Quick Start
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A tutorial with a typical setup for people in a rush.
MultiQC: Quick start
This tutorial covers installation and first run for a typical user. Itβs not meant to be comprehensive - see the rest of the documentation for that - itβs just to get the majority up and running quickly so you can get a taste for how to use MultiQC.
Install Conda
In order to install MultiQC, we first need Python. Arguably, the easiest way to do this is with Conda (see full docs).
- Download miniconda for your operating system.
- Run the bash script and follow the prompts.
- Restart your terminal shell.
- Configure your conda channels to work with BioConda:
- Create a new conda environment:
Install MultiQC
Now that we have Python, we can install MultiQC. As weβre already using Conda, we may as well install MultiQC with Conda too (see full docs).
Check that it worked by printing the MultiQC version (or --help
text):
Get some example data
To try MultiQC out quickly, you can fetch some example input data from the Example reports page.
Each example report has a link to Download input data. You should be able to recreate the example report using this.
For example, for the RNA-seq report:
You should now have a directory called data
which is full of analysis result files.
For the RNA-seq example, we have outputs from a bioinformatics analysis of some publicly available data. We have logs and reports from FastQC, TrimGalore! (Cutadapt), STAR and featureCounts.
Run MultiQC
There isnβt much to running MultiQC really - just point it at the directory that contains your files and it will search recursively for anything it recognises.
Assuming that you are still in the directory where you just extracted the data,
the current working directory (.
) contains your files:
Open the report
You can see in the log output that MultiQC created a file called multiqc_report.html
.
Open it and take a look (you can usually ctrl/cmd + click the filename in most terminals).
It should look basically the same as the example report on the MultiQC website.
Try using the toolbox features in the right hand sidebar, for example hiding and highlighting specific samples.
Also have a look at the directory multiqc_data
that was created.
This contains the parsed data in a nice friendly format, ready for any further downstream analysis.