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
- 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
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
- download the Miniconda installer for Python 3
bash Miniconda3-latest-Linux-x86_64.shto install Miniconda
- Answer the question "Do you wish the installer to prepend the Miniconda install location to PATH in your /home/.../.bashrc ?" with yes
PATH=dir/to/miniconda3:$PATHafter 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.
- Issue Tracker: github.molgen.mpg.de.com/loosolab/admire/issues
- Source Code: github.molgen.mpg.de.com/loosolab/admire
If you are having issues, please feel free to send an e-mail to Jens Preußner (email@example.com).
The project is licensed under the MIT license.