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    "info": {
        "author": "Magnus Marcin, Pawlowski Marcin, Bujnicki Janusz M",
        "author_email": "magnus@genesilico.pl",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "Welcome to MetaLocGramN\r\n---------------\r\n\r\nThe MetaLocGramN is a method for subcellular localization prediction of Gram-negative proteins. \r\nRead more: http://iimcb.genesilico.pl/MetaLocGramN/home\r\n\r\nHow does MetaLocGramN work?\r\n============\r\n\r\nThe MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors).\r\n\r\nThe MetaLocGramN integrates the primary methods and based on their outputs provides overall consensus prediction. \r\n\r\nRequirements\r\n============\r\n\r\n* suds = 0.4\r\n\r\nInstallation\r\n============\r\n\r\nInstall it with pip (or easy_install)::\r\n\r\n\tpip install MetaLocGramN\r\n\r\nHow to start?\r\n============\r\n\r\nIf you are really lazy try:\r\n       \r\n        $ ipython\r\n        \r\n\tIn [1]: from MetaLocGramN import *\r\n\tIn [2]: run_example()\r\n\t# job_id: 1X820N\r\n\t# status: queue\r\n\t# status: primary prediction::in progress\r\n\t# status: primary prediction::in progress\r\n\t# status: primary prediction::done\r\n\t# status: consenus::done\r\n\t# status: done\r\n\textracellular,47.541,0.0,0.0,0.0,52.459,\r\n\tprimary methods: CELLO,cytoplasmic,0.6138,0.036,0.1346,0.0612,0.1546,PSLpred,extracellular,0.2,0.531,PSORTb3,unknown,0.2,0.2,0.2,0.2,0.2,SosuiGramN,cytoplasmic\r\n\tIn [3]: run_example?\r\n\t# to get help!\r\n\tIn [4]: run_example??\r\n\t# to get even bigger help!\r\n\r\nif you want to find out more, see test.py inside the pkg.\r\n\r\n\timport MetaLocGramN\r\n\timport time\r\n\r\n\tif __name__ == \"__main__\":\r\n\t    mlgn = MetaLocGramN.MLGN()\r\n\r\n\t    seq = \"\"\">fasta\r\n\t    MKLSINKNTLESAVILCNAYVEKKDSSTITSHLFFHADEDKLLIKASDYEIGI\r\n\t    NYKIKKIRVESSGFATANAKSIADVIKSLNNEEVVLETIDNFLFVRQKNTKYK\r\n\t    \"\"\"\r\n\t    mlgn.predict(seq)\r\n\t    print '# job_id:', mlgn.get_job_id()\r\n\t    status = ''\r\n\t    while True:\r\n\t        status = mlgn.get_status()\r\n\t        print '# status:', status\r\n\t        if status == 'done':\r\n\t            break\r\n\t        time.sleep(5)\r\n\t    print mlgn.get_result()\r\n\r\nYou should get something like:\r\n\r\n\tpython test.py\r\n\t# job_id: K6Q10Q\r\n\t# status: queue\r\n\t# status: queue\r\n\t# status: primary prediction::in progress\r\n\t# status: primary prediction::in progress\r\n\t# status: primary prediction::done\r\n\t# status: done\r\n\textracellular,47.541,0.0,0.0,0.0,52.459,\r\n\tprimary methods: CELLO,cytoplasmic,0.6138,0.036,0.1346,0.0612,0.1546,PSLpred,extracellular,0.2,0.531,PSORTb3,unknown,0.2,0.2,0.2,0.2,0.2,SosuiGramN,cytoplasmic\r\n\r\nAuthors\r\n==================================================\r\n\r\nMarcin Magnus,\r\nMarcin Pawlowski,\r\nJanusz M. Bujnicki\r\n\r\nhttp://iimcb.genesilico.pl/\r\n\r\n\r\nHappy predictions!\r\n==================================================\r\n\r\nMarcin Magnus magnus@genesilico.pl",
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        "keywords": "bioinformatics,subcellular localization,prediction,gram-negative,sequence,analysis",
        "license": "GPLv3",
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        "name": "MetaLocGramN",
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