ViralGenome_analysis.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import os\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt \n",
      "import pandas as pd\n",
      "import glob \n",
      "import math\n",
      "import re\n",
      "from collections import defaultdict\n",
      "from matplotlib.font_manager import FontProperties\n",
      "import subprocess\n",
      "import scipy\n",
      "from scipy.stats.stats import pearsonr \n",
      "%matplotlib inline\n",
      "matplotlib.rcParams['savefig.dpi'] = 3 * matplotlib.rcParams['savefig.dpi']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get raw data\n",
      "global outfilepath\n",
      "sampleName='jfh1v2'\n",
      "outfilepath=os.getcwd() + '/results/%s/'%sampleName\n",
      "print outfilepath"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/arrayAhome/lmartin/CLIP/results/jfh1v2/\n"
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def modifyName(filepath,newTag):\n",
      "\t# Useage: Modifies the filepath name. \n",
      "\t# Input: File path of format <path>/<name>.fastq and a string to add to the name.\n",
      "\t# Output: Returns the modified path of type <old path>_<new modifier>.fastq\n",
      "    head, tail = os.path.split(filepath)\n",
      "    oldname = tail.split('.')[0]\n",
      "    newName = head+\"/\"+oldname+\"_\"+newTag\n",
      "    return newName\n",
      "\n",
      "def runBowtie(fastqFiles,viralGenome):\n",
      "    # Useage: Short read mapping to reference (hg19).\n",
      "    # Input: Fastq files of replicates (not paired end).\n",
      "    # Output: Path to samfile for each read.\n",
      "    program = 'bowtie2'\n",
      "    mappedReads=[]\n",
      "    unMappedReads=[]\n",
      "    print \"Performing Bowtie...\"\n",
      "    # Parameters\n",
      "    # In -k mode, Bowtie 2 searches for up to N distinct, valid alignments for each read.\n",
      "    # N equals the integer specified with the -k parameter. \n",
      "    # That is, if -k 2 is specified, Bowtie 2 will search for at most 2 distinct alignments\n",
      "    # See http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml\n",
      "    for infastq in fastqFiles:\n",
      "\n",
      "        index = os.getcwd() + '/docs/%s/%s'%(viralGenome,viralGenome)\n",
      "        outfile = modifyName(infastq,\"mappedTo%s.sam\"%viralGenome)\n",
      "        unmapped = modifyName(infastq,\"notMappedTo%s.fastq\"%viralGenome)\n",
      "        \n",
      "        print \"Input file:\"\n",
      "        print infastq \n",
      "        print 'Genome index:'\n",
      "        print index\n",
      "        print \"Output file (mapped):\"\n",
      "        print outfile\n",
      "        print \"Output file (unmapped)\"\n",
      "        print unmapped\n",
      "        # Remove -a\n",
      "        proc = subprocess.Popen([program,'-x', index,'-U',infastq,'--un',unmapped,'-S',outfile],stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n",
      "        out, err = proc.communicate()\n",
      "        result = out.decode()\n",
      "        error = err.decode()\n",
      "        print \"Result : \",result \n",
      "        print \"Error : \",error\n",
      "        mappedReads = mappedReads + [outfile]\n",
      "        unMappedReads = unMappedReads + [unmapped]\n",
      "    return (mappedReads,unMappedReads)\n",
      "\n",
      "def runSamtools(samfiles):\n",
      "    # Useage: Samfile processing.\n",
      "    # Input: Sam files from Bowtie mapping.\n",
      "    # Output: Duplicate removed, sorted bedFiles.\n",
      "    program = 'samtools'\n",
      "    program2 = 'bamToBed'\n",
      "    outBedFiles=[]\n",
      "    \n",
      "    for samfile in samfiles:\n",
      "            \n",
      "        # Convert to bamfile\n",
      "        bamfile = samfile.replace('.sam', '.bam')  \n",
      "        proc = subprocess.Popen( [program, 'view', '-bS', '-o', bamfile, samfile])\n",
      "        proc.communicate()\n",
      "        \n",
      "        # Sort the bamfile and note that samtools sort adds the .bam handle\n",
      "        bamfile_sort = bamfile.replace('.bam', '_sorted') \n",
      "        proc2 = subprocess.Popen( [program, 'sort', bamfile, bamfile_sort])\n",
      "        proc2.communicate()\n",
      "        \n",
      "        # Convert to bedFile\n",
      "        bedFile = bamfile_sort.replace('_sorted', '_withDupes.bed')\n",
      "        outfh = open(bedFile, 'w')\n",
      "        proc3 = subprocess.Popen( [program2,'-i', bamfile_sort+'.bam'],stdout=outfh)\n",
      "        proc3.communicate()\n",
      "        \n",
      "        outBedFiles=outBedFiles+[bedFile]\n",
      "        \n",
      "    return outBedFiles\n",
      "\n",
      "# Run bowtie\n",
      "print \"Mapping to viral index.\"  \n",
      "trimmedReads5p=glob.glob(outfilepath+'*5ptrimmed.fastq')\n",
      "mappedReads,unMappedReads=runBowtie(trimmedReads5p,'jfh1')\n",
      "\n",
      "# Run Samtools\n",
      "print \"Process mapped data\"  \n",
      "mappedBedFiles=runSamtools(mappedReads)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mapping to viral index.\n",
        "Performing Bowtie..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Input file:\n",
        "/arrayAhome/lmartin/CLIP/results/hcvwt/hcvwt_R2_3ptrimmed_filter_nodupe_5ptrimmed.fastq\n",
        "Genome index:\n",
        "/arrayAhome/lmartin/CLIP/docs/jfh1/jfh1\n",
        "Output file (mapped):\n",
        "/arrayAhome/lmartin/CLIP/results/hcvwt/hcvwt_R2_3ptrimmed_filter_nodupe_5ptrimmed_mappedTojfh1.sam\n",
        "Output file (unmapped)\n",
        "/arrayAhome/lmartin/CLIP/results/hcvwt/hcvwt_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedTojfh1.fastq\n",
        "Result : "
       ]
      }
     ],
     "prompt_number": "*"
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Process - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Index\n",
      "outfilepathjfh1=os.getcwd() + '/docs/jfh1/HCV_JFHAB047639.fa'\n",
      "jfh1_genome=np.genfromtxt(outfilepathjfh1,dtype='string')\n",
      "jfh1_genome_size=len(jfh1_genome[1])\n",
      "\n",
      "# Data frames to store HCV data\n",
      "storageDFHCV=pd.DataFrame()\n",
      "indexPositions=np.arange(0,jfh1_genome_size,1)\n",
      "storeGenomeHits=pd.DataFrame(index=indexPositions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Process mapped files\n",
      "mappedBedFiles1=[]\n",
      "sampleName='jfh1v2'\n",
      "outfilepathjfh1=os.getcwd() + '/results/'+sampleName+'/'\n",
      "mappedBedFiles2=glob.glob(outfilepathjfh1+'*mappedTojfh1_withDupes.bed')\n",
      "sampleName='hcvwt'\n",
      "outfilepathjfh1=os.getcwd() + '/results/'+sampleName+'/'\n",
      "mappedBedFiles3=glob.glob(outfilepathjfh1+'*mappedTojfh1_withDupes.bed')\n",
      "mappedBedFiles=mappedBedFiles1+mappedBedFiles2+mappedBedFiles3\n",
      "\n",
      "# Global plotting stats\n",
      "bins=np.array(range(0,jfh1_genome_size,5)) # Bin the genome by 5 bp\n",
      "width=0.7*(bins[1]-bins[0]) # Bar width for plotting\n",
      "center=(bins[:-1]+bins[1:])/2 # Center position of each bin\n",
      "\n",
      "# Compute histogram for each\n",
      "coverageHistograms=[]\n",
      "for bedFile in mappedBedFiles:\n",
      "    \n",
      "    handle=os.path.split(bedFile)[1].split('_3ptrimmed')[0]\n",
      "    data=np.genfromtxt(bedFile,usecols=(1,),delimiter='\\t',dtype='int') # Read the 5' coordinate (RT stop).\n",
      "    hist,bins=np.histogram(data,bins=bins) # Compute histogram.\n",
      "    \n",
      "    storageDFHCV[handle+'_raw']=hist\n",
      "    hist=hist/float(len(data)) # Normalize to total number of reads mapped to viral genome.\n",
      "    storageDFHCV[handle+'_norm']=hist\n",
      "    coverageHistograms.append(hist)\n",
      "    \n",
      "    genomeHits=pd.DataFrame(data)\n",
      "    genomeHits.columns=['RT_stop_position']\n",
      "    hits=genomeHits.groupby('RT_stop_position').size()\n",
      "    storeGenomeHits[handle+'_rawReads']=hits\n",
      "\n",
      "storageDFHCV['Bin_Center']=center\n",
      "bases=[jfh1_genome[1][pos] for pos in center]\n",
      "storageDFHCV['Base']=bases\n",
      "storageDFHCV.index=storageDFHCV['Base']\n",
      "    \n",
      "bases=[jfh1_genome[1][pos] for pos in storeGenomeHits.index]\n",
      "storeGenomeHits['Base']=bases\n",
      "storeGenomeHits.fillna(0,inplace=True)\n",
      "saveName='JFH-1_CLIP_RawData'\n",
      "storeGenomeHits.to_csv(outfilepath+saveName)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 257
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print storeGenomeHits.groupby('Base').jfh1v2_R1_rawReads.sum()/storeGenomeHits['jfh1v2_R1_rawReads'].sum()\n",
      "print storeGenomeHits.groupby('Base').jfh1v2_R2_rawReads.sum()/storeGenomeHits['jfh1v2_R2_rawReads'].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Base\n",
        "A       0.078587\n",
        "C       0.287547\n",
        "G       0.071360\n",
        "T       0.562506\n",
        "Name: jfh1v2_R1_rawReads, dtype: float64\n",
        "Base\n",
        "A       0.067809\n",
        "C       0.293841\n",
        "G       0.066292\n",
        "T       0.572057\n",
        "Name: jfh1v2_R2_rawReads, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cutoff=5\n",
      "tmpR1=storeGenomeHits[storeGenomeHits['jfh1v2_R1_rawReads']>cutoff]\n",
      "tmpR2=storeGenomeHits[storeGenomeHits['jfh1v2_R2_rawReads']>cutoff]\n",
      "print tmpR1.groupby('Base').jfh1v2_R1_rawReads.sum()/tmpR1['jfh1v2_R1_rawReads'].sum()\n",
      "print tmpR2.groupby('Base').jfh1v2_R2_rawReads.sum()/tmpR2['jfh1v2_R2_rawReads'].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Base\n",
        "A       0.044455\n",
        "C       0.274061\n",
        "G       0.032189\n",
        "T       0.649296\n",
        "Name: jfh1v2_R1_rawReads, dtype: float64\n",
        "Base\n",
        "A       0.022937\n",
        "C       0.256262\n",
        "G       0.014237\n",
        "T       0.706565\n",
        "Name: jfh1v2_R2_rawReads, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 291
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "storeGenomeHits.tail(150)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>jfh1v2_R1_rawReads</th>\n",
        "      <th>jfh1v2_R2_rawReads</th>\n",
        "      <th>hcvwt_R1_rawReads</th>\n",
        "      <th>hcvwt_R2_rawReads</th>\n",
        "      <th>Base</th>\n",
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        "      <th>9528</th>\n",
        "      <td>  10</td>\n",
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        "      <td>  13</td>\n",
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        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "      <td>  37</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9533</th>\n",
        "      <td>  90</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9534</th>\n",
        "      <td> 174</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9535</th>\n",
        "      <td> 115</td>\n",
        "      <td> 14</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9536</th>\n",
        "      <td>  87</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9537</th>\n",
        "      <td>  13</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "      <th>9538</th>\n",
        "      <td>  52</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9539</th>\n",
        "      <td>  34</td>\n",
        "      <td> 12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9540</th>\n",
        "      <td>  38</td>\n",
        "      <td> 14</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
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        "    <tr>\n",
        "      <th>9541</th>\n",
        "      <td>  22</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
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        "      <td>  42</td>\n",
        "      <td> 21</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "      <th>9543</th>\n",
        "      <td>  18</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
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        "      <td>  28</td>\n",
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        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9545</th>\n",
        "      <td>  63</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9546</th>\n",
        "      <td>  12</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9547</th>\n",
        "      <td>  13</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
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        "    <tr>\n",
        "      <th>9548</th>\n",
        "      <td>   7</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9549</th>\n",
        "      <td>   7</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
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        "    <tr>\n",
        "      <th>9550</th>\n",
        "      <td>   7</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
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        "    <tr>\n",
        "      <th>9551</th>\n",
        "      <td>   1</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9552</th>\n",
        "      <td>   5</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9553</th>\n",
        "      <td>  18</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9554</th>\n",
        "      <td>  12</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9555</th>\n",
        "      <td>  13</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9556</th>\n",
        "      <td>   6</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9557</th>\n",
        "      <td>  98</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9558</th>\n",
        "      <td>  11</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9559</th>\n",
        "      <td>   9</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9560</th>\n",
        "      <td>  26</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9561</th>\n",
        "      <td>  29</td>\n",
        "      <td> 12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9562</th>\n",
        "      <td>  61</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9563</th>\n",
        "      <td>  17</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9564</th>\n",
        "      <td>  24</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9565</th>\n",
        "      <td>   8</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9566</th>\n",
        "      <td>  11</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9567</th>\n",
        "      <td> 104</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9568</th>\n",
        "      <td>   3</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9569</th>\n",
        "      <td>   8</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9570</th>\n",
        "      <td>  41</td>\n",
        "      <td> 12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9571</th>\n",
        "      <td>  84</td>\n",
        "      <td> 16</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9572</th>\n",
        "      <td>  31</td>\n",
        "      <td> 12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9573</th>\n",
        "      <td>  24</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9574</th>\n",
        "      <td>  64</td>\n",
        "      <td> 23</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9575</th>\n",
        "      <td>  98</td>\n",
        "      <td> 12</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9576</th>\n",
        "      <td>  39</td>\n",
        "      <td>  7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9577</th>\n",
        "      <td>  10</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9578</th>\n",
        "      <td>  38</td>\n",
        "      <td>  7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9579</th>\n",
        "      <td>  27</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9580</th>\n",
        "      <td>   2</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> G</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9581</th>\n",
        "      <td>   0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> G</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9582</th>\n",
        "      <td>   1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9583</th>\n",
        "      <td>   1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> G</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9584</th>\n",
        "      <td>   2</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> G</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9585</th>\n",
        "      <td>   6</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9586</th>\n",
        "      <td>  21</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9587</th>\n",
        "      <td>   3</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>150 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 299,
       "text": [
        "      jfh1v2_R1_rawReads  jfh1v2_R2_rawReads  hcvwt_R1_rawReads  \\\n",
        "9528                  10                   1                  0   \n",
        "9529                  11                   0                  0   \n",
        "9530                  13                   2                  0   \n",
        "9531                  19                   1                  0   \n",
        "9532                  37                   3                  0   \n",
        "9533                  90                  19                  0   \n",
        "9534                 174                  31                  0   \n",
        "9535                 115                  14                  0   \n",
        "9536                  87                  17                  0   \n",
        "9537                  13                   3                  0   \n",
        "9538                  52                   6                  0   \n",
        "9539                  34                  12                  0   \n",
        "9540                  38                  14                  0   \n",
        "9541                  22                  17                  0   \n",
        "9542                  42                  21                  0   \n",
        "9543                  18                   4                  0   \n",
        "9544                  28                   4                  0   \n",
        "9545                  63                  11                  0   \n",
        "9546                  12                   8                  0   \n",
        "9547                  13                   4                  0   \n",
        "9548                   7                   1                  0   \n",
        "9549                   7                   0                  0   \n",
        "9550                   7                   0                  0   \n",
        "9551                   1                   0                  0   \n",
        "9552                   5                   0                  0   \n",
        "9553                  18                   3                  0   \n",
        "9554                  12                   3                  0   \n",
        "9555                  13                   2                  0   \n",
        "9556                   6                   1                  0   \n",
        "9557                  98                  26                  0   \n",
        "9558                  11                   2                  0   \n",
        "9559                   9                   4                  0   \n",
        "9560                  26                   8                  0   \n",
        "9561                  29                  12                  0   \n",
        "9562                  61                  17                  0   \n",
        "9563                  17                   5                  0   \n",
        "9564                  24                   5                  0   \n",
        "9565                   8                   1                  0   \n",
        "9566                  11                   1                  0   \n",
        "9567                 104                  26                  0   \n",
        "9568                   3                   2                  0   \n",
        "9569                   8                   1                  0   \n",
        "9570                  41                  12                  0   \n",
        "9571                  84                  16                  0   \n",
        "9572                  31                  12                  0   \n",
        "9573                  24                   2                  0   \n",
        "9574                  64                  23                  0   \n",
        "9575                  98                  12                  0   \n",
        "9576                  39                   7                  0   \n",
        "9577                  10                   5                  0   \n",
        "9578                  38                   7                  0   \n",
        "9579                  27                   9                  0   \n",
        "9580                   2                   1                  0   \n",
        "9581                   0                   1                  3   \n",
        "9582                   1                   1                  0   \n",
        "9583                   1                   1                  0   \n",
        "9584                   2                   2                  0   \n",
        "9585                   6                   0                  0   \n",
        "9586                  21                   5                  0   \n",
        "9587                   3                   0                  0   \n",
        "                     ...                 ...                ...   \n",
        "\n",
        "      hcvwt_R2_rawReads Base  \n",
        "9528                  0    T  \n",
        "9529                  0    T  \n",
        "9530                  0    T  \n",
        "9531                  0    T  \n",
        "9532                  0    T  \n",
        "9533                  0    T  \n",
        "9534                  0    T  \n",
        "9535                  0    T  \n",
        "9536                  0    T  \n",
        "9537                  0    T  \n",
        "9538                  0    T  \n",
        "9539                  0    C  \n",
        "9540                  0    C  \n",
        "9541                  0    C  \n",
        "9542                  0    T  \n",
        "9543                  0    C  \n",
        "9544                  0    T  \n",
        "9545                  0    T  \n",
        "9546                  0    T  \n",
        "9547                  0    C  \n",
        "9548                  0    T  \n",
        "9549                  0    T  \n",
        "9550                  0    C  \n",
        "9551                  0    C  \n",
        "9552                  0    C  \n",
        "9553                  0    T  \n",
        "9554                  0    T  \n",
        "9555                  0    C  \n",
        "9556                  0    T  \n",
        "9557                  0    C  \n",
        "9558                  0    A  \n",
        "9559                  0    T  \n",
        "9560                  0    C  \n",
        "9561                  0    T  \n",
        "9562                  0    T  \n",
        "9563                  0    A  \n",
        "9564                  0    T  \n",
        "9565                  0    T  \n",
        "9566                  0    C  \n",
        "9567                  0    T  \n",
        "9568                  0    A  \n",
        "9569                  0    C  \n",
        "9570                  0    T  \n",
        "9571                  0    T  \n",
        "9572                  0    T  \n",
        "9573                  0    C  \n",
        "9574                  0    T  \n",
        "9575                  0    T  \n",
        "9576                  0    T  \n",
        "9577                  1    C  \n",
        "9578                  0    T  \n",
        "9579                  0    T  \n",
        "9580                  0    G  \n",
        "9581                  0    G  \n",
        "9582                  0    T  \n",
        "9583                  0    G  \n",
        "9584                  0    G  \n",
        "9585                  0    C  \n",
        "9586                  0    T  \n",
        "9587                  0    C  \n",
        "                    ...  ...  \n",
        "\n",
        "[150 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "storageDFHCV.tail(50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>jfh1v2_R1_raw</th>\n",
        "      <th>jfh1v2_R1_norm</th>\n",
        "      <th>jfh1v2_R2_raw</th>\n",
        "      <th>jfh1v2_R2_norm</th>\n",
        "      <th>hcvwt_R1_raw</th>\n",
        "      <th>hcvwt_R1_norm</th>\n",
        "      <th>hcvwt_R2_raw</th>\n",
        "      <th>hcvwt_R2_norm</th>\n",
        "      <th>Bin_Center</th>\n",
        "      <th>Base</th>\n",
        "      <th>JFH-1_CLIP_Rep_1</th>\n",
        "      <th>JFH-1_CLIP_Rep_2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Base</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td> 586</td>\n",
        "      <td> 0.028046</td>\n",
        "      <td> 191</td>\n",
        "      <td> 0.028975</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9427</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.028046</td>\n",
        "      <td> 0.028975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  16</td>\n",
        "      <td> 0.000766</td>\n",
        "      <td>   5</td>\n",
        "      <td> 0.000758</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9432</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.000766</td>\n",
        "      <td> 0.000758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td> 332</td>\n",
        "      <td> 0.015890</td>\n",
        "      <td>  88</td>\n",
        "      <td> 0.013350</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9437</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.015890</td>\n",
        "      <td> 0.013350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>   5</td>\n",
        "      <td> 0.000239</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000910</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9442</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.000239</td>\n",
        "      <td> 0.000910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  99</td>\n",
        "      <td> 0.004738</td>\n",
        "      <td>  58</td>\n",
        "      <td> 0.008799</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9447</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.004738</td>\n",
        "      <td> 0.008799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 159</td>\n",
        "      <td> 0.007610</td>\n",
        "      <td>  93</td>\n",
        "      <td> 0.014108</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9452</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.007610</td>\n",
        "      <td> 0.014108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>  31</td>\n",
        "      <td> 0.001484</td>\n",
        "      <td>  18</td>\n",
        "      <td> 0.002731</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9457</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.001484</td>\n",
        "      <td> 0.002731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  17</td>\n",
        "      <td> 0.000814</td>\n",
        "      <td>   7</td>\n",
        "      <td> 0.001062</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9462</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.000814</td>\n",
        "      <td> 0.001062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   8</td>\n",
        "      <td> 0.000383</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000910</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9467</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000383</td>\n",
        "      <td> 0.000910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>   8</td>\n",
        "      <td> 0.000383</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.000303</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.006390</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.003165</td>\n",
        "      <td> 9472</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 126</td>\n",
        "      <td> 0.006030</td>\n",
        "      <td>  63</td>\n",
        "      <td> 0.009557</td>\n",
        "      <td>  16</td>\n",
        "      <td> 0.051118</td>\n",
        "      <td>   9</td>\n",
        "      <td> 0.028481</td>\n",
        "      <td> 9477</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 627</td>\n",
        "      <td> 0.030009</td>\n",
        "      <td> 282</td>\n",
        "      <td> 0.042779</td>\n",
        "      <td> 116</td>\n",
        "      <td> 0.370607</td>\n",
        "      <td> 115</td>\n",
        "      <td> 0.363924</td>\n",
        "      <td> 9482</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 452</td>\n",
        "      <td> 0.021633</td>\n",
        "      <td> 216</td>\n",
        "      <td> 0.032767</td>\n",
        "      <td>  57</td>\n",
        "      <td> 0.182109</td>\n",
        "      <td>  51</td>\n",
        "      <td> 0.161392</td>\n",
        "      <td> 9487</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 408</td>\n",
        "      <td> 0.019527</td>\n",
        "      <td> 210</td>\n",
        "      <td> 0.031857</td>\n",
        "      <td>  46</td>\n",
        "      <td> 0.146965</td>\n",
        "      <td>  41</td>\n",
        "      <td> 0.129747</td>\n",
        "      <td> 9492</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 475</td>\n",
        "      <td> 0.022734</td>\n",
        "      <td> 220</td>\n",
        "      <td> 0.033374</td>\n",
        "      <td>  32</td>\n",
        "      <td> 0.102236</td>\n",
        "      <td>  35</td>\n",
        "      <td> 0.110759</td>\n",
        "      <td> 9497</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 441</td>\n",
        "      <td> 0.021107</td>\n",
        "      <td> 215</td>\n",
        "      <td> 0.032615</td>\n",
        "      <td>  22</td>\n",
        "      <td> 0.070288</td>\n",
        "      <td>  40</td>\n",
        "      <td> 0.126582</td>\n",
        "      <td> 9502</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>  58</td>\n",
        "      <td> 0.002776</td>\n",
        "      <td>  17</td>\n",
        "      <td> 0.002579</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.003195</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9507</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000287</td>\n",
        "      <td>   5</td>\n",
        "      <td> 0.000758</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.006329</td>\n",
        "      <td> 9512</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>  10</td>\n",
        "      <td> 0.000479</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.006329</td>\n",
        "      <td> 9517</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   7</td>\n",
        "      <td> 0.000335</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9522</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000335</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>  42</td>\n",
        "      <td> 0.002010</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000910</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9527</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.002010</td>\n",
        "      <td> 0.000910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 333</td>\n",
        "      <td> 0.015938</td>\n",
        "      <td>  56</td>\n",
        "      <td> 0.008495</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9532</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.015938</td>\n",
        "      <td> 0.008495</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 301</td>\n",
        "      <td> 0.014406</td>\n",
        "      <td>  52</td>\n",
        "      <td> 0.007888</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9537</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.014406</td>\n",
        "      <td> 0.007888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 148</td>\n",
        "      <td> 0.007083</td>\n",
        "      <td>  60</td>\n",
        "      <td> 0.009102</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9542</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.007083</td>\n",
        "      <td> 0.009102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td> 102</td>\n",
        "      <td> 0.004882</td>\n",
        "      <td>  24</td>\n",
        "      <td> 0.003641</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9547</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.004882</td>\n",
        "      <td> 0.003641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  43</td>\n",
        "      <td> 0.002058</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000910</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9552</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.002058</td>\n",
        "      <td> 0.000910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td> 137</td>\n",
        "      <td> 0.006557</td>\n",
        "      <td>  35</td>\n",
        "      <td> 0.005309</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9557</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.006557</td>\n",
        "      <td> 0.005309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 157</td>\n",
        "      <td> 0.007514</td>\n",
        "      <td>  47</td>\n",
        "      <td> 0.007130</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9562</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.007514</td>\n",
        "      <td> 0.007130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 134</td>\n",
        "      <td> 0.006413</td>\n",
        "      <td>  31</td>\n",
        "      <td> 0.004703</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9567</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.006413</td>\n",
        "      <td> 0.004703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td> 244</td>\n",
        "      <td> 0.011678</td>\n",
        "      <td>  65</td>\n",
        "      <td> 0.009860</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9572</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.011678</td>\n",
        "      <td> 0.009860</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td> 212</td>\n",
        "      <td> 0.010146</td>\n",
        "      <td>  40</td>\n",
        "      <td> 0.006068</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.003165</td>\n",
        "      <td> 9577</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000287</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000910</td>\n",
        "      <td>   3</td>\n",
        "      <td> 0.009585</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9582</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  39</td>\n",
        "      <td> 0.001867</td>\n",
        "      <td>  14</td>\n",
        "      <td> 0.002124</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9587</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.001867</td>\n",
        "      <td> 0.002124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>  32</td>\n",
        "      <td> 0.001532</td>\n",
        "      <td>   4</td>\n",
        "      <td> 0.000607</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9592</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.001532</td>\n",
        "      <td> 0.000607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td>   4</td>\n",
        "      <td> 0.000607</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9597</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td> 0.000607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9602</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.000191</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.000303</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9607</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000191</td>\n",
        "      <td> 0.000303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9612</td>\n",
        "      <td> C</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.000287</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9617</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.000287</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.000096</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9622</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000096</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td>   2</td>\n",
        "      <td> 0.000303</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9627</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000144</td>\n",
        "      <td> 0.000303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   5</td>\n",
        "      <td> 0.000239</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9632</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000239</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9637</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9642</td>\n",
        "      <td> A</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000152</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9647</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.000431</td>\n",
        "      <td>   3</td>\n",
        "      <td> 0.000455</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9652</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000431</td>\n",
        "      <td> 0.000455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td>   3</td>\n",
        "      <td> 0.000455</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9657</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9662</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>G</th>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9667</td>\n",
        "      <td> G</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T</th>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 9672</td>\n",
        "      <td> T</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 298,
       "text": [
        "      jfh1v2_R1_raw  jfh1v2_R1_norm  jfh1v2_R2_raw  jfh1v2_R2_norm  \\\n",
        "Base                                                                 \n",
        "C               586        0.028046            191        0.028975   \n",
        "C                16        0.000766              5        0.000758   \n",
        "G               332        0.015890             88        0.013350   \n",
        "A                 5        0.000239              6        0.000910   \n",
        "C                99        0.004738             58        0.008799   \n",
        "A               159        0.007610             93        0.014108   \n",
        "G                31        0.001484             18        0.002731   \n",
        "C                17        0.000814              7        0.001062   \n",
        "T                 8        0.000383              6        0.000910   \n",
        "A                 8        0.000383              2        0.000303   \n",
        "T               126        0.006030             63        0.009557   \n",
        "T               627        0.030009            282        0.042779   \n",
        "T               452        0.021633            216        0.032767   \n",
        "T               408        0.019527            210        0.031857   \n",
        "T               475        0.022734            220        0.033374   \n",
        "T               441        0.021107            215        0.032615   \n",
        "T                58        0.002776             17        0.002579   \n",
        "T                 6        0.000287              5        0.000758   \n",
        "T                10        0.000479              1        0.000152   \n",
        "T                 7        0.000335              1        0.000152   \n",
        "T                42        0.002010              6        0.000910   \n",
        "T               333        0.015938             56        0.008495   \n",
        "T               301        0.014406             52        0.007888   \n",
        "T               148        0.007083             60        0.009102   \n",
        "C               102        0.004882             24        0.003641   \n",
        "C                43        0.002058              6        0.000910   \n",
        "C               137        0.006557             35        0.005309   \n",
        "T               157        0.007514             47        0.007130   \n",
        "T               134        0.006413             31        0.004703   \n",
        "T               244        0.011678             65        0.009860   \n",
        "C               212        0.010146             40        0.006068   \n",
        "T                 6        0.000287              6        0.000910   \n",
        "C                39        0.001867             14        0.002124   \n",
        "T                32        0.001532              4        0.000607   \n",
        "C                 3        0.000144              4        0.000607   \n",
        "T                 3        0.000144              0        0.000000   \n",
        "G                 4        0.000191              2        0.000303   \n",
        "C                 1        0.000048              1        0.000152   \n",
        "A                 6        0.000287              1        0.000152   \n",
        "T                 2        0.000096              1        0.000152   \n",
        "G                 3        0.000144              2        0.000303   \n",
        "G                 5        0.000239              1        0.000152   \n",
        "A                 1        0.000048              0        0.000000   \n",
        "A                 0        0.000000              0        0.000000   \n",
        "G                 1        0.000048              1        0.000152   \n",
        "G                 9        0.000431              3        0.000455   \n",
        "T                 1        0.000048              3        0.000455   \n",
        "T                 0        0.000000              0        0.000000   \n",
        "G                 0        0.000000              0        0.000000   \n",
        "T                 0        0.000000              0        0.000000   \n",
        "\n",
        "      hcvwt_R1_raw  hcvwt_R1_norm  hcvwt_R2_raw  hcvwt_R2_norm  Bin_Center  \\\n",
        "Base                                                                         \n",
        "C                0       0.000000             0       0.000000        9427   \n",
        "C                0       0.000000             0       0.000000        9432   \n",
        "G                0       0.000000             0       0.000000        9437   \n",
        "A                0       0.000000             0       0.000000        9442   \n",
        "C                0       0.000000             0       0.000000        9447   \n",
        "A                0       0.000000             0       0.000000        9452   \n",
        "G                0       0.000000             0       0.000000        9457   \n",
        "C                0       0.000000             0       0.000000        9462   \n",
        "T                0       0.000000             0       0.000000        9467   \n",
        "A                2       0.006390             1       0.003165        9472   \n",
        "T               16       0.051118             9       0.028481        9477   \n",
        "T              116       0.370607           115       0.363924        9482   \n",
        "T               57       0.182109            51       0.161392        9487   \n",
        "T               46       0.146965            41       0.129747        9492   \n",
        "T               32       0.102236            35       0.110759        9497   \n",
        "T               22       0.070288            40       0.126582        9502   \n",
        "T                1       0.003195             0       0.000000        9507   \n",
        "T                0       0.000000             2       0.006329        9512   \n",
        "T                0       0.000000             2       0.006329        9517   \n",
        "T                0       0.000000             0       0.000000        9522   \n",
        "T                0       0.000000             0       0.000000        9527   \n",
        "T                0       0.000000             0       0.000000        9532   \n",
        "T                0       0.000000             0       0.000000        9537   \n",
        "T                0       0.000000             0       0.000000        9542   \n",
        "C                0       0.000000             0       0.000000        9547   \n",
        "C                0       0.000000             0       0.000000        9552   \n",
        "C                0       0.000000             0       0.000000        9557   \n",
        "T                0       0.000000             0       0.000000        9562   \n",
        "T                0       0.000000             0       0.000000        9567   \n",
        "T                0       0.000000             0       0.000000        9572   \n",
        "C                0       0.000000             1       0.003165        9577   \n",
        "T                3       0.009585             0       0.000000        9582   \n",
        "C                0       0.000000             0       0.000000        9587   \n",
        "T                0       0.000000             0       0.000000        9592   \n",
        "C                0       0.000000             0       0.000000        9597   \n",
        "T                0       0.000000             0       0.000000        9602   \n",
        "G                0       0.000000             0       0.000000        9607   \n",
        "C                0       0.000000             0       0.000000        9612   \n",
        "A                0       0.000000             0       0.000000        9617   \n",
        "T                0       0.000000             0       0.000000        9622   \n",
        "G                0       0.000000             0       0.000000        9627   \n",
        "G                0       0.000000             0       0.000000        9632   \n",
        "A                0       0.000000             0       0.000000        9637   \n",
        "A                0       0.000000             0       0.000000        9642   \n",
        "G                0       0.000000             0       0.000000        9647   \n",
        "G                0       0.000000             0       0.000000        9652   \n",
        "T                0       0.000000             0       0.000000        9657   \n",
        "T                0       0.000000             0       0.000000        9662   \n",
        "G                0       0.000000             0       0.000000        9667   \n",
        "T                0       0.000000             0       0.000000        9672   \n",
        "\n",
        "     Base  JFH-1_CLIP_Rep_1  JFH-1_CLIP_Rep_2  \n",
        "Base                                           \n",
        "C       C          0.028046          0.028975  \n",
        "C       C          0.000766          0.000758  \n",
        "G       G          0.015890          0.013350  \n",
        "A       A          0.000239          0.000910  \n",
        "C       C          0.004738          0.008799  \n",
        "A       A          0.007610          0.014108  \n",
        "G       G          0.001484          0.002731  \n",
        "C       C          0.000814          0.001062  \n",
        "T       T          0.000383          0.000910  \n",
        "A       A          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "T       T          0.000335          0.000152  \n",
        "T       T          0.002010          0.000910  \n",
        "T       T          0.015938          0.008495  \n",
        "T       T          0.014406          0.007888  \n",
        "T       T          0.007083          0.009102  \n",
        "C       C          0.004882          0.003641  \n",
        "C       C          0.002058          0.000910  \n",
        "C       C          0.006557          0.005309  \n",
        "T       T          0.007514          0.007130  \n",
        "T       T          0.006413          0.004703  \n",
        "T       T          0.011678          0.009860  \n",
        "C       C          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "C       C          0.001867          0.002124  \n",
        "T       T          0.001532          0.000607  \n",
        "C       C          0.000144          0.000607  \n",
        "T       T          0.000144          0.000000  \n",
        "G       G          0.000191          0.000303  \n",
        "C       C          0.000048          0.000152  \n",
        "A       A          0.000287          0.000152  \n",
        "T       T          0.000096          0.000152  \n",
        "G       G          0.000144          0.000303  \n",
        "G       G          0.000239          0.000152  \n",
        "A       A          0.000048          0.000000  \n",
        "A       A          0.000000          0.000000  \n",
        "G       G          0.000048          0.000152  \n",
        "G       G          0.000431          0.000455  \n",
        "T       T          0.000048          0.000455  \n",
        "T       T          0.000000          0.000000  \n",
        "G       G          0.000000          0.000000  \n",
        "T       T          0.000000          0.000000  \n",
        "\n",
        "[50 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Check - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data=np.genfromtxt(mappedBedFiles2[0],usecols=(1,),delimiter='\\t',dtype='int') # Read the 5' coordinate (RT stop).\n",
      "print len(data[data==0])\n",
      "data=np.genfromtxt(mappedBedFiles2[1],usecols=(1,),delimiter='\\t',dtype='int') # Read the 5' coordinate (RT stop).\n",
      "print len(data[data==3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "107\n",
        "12"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reads=pd.read_table(mappedBedFiles2[0],header=None)\n",
      "reads.columns=['ReadName','Start','End','M1','M2','Strand']\n",
      "reads[reads['Start']==0].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 233,
       "text": [
        "(107, 6)"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Store filtered data - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Histograms to filter\n",
      "HistToFilter=[0,1] # jfh1v2\n",
      "Background=[2,3] # hcvwt\n",
      "filteredHist=[]\n",
      "# For each histogram\n",
      "for i in HistToFilter:\n",
      "    # Copy the values into temp object\n",
      "    tempHist=coverageHistograms[i]\n",
      "    # For each index in the histogram\n",
      "    for index in range(0,len(tempHist)):\n",
      "        # Check if background histograms (no infection) have counts\n",
      "        if coverageHistograms[Background[0]][index] > 0 or coverageHistograms[Background[1]][index] > 0:\n",
      "            # If so, then do not count\n",
      "            tempHist[index]=0\n",
      "    filteredHist.append(tempHist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 258
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save final data\n",
      "storageDFHCV['JFH-1_CLIP_Rep_1']=filteredHist[0]\n",
      "storageDFHCV['JFH-1_CLIP_Rep_2']=filteredHist[1]\n",
      "saveName='JFH-1_CLIP_CoverageData'\n",
      "storageDFHCV.to_csv(outfilepath+saveName)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 259
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Scatter plot\n",
      "geneType='HCV genome'\n",
      "sampleName1='JFH-1 Rep1'\n",
      "sampleName2='JFH-1 Rep2'\n",
      " \n",
      "# Compute based upon histograms bins\n",
      "bins=np.array(range(0,jfh1_genome_size,5)) # Bin the genome by 5 bp\n",
      "width=0.7*(bins[1]-bins[0]) # Bar width for plotting\n",
      "center=(bins[:-1] + bins[1:])/2 # Center position of each bin\n",
      "\n",
      "# Annotation\n",
      "UTR5end=340\n",
      "UTR3start=9280\n",
      "\n",
      "# Isolate UTR data from new JFH-1 histograms\n",
      "boolarray=[element < UTR5end or element > UTR3start for element in center]\n",
      "boolarray=np.array(boolarray,dtype=bool)\n",
      "center=center[boolarray]\n",
      "sample1=filteredHist[0][boolarray]\n",
      "sample2=filteredHist[1][boolarray]\n",
      "\n",
      "# Fit_fn is now a function which takes in x and returns an estimate for y\n",
      "fit = np.polyfit(sample1,sample2,1)\n",
      "fit_fn = np.poly1d(fit)\n",
      "slope,intercept,r_value,p_value,std_err=scipy.stats.linregress(sample1,sample2)\n",
      "print \"r-squared:\", r_value**2\n",
      "\n",
      "fig1=plt.figure(1)\n",
      "\n",
      "plt.subplot(2,2,2)\n",
      "for i in range(0,len(center)):\n",
      "    # Get position at the point\n",
      "    position=center[i]\n",
      "    # Annotate\n",
      "    if position < UTR5end:\n",
      "        color='blue'\n",
      "        plt.scatter(sample1[i],sample2[i],color=color,alpha=0.35) \n",
      "    elif position > UTR3start:\n",
      "        color='red'\n",
      "        plt.scatter(sample1[i],sample2[i],color=color,alpha=0.35)\n",
      "plt.plot(sample1,fit_fn(sample1),alpha=0.5,color='black')\n",
      "# plt.xlabel(\"Normalized gene read count: %s\"%sampleName1,fontsize=5)\n",
      "# plt.ylabel(\"Normalized gene read count: %s\"%sampleName2,fontsize=5)\n",
      "plt.tick_params(axis='xticks',labelsize=5) \n",
      "plt.tick_params(axis='yticks',labelsize=5)\n",
      "plt.xticks(rotation=90,fontsize=5)\n",
      "\n",
      "plt.yticks(fontsize=5)\n",
      "plt.tight_layout()\t\n",
      "\n",
      "fig1.savefig(outfilepath+'ViralScatter.png',format='png',bbox_inches='tight',dpi=200,pad_inches=0.5)\n",
      "fig1.savefig(outfilepath+'ViralScatter.pdf',format='pdf',bbox_inches='tight',dpi=200,pad_inches=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "r-squared: 0.933532945875\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nTpxw22YwGPDEE0/AaDSq3CsiUhMDKBERqaK+vh6HDx/22P74448jJiZGxR4R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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x25b9ce50>"
       ]
      }
     ],
     "prompt_number": 272
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utr5p=storageDFHCV[storageDFHCV['Bin_Center']<400]\n",
      "utr3p=storageDFHCV[storageDFHCV['Bin_Center']>9280]\n",
      "\n",
      "fig2=plt.figure(2)\n",
      "\n",
      "plt.subplot(2,2,1)\n",
      "color='blue'\n",
      "plt.bar(utr5p['Bin_Center'],utr5p['JFH-1_CLIP_Rep_1'],align='center',width=width,color=color,alpha=0.3)\n",
      "plt.bar(utr5p['Bin_Center'],utr5p['JFH-1_CLIP_Rep_2'],align='center',width=width,color=color,alpha=0.3)\n",
      "plt.tick_params(axis='x',labelsize=5)\n",
      "plt.tick_params(axis='y',labelsize=5)\n",
      "plt.xlim([-10,400])\n",
      "\n",
      "plt.subplot(2,2,2)\n",
      "color='red'\n",
      "plt.bar(utr3p['Bin_Center'],utr3p['JFH-1_CLIP_Rep_1'],align='center',width=width,color=color,alpha=0.3)\n",
      "plt.bar(utr3p['Bin_Center'],utr3p['JFH-1_CLIP_Rep_2'],align='center',width=width,color=color,alpha=0.3)\n",
      "plt.tick_params(axis='x',labelsize=5)\n",
      "plt.tick_params(axis='y',labelsize=5)\n",
      "plt.xlim([9280,jfh1_genome_size+10])\n",
      "\n",
      "fig2.savefig(outfilepath+'ViralUTRs.png',format='png',bbox_inches='tight',dpi=150,pad_inches=0.5)\n",
      "fig2.savefig(outfilepath+'ViralUTRs.pdf',format='pdf',bbox_inches='tight',dpi=150,pad_inches=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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axML1yPXXX49t27ZhwoQJaG5uxu9//3ssXrwY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\nDz/E7t27ceONN+Luu+/Gk08+CaBjfumwYcOg0WgU++Xk5KChoSEaTe6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       "text": [
        "<matplotlib.figure.Figure at 0x3c52a6d0>"
       ]
      }
     ],
     "prompt_number": 273
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig3=plt.figure(3)\n",
      "\n",
      "# Compute based upon histograms bins\n",
      "bins=np.array(range(0,jfh1_genome_size,5)) # Bin the genome by 5 bp\n",
      "width=0.7*(bins[1]-bins[0]) # Bar width for plotting\n",
      "center=(bins[:-1] + bins[1:])/2 # Center position of each bin\n",
      "\n",
      "# Scatter plot\n",
      "geneType='HCV genome'\n",
      "sampleName1='JFH-1 Rep1'\n",
      "sampleName2='JFH-1 Rep2'\n",
      "\n",
      "# Annotation\n",
      "UTR5end=340\n",
      "UTR3start=9280\n",
      " \n",
      "# Fit_fn is now a function which takes in x and returns an estimate for y\n",
      "fit=np.polyfit(filteredHist[0],filteredHist[1],1)\n",
      "fit_fn=np.poly1d(fit)\n",
      "slope,intercept,r_value,p_value,std_err=scipy.stats.linregress(filteredHist[0],filteredHist[1])\n",
      "print \"r-squared:\", r_value**2\n",
      "\n",
      "# Iterate through cdenter index of each bin\n",
      "plt.subplot(1,2,1)\n",
      "for i in range(0,len(center)):\n",
      "    # Get position at the point\n",
      "    position=center[i]\n",
      "    # Annotate\n",
      "    if position < UTR5end:\n",
      "        color='blue'\n",
      "    elif position > UTR3start:\n",
      "        color='red'\n",
      "    else:\n",
      "        color='cyan'\n",
      "    # Extract points from newer JFH-1 data histograms\n",
      "    plt.scatter(filteredHist[0][i],filteredHist[1][i],color=color,alpha=0.35)\n",
      "    plt.tick_params(axis='x',labelsize=5)\n",
      "    plt.tick_params(axis='y',labelsize=5)\n",
      "plt.plot(filteredHist[0],fit_fn(filteredHist[0]),alpha=0.5,color='black')\n",
      "plt.xlabel(\"Normalized gene read count: %s\"%sampleName1,fontsize=5)\n",
      "plt.ylabel(\"Normalized gene read count: %s\"%sampleName2,fontsize=5)\n",
      "plt.title(\"Comparison for %s\"%geneType)\n",
      "#plt.tick_params(axis='xticks',labelsize=5) \n",
      "#plt.tick_params(axis='yticks',labelsize=5)\n",
      "plt.xticks(rotation=90)\n",
      "maxVal=0.04\n",
      "plt.xlim(-0.1*maxVal,maxVal)\n",
      "plt.ylim(-0.1*maxVal,maxVal)\n",
      "\n",
      "fig3.savefig(outfilepath+'S4_HCV_fullGenomeCoor_Supp.png',format='png',bbox_inches='tight',dpi=200,pad_inches=0.5)\n",
      "fig3.savefig(outfilepath+'S4_HCV_fullGenomeCoor_Supp.pdf',format='pdf',bbox_inches='tight',dpi=200,pad_inches=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "r-squared: 0.903140669239\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hAh4+fKi1XSsrKwwaNEianj9/vs7JqLGof0e2bNmCS5cuaay/ePFi2fAuhf1Y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\n8A78nmHCmA8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2f2d25d0>"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig4=plt.figure(4)\n",
      "\n",
      "color='cyan'\n",
      "\n",
      "plt.subplot2grid((2,3),(0,0),colspan=3)\n",
      "for i in range(0,2): # Full genome \n",
      "    plt.bar(center,filteredHist[i],align='center',width=width,color=color,alpha=0.3)\n",
      "    plt.tick_params(axis='x',labelsize=5)\n",
      "    plt.tick_params(axis='y',labelsize=5)\n",
      "plt.title(\"Coverage histogram for JFH-1.\",fontsize=7.5)\n",
      "plt.ylim([0,0.04])\n",
      "\n",
      "plt.subplot2grid((2,3),(1,0),colspan=1)\n",
      "for i in range(0,2):    \n",
      "    plt.bar(center,filteredHist[i],align='center',width=width,color=color,alpha=0.3)\n",
      "    plt.tick_params(axis='x',labelsize=5)\n",
      "    plt.tick_params(axis='y',labelsize=5)\n",
      "plt.xlim([400,2500])\n",
      "plt.ylim([0,0.01])\n",
      "\n",
      "plt.subplot2grid((2,3),(1,1),colspan=1)\n",
      "for i in range(0,2):    \n",
      "    plt.bar(center,filteredHist[i],align='center',width=width,color=color,alpha=0.3)\n",
      "    plt.tick_params(axis='x',labelsize=5)\n",
      "    plt.tick_params(axis='y',labelsize=5)\n",
      "plt.xlim([6000,7000])\n",
      "plt.ylim([0,0.01])\n",
      "\n",
      "plt.subplot2grid((2,3),(1,2),colspan=1)\n",
      "for i in range(0,2):    \n",
      "    plt.bar(center,filteredHist[i],align='center',width=width,color=color,alpha=0.3)\n",
      "    plt.tick_params(axis='x',labelsize=5)\n",
      "    plt.tick_params(axis='y',labelsize=5)\n",
      "plt.xlim([8000,9280])\n",
      "plt.ylim([0,0.01])\n",
      "plt.tight_layout()\n",
      "\n",
      "fig4.savefig(outfilepath+'S5_HCV_fullGenomeCoor_hist_Supp.png',format='png',bbox_inches='tight',dpi=200,pad_inches=0.5)\n",
      "fig4.savefig(outfilepath+'S5_HCV_fullGenomeCoor_hist_Supp.pdf',format='pdf',bbox_inches='tight',dpi=200,pad_inches=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bIxBADwQwTbPezRFCCCFWHAn0zdOJ3l7i8Xi9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RySZXnMrRFMOiLQJxBcE2RZZlOSADBNk5tjbo9gdAS2TVOS2NzY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       "text": [
        "<matplotlib.figure.Figure at 0x2aaacb767e90>"
       ]
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}