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    "info": {
        "author": "Guanlan Dong",
        "author_email": "guanlan.dong@wustl.edu",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# AeQTL\n\neQTL analysis using region-based aggregation of rare variants. \n\n## Requirements\n\n- python 3.5\n- pip\n- bx_interval_tree (see installation instructions below)\n- git (optional)\n\n## Installation\n\nFirst, install IntervalTree from bx-python. We strongly recommend using a standalone package called *bx_interval_tree* which is smaller and easier to compile than bx-python. \n\n```\ngit clone https://github.com/ccwang002/bx_interval_tree\ncd bx_interval_tree\npython setup.py install\ncd ..\n```\n\nContinue to install AeQTL by choosing one of the options below.\n\n#### (1) From PyPI\n\nThe easiest way to install AeQTL is from PyPI.\n\n\tpip install aeqtl\n\n#### (2) From source code\n\nAlternatively, download the source code of AeQTL\n\n\tgit clone https://github.com/Huang-lab/AeQTL\n\nThen install AeQTL\n\n\tcd AeQTL\n\tpip install .\n\n#### Optional (but recommended)\n\nAppend the path to AeQTL to your PATH environment variable\n\n\texport PATH=/path/to/AeQTL/bin:$PATH\n\n## Run\n\n\taeqtl -v <vcf file> -b <bed file> -e <expression file> \\\n    \t  -cn <numerical covariates> -cc <categorical covariates> -s <covariate file> \\\n          -o <output directory>\n\n## Input data format\n\n*Note: demo input files with compatible format can be found in the \"demo\" folder*\n\n#### VCF file\n\nA standard multi-sample VCF file with file extension .vcf (or .vcf.gz). Sample IDs in VCF file, expression file, and covariate file should match exactly. \n\n#### BED file\n\nA BED file (tab separated) with at least four columns and without header. The format of the file should follow:\n\n\t<chromosome>\t<start>\t\t<end>\t\t<region_name>\t\t<tested_genes>\n\nAn example row:\n\n\tchr17\t\t\t41197693\t41197821\tBRCA1\t\t\t\tBRCA1;SLC25A39;HEXIM2\n\nThe first four columns are required. The fifth column is a list of genes separated by \";\". If the fifth column (tested_genes) is not provided, AeQTL by default will test each region with every gene from the expression file. \n\n#### Expression file\n\nA matrix-format, tab separated .tsv file with gene expression from RNA-seq. The first row (header) of the file should follow:\n\n\tgene_id\t\t<sample_id_1>\t\t<sample_id_2>\t\t<sample_id_3>\t\t...\n\nand the first column of the file should follow:\n\n\tgene_id\n\t<gene_1>\n\t<gene_2>\n\t...\n\n#### Covariate file\n\nA tab separated .tsv file with column names corresponding to covariates. A column of sample IDs with column name \"sample_id\" is required. Covariates entered in AeQTL and their corresponding column names must match exactly. However, the covariate file can contain other unused columns as well. If entering a categorical covariate, please make sure each category has the same value throughout the file (i.e. avoid instances such as having both \"FEMALE\" and \"female\" in the same column).\n\n\n## Output data format\n\nA tab separated .tsv file of summary statistics (up to 5 digits after the decimal point). Each row is an eQTL test between a region and a gene. The file contains the following fields:\n\n- region\n- gene\n- coef_intercept\n- coef_genotype\n- coef_\\<covariate> *(for each covariate)*\n- pvalue_intercept\n- pvalue_genotype\n- pvalue_\\<covariate> *(for each covariate)*\n\n\n\n\n\n\n\n\n\n\n",
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        "summary": "eQTL analysis using region-based aggregation of rare variants.",
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