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        "description": "=============\nPre-requisite\n=============\n\n* Python 2.7\n* pip (http://pypi.python.org/pypi/pip)\n* numpy (http://pypi.python.org/pypi/numpy)\n* matplotlib (http://matplotlib.org)\n\n============\nInstallation\n============\n\nTo install this module, run the following commands:\n\n    sudo python setup.py install\n\n===========================\nDownload reference database\n===========================\n\nAfter the installation is complete, type\n\n    CombiVEP_reference_updater\n\nThis application will automatically check with UCSC and LJB database, and see\nif it is required to download the new one. The original database size is\naround 1GB each. The total operation time for each database should be\naround 30-60 mins.\n\n========\nTraining\n========\n\nAfter having reference database installed, the CombiVEP model can be trained\nusing\n\n    CombiVEP_trainer <training_data_file>\n\n<training_data_file> must be in CBV format: CHROM, POS, REF, ALT,\nACTUAL_DELETERIOUS_EFFECT. Each field is separated by a tab. SNP Position(POS)\nis 1-based index. The VariBench training data file in CBV format can be found at\n\n    combivep/data/CBV/training.cbv\n\n==========\nPrediction\n==========\n\nTo use the trained model to predict the effect, you can do it using\n\n    CombiVEP_predictor <input_file> [-F FORMAT]\n\nThe input file can be either in VCF or above CBV format. Default is in\nVCF format. So if you want to use input file in VCF format, simply type\n\n    CombiVEP_predictor <vcf_file>\n\nIf you to do the prediction using file in CBV format, you can \n\n    CombiVEP_predictor <cbv_file> -F CBV\n\nThe VariBench test data file in CBV format can be found at\n\n    combivep/data/CBV/test.cbv",
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