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        "author": "Complete Genomics",
        "author_email": "eanderson@genomics.cn",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# ExtremeVariantFilter\n\nExtreme Variant Filter is a set of tools developed to aid in the identification of false positive variants in Genomic Variant Call Files based on XGBoost.\n\n### Functions\n\n__apply_filter__\n\n    Usage:\n        apply_filter (--vcf STR) (--snp-model STR) (--indel-model STR) [--verbose]\n\n    Description:\n        Apply models generated by train_model to a VCF.\n\n    Arguments:\n        --vcf STR                     VCF to be filtered\n        --snp-model STR               Model for applying to SNPs\n        --indel-model INT             Model for applying to InDels\n\n    Options:\n        -h, --help                      Show this help message and exit.\n        -v, --version                   Show version and exit.\n        --verbose                       Log output\n\n    Examples:\n        apply_filter --vcf <table> --snp-model <snp.pickle.dat> --indel-model <indel.pickle.dat>\n\n__train_model__\n\n    Usage:\n        train_model (--true-pos STR) (--false-pos STR) (--type STR) [--out STR] [--njobs INT] [--verbose]\n\n    Description:\n        Train a model to be saved and used to filter VCFs.\n\n    Arguments:\n        --true-pos STR          Path to true-positive VCF from VCFeval or comma-seperated list of paths\n        --false-pos STR         Path to false-positive VCF from VCFeval or comma-seperated list of paths\n        --type STR              SNP or INDEL\n\n    Options:\n        -o, --out <STR>                 Outfile name for writing model [default: (type).filter.pickle.dat]\n        -n, --njobs <INT>               Number of threads to run in parallel [default: 2]\n        -h, --help                      Show this help message and exit.\n        -v, --version                   Show version and exit.\n        --verbose                       Log output\n\n    Examples:\n        train_table --true-pos <path/to/tp/vcf(s)> --false-pos <path/to/fp/vcf(s)> --type [SNP, INDEL] --njobs 20\n\n### Install\n\nTo install and run EVF simply type:\n\n    pip install extremevariantfilter\n\nAlternatively, clone this repo and build using the following commands:\n\n    git clone https://github.com/stLFR/extremevariantfilter.git\n    cd extremevariantfilter\n    pip install .\n\n### stLFR Paper Results\n\nIf you'd like to use this tool to corroborate the results from the \n[stLFR Paper on Bioarxiv](https://www.biorxiv.org/content/early/2018/05/17/324392.1) paper, \nthe models used for variant filtering are available within the `models/` directory. \nIn order to get identical results, after installation, use the command\n`pip install -r requirements.txt` from within this directory to ensure your \nenvironment matches the one we used for our results. \nDifferent versions of certain packages will result in variable results.  \n\n\n",
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        "name": "extremevariantfilter",
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