctly formatted / configured. See the MultiQC documentation for more information.

Please note that some modules only recognise output from certain tool subcommands. Please follow the links in the above table to the module documentation for more information.

More modules are being written all of the time. Please suggest any ideas as a new issue (include an example log file if possible).

Installation

You can install MultiQC from PyPI using pip as follows:

pip install multiqc

Alternatively, you can install using Conda from the bioconda channel:

conda install -c bioconda multiqc

If you would like the development version instead, the command is:

pip install --upgrade --force-reinstall git+https://github.com/ewels/MultiQC.git

MultiQC is also available in the Galaxy Toolshed.

Usage

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.

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.

Citation

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

@article{doi:10.1093/bioinformatics/btw354,
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 = { + http://dx.doi.org/10.1093/bioinformatics/btw354},
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. MultiQC has extensive documentation describing how to write new modules, plugins and templates.

There is a chat room for the package hosted on Gitter where you can discuss things with the package author and other developers: https://gitter.im/ewels/MultiQC

If in doubt, feel free to get in touch with the author directly: @ewels (phil.ewels@scilifelab.se)

Contributors

Project lead and main author: @ewels

Code contributions from: @moonso, @lpantano, @dakl, @robinandeer, @mlusignan, @HLWiencko, @guillermo-carrasco, @avilella, @vladsaveliev, @t-neumann, @ahvigil, @bschiffthaler, @jrderuiter @iimog @rlegendre and many others. Thanks for your support!