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    "info": {
        "author": "YoSon Park",
        "author_email": "yoson.park@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "\n\n****\ncimr\n****\n\n\n***************************************\ncimr is not yet released for public use\n***************************************\n\n\n================================================================================\ncontinuous integration and analysis using variant association summary statistics\n================================================================================\n\n==========\nYoSon Park\n==========\n\n**Useful links**:\n`Source repository <https://github.com/greenelab/cimr>`_ |\n`Issues & Ideas <https://github.com/greenelab/cimr/issues>`_ | \n`Documentation <https://cimr.readthedocs.io>`_ | \n`cimr-d <https://github.com/greenelab/cimr-d>`_\n\n\ncimr is a convenience tool for continuous analyses of variant-based \nassociation results from GWAS (genome-wide association studies), eQTL \n(expression-quantitative trait loci mapping) or other association studies. \ncimr began as a python module to run large-scale Mendelian randomization \nanalysis (hence the name). As the project developed, it became more \nevident that there are many parts preceding the analyses that require \npipelining. So the current incarnation of cimr aims to streamline the \npre-analysis processing steps, provide standardized input files and write \nexample scripts to run various downstream methods seamlessly.\n\n\n\n============\nInstallation\n============\n\n-----------------\nInstalling python\n-----------------\n\ncimr requires python :math: `\\ge` 3.6. Installation of data analysis bundles \nsuch as `miniconda <https://conda.io/miniconda.html>`_ or \n`anaconda <https://www.anaconda.com/download/>`_ are recommended and will \ninstall all python packages cimr depends on. However, all required python \npackages can be downloaded and installed with setup.py or requirements.txt \nprovided here.\n\n\n------------------\nInstalling git lfs\n------------------\n\ncimr-d and some functionalities in cimr may use \n`git large file storage (LFS) <https://git-lfs.github.com/>`_ . \nSee how to install `git <https://www.atlassian.com/git/tutorials/install-git>`_ .\ngit-lfs is not required for using cimr as a standalone tool without cimr-d.\n\n\nTo install git-lfs on Ubuntu, run:\n\n\n>>> curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n>>> sudo apt-get install -y git git-lfs\n>>> git-lfs install\n\n\nAlternatively, you can install git-lfs through conda:\n\n>>> conda install -c conda-forge git-lfs && git lfs install\n\n\n---------------\nInstalling cimr\n---------------\n\nYou can use pip to install the latest stable release of cimr.\n\n>>> pip3 install cimr\n\n\nIf you want to try out the nightly build of cimr at your own risk, \nclone the repository from git.\n\n\n>>> git clone https://github.com/greenelab/cimr.git\n>>> cd cimr\n>>> python3 setup.py build\n>>> python3 setup.py install\n\n\n=================\nAnalysis examples\n=================\n\n------------------------------------------------------------------------\nQuality assurance and processing of association summary statistics files\n------------------------------------------------------------------------\n\ncimr contains various functionalities in \n`processor <https://cimr.readthedocs.io/cimr/processor>`_ \nfor processing summary statistics files for downstream analysis.\n\n\n====================\nContributing to cimr\n====================\n\n-----------------\nContributing data\n-----------------\n\nYou may contribute summary statistics from GWAS, eQTL and other similar studies. \ncimr currently expects hg20/GRCh38 reference for genomic position mapping.\nHowever, variants mapped to hg19/GRCh37 may be used if updated using the\nfollowing command:\n\n\n>>> cimr processor --datatype {datatype} --filename {filename} --update-map\n\n\nFollowing columns are expected for association summary statistics files::\n\n  gene : gene id in ensembl format\n  rsnum : rs id of the variant\n  constant_id : chromosome\\_position\\_referenceallele\\_alternateallele\\_genomebuild \n  e.g. chr2_128747549_G_T_hg19\n  inc_allele : allele with respect to which variant's effect sizes are estimated\n  inc_afrq : allele frequency of inc_allele\n  beta : beta coefficient estimate for the association effect of the variant \n  se : standard error of the beta\n  pval : p-value of the beta estimate\n\n\n\nHere is an eQTL input file example::\n\n  gene_id rsnum constant_id inc_allele inc_afrq beta se pval  \n  GPR17 rs17262104 chr2_128747549_G_T G 0.06457 0.73698 0.11432743 5.5415e-10\n\n\n\n----------------------------------\nContributing to cimr python module\n----------------------------------\n\n\nContribute to cimr-d code or resources `here <https://github.com/greenelab/cimr-d>`_ .\nGuidelines are provided `here <https://github.com/greenelab/cimr-d/CONTRIBUTING.md>`_ .\n\nContribute to cimr code or resources `here <https://github.com/greenelab/cimr>`_ .\nGuidelines are provided `here <https://github.com/greenelab/cimr/CONTRIBUTING.md>`_ .\n\n\n\n",
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