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        "author": "Fang Wang",
        "author_email": "wangfang@ems.hrbmu.edu.cn",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Python Software Foundation License",
            "Operating System :: MacOS :: MacOS X",
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            "Operating System :: POSIX :: Linux",
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            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "CellMethy_v1\n\nDNA methylation patterns within a cell population from individual somatic tissues are highly heterogeneous and polymorphic. We developed CellMethy method to quantify fraction of focal full methylation of cell subpopulation (FMC fraction) and identify fully methylated regions of cell subpopulation (CellMethy) based on single base resolution DNA methylation data. \nThis script is used for infering fraction of focal full methylation cell subpopulation.\n        \n\n***************\nEasy to start:\n***************\n\nInputfile: File sepearated by \"\\\\t\" after bismark processed including read id, strand, chromosome, position of CpGc and methylation state (Z or z).\n\nThe result would be placed into input folder:  outputfile.\n\nIf your fastq mapping is done with bismark and extracted methylation state, use following command:\n        \n        ::\n        \n             python CellMethy.py -f inputfile -o outputfile\n\nOptions:\n^^^^^^^^\n\n        -f: The file name of input file after bismark processed, include five columns: read ID, strand, chromosome, position of CpG and methylation states Z (methylated) or z (unmethylated) separated by '\\\\t'.\n    \n        -c: Lowest coverage cutoff in each bin, default is 10.\n        \n        -b: number of CpGs in each bin, default is 5.\n    \n        -o: The file name of output file showing full methylation of cell subpopulation, include five columns: chromosome, start, end, FMC fraction, and CpGs number in the region separated by \"\\\\t\".\n        \nexamples:\n\"\"\"\"\"\"\"\"\" \n        \n        ::\n                 \n             cd ~/CellMethy-*.*.*\n             python ./CellMethy/bin/CellMethy.py -f ./data/data_file -b5 -c 10 -o myoutput \n\n    \nHow to get Input file\n^^^^^^^^^^^^^^^^^^^^^^^^^^\nIf you have fastq data, you can mapping with bismark tools.\n\nexamples:\n\"\"\"\"\"\"\"\"\"\n        \n        ::\n                \n             bismark ./referenceGenome --bowtie2 test.fastq -o test.sam   \n             bismark_methylation_extractor -s --comprehensive test.sam\n  \n  \nThe output file named \"CpG_context_test.txt\" is inputfile of  CellMethy.\nThe format of  CellMethy inputfile  include read id, strand, chromosome, position of CpGc and methylation state (Z or z) separated by '\\\\t'.\n\n        \n        ::\n                \n            Read1\t+\tchr21\t9827508\tZ\n            Read1\t-\tchr21\t9827503\tz\n            Read1\t-\tchr21\t9827484\tz\n            Read2\t+\tchr21\t9827434\tZ\n            Read2\t+\tchr21\t9827454\tZ\n            Read2\t-\tchr21\t9827483\tz",
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        "keywords": "CellMethy setuptools development",
        "license": "Harbin Medical University, Python Software Foundation License",
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        "name": "CellMethy",
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        "summary": "Identifying focal full methylation of cell subpopulation and inferring fraction",
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