ADMIRE - Analysis of DNA methylation in genomic regions

ADMIRE is a semi-automatic analysis pipeline and visualization tool for Infinium HumanMethylation450K and Infinium MethylationEpic assays.

Use ADMIRE online: https://bioinformatics.mpi-bn.mpg.de

Features

Automatic filtering and normalization
Statistical testing and multiple testing correction
Supports arbitrary number of samples and sample groups
Differential methylation analysis on pre-calculated and individual genomic regions
Provides ready-to-plug-in files for genome browsers (like IGV)
Provides publication-ready figures for the most differentially methylated regions
Performs gene set enrichment analysis on predefined and individual gene sets

Documentation

We have a extensive documentation with a use case available here.

Installation and Command-line usage

We recommend to install prerequisites using the conda package manager. Make sure to have conda installed, e.g. via

Miniconda
- download the Miniconda installer for Python 3
- run bash Miniconda3-latest-Linux-x86_64.sh to install Miniconda
- Answer the question "Do you wish the installer to prepend the Miniconda install location to PATH in your /home/.../.bashrc ?" with yes OR do PATH=dir/to/miniconda3:$PATH after installation process

Clone the ADMIRE repository and populate an environment with all prerequisites:

$ git clone https://github.molgen.mpg.de/loosolab/admire
$ conda env create -f admire/environment.yaml
$ export PATH=$PATH:dir/to/admire/src

Every time you intent to use ADMIRE, make sure the environment is activated:

$ source activate admire
$ admire -h
Usage: admire [options]

Available options:
-c | Comma separated sample definition file (SampleSheet.csv)
-s | Tab separated sample definition file (design.txt)
-z | Compressed input of idat files (requires -c).
-e | Create quality control report in PDF
-r | Region file in bed format (regions.bed), use multiple -r parameters to calculate for multiple region files
-p | Detection p-value to exclude probes prior to analysis (0.01)
-t | Exclude probes where more than t% samples failed according to the detection p-value. (0.4)
-n | Normalization method (fn,swan,noob,illumina,raw,quantile)
-b | In case of functional normalization, skip noob background correction step
-d | In case of noob or functional normalization, skip dye correction step
-f | In case of quantile normalization, skip fixing outliers prior to analysis
-l | In case of quantile normalization, label samples as bad if their median signals are below a given value (10.5)
-m | In case of quantile normalization, remove bad samples
-q | Q-value cutoff for multiple testing correction (0.05)
-i | Render advanced plots for the best i regions (20)
-g | Gene set file for enrichment analysis, use multiple -g parameters to calculate enrichment over many gene sets
-o | tar-gz compress output into file given
-h | shows this help message
-v | shows version information

Options -c and -s are mutually exclusive.

How to cite?

Please cite Preussner J, Bayer J, Kuenne C and Looso M. ADMIRE: ADMIRE: analysis and visualization of differential methylation in genomic regions using the Infinium HumanMethylation450 Assay. Epigenetics & Chromatin (2015), doi:10.1186/s13072-015-0045-1, when using admire in your work.

Contribute

Issue Tracker: github.molgen.mpg.de.com/loosolab/admire/issues
Source Code: github.molgen.mpg.de.com/loosolab/admire

Support

If you are having issues, please feel free to send an e-mail to Jens Preußner (jens.preussner@mpi-bn.mpg.de).

License

The project is licensed under the MIT license.

License

loosolab/admire

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