{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating CpG ratio for the *Stylophora pistillata* transcriptome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workflow calculates CpG ratio, or CpG O/E, for contigs in the *Stylophora pistillata* [transcriptome](http://data.centrescientifique.mc/Data/454Isotigs.fas.zip). CpG ratio is an estimate of germline DNA methylation.\n",
    "\n",
    "This workflow is an extension of another IPython notebook workflow, `Spist_blast_anno.ipynb`, that generates an annotation of the same transcriptome. This workflow assumes that you have created the directories and files specified in the annotation workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jd/Documents/Projects/Coral-CpG-ratio-MS/data/Spist\n"
     ]
    }
   ],
   "source": [
    "cd ../data/Spist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Spi_contig00032  gene=isogroup00001  length=1566\n",
      "GgATCCATCGAAAGAAAAaGTCGTAGTTGACAGTGATTGCATGACTTCTGTACAACGTCCGATTAGTTTTtGCGTTTTATTGTCTtCTTTTGTACTGgAAATTCGACTCTAGCAAGTTGATTTCGTTTAGTTATGTCTGGCATTGCTTTGGGCAGATTATCAGAAGAGAGAAAAGCTTGGCGTAAAGACcATCCTTTCGGATTTGTGGCCAAACCGGTTAAAAATCCTGATGGTACTCTAAATCTGATGAACTGGGAATGTGCGATCCCaGGAAAGAAAGCGACCCCATGGGAAGGAGGCTCTTTCAAATTGAAGATGATATTTAAAGACGACTATCCATCCTCTCCGCCAAAATGTAAATTTGATCCTCCAATTTTCCATCCTAATGTATACCCATCTGGCACAGTGTGTCTGTCTCTTCTAGATGAAGAGAAAGACTGGAGACCTGCCGTCACTATAAAACAGATTTTATTGGGAATTCAAGACTTACTGAaTGATCCAAaCaTAaGaGATCCAGCTCAAGCAGAAGCATACaCCATTTACTGCCAAAACAGATCAGAATATGAGAAACGAGTCAGAAGTCAAGCTGCAAaGTTCTCAAGTTCATAGTGAATAAAGAAAAATATAAAATACTTCGCTAGCAGACGTGTAGGTTAACaGTGACTGGCAGAAATTGGATTTATTTTTtCTCcGTTtGTAAacAaCAaTGTGGAGTGGAGCAaGAATTCATCCgTAACTGCGTTAATACCACAGCTGTTGTTGTGAATTGGAaGGACAATCATGTCTGTTATTTtAGGAAGTTTCACAATGTACAGTTCAACTAACTTATACTAAAAAaCAGCTAAAGTtCACCTTAGTCTATGTtAGTTTtGTTAAAGCTTCTCTGTAgAAGGTTTGGGAGGgTtGTAATGGCTGTAACTATTCAGGgAGATCTGTCAAAGTACACTCTCTAATCCCATTGTTTCTTAAGGAATGAGTCATTATTTATTGTCTGGTGGGACGGaGGaTTTTAATCGTGTCACAATAAAATTTACCTGATCTTCCCatAagACCTGTAGTAATCTAaTGGTCCCTGTCATTGGCAGTCAaTTTTCTATAGTTTCCTGTTAACCTCTGTTAGCTACAACTGATGCCCCcTCTGTACCCCCCTAAAATTGTGTTCCCCCAAAgAAaTCCTTCCACCCACCCCATaCCTCCAGGTGATAACTAATGActGGTCCTTAATTCATTCTCTTTTTTTtGTTCTTGATTATTTGACTAAACCAAAGGCGatAAGTAAGATATACCCcAAtACGATTTGACGAACAATCTCTAGATTTTTTATTtAATCTTTAGTTGTAGATGTAATCAGAGGTGCAAAGCTTTACTGTTTtGAGTGTAAAGTGCCTGTTTCTAGGGATCCAGTGAAACCAGTAGTGTTGGGATTGTAAAGAGaTCAAAGAaTACCTCAAAGCACCAATCATAATGCTCTAATGTTGACAACTGCTAATGATTGGTtCAGCCTaTGGCTTTCATTAAAAaCGTCAAAAAaTAACGCTTCACTTCATTGCAAGTGATTCAGGTGGa\n",
      "\n",
      "number of seqs =\n",
      "15052\n"
     ]
    }
   ],
   "source": [
    "#fasta file\n",
    "!head -2 Spist.fasta\n",
    "!echo \n",
    "!echo number of seqs =\n",
    "!fgrep -c \">\" Spist.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "awk: cannot open Spist.fasta (No such file or directory)\r\n"
     ]
    }
   ],
   "source": [
    "#Just printing first line w/out comments and looking at contig names\n",
    "!awk '{print $1}' Spist.fasta > Spist2.fasta\n",
    "!head -10 Spist2.fasta\n",
    "!tail -10 Spist2.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Converted 15052 FASTA records in 30104 lines to tabular format\r\n",
      "Total sequence length: 16227896\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#Converting FASTA to tabular format and placing output file in analyses directory\n",
    "!perl -e '$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",
    "Spist2.fasta > ../../analyses/Spist/fasta2tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jay/Documents/fish546-2015/Analyses/Spist\n"
     ]
    }
   ],
   "source": [
    "cd ../../analyses/Spist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032\t\tGgATCCATCGAAAGAAAAaGTCGTAGTTGACAGTGATTGCATGACTTCTGTACAACGTCCGATTAGTTTTtGCGTTTTATTGTCTtCTTTTGTACTGgAAATTCGACTCTAGCAAGTTGATTTCGTTTAGTTATGTCTGGCATTGCTTTGGGCAGATTATCAGAAGAGAGAAAAGCTTGGCGTAAAGACcATCCTTTCGGATTTGTGGCCAAACCGGTTAAAAATCCTGATGGTACTCTAAATCTGATGAACTGGGAATGTGCGATCCCaGGAAAGAAAGCGACCCCATGGGAAGGAGGCTCTTTCAAATTGAAGATGATATTTAAAGACGACTATCCATCCTCTCCGCCAAAATGTAAATTTGATCCTCCAATTTTCCATCCTAATGTATACCCATCTGGCACAGTGTGTCTGTCTCTTCTAGATGAAGAGAAAGACTGGAGACCTGCCGTCACTATAAAACAGATTTTATTGGGAATTCAAGACTTACTGAaTGATCCAAaCaTAaGaGATCCAGCTCAAGCAGAAGCATACaCCATTTACTGCCAAAACAGATCAGAATATGAGAAACGAGTCAGAAGTCAAGCTGCAAaGTTCTCAAGTTCATAGTGAATAAAGAAAAATATAAAATACTTCGCTAGCAGACGTGTAGGTTAACaGTGACTGGCAGAAATTGGATTTATTTTTtCTCcGTTtGTAAacAaCAaTGTGGAGTGGAGCAaGAATTCATCCgTAACTGCGTTAATACCACAGCTGTTGTTGTGAATTGGAaGGACAATCATGTCTGTTATTTtAGGAAGTTTCACAATGTACAGTTCAACTAACTTATACTAAAAAaCAGCTAAAGTtCACCTTAGTCTATGTtAGTTTtGTTAAAGCTTCTCTGTAgAAGGTTTGGGAGGgTtGTAATGGCTGTAACTATTCAGGgAGATCTGTCAAAGTACACTCTCTAATCCCATTGTTTCTTAAGGAATGAGTCATTATTTATTGTCTGGTGGGACGGaGGaTTTTAATCGTGTCACAATAAAATTTACCTGATCTTCCCatAagACCTGTAGTAATCTAaTGGTCCCTGTCATTGGCAGTCAaTTTTCTATAGTTTCCTGTTAACCTCTGTTAGCTACAACTGATGCCCCcTCTGTACCCCCCTAAAATTGTGTTCCCCCAAAgAAaTCCTTCCACCCACCCCATaCCTCCAGGTGATAACTAATGActGGTCCTTAATTCATTCTCTTTTTTTtGTTCTTGATTATTTGACTAAACCAAAGGCGatAAGTAAGATATACCCcAAtACGATTTGACGAACAATCTCTAGATTTTTTATTtAATCTTTAGTTGTAGATGTAATCAGAGGTGCAAAGCTTTACTGTTTtGAGTGTAAAGTGCCTGTTTCTAGGGATCCAGTGAAACCAGTAGTGTTGGGATTGTAAAGAGaTCAAAGAaTACCTCAAAGCACCAATCATAATGCTCTAATGTTGACAACTGCTAATGATTGGTtCAGCCTaTGGCTTTCATTAAAAaCGTCAAAAAaTAACGCTTCACTTCATTGCAAGTGATTCAGGTGGa\r\n",
      "Spi_contig00035\t\tAGCAGAGATCTATCAGACGACTATGTTACGACATGAAAaCCTTTTAGGCTTtGTAGCTGCTGACAaCAAaGATAATGGTGCCTGGACACAGCTTTGGCTGGTTACAGATTATTTagAAAGGGgCTCATTGTATGACTATCTTCAACTTGTTACCCTGAATGTTGAATCCATGCTGAAGTTAGCAGTGTCAATAGCCAGTGGACTTGCTCATCTCCACATtGAGATtGTAGGCACCCAAGgAAAGCCAGCTATTGCACATCGTGACTTAAAAaGTAAAAaCATTCTGGTTAAAAGAAATGGGACATGTTGCATAGGTGATTTGGGTCTTGCTGTTCGTCATAGTTCCATTACTGACACAGTTGATGTTCCTCCTGGGAACAGAgTTGGTACAAAGCGCTACATGCCACCTGAATTTTtAGATGACAATATACAGGTGCGGCACTTTGATGCATACAAGCGAGGTGATATGTATGCATTTGGTCTAGTGTTATGGGAAaTTGCAAGGACgTGTGTTTGTGGTGGATTGTGTGATGAATATCAGCTGCCCTACTATGACAGAGTTCCCTGTGATCCCAGCATTGAAGAtATGAGAAAGGTGGTCTGTGTGGAGAGGTATCGCCCTGCCTTTCCAAATAGGTGGATACAAGATCAGACACTACAGTCTATGTCCAAGTTGATGAAAGAATGTTGGTATGCCAATAGTGCTGCAAGACTTACAGCTCTCCGTGTTAAGAAGACTCTTACAACAcTGTGTCAGTTACATGATGTTGATATTATGGTTTAGAGGGaTCCATCAaGAAAACTCATGTAAAACAAGTgCAAGATGGGTGTATCACCAGGACTTATATAACAGGAGGAACCTCCTGATAATCCTTGCAGGCAATTTGTAGGTCCTGTAATGTGATACAGTACATGTCTACCATCATGCTATTGgTTTGCAGTTCAAACaGCAATGCTGTCTTTtATTTTTtGATAAAGCCATGAAGAATAAACGCGACAGAGAAAaTTTATGACTTCCTAGTTAACAAGAAGCATTCtCATTTTTTTTTTTTtttGCCTGCATAATTcTTGtATTTATATCCACAGCAACTGTCACTGACTCAAAGTgTtGgtAaTCatCAGCAACAAAGAACCCAACTTGgTTTTATGAAAGCACGTACATtCAAATGAGCAATTTAAATTGAAAACCTAAAAGGACATATCCATGTGCAACAACTGTAGAACTATGTATGAGCAGTGTCCACTATTCTCCTCCTTAGTTTATTTTAGATGTTTCGAATGAGAATACAGAATAGAGCTTCATCCTTTTGACATTGAAAGCATTTCATACTCAAGTATtGAAGGGTATGATTCTTTGTTGTGAGGAACGACACTCACCAAGTAATCCTCAAAATATTTAAGAGCATACATGGATTTTACCAAAAGATGTGGGTTGGTCATATCTACATTAAGAATTTGTTACAATAGTAAGGTAGTTGCAGTGCTGTAGCTAGCTCTTAGATGAAGCGTGTTTATTGCATTCAAATTGAAATGTTATTGTTTAAAGTGATTCTCAAATTAAAAGACATTGTATTTTTAGACAACTTTTTATCTTCGAAAACCCAGTGGTGTAAGGTAATTACATTATTTTATGTTCATAACAAATACATTTTAACTGTATTCCACCAAACAATGTGAAAGGGGCAGTTTAAGAATTAATGAAATCTTTCTTGATACCCGTTCTtcAGAACGCTTTATAGTATCTTCTTTATAGTTGTAAATAAaCCGAaTACTTTGCGTGTGAGGGGCATTGAaGAAACTGAAGGGTTTTGTTTGACTCTGTTCTAACAGAAtCAAACATAATGTAATTACTCGGAACTTCATTAAACTGGTGGCC\r\n"
     ]
    }
   ],
   "source": [
    "#Checking header on new tabular format file\n",
    "!head -2 fasta2tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Added column with length of column 2 for 15052 lines.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#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",
    "fasta2tab > tab_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   15052    45156 16565757 tab_1\r\n"
     ]
    }
   ],
   "source": [
    "!wc tab_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#The file used to count Cs and Gs will only include the sequence\n",
    "!awk '{print $2}' tab_1 > tab_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This counts CGs - both cases\n",
    "!echo \"CG\" | awk -F\\[Cc][Gg] '{print NF-1}' tab_2 > CG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Counts Cs\n",
    "!echo \"C\" | awk -F\\[Cc] '{print NF-1}' tab_2 > C "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Counts Gs\n",
    "!echo \"G\" | awk -F\\[Gg] '{print NF-1}' tab_2 > G "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032\t\tGgATCCATCGAAAGAAAAaGTCGTAGTTGACAGTGATTGCATGACTTCTGTACAACGTCCGATTAGTTTTtGCGTTTTATTGTCTtCTTTTGTACTGgAAATTCGACTCTAGCAAGTTGATTTCGTTTAGTTATGTCTGGCATTGCTTTGGGCAGATTATCAGAAGAGAGAAAAGCTTGGCGTAAAGACcATCCTTTCGGATTTGTGGCCAAACCGGTTAAAAATCCTGATGGTACTCTAAATCTGATGAACTGGGAATGTGCGATCCCaGGAAAGAAAGCGACCCCATGGGAAGGAGGCTCTTTCAAATTGAAGATGATATTTAAAGACGACTATCCATCCTCTCCGCCAAAATGTAAATTTGATCCTCCAATTTTCCATCCTAATGTATACCCATCTGGCACAGTGTGTCTGTCTCTTCTAGATGAAGAGAAAGACTGGAGACCTGCCGTCACTATAAAACAGATTTTATTGGGAATTCAAGACTTACTGAaTGATCCAAaCaTAaGaGATCCAGCTCAAGCAGAAGCATACaCCATTTACTGCCAAAACAGATCAGAATATGAGAAACGAGTCAGAAGTCAAGCTGCAAaGTTCTCAAGTTCATAGTGAATAAAGAAAAATATAAAATACTTCGCTAGCAGACGTGTAGGTTAACaGTGACTGGCAGAAATTGGATTTATTTTTtCTCcGTTtGTAAacAaCAaTGTGGAGTGGAGCAaGAATTCATCCgTAACTGCGTTAATACCACAGCTGTTGTTGTGAATTGGAaGGACAATCATGTCTGTTATTTtAGGAAGTTTCACAATGTACAGTTCAACTAACTTATACTAAAAAaCAGCTAAAGTtCACCTTAGTCTATGTtAGTTTtGTTAAAGCTTCTCTGTAgAAGGTTTGGGAGGgTtGTAATGGCTGTAACTATTCAGGgAGATCTGTCAAAGTACACTCTCTAATCCCATTGTTTCTTAAGGAATGAGTCATTATTTATTGTCTGGTGGGACGGaGGaTTTTAATCGTGTCACAATAAAATTTACCTGATCTTCCCatAagACCTGTAGTAATCTAaTGGTCCCTGTCATTGGCAGTCAaTTTTCTATAGTTTCCTGTTAACCTCTGTTAGCTACAACTGATGCCCCcTCTGTACCCCCCTAAAATTGTGTTCCCCCAAAgAAaTCCTTCCACCCACCCCATaCCTCCAGGTGATAACTAATGActGGTCCTTAATTCATTCTCTTTTTTTtGTTCTTGATTATTTGACTAAACCAAAGGCGatAAGTAAGATATACCCcAAtACGATTTGACGAACAATCTCTAGATTTTTTATTtAATCTTTAGTTGTAGATGTAATCAGAGGTGCAAAGCTTTACTGTTTtGAGTGTAAAGTGCCTGTTTCTAGGGATCCAGTGAAACCAGTAGTGTTGGGATTGTAAAGAGaTCAAAGAaTACCTCAAAGCACCAATCATAATGCTCTAATGTTGACAACTGCTAATGATTGGTtCAGCCTaTGGCTTTCATTAAAAaCGTCAAAAAaTAACGCTTCACTTCATTGCAAGTGATTCAGGTGGa\t1566\t28\t297\t305\r\n"
     ]
    }
   ],
   "source": [
    "#Combining counts\n",
    "!paste tab_1 \\\n",
    "CG \\\n",
    "C \\\n",
    "G \\\n",
    "> comb\n",
    "!head -1 comb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating CpGo/e based on [Gavery and Roberts (2010)](http://www.biomedcentral.com/1471-2164/11/483)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"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\" alt=\"BMC_Genomics___Full_text___DNA_methylation_patterns_provide_insight_into_epigenetic_regulation_in_the_Pacific_oyster__Crassostrea_gigas__1A0683A5.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!awk '{print $1, \"\\t\", (($4)/($5*$6))*(($3^2)/($3-1))}' comb > ID_CpG #use ^ instead of ** for exponent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032 \t 0.484363\n",
      "Spi_contig00035 \t 0.335179\n",
      "Spi_contig00040 \t 0.854266\n",
      "Spi_contig00044 \t 0.867137\n",
      "Spi_contig00046 \t 0.196592\n",
      "Spi_contig00075 \t 0.871739\n",
      "Spi_contig00091 \t 0.937628\n",
      "Spi_contig00094 \t 0.131292\n",
      "Spi_contig00095 \t 0.684932\n",
      "Spi_contig00098 \t 0.584255\n",
      " 15052  30104 402540 ID_CpG\n"
     ]
    }
   ],
   "source": [
    "!head ID_CpG\n",
    "!wc ID_CpG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now joining CpG to annotation, but first must sort files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00035\tsp\tP80204\tTGFR1_RAT\t69.77\t258\t78\t0\t2\t775\t244\t501\t2e-129\t  397\r\n",
      "Spi_contig00040\tsp\tQ35101\tCOX1_METSE\t82.85\t449\t72\t1\t605\t1936\t70\t518\t0.0\t  665\r\n",
      "Spi_contig00046\tsp\tE7EY42\tPTSS2_DANRE\t56.41\t234\t101\t1\t19\t720\t26\t258\t6e-91\t  281\r\n",
      "Spi_contig00075\tsp\tQ37556\tNU1M_METSE\t80.16\t247\t48\t1\t1719\t2456\t29\t275\t3e-104\t  333\r\n",
      "Spi_contig00094\tsp\tP00519\tABL1_HUMAN\t48.15\t54\t28\t0\t2494\t2333\t443\t496\t2e-09\t65.1\r\n",
      "Spi_contig00095\tsp\tP20693\tFCER2_MOUSE\t28.57\t203\t122\t5\t831\t229\t128\t309\t6e-18\t87.0\r\n",
      "Spi_contig00834\tsp\tP19615\tMYP_STRPU\t32.94\t598\t321\t18\t50\t1741\t132\t683\t2e-84\t  294\r\n",
      "Spi_contig04187\tsp\tQ9CYA0\tCREL2_MOUSE\t43.57\t140\t77\t2\t82\t501\t193\t330\t1e-27\t  110\r\n",
      "Spi_isotig00002\tsp\tQ5I2B1\tACTPG_OULOR\t56.49\t131\t56\t1\t1429\t1037\t7\t136\t6e-41\t  151\r\n",
      "Spi_isotig00005\tsp\tC9EIC7\tACTP1_URTCR\t51.67\t120\t57\t1\t1444\t1085\t54\t172\t2e-31\t  126\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting Ahya Uniprot/Swissprot annotation file. This file was the result of work done in another notebook: Ahya_blast_anno.ipynb\n",
    "!sort Spist_blastx_uniprot_sql.tab | tail -n +2 > Spist_blastx_uniprot_sql.tab.sorted\n",
    "!head Spist_blastx_uniprot_sql.tab.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sort: open failed: Spist_GOSlim.tab: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting GOSlim annotation file. This file was the result of work done in another notebook: Spist_blast_anno.ipynb\n",
    "!sort Spist_GOSlim.tab | tail -n +2 > Spist_GOSlim.sorted\n",
    "!head Spist_GOSlim.sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032 \t 0.484363\r\n",
      "Spi_contig00035 \t 0.335179\r\n",
      "Spi_contig00040 \t 0.854266\r\n",
      "Spi_contig00044 \t 0.867137\r\n",
      "Spi_contig00046 \t 0.196592\r\n",
      "Spi_contig00075 \t 0.871739\r\n",
      "Spi_contig00091 \t 0.937628\r\n",
      "Spi_contig00094 \t 0.131292\r\n",
      "Spi_contig00095 \t 0.684932\r\n",
      "Spi_contig00098 \t 0.584255\r\n"
     ]
    }
   ],
   "source": [
    "#Sorting CpG file\n",
    "!sort ID_CpG > ID_CpG.sorted\n",
    "!head ID_CpG.sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For this analysis, *Symbiodinium* sequences were removed. Using file generated from Spist_zoox_removal.ipynb, ID_CpG.sorted2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!join ID_CpG.sorted2 Spist_blastx_uniprot_sql.tab.sorted | awk '{print $1, \"\\t\", $2}' > Spist_cpg_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00035 \t 0.335179\n",
      "Spi_contig00040 \t 0.854266\n",
      "Spi_contig00046 \t 0.196592\n",
      "Spi_contig00075 \t 0.871739\n",
      "Spi_contig00094 \t 0.131292\n",
      "Spi_contig00095 \t 0.684932\n",
      "Spi_contig00834 \t 0.654594\n",
      "Spi_contig04187 \t 0.876088\n",
      "Spi_isotig00002 \t 0.910243\n",
      "Spi_isotig00005 \t 0.900018\n",
      "    7061   14122  189272 Spist_cpg_anno\n"
     ]
    }
   ],
   "source": [
    "!head Spist_cpg_anno\n",
    "!wc Spist_cpg_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!join ID_CpG.sorted2 Spist_GOSlim.sorted > Spist_cpg_GOslim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032 0.484363 cell cycle and proliferation\r\n",
      "Spi_contig00032 0.484363 cell organization and biogenesis\r\n",
      "Spi_contig00032 0.484363 developmental processes\r\n",
      "Spi_contig00032 0.484363 other biological processes\r\n",
      "Spi_contig00032 0.484363 protein metabolism\r\n",
      "Spi_contig00035 0.335179 cell cycle and proliferation\r\n",
      "Spi_contig00035 0.335179 cell organization and biogenesis\r\n",
      "Spi_contig00035 0.335179 death\r\n",
      "Spi_contig00035 0.335179 developmental processes\r\n",
      "Spi_contig00035 0.335179 other biological processes\r\n"
     ]
    }
   ],
   "source": [
    "!head Spist_cpg_GOslim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spi_contig00032 \t 0.484363 \t cell cycle and proliferation\r\n",
      "Spi_contig00032 \t 0.484363 \t cell organization and biogenesis\r\n",
      "Spi_contig00032 \t 0.484363 \t developmental processes  \r\n",
      "Spi_contig00032 \t 0.484363 \t other biological processes \r\n",
      "Spi_contig00032 \t 0.484363 \t protein metabolism  \r\n",
      "Spi_contig00035 \t 0.335179 \t cell cycle and proliferation\r\n",
      "Spi_contig00035 \t 0.335179 \t cell organization and biogenesis\r\n",
      "Spi_contig00035 \t 0.335179 \t death   \r\n",
      "Spi_contig00035 \t 0.335179 \t developmental processes  \r\n",
      "Spi_contig00035 \t 0.335179 \t other biological processes \r\n"
     ]
    }
   ],
   "source": [
    "#Putting tabs in between columns\n",
    "!awk '{print $1, \"\\t\", $2, \"\\t\", $3, $4, $5, $6}' Spist_cpg_GOslim > Spist_cpg_GOslim.tab\n",
    "!head Spist_cpg_GOslim.tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now time to plot data using pandas and matplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td> Spi_contig00032 </td>\n",
       "      <td> 0.484363</td>\n",
       "      <td>      cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td> Spi_contig00032 </td>\n",
       "      <td> 0.484363</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td> Spi_contig00032 </td>\n",
       "      <td> 0.484363</td>\n",
       "      <td>         developmental processes  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td> Spi_contig00032 </td>\n",
       "      <td> 0.484363</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td> Spi_contig00032 </td>\n",
       "      <td> 0.484363</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>      cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>                          death   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>         developmental processes  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>                  RNA metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>             signal transduction  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>                 stress response  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td> Spi_contig00035 </td>\n",
       "      <td> 0.335179</td>\n",
       "      <td>                      transport   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td> Spi_contig00040 </td>\n",
       "      <td> 0.854266</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td> Spi_contig00040 </td>\n",
       "      <td> 0.854266</td>\n",
       "      <td>                      transport   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td> Spi_contig00046 </td>\n",
       "      <td> 0.196592</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td> Spi_contig00075 </td>\n",
       "      <td> 0.871739</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td> Spi_contig00075 </td>\n",
       "      <td> 0.871739</td>\n",
       "      <td>                      transport   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>                   cell adhesion  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>      cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>                          death   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>         developmental processes  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>                  DNA metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td> Spi_contig00094 </td>\n",
       "      <td> 0.131292</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13603</th>\n",
       "      <td> Spi_isotig14901 </td>\n",
       "      <td> 0.815894</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13604</th>\n",
       "      <td> Spi_isotig14913 </td>\n",
       "      <td> 0.841359</td>\n",
       "      <td>             signal transduction  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13605</th>\n",
       "      <td> Spi_isotig14926 </td>\n",
       "      <td> 0.391261</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td> Spi_isotig14932 </td>\n",
       "      <td> 0.626017</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13607</th>\n",
       "      <td> Spi_isotig14936 </td>\n",
       "      <td> 0.253558</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13608</th>\n",
       "      <td> Spi_isotig14955 </td>\n",
       "      <td> 0.534380</td>\n",
       "      <td>      cell cycle and proliferation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13609</th>\n",
       "      <td> Spi_isotig14955 </td>\n",
       "      <td> 0.534380</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13610</th>\n",
       "      <td> Spi_isotig14955 </td>\n",
       "      <td> 0.534380</td>\n",
       "      <td>         developmental processes  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13611</th>\n",
       "      <td> Spi_isotig14956 </td>\n",
       "      <td> 0.431596</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13612</th>\n",
       "      <td> Spi_isotig14959 </td>\n",
       "      <td> 0.958655</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13613</th>\n",
       "      <td> Spi_isotig14962 </td>\n",
       "      <td> 0.348816</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13614</th>\n",
       "      <td> Spi_isotig14962 </td>\n",
       "      <td> 0.348816</td>\n",
       "      <td>                      transport   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13615</th>\n",
       "      <td> Spi_isotig14966 </td>\n",
       "      <td> 0.683519</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13616</th>\n",
       "      <td> Spi_isotig14969 </td>\n",
       "      <td> 0.480887</td>\n",
       "      <td>                  DNA metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13617</th>\n",
       "      <td> Spi_isotig14969 </td>\n",
       "      <td> 0.480887</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13618</th>\n",
       "      <td> Spi_isotig14969 </td>\n",
       "      <td> 0.480887</td>\n",
       "      <td>             signal transduction  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13619</th>\n",
       "      <td> Spi_isotig14969 </td>\n",
       "      <td> 0.480887</td>\n",
       "      <td>                 stress response  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13620</th>\n",
       "      <td> Spi_isotig14970 </td>\n",
       "      <td> 0.269739</td>\n",
       "      <td>  cell organization and biogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13621</th>\n",
       "      <td> Spi_isotig14970 </td>\n",
       "      <td> 0.269739</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13622</th>\n",
       "      <td> Spi_isotig14970 </td>\n",
       "      <td> 0.269739</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13623</th>\n",
       "      <td> Spi_isotig14970 </td>\n",
       "      <td> 0.269739</td>\n",
       "      <td>                  RNA metabolism  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13624</th>\n",
       "      <td> Spi_isotig14970 </td>\n",
       "      <td> 0.269739</td>\n",
       "      <td>                 stress response  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13625</th>\n",
       "      <td> Spi_isotig14981 </td>\n",
       "      <td> 0.202625</td>\n",
       "      <td>             signal transduction  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13626</th>\n",
       "      <td> Spi_isotig14993 </td>\n",
       "      <td> 0.663237</td>\n",
       "      <td>                          death   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13627</th>\n",
       "      <td> Spi_isotig14993 </td>\n",
       "      <td> 0.663237</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13628</th>\n",
       "      <td> Spi_isotig14993 </td>\n",
       "      <td> 0.663237</td>\n",
       "      <td>                 stress response  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13629</th>\n",
       "      <td> Spi_isotig14993 </td>\n",
       "      <td> 0.663237</td>\n",
       "      <td>                      transport   </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13630</th>\n",
       "      <td> Spi_isotig14997 </td>\n",
       "      <td> 0.264544</td>\n",
       "      <td>        other metabolic processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13631</th>\n",
       "      <td> Spi_isotig14998 </td>\n",
       "      <td> 0.575532</td>\n",
       "      <td>       other biological processes </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13632</th>\n",
       "      <td> Spi_isotig15025 </td>\n",
       "      <td> 0.885773</td>\n",
       "      <td>              protein metabolism  </td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0         1                                  2\n",
       "0      Spi_contig00032   0.484363       cell cycle and proliferation\n",
       "1      Spi_contig00032   0.484363   cell organization and biogenesis\n",
       "2      Spi_contig00032   0.484363          developmental processes  \n",
       "3      Spi_contig00032   0.484363        other biological processes \n",
       "4      Spi_contig00032   0.484363               protein metabolism  \n",
       "5      Spi_contig00035   0.335179       cell cycle and proliferation\n",
       "6      Spi_contig00035   0.335179   cell organization and biogenesis\n",
       "7      Spi_contig00035   0.335179                           death   \n",
       "8      Spi_contig00035   0.335179          developmental processes  \n",
       "9      Spi_contig00035   0.335179        other biological processes \n",
       "10     Spi_contig00035   0.335179         other metabolic processes \n",
       "11     Spi_contig00035   0.335179               protein metabolism  \n",
       "12     Spi_contig00035   0.335179                   RNA metabolism  \n",
       "13     Spi_contig00035   0.335179              signal transduction  \n",
       "14     Spi_contig00035   0.335179                  stress response  \n",
       "15     Spi_contig00035   0.335179                       transport   \n",
       "16     Spi_contig00040   0.854266         other metabolic processes \n",
       "17     Spi_contig00040   0.854266                       transport   \n",
       "18     Spi_contig00046   0.196592         other metabolic processes \n",
       "19     Spi_contig00075   0.871739         other metabolic processes \n",
       "20     Spi_contig00075   0.871739                       transport   \n",
       "21     Spi_contig00094   0.131292                    cell adhesion  \n",
       "22     Spi_contig00094   0.131292       cell cycle and proliferation\n",
       "23     Spi_contig00094   0.131292   cell organization and biogenesis\n",
       "24     Spi_contig00094   0.131292                           death   \n",
       "25     Spi_contig00094   0.131292          developmental processes  \n",
       "26     Spi_contig00094   0.131292                   DNA metabolism  \n",
       "27     Spi_contig00094   0.131292        other biological processes \n",
       "28     Spi_contig00094   0.131292         other metabolic processes \n",
       "29     Spi_contig00094   0.131292               protein metabolism  \n",
       "...                 ...       ...                                ...\n",
       "13603  Spi_isotig14901   0.815894         other metabolic processes \n",
       "13604  Spi_isotig14913   0.841359              signal transduction  \n",
       "13605  Spi_isotig14926   0.391261               protein metabolism  \n",
       "13606  Spi_isotig14932   0.626017               protein metabolism  \n",
       "13607  Spi_isotig14936   0.253558         other metabolic processes \n",
       "13608  Spi_isotig14955   0.534380       cell cycle and proliferation\n",
       "13609  Spi_isotig14955   0.534380   cell organization and biogenesis\n",
       "13610  Spi_isotig14955   0.534380          developmental processes  \n",
       "13611  Spi_isotig14956   0.431596               protein metabolism  \n",
       "13612  Spi_isotig14959   0.958655   cell organization and biogenesis\n",
       "13613  Spi_isotig14962   0.348816         other metabolic processes \n",
       "13614  Spi_isotig14962   0.348816                       transport   \n",
       "13615  Spi_isotig14966   0.683519               protein metabolism  \n",
       "13616  Spi_isotig14969   0.480887                   DNA metabolism  \n",
       "13617  Spi_isotig14969   0.480887               protein metabolism  \n",
       "13618  Spi_isotig14969   0.480887              signal transduction  \n",
       "13619  Spi_isotig14969   0.480887                  stress response  \n",
       "13620  Spi_isotig14970   0.269739   cell organization and biogenesis\n",
       "13621  Spi_isotig14970   0.269739        other biological processes \n",
       "13622  Spi_isotig14970   0.269739         other metabolic processes \n",
       "13623  Spi_isotig14970   0.269739                   RNA metabolism  \n",
       "13624  Spi_isotig14970   0.269739                  stress response  \n",
       "13625  Spi_isotig14981   0.202625              signal transduction  \n",
       "13626  Spi_isotig14993   0.663237                           death   \n",
       "13627  Spi_isotig14993   0.663237        other biological processes \n",
       "13628  Spi_isotig14993   0.663237                  stress response  \n",
       "13629  Spi_isotig14993   0.663237                       transport   \n",
       "13630  Spi_isotig14997   0.264544         other metabolic processes \n",
       "13631  Spi_isotig14998   0.575532        other biological processes \n",
       "13632  Spi_isotig15025   0.885773               protein metabolism  \n",
       "\n",
       "[13633 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jData = pd.read_table('Spist_cpg_GOslim.tab', header=None)\n",
    "jData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAD7CAYAAADtuXeEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzMzMknjyYGZmZkl+AjPXJAGb0UKEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e4b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jData.groupby(2)[1].mean().plot(kind='barh', color=list('myb'))\n",
    "plt.axis([0.5, 0.75, -1, 14])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.484363\r\n",
      "0.335179\r\n",
      "0.854266\r\n",
      "0.867137\r\n",
      "0.196592\r\n",
      "0.871739\r\n",
      "0.937628\r\n",
      "0.131292\r\n",
      "0.684932\r\n",
      "0.584255\r\n"
     ]
    }
   ],
   "source": [
    "##To plot density curve, must use CpG data WITHOUT annotation. Also must remove \"-nan\" in order to plot.\n",
    "!awk '{print $2}' ID_CpG.sorted2 | sed '/-nan/d' > CpG\n",
    "!head CpG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td> 0.484363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1    </th>\n",
       "      <td> 0.335179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2    </th>\n",
       "      <td> 0.854266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3    </th>\n",
       "      <td> 0.867137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4    </th>\n",
       "      <td> 0.196592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5    </th>\n",
       "      <td> 0.871739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6    </th>\n",
       "      <td> 0.937628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7    </th>\n",
       "      <td> 0.131292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8    </th>\n",
       "      <td> 0.684932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9    </th>\n",
       "      <td> 0.584255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10   </th>\n",
       "      <td> 0.857523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11   </th>\n",
       "      <td> 1.046390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12   </th>\n",
       "      <td> 0.981477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13   </th>\n",
       "      <td> 0.654594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14   </th>\n",
       "      <td> 0.631948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15   </th>\n",
       "      <td> 0.798651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16   </th>\n",
       "      <td> 0.876088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17   </th>\n",
       "      <td> 0.926440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18   </th>\n",
       "      <td> 0.910725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19   </th>\n",
       "      <td> 0.910243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20   </th>\n",
       "      <td> 0.896155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21   </th>\n",
       "      <td> 0.895683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22   </th>\n",
       "      <td> 0.900018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23   </th>\n",
       "      <td> 0.844576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24   </th>\n",
       "      <td> 0.932216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25   </th>\n",
       "      <td> 0.987775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26   </th>\n",
       "      <td> 0.987220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27   </th>\n",
       "      <td> 0.908013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28   </th>\n",
       "      <td> 0.972397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29   </th>\n",
       "      <td> 0.971853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14883</th>\n",
       "      <td> 0.077346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14884</th>\n",
       "      <td> 0.995899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14885</th>\n",
       "      <td> 0.805340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14886</th>\n",
       "      <td> 0.193988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14887</th>\n",
       "      <td> 1.762730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14888</th>\n",
       "      <td> 1.116090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14889</th>\n",
       "      <td> 0.735748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14890</th>\n",
       "      <td> 0.629234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14891</th>\n",
       "      <td> 1.161310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14892</th>\n",
       "      <td> 0.552857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14893</th>\n",
       "      <td> 0.122963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14894</th>\n",
       "      <td> 0.717703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14895</th>\n",
       "      <td> 0.896540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14896</th>\n",
       "      <td> 1.407900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14897</th>\n",
       "      <td> 0.808355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14898</th>\n",
       "      <td> 0.322698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14899</th>\n",
       "      <td> 1.115180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14900</th>\n",
       "      <td> 0.466681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14901</th>\n",
       "      <td> 0.419544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14902</th>\n",
       "      <td> 0.427099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14903</th>\n",
       "      <td> 0.843343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14904</th>\n",
       "      <td> 0.790032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14905</th>\n",
       "      <td> 0.885773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14906</th>\n",
       "      <td> 0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14907</th>\n",
       "      <td> 1.242950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14908</th>\n",
       "      <td> 1.104080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14909</th>\n",
       "      <td> 0.430511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14910</th>\n",
       "      <td> 0.640071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14911</th>\n",
       "      <td> 0.899106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14912</th>\n",
       "      <td> 0.506757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14913 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "0      0.484363\n",
       "1      0.335179\n",
       "2      0.854266\n",
       "3      0.867137\n",
       "4      0.196592\n",
       "5      0.871739\n",
       "6      0.937628\n",
       "7      0.131292\n",
       "8      0.684932\n",
       "9      0.584255\n",
       "10     0.857523\n",
       "11     1.046390\n",
       "12     0.981477\n",
       "13     0.654594\n",
       "14     0.631948\n",
       "15     0.798651\n",
       "16     0.876088\n",
       "17     0.926440\n",
       "18     0.910725\n",
       "19     0.910243\n",
       "20     0.896155\n",
       "21     0.895683\n",
       "22     0.900018\n",
       "23     0.844576\n",
       "24     0.932216\n",
       "25     0.987775\n",
       "26     0.987220\n",
       "27     0.908013\n",
       "28     0.972397\n",
       "29     0.971853\n",
       "...         ...\n",
       "14883  0.077346\n",
       "14884  0.995899\n",
       "14885  0.805340\n",
       "14886  0.193988\n",
       "14887  1.762730\n",
       "14888  1.116090\n",
       "14889  0.735748\n",
       "14890  0.629234\n",
       "14891  1.161310\n",
       "14892  0.552857\n",
       "14893  0.122963\n",
       "14894  0.717703\n",
       "14895  0.896540\n",
       "14896  1.407900\n",
       "14897  0.808355\n",
       "14898  0.322698\n",
       "14899  1.115180\n",
       "14900  0.466681\n",
       "14901  0.419544\n",
       "14902  0.427099\n",
       "14903  0.843343\n",
       "14904  0.790032\n",
       "14905  0.885773\n",
       "14906  0.000000\n",
       "14907  1.242950\n",
       "14908  1.104080\n",
       "14909  0.430511\n",
       "14910  0.640071\n",
       "14911  0.899106\n",
       "14912  0.506757\n",
       "\n",
       "[14913 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To plot density curve, must use CpG data with original annotation\n",
    "CpG = pd.read_table('Spist_cpg_anno', header=None, )\n",
    "CpG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.3, 1.7, 0, 1.7]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD7CAYAAABt0P8jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndRlP/M4512U88TvnXJf5P0KvO+S5PjdzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e4b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pandas density plot\n",
    "CpG[0].plot(kind='kde', linewidth=3);\n",
    "plt.axis([-0.3, 1.7, 0, 1.7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}