notebooks/Jay-CpG.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Determing CpG ratio per Gene Function"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Step 1: Obtain GO slim information (gene function)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#from 00 notebook\n",
      "!head ../analyses/Ahyacinthus_GOslim.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ContigID\tGOSlim_bin\r",
        "\r\n",
        "contig70128\tother biological processes\r",
        "\r\n",
        "contig82454\tDNA metabolism\r",
        "\r\n",
        "contig174510\tprotein metabolism\r",
        "\r\n",
        "contig209629\tother biological processes\r",
        "\r\n",
        "contig10816_153279\tsignal transduction\r",
        "\r\n",
        "contig109222\ttransport\r",
        "\r\n",
        "contig79068\tsignal transduction\r",
        "\r\n",
        "contig119835\tstress response\r",
        "\r\n",
        "contig110229\ttransport\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Step 2: Determine CpG ratio for each gene "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Step 2a: Convert fasta to tab-delimited"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!perl -e '$count=0; $len=0; while(<>) {s/\\r?\\n//; s/\\t/ /g; if (s/^>//) { if ($. != 1) {print \"\\n\"} s/ |$/\\t/; $count++; $_ .= \"\\t\";} else {s/ //g; $len += length($_)} print $_;} print \"\\n\"; warn \"\\nConverted $count FASTA records in $. lines to tabular format\\nTotal sequence length: $len\\n\\n\";' \\\n",
      "../data/fa/Ahyacinthus_v1.fasta > ../analyses/Ahyacinthus_v1.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Converted 33496 FASTA records in 66992 lines to tabular format\r\n",
        "Total sequence length: 17056543\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -1 ../analyses/Ahyacinthus_v1.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig27\t\tCAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#temp replace name so c or g will not confound\n",
      "#!sed 's/PiuraChilensis_v1_contig/999999/g' <../data/Piura_v1_contigs.tab> ../data/Piura_v1-99_contigs.tab\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!head -1 ../data/Piura_v1-99_contigs.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "999999_1\t\tATTTACAATACGAAGTAAAATAGATAACGTGAAAATAATCTTGGTGCTGGATGATCGATCAAGTTCACCAATATTTTATTGTAAAAAATCATTCTAAACAGCATGAAATCGTGTACAATGTATAAACAAGCAAATATATAACACTAAAGCAAGAGGGCGTAAGTGGGGGGGTGGGTGAGAGTAAAAAATTCAAACATGTCAAATACCCCGGCGTTAGCCTTAAAAGCACCATGGACTTCTGCCTTCAATAAGCATAAAATTAAAACACCTAATACACAATGAATATACAGATAAAACAGATTTATGAATAGTTGGTGTTACATCTTTTACAGCCATAAGCCTTCATTTTGCTTCCAAACGTATAAAATCTGACTTGGAACAATATACAGCCATGAGATATGACACAGCGAGCACTACAATATATATTTATCTTGTACTATACAGCCTGTACAAGAAAATTCTGGAATTGTCTTCACAAGAGACAGAAAAATAGTTGCAATGTGAATGCTAGTCTACTATTTGATCACAATTGGATAGAAAAGTACAGCACATAAATGTTGGTGATACCTTAAAGAAAAGTGCAACAATATCAAAGGAATTAGTACCAGCATGCATTAGAAAAGTAAAAGTCTTGCTTATTACACAAAGCTGACTATATGATGTTCACCGCTTCTGGTGTGCAAAGAATTAAAAACAATGCAATTTCGGTCAGTTTTAACAAGGAATTAACAATTCATAGGAAAAATACAAGCATATGGTCTCAGGCCAATTGCTAGGACATAAAAAAAGCCTGCATATCACGAAAAGCCAAGTGCATGCATCGTATCCTGAAGACACCTTGATATTAACATGTAAGAAATTTAGCTTGCCACATTTCCATATTCCATAATTTCATTTTGAACACCGTGCCAGCAAATTCATCTGATATAAACACACAGGCAACTAATTTGGACACTTTCTAACTAGGTAGTTCAGAAAATACAGCTTTCAACAGGTACACATTTCTATAATAATAATAATAGCAAATGTCAGTGTGGCAGTTTTTGGACAAGTCCCTTTCAGAGGCCAAAATATCTATTTTGTATTAATTAATTATCCATTTTTTGGACTATACGGCTGTATCAAAACCATGGGTAACTGGGACTTGCCTAGCTTTGGGGGTGGAGTC\r\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#add column with length of sequence\n",
      "!perl -e '$col = 2;' -e 'while (<>) { s/\\r?\\n//; @F = split /\\t/, $_; $len = length($F[$col]); print \"$_\\t$len\\n\" } warn \"\\nAdded column with length of column $col for $. lines.\\n\\n\";' \\\n",
      "../analyses/Ahyacinthus_v1.tab > ../analyses/Ahyacinthus_v1_len.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Added column with length of column 2 for 33496 lines.\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -1 ../analyses/Ahyacinthus_v1_len.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig27\t\tCAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\t223\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F\\CG '{print NF-1}' ../analyses/Ahyacinthus_v1_len.tab > ../analyses/Ahyacinthus_v1_CG.tab\n",
      "!awk -F\\C '{print NF-1}' ../analyses/Ahyacinthus_v1_len.tab > ../analyses/Ahyacinthus_v1_C.tab\n",
      "!awk -F\\G '{print NF-1}' ../analyses/Ahyacinthus_v1_len.tab > ../analyses/Ahyacinthus_v1_G.tab\n",
      "!paste ../analyses/Ahyacinthus_v1_len.tab \\\n",
      "../analyses/Ahyacinthus_v1_CG.tab \\\n",
      "../analyses/Ahyacinthus_v1_C.tab \\\n",
      "../analyses/Ahyacinthus_v1_G.tab \\\n",
      "> ../analyses/Ahyacinthus_v1_C-G.tab\n",
      "!head -1 ../analyses/Ahyacinthus_v1_C-G.tab\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig27\t\tCAAAATTCCAGCACTCCGTTTTGCATGGTAAACTTGTCTTAGTAGGACACTGTGGAAGATGTACAGCGCAAGACATCACAGTTGCAAGCGCCGACGAACAGCTGTTAAACTCTCCTCTCATATTCTCGAACAAACCAAATATTTCTTCCTCTCTGTTGTTGCTAACCTTTGAATATATGAAGCTGGCATTAGCACAGGACTCAAAGTTTCCGCCGAGCAGTTT\t223\t8\t55\t42\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://eagle.fish.washington.edu/cnidarian/skitch/BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk '{print $1, \"\\t\", (($4)/($5*$6))*(($3**2)/($3-1))}' \\\n",
      "../analyses/Ahyacinthus_v1_C-G.tab > ../analyses/Ahyacinthus_v1_CpG.tab\n",
      "!head ../analyses/Ahyacinthus_v1_CpG.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig27 \t 0.775773\r\n",
        "contig88 \t 0.459903\r\n",
        "contig100 \t 0.254614\r\n",
        "contig211 \t 0.885658\r\n",
        "contig405 \t 0.689373\r\n",
        "contig443 \t 1.34126\r\n",
        "contig470 \t 0.323368\r\n",
        "contig503 \t 0.941889\r\n",
        "contig583 \t 0.625727\r\n",
        "contig590 \t 1.21135\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls ../analyses/A*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "../analyses/Ahyacinthus_sprot.tab  ../analyses/Ahyacinthus_v1_CG.tab\r\n",
        "../analyses/Ahyacinthus_v1.tab     ../analyses/Ahyacinthus_v1_CpG.tab\r\n",
        "../analyses/Ahyacinthus_v1_C-G.tab ../analyses/Ahyacinthus_v1_G.tab\r\n",
        "../analyses/Ahyacinthus_v1_C.tab   ../analyses/Ahyacinthus_v1_len.tab\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Join with GO Slim"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head ../analyses/Ahyacinthus_GOslim.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ContigID\tGOSlim_bin\r",
        "\r\n",
        "contig70128\tother biological processes\r",
        "\r\n",
        "contig82454\tDNA metabolism\r",
        "\r\n",
        "contig174510\tprotein metabolism\r",
        "\r\n",
        "contig209629\tother biological processes\r",
        "\r\n",
        "contig10816_153279\tsignal transduction\r",
        "\r\n",
        "contig109222\ttransport\r",
        "\r\n",
        "contig79068\tsignal transduction\r",
        "\r\n",
        "contig119835\tstress response\r",
        "\r\n",
        "contig110229\ttransport\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!tr ',' \"\\t\" <../data/Piura_v1_GOslim.csv> ../data/Piura_v1_GOslim.tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sort ../analyses/Ahyacinthus_GOslim.tab | tail -n +2 > ../analyses/Ahyacinthus_GOslim.sorted\n",
      "!head ../analyses/Ahyacinthus_GOslim.sorted\n",
      "!awk -F $'\\t' '{print $1, \"\\t\", $2}' ../analyses/Ahyacinthus_GOslim.sorted > ../analyses/Ahyacinthus_GOslim.sortedtab\n",
      "!head ../analyses/Ahyacinthus_GOslim.sortedtab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig100010\tcell adhesion\r",
        "\r\n",
        "contig100010\tcell-cell signaling\r",
        "\r\n",
        "contig100010\tdevelopmental processes\r",
        "\r\n",
        "contig100021_110093_105915\tDNA metabolism\r",
        "\r\n",
        "contig100021_110093_105915\tprotein metabolism\r",
        "\r\n",
        "contig100025\tdevelopmental processes\r",
        "\r\n",
        "contig100040\tRNA metabolism\r",
        "\r\n",
        "contig100040\tdevelopmental processes\r",
        "\r\n",
        "contig100055\ttransport\r",
        "\r\n",
        "contig100067\tdevelopmental processes\r",
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig100010 \t cell adhesion\r",
        "\r\n",
        "contig100010 \t cell-cell signaling\r",
        "\r\n",
        "contig100010 \t developmental processes\r",
        "\r\n",
        "contig100021_110093_105915 \t DNA metabolism\r",
        "\r\n",
        "contig100021_110093_105915 \t protein metabolism\r",
        "\r\n",
        "contig100025 \t developmental processes\r",
        "\r\n",
        "contig100040 \t RNA metabolism\r",
        "\r\n",
        "contig100040 \t developmental processes\r",
        "\r\n",
        "contig100055 \t transport\r",
        "\r\n",
        "contig100067 \t developmental processes\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sort ../analyses/Ahyacinthus_v1_CpG.tab > ../analyses/Ahyacinthus_v1_CpG.sorted\n",
      "!head ../analyses/Ahyacinthus_v1_CpG.sorted\n",
      "#!awk -F $'\\t' '{print $1, \"\\t\", $2}' ../data/Piura_v1_CpG.sorted > ../data/Piura_v1_CpG.sortedtab\n",
      "#!head ../data/Piura_v1_CpG.sortedtab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contig100 \t 0.254614\r\n",
        "contig100001 \t 0.431531\r\n",
        "contig100008 \t 0.276093\r\n",
        "contig100010 \t 0.476931\r\n",
        "contig100021_110093_105915 \t 2.0758\r\n",
        "contig100025 \t 0.299187\r\n",
        "contig100026 \t 1.0599\r\n",
        "contig100030 \t 0.854552\r\n",
        "contig100031 \t 0.64616\r\n",
        "contig100038_111047 \t 1.60515\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!join -t $'\\t' ../analyses/Ahyacinthus_v1_CpG.sorted ../analyses/Ahyacinthus_GOslim.sortedtab \\\n",
      "> ../analyses/Ahyacinthus_v1_CpG_GOslim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Graph"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ahya = pd.read_table('../analyses/Ahyacinthus_v1_CpG_GOslim', header=None)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ahya"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 25258 entries, 0 to 25257\n",
        "Data columns (total 3 columns):\n",
        "0    25258  non-null values\n",
        "1    25258  non-null values\n",
        "2    25258  non-null values\n",
        "dtypes: float64(1), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 25258 entries, 0 to 25257\n",
        "Data columns (total 3 columns):\n",
        "0    25258  non-null values\n",
        "1    25258  non-null values\n",
        "2    25258  non-null values\n",
        "dtypes: float64(1), object(2)"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ahya.groupby(2)[1].mean().plot(kind='barh', color=list('myb'))\n",
      "plt.axis([0.7, 0.9, 0, 15])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "[0.7, 0.9, 0, 15]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PArTPdbG8Sl67nAchhL4E3W6E4/fff/9JXnW1llL+U5w5c2YymX+kNZ9zpYWL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\nB0IIIYS63P8DpD4wvYNzZzoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10760f290>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pandas density plot\n",
      "Ahya[1].plot(kind='kde', linewidth=3);\n",
      "plt.axis([0, 1.5, 0, 1.9])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "[0, 1.5, 0, 1.9]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1076bbbd0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#using whole transcriptome data \n",
      "%matplotlib inline\n",
      "\n",
      "import matplotlib.pyplot as plt \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ahya_all = pd.read_table('../analyses/Ahyacinthus_v1_CpG.tab', header=None)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pandas density plot\n",
      "Ahya_all[1].plot(kind='kde', linewidth=3);\n",
      "plt.axis([0, 1.5, 0, 1.9])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[0, 1.5, 0, 1.9]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a82e190>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}