Retroviral_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": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get raw data\n",
      "global outfilepath\n",
      "sampleName='ddx3wt'\n",
      "outfilepath=os.getcwd() + '/results/%s/'%sampleName"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "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):\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",
      "        #k='1' ;; '-k',k,\n",
      "        index = os.getcwd() + '/docs/viralrepeat/retroViral'\n",
      "        outfile = modifyName(infastq,\"mappedToRetroviral.sam\")\n",
      "        unmapped = modifyName(infastq,\"notMappedToRetroviral.fastq\")\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",
      "        proc = subprocess.Popen([program,'-x', index,'-a','-U',infastq,'--un',unmapped,'-S',outfile,'2>>'],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",
      "# Run Bowtie\n",
      "print \"Run mapping\"  \n",
      "readsForRetroviralmapping=glob.glob(outfilepath+\"*_notMappedToRepeat.fastq\")\n",
      "mappedReads,unmappedReads=runBowtie(readsForRetroviralmapping)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Run mapping\n",
        "Performing Bowtie..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Input file:\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat.fastq\n",
        "Genome index:\n",
        "/arrayAhome/lmartin/CLIP/docs/viralrepeat/retroViral\n",
        "Output file (mapped):\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_mappedToRetroviral.sam\n",
        "Output file (unmapped)\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_notMappedToRetroviral.fastq\n",
        "Result : "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "Error :  Use of uninitialized value $l in scalar chomp at /seq/bowtie2-2.1.0/bowtie2 line 437, <BT> line 12380010.\n",
        "Use of uninitialized value $l in substitution (s///) at /seq/bowtie2-2.1.0/bowtie2 line 438, <BT> line 12380010.\n",
        "Use of uninitialized value $l in print at /seq/bowtie2-2.1.0/bowtie2 line 442, <BT> line 12380010.\n",
        "\n",
        "Input file:\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat.fastq\n",
        "Genome index:\n",
        "/arrayAhome/lmartin/CLIP/docs/viralrepeat/retroViral\n",
        "Output file (mapped):\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_mappedToRetroviral.sam\n",
        "Output file (unmapped)\n",
        "/arrayAhome/lmartin/CLIP/results/ddx3wt/ddx3wt_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_notMappedToRetroviral.fastq\n",
        "Result : "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "Error :  Use of uninitialized value $l in scalar chomp at /seq/bowtie2-2.1.0/bowtie2 line 437, <BT> line 12527954.\n",
        "Use of uninitialized value $l in substitution (s///) at /seq/bowtie2-2.1.0/bowtie2 line 438, <BT> line 12527954.\n",
        "Use of uninitialized value $l in print at /seq/bowtie2-2.1.0/bowtie2 line 442, <BT> line 12527954.\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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 Samtools\n",
      "print \"Process mapped data\"  \n",
      "mappedReads=glob.glob(outfilepath+\"*mappedToRetroviral.sam\")\n",
      "mappedBedFiles=runSamtools(mappedReads)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Process mapped data\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeRepeatAnnotation():\n",
      "    \n",
      "    # Repeat index sequence \n",
      "    repeatGenomeBuild=os.getcwd() + '/docs/viralrepeat/Hs_viralRepeat.fa'\n",
      "    repeat_genome=np.genfromtxt(repeatGenomeBuild,dtype='string')\n",
      "    repeat_genome_bases=repeat_genome[1]\n",
      "    repeat_genome_size=len(repeat_genome[1])\n",
      "    # Repeat index positions\n",
      "    repeatAnnotation=os.getcwd() + '/docs/viralrepeat/Hs_viralindex_positions.txt'\n",
      "    repeatAnnotDF=pd.DataFrame(pd.read_table(repeatAnnotation,header=None))\n",
      "    repeatAnnotDF.columns=['Name','Length','IndexStart','IndexEnd']\n",
      "    # Python list extraction is not end index inclusive; to extract sequence, use end + 1.\n",
      "    repeatAnnotDF['End_for_extraction']=repeatAnnotDF['IndexEnd']+1 \n",
      "    return (repeat_genome_bases,repeatAnnotDF)\n",
      "\n",
      "# - Repeat annotation - \n",
      "repeat_genome_bases,repeatAnnotDF=makeRepeatAnnotation()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "repeatAnnotDF.set_index('Name',inplace=True,drop=False)\n",
      "repeatAnnotDF.head(25)"
     ],
     "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>Name</th>\n",
        "      <th>Length</th>\n",
        "      <th>IndexStart</th>\n",
        "      <th>IndexEnd</th>\n",
        "      <th>End_for_extraction</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Name</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>HERVH_ERV1_Eutheria</th>\n",
        "      <td>                       HERVH_ERV1_Eutheria</td>\n",
        "      <td> 7713</td>\n",
        "      <td>     0</td>\n",
        "      <td>  7713</td>\n",
        "      <td>  7714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X21_LINE_CR1_Mammalia</th>\n",
        "      <td>                     X21_LINE_CR1_Mammalia</td>\n",
        "      <td>  185</td>\n",
        "      <td>  7713</td>\n",
        "      <td>  7898</td>\n",
        "      <td>  7899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR16A_ERV3_Eutheria</th>\n",
        "      <td>                      LTR16A_ERV3_Eutheria</td>\n",
        "      <td>  450</td>\n",
        "      <td>  7898</td>\n",
        "      <td>  8348</td>\n",
        "      <td>  8349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR78_ERV1_Mammalia</th>\n",
        "      <td>                       LTR78_ERV1_Mammalia</td>\n",
        "      <td> 1304</td>\n",
        "      <td>  8348</td>\n",
        "      <td>  9652</td>\n",
        "      <td>  9653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MARE5_hAT_Mammalia</th>\n",
        "      <td>                        MARE5_hAT_Mammalia</td>\n",
        "      <td>  564</td>\n",
        "      <td>  9652</td>\n",
        "      <td> 10216</td>\n",
        "      <td> 10217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UCON50_hAT_Mammalia</th>\n",
        "      <td>                       UCON50_hAT_Mammalia</td>\n",
        "      <td>  133</td>\n",
        "      <td> 10216</td>\n",
        "      <td> 10349</td>\n",
        "      <td> 10350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Charlie22a_hAT_Mammalia</th>\n",
        "      <td>                   Charlie22a_hAT_Mammalia</td>\n",
        "      <td>  491</td>\n",
        "      <td> 10349</td>\n",
        "      <td> 10840</td>\n",
        "      <td> 10841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PrimLTR79_ERV1_Homo_sapiens</th>\n",
        "      <td>               PrimLTR79_ERV1_Homo_sapiens</td>\n",
        "      <td>  503</td>\n",
        "      <td> 10840</td>\n",
        "      <td> 11343</td>\n",
        "      <td> 11344</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MLT1J-int_ERV3_Eutheria</th>\n",
        "      <td>                   MLT1J-int_ERV3_Eutheria</td>\n",
        "      <td> 1387</td>\n",
        "      <td> 11343</td>\n",
        "      <td> 12730</td>\n",
        "      <td> 12731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MIR3_SINE2/tRNA_Mammalia</th>\n",
        "      <td>                  MIR3_SINE2/tRNA_Mammalia</td>\n",
        "      <td>  224</td>\n",
        "      <td> 12730</td>\n",
        "      <td> 12954</td>\n",
        "      <td> 12955</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR35A_ERV1_Primates</th>\n",
        "      <td>                      LTR35A_ERV1_Primates</td>\n",
        "      <td>  547</td>\n",
        "      <td> 12954</td>\n",
        "      <td> 13501</td>\n",
        "      <td> 13502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X4a_DNA_DNA_transposon_Mammalia</th>\n",
        "      <td>           X4a_DNA_DNA_transposon_Mammalia</td>\n",
        "      <td>  215</td>\n",
        "      <td> 13501</td>\n",
        "      <td> 13716</td>\n",
        "      <td> 13717</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR89_ERV3_Mammalia</th>\n",
        "      <td>                       LTR89_ERV3_Mammalia</td>\n",
        "      <td>  879</td>\n",
        "      <td> 13716</td>\n",
        "      <td> 14595</td>\n",
        "      <td> 14596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TIGGER5_Mariner/Tc1_Homo_sapiens</th>\n",
        "      <td>          TIGGER5_Mariner/Tc1_Homo_sapiens</td>\n",
        "      <td> 2302</td>\n",
        "      <td> 14595</td>\n",
        "      <td> 16897</td>\n",
        "      <td> 16898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR21A_ERV1_Homo_sapiens</th>\n",
        "      <td>                  LTR21A_ERV1_Homo_sapiens</td>\n",
        "      <td>  505</td>\n",
        "      <td> 16897</td>\n",
        "      <td> 17402</td>\n",
        "      <td> 17403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR25-int_ERV1_Primates</th>\n",
        "      <td>                   LTR25-int_ERV1_Primates</td>\n",
        "      <td> 7188</td>\n",
        "      <td> 17402</td>\n",
        "      <td> 24590</td>\n",
        "      <td> 24591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERV18_Endogenous_Retrovirus_Homo_sapiens</th>\n",
        "      <td> HERV18_Endogenous_Retrovirus_Homo_sapiens</td>\n",
        "      <td> 6053</td>\n",
        "      <td> 24590</td>\n",
        "      <td> 30643</td>\n",
        "      <td> 30644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER22_MER22_Homo_sapiens</th>\n",
        "      <td>                  MER22_MER22_Homo_sapiens</td>\n",
        "      <td> 1563</td>\n",
        "      <td> 30643</td>\n",
        "      <td> 32206</td>\n",
        "      <td> 32207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MamRep605_Transposable_Element_Mammalia</th>\n",
        "      <td>   MamRep605_Transposable_Element_Mammalia</td>\n",
        "      <td>  876</td>\n",
        "      <td> 32206</td>\n",
        "      <td> 33082</td>\n",
        "      <td> 33083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UCON60_Repetitive_element_Mammalia</th>\n",
        "      <td>        UCON60_Repetitive_element_Mammalia</td>\n",
        "      <td>  137</td>\n",
        "      <td> 33082</td>\n",
        "      <td> 33219</td>\n",
        "      <td> 33220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CER_SAT_Homo_sapiens</th>\n",
        "      <td>                      CER_SAT_Homo_sapiens</td>\n",
        "      <td>  384</td>\n",
        "      <td> 33219</td>\n",
        "      <td> 33603</td>\n",
        "      <td> 33604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UCON55_SINE2/tRNA_Mammalia</th>\n",
        "      <td>                UCON55_SINE2/tRNA_Mammalia</td>\n",
        "      <td>  170</td>\n",
        "      <td> 33603</td>\n",
        "      <td> 33773</td>\n",
        "      <td> 33774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER21I_ERV1_Homo_sapiens</th>\n",
        "      <td>                  MER21I_ERV1_Homo_sapiens</td>\n",
        "      <td> 4191</td>\n",
        "      <td> 33773</td>\n",
        "      <td> 37964</td>\n",
        "      <td> 37965</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER6_Mariner/Tc1_Eutheria</th>\n",
        "      <td>                 MER6_Mariner/Tc1_Eutheria</td>\n",
        "      <td>  865</td>\n",
        "      <td> 37964</td>\n",
        "      <td> 38829</td>\n",
        "      <td> 38830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER57E1_ERV1_Eutheria</th>\n",
        "      <td>                     MER57E1_ERV1_Eutheria</td>\n",
        "      <td>  373</td>\n",
        "      <td> 38829</td>\n",
        "      <td> 39202</td>\n",
        "      <td> 39203</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>25 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                                                                                Name  \\\n",
        "Name                                                                                   \n",
        "HERVH_ERV1_Eutheria                                              HERVH_ERV1_Eutheria   \n",
        "X21_LINE_CR1_Mammalia                                          X21_LINE_CR1_Mammalia   \n",
        "LTR16A_ERV3_Eutheria                                            LTR16A_ERV3_Eutheria   \n",
        "LTR78_ERV1_Mammalia                                              LTR78_ERV1_Mammalia   \n",
        "MARE5_hAT_Mammalia                                                MARE5_hAT_Mammalia   \n",
        "UCON50_hAT_Mammalia                                              UCON50_hAT_Mammalia   \n",
        "Charlie22a_hAT_Mammalia                                      Charlie22a_hAT_Mammalia   \n",
        "PrimLTR79_ERV1_Homo_sapiens                              PrimLTR79_ERV1_Homo_sapiens   \n",
        "MLT1J-int_ERV3_Eutheria                                      MLT1J-int_ERV3_Eutheria   \n",
        "MIR3_SINE2/tRNA_Mammalia                                    MIR3_SINE2/tRNA_Mammalia   \n",
        "LTR35A_ERV1_Primates                                            LTR35A_ERV1_Primates   \n",
        "X4a_DNA_DNA_transposon_Mammalia                      X4a_DNA_DNA_transposon_Mammalia   \n",
        "LTR89_ERV3_Mammalia                                              LTR89_ERV3_Mammalia   \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens                    TIGGER5_Mariner/Tc1_Homo_sapiens   \n",
        "LTR21A_ERV1_Homo_sapiens                                    LTR21A_ERV1_Homo_sapiens   \n",
        "LTR25-int_ERV1_Primates                                      LTR25-int_ERV1_Primates   \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens  HERV18_Endogenous_Retrovirus_Homo_sapiens   \n",
        "MER22_MER22_Homo_sapiens                                    MER22_MER22_Homo_sapiens   \n",
        "MamRep605_Transposable_Element_Mammalia      MamRep605_Transposable_Element_Mammalia   \n",
        "UCON60_Repetitive_element_Mammalia                UCON60_Repetitive_element_Mammalia   \n",
        "CER_SAT_Homo_sapiens                                            CER_SAT_Homo_sapiens   \n",
        "UCON55_SINE2/tRNA_Mammalia                                UCON55_SINE2/tRNA_Mammalia   \n",
        "MER21I_ERV1_Homo_sapiens                                    MER21I_ERV1_Homo_sapiens   \n",
        "MER6_Mariner/Tc1_Eutheria                                  MER6_Mariner/Tc1_Eutheria   \n",
        "MER57E1_ERV1_Eutheria                                          MER57E1_ERV1_Eutheria   \n",
        "\n",
        "                                           Length  IndexStart  IndexEnd  \\\n",
        "Name                                                                      \n",
        "HERVH_ERV1_Eutheria                          7713           0      7713   \n",
        "X21_LINE_CR1_Mammalia                         185        7713      7898   \n",
        "LTR16A_ERV3_Eutheria                          450        7898      8348   \n",
        "LTR78_ERV1_Mammalia                          1304        8348      9652   \n",
        "MARE5_hAT_Mammalia                            564        9652     10216   \n",
        "UCON50_hAT_Mammalia                           133       10216     10349   \n",
        "Charlie22a_hAT_Mammalia                       491       10349     10840   \n",
        "PrimLTR79_ERV1_Homo_sapiens                   503       10840     11343   \n",
        "MLT1J-int_ERV3_Eutheria                      1387       11343     12730   \n",
        "MIR3_SINE2/tRNA_Mammalia                      224       12730     12954   \n",
        "LTR35A_ERV1_Primates                          547       12954     13501   \n",
        "X4a_DNA_DNA_transposon_Mammalia               215       13501     13716   \n",
        "LTR89_ERV3_Mammalia                           879       13716     14595   \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens             2302       14595     16897   \n",
        "LTR21A_ERV1_Homo_sapiens                      505       16897     17402   \n",
        "LTR25-int_ERV1_Primates                      7188       17402     24590   \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens    6053       24590     30643   \n",
        "MER22_MER22_Homo_sapiens                     1563       30643     32206   \n",
        "MamRep605_Transposable_Element_Mammalia       876       32206     33082   \n",
        "UCON60_Repetitive_element_Mammalia            137       33082     33219   \n",
        "CER_SAT_Homo_sapiens                          384       33219     33603   \n",
        "UCON55_SINE2/tRNA_Mammalia                    170       33603     33773   \n",
        "MER21I_ERV1_Homo_sapiens                     4191       33773     37964   \n",
        "MER6_Mariner/Tc1_Eutheria                     865       37964     38829   \n",
        "MER57E1_ERV1_Eutheria                         373       38829     39202   \n",
        "\n",
        "                                           End_for_extraction  \n",
        "Name                                                           \n",
        "HERVH_ERV1_Eutheria                                      7714  \n",
        "X21_LINE_CR1_Mammalia                                    7899  \n",
        "LTR16A_ERV3_Eutheria                                     8349  \n",
        "LTR78_ERV1_Mammalia                                      9653  \n",
        "MARE5_hAT_Mammalia                                      10217  \n",
        "UCON50_hAT_Mammalia                                     10350  \n",
        "Charlie22a_hAT_Mammalia                                 10841  \n",
        "PrimLTR79_ERV1_Homo_sapiens                             11344  \n",
        "MLT1J-int_ERV3_Eutheria                                 12731  \n",
        "MIR3_SINE2/tRNA_Mammalia                                12955  \n",
        "LTR35A_ERV1_Primates                                    13502  \n",
        "X4a_DNA_DNA_transposon_Mammalia                         13717  \n",
        "LTR89_ERV3_Mammalia                                     14596  \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens                        16898  \n",
        "LTR21A_ERV1_Homo_sapiens                                17403  \n",
        "LTR25-int_ERV1_Primates                                 24591  \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens               30644  \n",
        "MER22_MER22_Homo_sapiens                                32207  \n",
        "MamRep605_Transposable_Element_Mammalia                 33083  \n",
        "UCON60_Repetitive_element_Mammalia                      33220  \n",
        "CER_SAT_Homo_sapiens                                    33604  \n",
        "UCON55_SINE2/tRNA_Mammalia                              33774  \n",
        "MER21I_ERV1_Homo_sapiens                                37965  \n",
        "MER6_Mariner/Tc1_Eutheria                               38830  \n",
        "MER57E1_ERV1_Eutheria                                   39203  \n",
        "\n",
        "[25 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Get mapped bedfiles - \n",
      "def readBed(path):\n",
      "    bedFile = pd.read_table(path,dtype=str,header=None)\n",
      "    bedFile.columns=['Index','Start','Stop','Name','QS','Strand']\n",
      "    bedFile['Start']=bedFile['Start'].astype(int)\n",
      "    return bedFile\n",
      "\n",
      "mappedBed=glob.glob(outfilepath+\"*mappedToRetroviral_withDupes.bed\")\n",
      "bedR1=readBed(mappedBed[0])\n",
      "bedR2=readBed(mappedBed[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Record gene position of RT stop - \n",
      "recordHits=pd.DataFrame()\n",
      "for ix in repeatAnnotDF.index:\n",
      "    end=repeatAnnotDF.loc[ix,'IndexEnd']\n",
      "    repName=repeatAnnotDF.loc[ix,'Name']\n",
      "    hits_r1=bedR1[(bedR1['Start']<int(repeatAnnotDF.loc[ix,'IndexEnd'])) & (bedR1['Start']>int(repeatAnnotDF.loc[ix,'IndexStart']))].shape[0]\n",
      "    hits_r2=bedR2[(bedR2['Start']<int(repeatAnnotDF.loc[ix,'IndexEnd'])) & (bedR2['Start']>int(repeatAnnotDF.loc[ix,'IndexStart']))].shape[0]\n",
      "    recordHits.loc[repName,'hits_r1']=hits_r1\n",
      "    recordHits.loc[repName,'hits_r2']=hits_r2\n",
      "recordHits['sum']=recordHits['hits_r1']+recordHits['hits_r2']\n",
      "recordHits.fillna(0,inplace=True)\n",
      "recordHits.sort(['sum'],inplace=True,ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Evaluate consistency between replicates - \n",
      "plt.subplot(2,2,1)\n",
      "plt.scatter(recordHits['hits_r1']/bedR1.shape[0],recordHits['hits_r2']/bedR2.shape[0])\n",
      "plt.xticks(fontsize=5)\n",
      "plt.yticks(fontsize=5)\n",
      "plt.xlabel('Normalized Hits / repeat RNA (Rep1)',fontsize=5)\n",
      "plt.ylabel('Normalized Hits / repeat RNA (Rep2)',fontsize=5)\n",
      "\n",
      "plt.subplot(2,2,2)\n",
      "plt.scatter(np.log10(recordHits['hits_r1']),np.log10(recordHits['hits_r2']))\n",
      "plt.xticks(fontsize=5)\n",
      "plt.yticks(fontsize=5)\n",
      "plt.xlabel('Log10 (Raw hits / repeat RNA, Rep1)',fontsize=5)\n",
      "plt.ylabel('Log10 (Raw hits / repeat RNA, Rep2)',fontsize=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x2aaaac692050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BSoE9jJGGrdjZvn07M2bMoFevXvznP/9h9+7djB07loULF9pVUGf9+vX4+fmR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\n+6NRo0bw8PBAZmamRvk7deqEI0eO4MiRI+jcuTOAF3Pv7Nu3DydPnhSX6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       "text": [
        "<matplotlib.figure.Figure at 0x6bf6b10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grZero=recordHits[recordHits['sum']>0]\n",
      "grZero.head(20)"
     ],
     "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>hits_r1</th>\n",
        "      <th>hits_r2</th>\n",
        "      <th>sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>L1HS_L1_Homo_sapiens</th>\n",
        "      <td> 5820</td>\n",
        "      <td> 6014</td>\n",
        "      <td> 11834</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERV49I_ERV1_Homo_sapiens</th>\n",
        "      <td> 3789</td>\n",
        "      <td> 3439</td>\n",
        "      <td>  7228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1_L1_Homo_sapiens</th>\n",
        "      <td> 3098</td>\n",
        "      <td> 3305</td>\n",
        "      <td>  6403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PREC1_L1_Homo_sapiens</th>\n",
        "      <td> 1771</td>\n",
        "      <td> 1950</td>\n",
        "      <td>  3721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ALU_SINE1/7SL_Primates</th>\n",
        "      <td> 1791</td>\n",
        "      <td> 1860</td>\n",
        "      <td>  3651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA4_L1_Homo_sapiens</th>\n",
        "      <td> 1260</td>\n",
        "      <td> 1321</td>\n",
        "      <td>  2581</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA3_L1_Homo_sapiens</th>\n",
        "      <td> 1124</td>\n",
        "      <td> 1264</td>\n",
        "      <td>  2388</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA5_L1_Homo_sapiens</th>\n",
        "      <td> 1125</td>\n",
        "      <td> 1220</td>\n",
        "      <td>  2345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA2_L1_Homo_sapiens</th>\n",
        "      <td> 1049</td>\n",
        "      <td> 1110</td>\n",
        "      <td>  2159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA6_L1_Homo_sapiens</th>\n",
        "      <td>  953</td>\n",
        "      <td> 1037</td>\n",
        "      <td>  1990</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SVA_A_SINE_Homo_sapiens</th>\n",
        "      <td>  893</td>\n",
        "      <td>  990</td>\n",
        "      <td>  1883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA7_L1_Homo_sapiens</th>\n",
        "      <td>  783</td>\n",
        "      <td>  809</td>\n",
        "      <td>  1592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA8_L1_Homo_sapiens</th>\n",
        "      <td>  666</td>\n",
        "      <td>  727</td>\n",
        "      <td>  1393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PREC2_L1_Homo_sapiens</th>\n",
        "      <td>  625</td>\n",
        "      <td>  711</td>\n",
        "      <td>  1336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERVKC4_ERV2_Eutheria</th>\n",
        "      <td>  261</td>\n",
        "      <td>  557</td>\n",
        "      <td>   818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA10_L1_Homo_sapiens</th>\n",
        "      <td>  334</td>\n",
        "      <td>  412</td>\n",
        "      <td>   746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA13_L1_Homo_sapiens</th>\n",
        "      <td>  319</td>\n",
        "      <td>  350</td>\n",
        "      <td>   669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA11_L1_Homo_sapiens</th>\n",
        "      <td>  306</td>\n",
        "      <td>  312</td>\n",
        "      <td>   618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA12_L1_Homo_sapiens</th>\n",
        "      <td>  293</td>\n",
        "      <td>  311</td>\n",
        "      <td>   604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PB2c_L1_Homo_sapiens</th>\n",
        "      <td>  268</td>\n",
        "      <td>  283</td>\n",
        "      <td>   551</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                           hits_r1  hits_r2    sum\n",
        "L1HS_L1_Homo_sapiens          5820     6014  11834\n",
        "HERV49I_ERV1_Homo_sapiens     3789     3439   7228\n",
        "L1_L1_Homo_sapiens            3098     3305   6403\n",
        "L1PREC1_L1_Homo_sapiens       1771     1950   3721\n",
        "ALU_SINE1/7SL_Primates        1791     1860   3651\n",
        "L1PA4_L1_Homo_sapiens         1260     1321   2581\n",
        "L1PA3_L1_Homo_sapiens         1124     1264   2388\n",
        "L1PA5_L1_Homo_sapiens         1125     1220   2345\n",
        "L1PA2_L1_Homo_sapiens         1049     1110   2159\n",
        "L1PA6_L1_Homo_sapiens          953     1037   1990\n",
        "SVA_A_SINE_Homo_sapiens        893      990   1883\n",
        "L1PA7_L1_Homo_sapiens          783      809   1592\n",
        "L1PA8_L1_Homo_sapiens          666      727   1393\n",
        "L1PREC2_L1_Homo_sapiens        625      711   1336\n",
        "HERVKC4_ERV2_Eutheria          261      557    818\n",
        "L1PA10_L1_Homo_sapiens         334      412    746\n",
        "L1PA13_L1_Homo_sapiens         319      350    669\n",
        "L1PA11_L1_Homo_sapiens         306      312    618\n",
        "L1PA12_L1_Homo_sapiens         293      311    604\n",
        "L1PB2c_L1_Homo_sapiens         268      283    551\n",
        "\n",
        "[20 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "repeatAnnotDF.head(20)"
     ],
     "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>Name</th>\n",
        "      <th>Length</th>\n",
        "      <th>IndexStart</th>\n",
        "      <th>IndexEnd</th>\n",
        "      <th>End_for_extraction</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Name</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>HERVH_ERV1_Eutheria</th>\n",
        "      <td>                       HERVH_ERV1_Eutheria</td>\n",
        "      <td> 7713</td>\n",
        "      <td>     0</td>\n",
        "      <td>  7713</td>\n",
        "      <td>  7714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X21_LINE_CR1_Mammalia</th>\n",
        "      <td>                     X21_LINE_CR1_Mammalia</td>\n",
        "      <td>  185</td>\n",
        "      <td>  7713</td>\n",
        "      <td>  7898</td>\n",
        "      <td>  7899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR16A_ERV3_Eutheria</th>\n",
        "      <td>                      LTR16A_ERV3_Eutheria</td>\n",
        "      <td>  450</td>\n",
        "      <td>  7898</td>\n",
        "      <td>  8348</td>\n",
        "      <td>  8349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR78_ERV1_Mammalia</th>\n",
        "      <td>                       LTR78_ERV1_Mammalia</td>\n",
        "      <td> 1304</td>\n",
        "      <td>  8348</td>\n",
        "      <td>  9652</td>\n",
        "      <td>  9653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MARE5_hAT_Mammalia</th>\n",
        "      <td>                        MARE5_hAT_Mammalia</td>\n",
        "      <td>  564</td>\n",
        "      <td>  9652</td>\n",
        "      <td> 10216</td>\n",
        "      <td> 10217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UCON50_hAT_Mammalia</th>\n",
        "      <td>                       UCON50_hAT_Mammalia</td>\n",
        "      <td>  133</td>\n",
        "      <td> 10216</td>\n",
        "      <td> 10349</td>\n",
        "      <td> 10350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Charlie22a_hAT_Mammalia</th>\n",
        "      <td>                   Charlie22a_hAT_Mammalia</td>\n",
        "      <td>  491</td>\n",
        "      <td> 10349</td>\n",
        "      <td> 10840</td>\n",
        "      <td> 10841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PrimLTR79_ERV1_Homo_sapiens</th>\n",
        "      <td>               PrimLTR79_ERV1_Homo_sapiens</td>\n",
        "      <td>  503</td>\n",
        "      <td> 10840</td>\n",
        "      <td> 11343</td>\n",
        "      <td> 11344</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MLT1J-int_ERV3_Eutheria</th>\n",
        "      <td>                   MLT1J-int_ERV3_Eutheria</td>\n",
        "      <td> 1387</td>\n",
        "      <td> 11343</td>\n",
        "      <td> 12730</td>\n",
        "      <td> 12731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MIR3_SINE2/tRNA_Mammalia</th>\n",
        "      <td>                  MIR3_SINE2/tRNA_Mammalia</td>\n",
        "      <td>  224</td>\n",
        "      <td> 12730</td>\n",
        "      <td> 12954</td>\n",
        "      <td> 12955</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR35A_ERV1_Primates</th>\n",
        "      <td>                      LTR35A_ERV1_Primates</td>\n",
        "      <td>  547</td>\n",
        "      <td> 12954</td>\n",
        "      <td> 13501</td>\n",
        "      <td> 13502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X4a_DNA_DNA_transposon_Mammalia</th>\n",
        "      <td>           X4a_DNA_DNA_transposon_Mammalia</td>\n",
        "      <td>  215</td>\n",
        "      <td> 13501</td>\n",
        "      <td> 13716</td>\n",
        "      <td> 13717</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR89_ERV3_Mammalia</th>\n",
        "      <td>                       LTR89_ERV3_Mammalia</td>\n",
        "      <td>  879</td>\n",
        "      <td> 13716</td>\n",
        "      <td> 14595</td>\n",
        "      <td> 14596</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TIGGER5_Mariner/Tc1_Homo_sapiens</th>\n",
        "      <td>          TIGGER5_Mariner/Tc1_Homo_sapiens</td>\n",
        "      <td> 2302</td>\n",
        "      <td> 14595</td>\n",
        "      <td> 16897</td>\n",
        "      <td> 16898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR21A_ERV1_Homo_sapiens</th>\n",
        "      <td>                  LTR21A_ERV1_Homo_sapiens</td>\n",
        "      <td>  505</td>\n",
        "      <td> 16897</td>\n",
        "      <td> 17402</td>\n",
        "      <td> 17403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR25-int_ERV1_Primates</th>\n",
        "      <td>                   LTR25-int_ERV1_Primates</td>\n",
        "      <td> 7188</td>\n",
        "      <td> 17402</td>\n",
        "      <td> 24590</td>\n",
        "      <td> 24591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERV18_Endogenous_Retrovirus_Homo_sapiens</th>\n",
        "      <td> HERV18_Endogenous_Retrovirus_Homo_sapiens</td>\n",
        "      <td> 6053</td>\n",
        "      <td> 24590</td>\n",
        "      <td> 30643</td>\n",
        "      <td> 30644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER22_MER22_Homo_sapiens</th>\n",
        "      <td>                  MER22_MER22_Homo_sapiens</td>\n",
        "      <td> 1563</td>\n",
        "      <td> 30643</td>\n",
        "      <td> 32206</td>\n",
        "      <td> 32207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MamRep605_Transposable_Element_Mammalia</th>\n",
        "      <td>   MamRep605_Transposable_Element_Mammalia</td>\n",
        "      <td>  876</td>\n",
        "      <td> 32206</td>\n",
        "      <td> 33082</td>\n",
        "      <td> 33083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UCON60_Repetitive_element_Mammalia</th>\n",
        "      <td>        UCON60_Repetitive_element_Mammalia</td>\n",
        "      <td>  137</td>\n",
        "      <td> 33082</td>\n",
        "      <td> 33219</td>\n",
        "      <td> 33220</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                                                                                Name  \\\n",
        "Name                                                                                   \n",
        "HERVH_ERV1_Eutheria                                              HERVH_ERV1_Eutheria   \n",
        "X21_LINE_CR1_Mammalia                                          X21_LINE_CR1_Mammalia   \n",
        "LTR16A_ERV3_Eutheria                                            LTR16A_ERV3_Eutheria   \n",
        "LTR78_ERV1_Mammalia                                              LTR78_ERV1_Mammalia   \n",
        "MARE5_hAT_Mammalia                                                MARE5_hAT_Mammalia   \n",
        "UCON50_hAT_Mammalia                                              UCON50_hAT_Mammalia   \n",
        "Charlie22a_hAT_Mammalia                                      Charlie22a_hAT_Mammalia   \n",
        "PrimLTR79_ERV1_Homo_sapiens                              PrimLTR79_ERV1_Homo_sapiens   \n",
        "MLT1J-int_ERV3_Eutheria                                      MLT1J-int_ERV3_Eutheria   \n",
        "MIR3_SINE2/tRNA_Mammalia                                    MIR3_SINE2/tRNA_Mammalia   \n",
        "LTR35A_ERV1_Primates                                            LTR35A_ERV1_Primates   \n",
        "X4a_DNA_DNA_transposon_Mammalia                      X4a_DNA_DNA_transposon_Mammalia   \n",
        "LTR89_ERV3_Mammalia                                              LTR89_ERV3_Mammalia   \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens                    TIGGER5_Mariner/Tc1_Homo_sapiens   \n",
        "LTR21A_ERV1_Homo_sapiens                                    LTR21A_ERV1_Homo_sapiens   \n",
        "LTR25-int_ERV1_Primates                                      LTR25-int_ERV1_Primates   \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens  HERV18_Endogenous_Retrovirus_Homo_sapiens   \n",
        "MER22_MER22_Homo_sapiens                                    MER22_MER22_Homo_sapiens   \n",
        "MamRep605_Transposable_Element_Mammalia      MamRep605_Transposable_Element_Mammalia   \n",
        "UCON60_Repetitive_element_Mammalia                UCON60_Repetitive_element_Mammalia   \n",
        "\n",
        "                                           Length  IndexStart  IndexEnd  \\\n",
        "Name                                                                      \n",
        "HERVH_ERV1_Eutheria                          7713           0      7713   \n",
        "X21_LINE_CR1_Mammalia                         185        7713      7898   \n",
        "LTR16A_ERV3_Eutheria                          450        7898      8348   \n",
        "LTR78_ERV1_Mammalia                          1304        8348      9652   \n",
        "MARE5_hAT_Mammalia                            564        9652     10216   \n",
        "UCON50_hAT_Mammalia                           133       10216     10349   \n",
        "Charlie22a_hAT_Mammalia                       491       10349     10840   \n",
        "PrimLTR79_ERV1_Homo_sapiens                   503       10840     11343   \n",
        "MLT1J-int_ERV3_Eutheria                      1387       11343     12730   \n",
        "MIR3_SINE2/tRNA_Mammalia                      224       12730     12954   \n",
        "LTR35A_ERV1_Primates                          547       12954     13501   \n",
        "X4a_DNA_DNA_transposon_Mammalia               215       13501     13716   \n",
        "LTR89_ERV3_Mammalia                           879       13716     14595   \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens             2302       14595     16897   \n",
        "LTR21A_ERV1_Homo_sapiens                      505       16897     17402   \n",
        "LTR25-int_ERV1_Primates                      7188       17402     24590   \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens    6053       24590     30643   \n",
        "MER22_MER22_Homo_sapiens                     1563       30643     32206   \n",
        "MamRep605_Transposable_Element_Mammalia       876       32206     33082   \n",
        "UCON60_Repetitive_element_Mammalia            137       33082     33219   \n",
        "\n",
        "                                           End_for_extraction  \n",
        "Name                                                           \n",
        "HERVH_ERV1_Eutheria                                      7714  \n",
        "X21_LINE_CR1_Mammalia                                    7899  \n",
        "LTR16A_ERV3_Eutheria                                     8349  \n",
        "LTR78_ERV1_Mammalia                                      9653  \n",
        "MARE5_hAT_Mammalia                                      10217  \n",
        "UCON50_hAT_Mammalia                                     10350  \n",
        "Charlie22a_hAT_Mammalia                                 10841  \n",
        "PrimLTR79_ERV1_Homo_sapiens                             11344  \n",
        "MLT1J-int_ERV3_Eutheria                                 12731  \n",
        "MIR3_SINE2/tRNA_Mammalia                                12955  \n",
        "LTR35A_ERV1_Primates                                    13502  \n",
        "X4a_DNA_DNA_transposon_Mammalia                         13717  \n",
        "LTR89_ERV3_Mammalia                                     14596  \n",
        "TIGGER5_Mariner/Tc1_Homo_sapiens                        16898  \n",
        "LTR21A_ERV1_Homo_sapiens                                17403  \n",
        "LTR25-int_ERV1_Primates                                 24591  \n",
        "HERV18_Endogenous_Retrovirus_Homo_sapiens               30644  \n",
        "MER22_MER22_Homo_sapiens                                32207  \n",
        "MamRep605_Transposable_Element_Mammalia                 33083  \n",
        "UCON60_Repetitive_element_Mammalia                      33220  \n",
        "\n",
        "[20 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Evaluate - \n",
      "### RANK (0 to N) of REPEAT RNA FROM ABOVE LIST \n",
      "rank=2\n",
      "####\n",
      "\n",
      "repName=grZero.index[rank]\n",
      "# Hits\n",
      "hits_r1=bedR1[(bedR1['Start']<int(repeatAnnotDF.loc[repName,'IndexEnd'])) & (bedR1['Start']>int(repeatAnnotDF.loc[repName,'IndexStart']))]\n",
      "hits_r2=bedR2[(bedR2['Start']<int(repeatAnnotDF.loc[repName,'IndexEnd'])) & (bedR2['Start']>int(repeatAnnotDF.loc[repName,'IndexStart']))]\n",
      "# Histogram\n",
      "binSize=1\n",
      "bins=range(repeatAnnotDF.loc[repName,'IndexStart'],repeatAnnotDF.loc[repName,'IndexEnd']+2,binSize) # Make sure bins are end coordinate inclusive\n",
      "histr1,bins=np.histogram(hits_r1,bins=bins)\n",
      "histr2,bins=np.histogram(hits_r2,bins=bins)\n",
      "width=0.7*(bins[1]-bins[0])\n",
      "center=(bins[:-1] + bins[1:])/2\n",
      "plt.bar(np.array(range(0,histr1.shape[0])),np.array(list(histr1)),width=1,align='center',color='blue',alpha=0.45)\n",
      "plt.bar(np.array(range(0,histr1.shape[0])),np.array(list(histr2)),width=1,align='center',color='red',alpha=0.45)\n",
      "plt.xlim(0,bins.shape[0])\n",
      "plt.xticks(fontsize=5)\n",
      "plt.yticks(fontsize=5)\n",
      "plt.title('Coverage histogram for %s'%repName,fontsize=10)\n",
      "plt.xlabel('RNA position',fontsize=5)\n",
      "plt.ylabel('# of reads per base',fontsize=5)\n",
      "\n",
      "# Save data\n",
      "record=pd.DataFrame()\n",
      "pos=range(0,bins[-1]-bins[0])\n",
      "record['Base']=pos\n",
      "record['R1_count']=histr1\n",
      "record['R2_count']=histr2\n",
      "pathToSave=outfilepath + '%s_coverage'%repName\n",
      "record.to_csv(pathToSave)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2aaaca76ec90>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "(970,)"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "histr1.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "(969,)"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins[-1]-bins[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "969"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "363913-362945"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "968"
       ]
      }
     ],
     "prompt_number": 61
    }
   ],
   "metadata": {}
  }
 ]
}