retroviral_mapping.ipynb

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  "name": ""
 },
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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": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get raw data\n",
      "global outfilepath\n",
      "sampleName='Rec'\n",
      "outfilepath=os.getcwd() + '/results/%s/'%sampleName"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "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'\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,'-k',k,'-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/ddx21v3/ddx21v3_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/ddx21v3/ddx21v3_R2_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_mappedToRetroviral.sam\n",
        "Output file (unmapped)\n",
        "/arrayAhome/lmartin/CLIP/results/ddx21v3/ddx21v3_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 14527624.\n",
        "Use of uninitialized value $l in substitution (s///) at /seq/bowtie2-2.1.0/bowtie2 line 438, <BT> line 14527624.\n",
        "Use of uninitialized value $l in print at /seq/bowtie2-2.1.0/bowtie2 line 442, <BT> line 14527624.\n",
        "\n",
        "Input file:\n",
        "/arrayAhome/lmartin/CLIP/results/ddx21v3/ddx21v3_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/ddx21v3/ddx21v3_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_mappedToRetroviral.sam\n",
        "Output file (unmapped)\n",
        "/arrayAhome/lmartin/CLIP/results/ddx21v3/ddx21v3_R1_3ptrimmed_filter_nodupe_5ptrimmed_notMappedToRepeat_notMappedToRetroviral.fastq\n",
        "Result : "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "Error :  \n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "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": 36
    },
    {
     "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": 49
    },
    {
     "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": 50,
       "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": 50
    },
    {
     "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": 51
    },
    {
     "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": 52
    },
    {
     "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": 53,
       "text": [
        "<matplotlib.text.Text at 0x2aaac6461150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SuXNnevfuzaeffprarnLlyoSEhPD2228DMHbsWB555BEeffRRTCYT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\nPQ0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aaac44bf8d0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "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>LTR5_Hs_ERV2_Primates</th>\n",
        "      <td> 98477</td>\n",
        "      <td> 192938</td>\n",
        "      <td> 291415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR5_ERV2_Homo_sapiens</th>\n",
        "      <td> 28557</td>\n",
        "      <td>  58291</td>\n",
        "      <td>  86848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR5B_ERV2_Homo_sapiens</th>\n",
        "      <td>  4994</td>\n",
        "      <td>   8672</td>\n",
        "      <td>  13666</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SVA_A_SINE_Homo_sapiens</th>\n",
        "      <td>  4157</td>\n",
        "      <td>   6630</td>\n",
        "      <td>  10787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR5A_ERV2_Primates</th>\n",
        "      <td>  3654</td>\n",
        "      <td>   6015</td>\n",
        "      <td>   9669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERV49I_ERV1_Homo_sapiens</th>\n",
        "      <td>  2435</td>\n",
        "      <td>   3113</td>\n",
        "      <td>   5548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERVH_ERV1_Eutheria</th>\n",
        "      <td>   964</td>\n",
        "      <td>   1981</td>\n",
        "      <td>   2945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERVKC4_ERV2_Eutheria</th>\n",
        "      <td>  1248</td>\n",
        "      <td>   1660</td>\n",
        "      <td>   2908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1HS_L1_Homo_sapiens</th>\n",
        "      <td>   641</td>\n",
        "      <td>   1458</td>\n",
        "      <td>   2099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HERVK_ERV2_Primates</th>\n",
        "      <td>   386</td>\n",
        "      <td>   1583</td>\n",
        "      <td>   1969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1_L1_Homo_sapiens</th>\n",
        "      <td>   491</td>\n",
        "      <td>   1024</td>\n",
        "      <td>   1515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ALU_SINE1/7SL_Primates</th>\n",
        "      <td>   555</td>\n",
        "      <td>    722</td>\n",
        "      <td>   1277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PREC1_L1_Homo_sapiens</th>\n",
        "      <td>   150</td>\n",
        "      <td>    355</td>\n",
        "      <td>    505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MER22_MER22_Homo_sapiens</th>\n",
        "      <td>   195</td>\n",
        "      <td>    219</td>\n",
        "      <td>    414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR7Y_ERV3_Primates</th>\n",
        "      <td>    72</td>\n",
        "      <td>    209</td>\n",
        "      <td>    281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR7A_ERV3_Homo_sapiens</th>\n",
        "      <td>    65</td>\n",
        "      <td>    192</td>\n",
        "      <td>    257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR13_ERV2_Homo_sapiens</th>\n",
        "      <td>   108</td>\n",
        "      <td>    138</td>\n",
        "      <td>    246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LTR12C_ERV1_Homo_sapiens</th>\n",
        "      <td>    90</td>\n",
        "      <td>    152</td>\n",
        "      <td>    242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA4_L1_Homo_sapiens</th>\n",
        "      <td>    65</td>\n",
        "      <td>    175</td>\n",
        "      <td>    240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L1PA3_L1_Homo_sapiens</th>\n",
        "      <td>    68</td>\n",
        "      <td>    168</td>\n",
        "      <td>    236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "                           hits_r1  hits_r2     sum\n",
        "LTR5_Hs_ERV2_Primates        98477   192938  291415\n",
        "LTR5_ERV2_Homo_sapiens       28557    58291   86848\n",
        "LTR5B_ERV2_Homo_sapiens       4994     8672   13666\n",
        "SVA_A_SINE_Homo_sapiens       4157     6630   10787\n",
        "LTR5A_ERV2_Primates           3654     6015    9669\n",
        "HERV49I_ERV1_Homo_sapiens     2435     3113    5548\n",
        "HERVH_ERV1_Eutheria            964     1981    2945\n",
        "HERVKC4_ERV2_Eutheria         1248     1660    2908\n",
        "L1HS_L1_Homo_sapiens           641     1458    2099\n",
        "HERVK_ERV2_Primates            386     1583    1969\n",
        "L1_L1_Homo_sapiens             491     1024    1515\n",
        "ALU_SINE1/7SL_Primates         555      722    1277\n",
        "L1PREC1_L1_Homo_sapiens        150      355     505\n",
        "MER22_MER22_Homo_sapiens       195      219     414\n",
        "LTR7Y_ERV3_Primates             72      209     281\n",
        "LTR7A_ERV3_Homo_sapiens         65      192     257\n",
        "LTR13_ERV2_Homo_sapiens        108      138     246\n",
        "LTR12C_ERV1_Homo_sapiens        90      152     242\n",
        "L1PA4_L1_Homo_sapiens           65      175     240\n",
        "L1PA3_L1_Homo_sapiens           68      168     236\n",
        "\n",
        "[20 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "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": 55,
       "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": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Evaluate - \n",
      "### RANK (0 to N) of REPEAT RNA FROM ABOVE LIST \n",
      "rank=0\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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dsteyxHba4qioqHSfgNtKb2pGR0+bUnvvvff04Ycf2p0y0t4PHWuAyerKlSvm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\nhqHnn39eAwcOVPHixc1WXGX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MCGwAAAAAyozA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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aaaaedba7d0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "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
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}