cimr

cimr is not yet released for public use

continuous integration and analysis of complex traits

YoSon Park

Useful links: Source repository | Issues & Ideas | Documentation | cimr-d

cimr (continuously integrated meta-resource) is a convenience tool for continuous analyses of variant-based association results from GWAS (genome-wide association studies), eQTL (expression-quantitative trait loci mapping) or other association studies. cimr aims to streamline the pre-analysis processing steps, provide standardized input files and automate scripting for standard downstream analyses.

Installation

Installing python

cimr requires python :math: ge 3.6. Installation of data analysis bundles such as miniconda or anaconda are recommended and will install all python packages cimr depends on. However, all required python packages can be downloaded and installed with setup.py or requirements.txt provided here.

Installing git lfs

cimr-d and some functionalities in cimr may use git large file storage (LFS) . See how to install git .

To install git-lfs on Ubuntu, run:

curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install -y git git-lfs
git-lfs install

Alternatively, you can install git-lfs through conda:

conda install -c conda-forge git-lfs && git lfs install

Installing cimr

You can use pip to install the latest stable release of cimr:

pip3 install cimr

If you want to try out the nightly build of cimr at your own risk, clone the repository from git:

git clone https://github.com/greenelab/cimr.git
cd cimr
pip3 install -r requirements.txt
python3 setup.py build
python3 setup.py install

Analysis examples

Quality assurance and processing of association summary statistics files

You can use cimr to standardize public datasets using a yaml file, e.g.:

# example.yaml

data_file:
    description: >-
        Global Lipid Genetics Consortium GWAS results for high-density
        cholesterol levels
    location:
        url: https://zenodo.org/record/3338180/files/HDL_Cholesterol.txt.gz
        md5: 2b28816a0a363db1a09ad9a6ba1a6620
    columns:
        variant_id: panel_variant_id
        variant_chrom: chromosome
        variant_pos: position
        rsnum: variant_id

data_info:
    citation: 10.1038/ng.2797
    data_source: http://lipidgenetics.org/
    data_type: gwas
    context: hdl cholesterol
    build: b38
    sample_size: 187167
    n_cases: na
    can_be_public: true

method:
    name: linear regression
    tool: PLINK;SNPTEST;EMMAX;Merlin;GENABEL;MMAP
    website: >-
        http://zzz.bwh.harvard.edu/plink/download.shtml;
        https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html;
        https://genome.sph.umich.edu/wiki/EMMAX;
        https://csg.sph.umich.edu/abecasis/Merlin/tour/assoc.html;
        http://www.genabel.org/sites/default/files/html_for_import/GenABEL_tutorial_html/GenABEL-tutorial.html;
        https://mmap.github.io/

contributor:
    name: Contributor Name
    github: contributorgithub
    email: [email protected]

Details can be found in the cimr-d contributions.md.

Once the yaml file is prepared, you can run cimr locally:

cimr processor -process -yaml-file example.yaml