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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Automating a Workflow: Beyond Blast - to GO Slim"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The concept is that you can take a fasta file in a working directory and end up with GO slim information all within a single notebook that is automated. Currently this work by writing (and overwriting) as scracth file to SQLShare. Assumptions are that you are working in a directory with fasta file named `query.fa`. And blast algorithms are in PATH. \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#allows plots to be shown inline\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Setting Working Directory\n",
      "wd=\"/Volumes/web/whale/fish546/qpx_go_val\"\n",
      "#Setting directory of Blast Databases\n",
      "dbd=\"/Volumes/Bay3/Software/ncbi-blast-2.2.29\\+/db/\"\n",
      "#Database name\n",
      "dbn=\"uniprot_sprot_r2013_12\"\n",
      "#Blast algorithim\n",
      "ba=\"blastx\"\n",
      "#Location of SQLShare python tools: you can empty (\"\") if tools are in PATH\n",
      "spd=\"/Users/sr320/sqlshare-pythonclient/tools/\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd {wd}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/qpx_go_val\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!{ba} -query query.fa -db {dbd}{dbn} -out {dbn}_{ba}_out.tab -evalue 1E-50 -num_threads 4 -max_hsps_per_subject 1 -max_target_seqs 1 -outfmt 6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BLAST Database error: No alias or index file found for protein database [/Volumes/Bay3/Software/ncbi-blast-2.2.29+/db/uniprot_sprot_r2013_12] in search path [/Volumes/web/whale/fish546/pipeline_test_dir4::]\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -1 {dbn}_{ba}_out.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "QPX_transcriptome_v1_Contig_2\tsp|P52712|CBPX_ORYSJ\t43.75\t416\t213\t12\t2095\t869\t6\t407\t3e-98\t 326\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Translate pipes to tab so SPID is in separate column for Joining\n",
      "!tr '|' \"\\t\" <{dbn}_{ba}_out.tab> {dbn}_{ba}_out2.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -1 {dbn}_{ba}_out2.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Uploads formatted blast table to SQLshare; currently has generic name and meant to be temporary: Warning will overwrite.\n",
      "!python {spd}singleupload.py -d scratchblast_out {dbn}_{ba}_out2.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 1512 (0.378393888474 s elapsed)\r\n",
        "pushing uniprot_sprot_r2013_12_blastx_out2.tab...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing 8A0C3E42...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished scratchblast_out\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python {spd}fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[scratchblast_out]blast Left Join [sr320@washington.edu].[uniprot-reviewed_wGO_010714]unp ON blast.Column3 = unp.Entry Left Join [sr320@washington.edu].[SPID and GO Numbers]go ON unp.Entry = go.SPID Left Join [sr320@washington.edu].[GO_to_GOslim]slim ON slim.GO_id = go.GOID\" -f tsv -o {dbn}_join2goslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -2 {dbn}_join2goslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Column1\tColumn2\tColumn3\tColumn4\tColumn5\tColumn6\tColumn7\tColumn8\tColumn9\tColumn10\tColumn11\tColumn12\tColumn13\tColumn14\tEntry\tEntry name\tGene ontology IDs\tInteracts with\tCross-reference (GO)\tGene ontology (GO)\tStatus\tInterPro\tPathway\tProtein names\tGene names\tOrganism\tLength\tSPID\tGOID\tGO_id\tterm\tGOSlim_bin\taspect\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_5704\tsp\tB0BNA9\tCNO11_RAT\t74.19\t217\t55\t1\t990\t343\t274\t490\t1E-103\t329\tB0BNA9\tCNO11_RAT\tGO:0030014; GO:0005737; GO:0031047; GO:0005634; GO:0006355; GO:0006417; GO:0006351\t\t\tCCR4-NOT complex; cytoplasm; gene silencing by RNA; nucleus; regulation of transcription, DNA-dependent; regulation of translation; transcription, DNA-dependent\treviewed\tIPR019312;\t\tCCR4-NOT transcription complex subunit 11\tCnot11\tRattus norvegicus (Rat)\t504\t\t\t\t\t\t\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python {spd}singleupload.py -d scratchjoin_slim {dbn}_join2goslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 18037 (0.0718240737915 s elapsed)\r\n",
        "pushing uniprot_sprot_r2013_12_join2goslim.txt...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing 9A18D989...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished scratchjoin_slim\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sets GO aspect \n",
      "!python {spd}fetchdata.py -s \"SELECT Distinct Column1 as query, Column3 as SPID, GOSlim_bin FROM [sr320@washington.edu].[scratchjoin_slim] Where aspect = 'P'\" -f tsv -o justslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head justslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "query\tSPID\tGOSlim_bin\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_10\tQ17770\tdevelopmental processes\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_10\tQ17770\tother biological processes\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_10\tQ17770\tother metabolic processes\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_10\tQ17770\tsignal transduction\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_10\tQ17770\tstress response\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_1003\tQ9UEW8\tother metabolic processes\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_1003\tQ9UEW8\tprotein metabolism\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_1003\tQ9UEW8\tstress response\r",
        "\r\n",
        "QPX_transcriptome_v1_Contig_1003\tQ9UEW8\ttransport\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import *\n",
      "\n",
      "jslim = read_table(\"justslim.txt\", # name of the data file\n",
      "            #sep=\",\", # what character separates each column?\n",
      "            na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jslim.groupby('GOSlim_bin').query.count().plot(kind='bar')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1067a5550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ei2f+tMChQ4fg7+8PAPD390d0dDQAICYmBpMmTYK+vj6USiXMzc2RmJjY2NgJgiCIBtCg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\n+/fHxo0b0a1bNy761amsrMSAAQO4TSD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3btympvvjjz/UZucaMmQIbG1tuWgD0k5G3VQ1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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106773fd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python {spd}singleupload.py -d scratchjoin_slim {dbn}_join2goslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 17730 (0.107241153717 s elapsed)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "pushing uniprot_sprot_r2013_12_join2goslim.txt...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing 9474190F...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished scratchjoin_slim\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sets GO aspect \n",
      "!python {spd}fetchdata.py -s \"SELECT Distinct Column1 as query, Column3 as SPID, GOSlim_bin FROM [sr320@washington.edu].[scratchjoin_slim] Where aspect = 'P'\" -f tsv -o justslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head justslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "query\tSPID\tGOSlim_bin\r",
        "\r\n",
        "ConsensusfromContig10\tQ9PVZ4\tcell organization and biogenesis\r",
        "\r\n",
        "ConsensusfromContig10\tQ9PVZ4\tdevelopmental processes\r",
        "\r\n",
        "ConsensusfromContig10\tQ9PVZ4\tother metabolic processes\r",
        "\r\n",
        "ConsensusfromContig10\tQ9PVZ4\tprotein metabolism\r",
        "\r\n",
        "ConsensusfromContig10\tQ9PVZ4\tsignal transduction\r",
        "\r\n",
        "ConsensusfromContig1003\tQ92922\tcell organization and biogenesis\r",
        "\r\n",
        "ConsensusfromContig1003\tQ92922\tdevelopmental processes\r",
        "\r\n",
        "ConsensusfromContig1003\tQ92922\tRNA metabolism\r",
        "\r\n",
        "ConsensusfromContig1003\tQ92922\tsignal transduction\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from pandas import *\n",
      "\n",
      "jslim = read_table(\"justslim.txt\", # name of the data file\n",
      "            #sep=\",\", # what character separates each column?\n",
      "            na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jslim.groupby('GOSlim_bin').query.count().plot(kind='bar')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1068c1790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l5uX7lUpNHHfKzAmCIOoxiuvMKTOvefc/exBnpvyzoj0o1C3Wr+RzRtRxUVxnThAEQZSF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\nkEaAkjkhhDQClMwJIaQRoGROCCGNwP8B7EUnRl2uY7MAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1053cff90>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!say \"hash tag winning\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Below is optional"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#could also upload again to get a simple table\n",
      "#could be done in pandas\n",
      "\n",
      "#!python {spd}singleupload.py -d scratchpie justslim.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 2538 (0.00250601768494 s elapsed)\r\n",
        "pushing justslim.txt...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing 87B0B7A8...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished scratchpie\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#fetching data grouped by GObin\n",
      "\n",
      "#!python {spd}fetchdata.py -s \"SELECT GOSlim_bin, COUNT(GOSlim_bin) as termcount from [sr320@washington.edu].[scratchpie] Group by GOSlim_bin\" -f tsv -o justpie.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}