Variant-Annotation-Comparsion-2017.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_venn import venn2, venn2_unweighted\n",
    "import seaborn\n",
    "%pylab inline\n",
    "pd.set_option('display.max_rows', 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "vep_cols = \"\"\"\\\n",
    "Allele|Annotation|Impact|Gene_name|Gene_id|Feature_type|Feature_id|Biotype|EXON\\\n",
    "|INTRON|HGVSc|HGVSp|cDNA_position|CDS_position|Protein_position|Amino_acids\\\n",
    "|Codons|Existing_variation|DISTANCE|STRAND|FLAGS|SYMBOL_SOURCE|HGNC_ID|CANONICAL\\\n",
    "|CCDS|HGVS_OFFSET\\\n",
    "\"\"\"\n",
    "vep_cols = vep_cols.split(\"|\")\n",
    "vep_cols = [term.strip().capitalize() for term in vep_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Import Data\n",
    "\n",
    "First we'll import the vcf files into Pandas dataframes. We split out the **ANN** INFO field and parse it into the component fields. Since an annotation is provided for each transcript, we split by transcript (ie splitting on `,`). Then for each transcript we break it into the fields specified by the tools documentation (splitting on `|`). Finally, we join back to the original dataframe, with each row representing a unique `POS`, `REF`, `ALT`, `Feature_id` combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>Feature_id</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Annotation</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Hgvsc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>CA</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         POS REF ALT       Feature_id               Biotype  \\\n",
       "0  117105737   C   A  ENST00000546407  processed_transcript   \n",
       "1  117105737   C   G  ENST00000546407  processed_transcript   \n",
       "2  117105737   C   T  ENST00000546407  processed_transcript   \n",
       "3  117105737   C  CA  ENST00000546407  processed_transcript   \n",
       "4  117105737   C  CG  ENST00000546407  processed_transcript   \n",
       "\n",
       "              Annotation    Impact Hgvsc  \n",
       "0  upstream_gene_variant  MODIFIER        \n",
       "1  upstream_gene_variant  MODIFIER        \n",
       "2  upstream_gene_variant  MODIFIER        \n",
       "3  upstream_gene_variant  MODIFIER        \n",
       "4  upstream_gene_variant  MODIFIER        "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df = pd.read_table('./cftr.grch37.vep.vcf', header=0, skiprows=6, usecols=range(10))\n",
    "info_df = vep_df[\"INFO\"].str.replace(\"ANN=\", \"\").str.split(\",\").apply(pd.Series, 1).stack()\n",
    "info_df = info_df.str.split(\"|\").apply(pd.Series, 1)\n",
    "info_df.index = info_df.index.droplevel(-1)\n",
    "info_df.columns = vep_cols\n",
    "vep_df = vep_df.join(info_df)\n",
    "vep_df = vep_df[['POS', 'REF', 'ALT', 'Feature_id', 'Biotype', 'Annotation', 'Impact', 'Hgvsc']]\n",
    "del info_df\n",
    "vep_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "snpeff_cols = \"\"\"\\\n",
    "Allele|Annotation|Impact|Gene_name|Gene_ID|Feature_type|Feature_ID|Biotype|Rank|HGVSc|HGVSp\\\n",
    "|cDNA_position|CDS_position|Protein_position|Distance|Errors\"\"\"\n",
    "snpeff_cols = snpeff_cols.split(\"|\")\n",
    "snpeff_cols = [term.strip().capitalize() for term in snpeff_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>Feature_id</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Annotation</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Hgvsc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>n.-101C&gt;A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>ENSG00000214684-ENSG00000001626</td>\n",
       "      <td></td>\n",
       "      <td>intergenic_region</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>n.117105737C&gt;A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>n.-101C&gt;G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>ENSG00000214684-ENSG00000001626</td>\n",
       "      <td></td>\n",
       "      <td>intergenic_region</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>n.117105737C&gt;G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117105737</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000546407</td>\n",
       "      <td>processed_transcript</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>n.-101C&gt;T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         POS REF ALT                       Feature_id               Biotype  \\\n",
       "0  117105737   C   A                  ENST00000546407  processed_transcript   \n",
       "0  117105737   C   A  ENSG00000214684-ENSG00000001626                         \n",
       "1  117105737   C   G                  ENST00000546407  processed_transcript   \n",
       "1  117105737   C   G  ENSG00000214684-ENSG00000001626                         \n",
       "2  117105737   C   T                  ENST00000546407  processed_transcript   \n",
       "\n",
       "              Annotation    Impact           Hgvsc  \n",
       "0  upstream_gene_variant  MODIFIER       n.-101C>A  \n",
       "0      intergenic_region  MODIFIER  n.117105737C>A  \n",
       "1  upstream_gene_variant  MODIFIER       n.-101C>G  \n",
       "1      intergenic_region  MODIFIER  n.117105737C>G  \n",
       "2  upstream_gene_variant  MODIFIER       n.-101C>T  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df = pd.read_table('./cftr.grch37.snpeff.vcf', header=0, skiprows=9, usecols=range(10))\n",
    "info_df = snpeff_df[\"INFO\"].str.split(\";\").apply(pd.Series, 1)[0] #snpeff includes two other INFO fields that we don't need\n",
    "info_df = info_df.str.replace(\"ANN=\", \"\").str.split(\",\").apply(pd.Series, 1).stack()\n",
    "info_df = info_df.str.split(\"|\").apply(pd.Series, 1)\n",
    "info_df.index = info_df.index.droplevel(-1)\n",
    "info_df.columns = snpeff_cols\n",
    "snpeff_df = snpeff_df.join(info_df)\n",
    "snpeff_df = snpeff_df[['POS', 'REF', 'ALT', 'Feature_id', 'Biotype', 'Annotation', 'Impact', 'Hgvsc']]\n",
    "del info_df\n",
    "snpeff_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Collapsing Annotations\n",
    "\n",
    "For each transcript-variant pair, the tool may provide multiple annotations. We'll only compare the most damaging as defined by the ranking in the ANN field spec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#From http://snpeff.sourceforge.net/VCFannotationformat_v1.0.pdf with additions:\n",
    "#VEP: non_coding_transcript_exon_variant, non_coding_transcript_variant, protein altering variant, \n",
    "#incomplete_terminal_codon_variant, NMD_transcript_variant\n",
    "#Snpeff: conservative_inframe_deletion, conservative_inframe_insertion, structural_interaction_variant, 5_prime_UTR_truncation\n",
    "\n",
    "ranked_terms = [\"chromosome_number_variation\",\"exon_loss_variant\",\"frameshift_variant\",\"stop_gained\",\"stop_lost\",\n",
    "                \"start_lost\",\"splice_acceptor_variant\",\"splice_donor_variant\",\"rare_amino_acid_variant\",\"missense_variant\",\n",
    "                \"inframe_insertion\",\"conservative_inframe_insertion\", \"disruptive_inframe_insertion\",\"inframe_deletion\",\"conservative_inframe_deletion\", \"disruptive_inframe_deletion\",\n",
    "                \"5_prime_UTR_truncation+exon_loss_variant\",\"5_prime_UTR_truncation\",\"3_prime_UTR_truncation+exon_loss\",\"splice_branch_variant\",\n",
    "                \"splice_region_variant\",\"stop_retained_variant\",\"initiator_codon_variant\",\n",
    "                \"synonymous_variant\",\"initiator_codon_variant+non_canonical_start_codon\",\"stop_retained_variant\",\n",
    "                \"5_prime_UTR_variant\",\"3_prime_UTR_variant\",\"5_prime_UTR_premature_start_codon_gain_variant\",\n",
    "                \"structural_interaction_variant\", \"protein_altering_variant\",\"upstream_gene_variant\",\"downstream_gene_variant\",\n",
    "                \"TF_binding_site_variant\",\"regulatory_region_variant\",\"miRNA\",\"custom\",\"sequence_feature\",\n",
    "                \"conserved_intron_variant\",\"intron_variant\",\"intragenic_variant\",\"conserved_intergenic_variant\",\n",
    "                \"intergenic_region\", \"coding_sequence_variant\", \"non_coding_exon_variant\",\"non_coding_transcript_exon_variant\",\n",
    "                \"nc_transcript_variant\",\"non_coding_transcript_variant\",\"NMD_transcript_variant\", \"incomplete_terminal_codon_variant\", \"gene_variant\",\"chromosome\"]\n",
    "def term_rank(term):\n",
    "    return ranked_terms.index(term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "vep_df[\"Effect\"] = vep_df.apply(lambda row: min(row[\"Annotation\"].split(\"&\"), key=term_rank), axis=1)\n",
    "snpeff_df[\"Effect\"] = snpeff_df.apply(lambda row: min(row[\"Annotation\"].split('&'), key=term_rank), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotations\n",
      "\n",
      "                                                     VEP    SnpEff\n",
      "3_prime_UTR_variant                               6654.0    6680.0\n",
      "5_prime_UTR_premature_start_codon_gain_variant       0.0     286.0\n",
      "5_prime_UTR_variant                               9987.0   10259.0\n",
      "coding_sequence_variant                             28.0       0.0\n",
      "conservative_inframe_deletion                        0.0    4619.0\n",
      "conservative_inframe_insertion                       0.0    8650.0\n",
      "disruptive_inframe_deletion                          0.0    8990.0\n",
      "disruptive_inframe_insertion                         0.0   17211.0\n",
      "downstream_gene_variant                          99177.0   99185.0\n",
      "exon_loss_variant                                    0.0       7.0\n",
      "frameshift_variant                              113706.0  113693.0\n",
      "incomplete_terminal_codon_variant                    8.0       0.0\n",
      "inframe_deletion                                 13604.0       0.0\n",
      "inframe_insertion                                18961.0       0.0\n",
      "initiator_codon_variant                              0.0       8.0\n",
      "intergenic_region                                    0.0    1403.0\n",
      "intron_variant                                  417918.0  416946.0\n",
      "missense_variant                                 30988.0   30988.0\n",
      "non_coding_transcript_exon_variant               29794.0   29567.0\n",
      "non_coding_transcript_variant                        0.0      42.0\n",
      "protein_altering_variant                          6834.0       0.0\n",
      "sequence_feature                                     0.0    5220.0\n",
      "splice_acceptor_variant                           2208.0    2776.0\n",
      "splice_donor_variant                              2208.0    2869.0\n",
      "splice_region_variant                            17865.0   18005.0\n",
      "start_lost                                          55.0      65.0\n",
      "stop_gained                                       4877.0    4867.0\n",
      "stop_lost                                           55.0      63.0\n",
      "stop_retained_variant                                5.0       5.0\n",
      "structural_interaction_variant                       0.0   78159.0\n",
      "synonymous_variant                                9056.0    9052.0\n",
      "upstream_gene_variant                            83853.0   83809.0\n",
      "\n",
      "Impacts\n",
      "             VEP  SnpEff\n",
      "Impact                  \n",
      "HIGH      123109  202492\n",
      "LOW        26698   31676\n",
      "MODERATE   70645   71365\n",
      "MODIFIER  647389  647891\n",
      "\n",
      "Counts\n",
      "               VEP  SnpEff\n",
      "POS         867841  953424\n",
      "REF         867841  953424\n",
      "ALT         867841  953424\n",
      "Feature_id  867841  953424\n",
      "Biotype     867841  953424\n",
      "Annotation  867841  953424\n",
      "Impact      867841  953424\n",
      "Hgvsc       867841  953424\n",
      "Effect      867841  953424\n"
     ]
    }
   ],
   "source": [
    "vc_vep = vep_df.groupby(['Effect']).size()\n",
    "vc_vep.name = \"VEP\"\n",
    "vc_snpeff = snpeff_df.groupby(['Effect']).size()\n",
    "vc_snpeff.name = \"SnpEff\"\n",
    "vc_df = pd.DataFrame([vc_vep, vc_snpeff])\n",
    "print(\"Annotations\\n\")\n",
    "print(vc_df.transpose().fillna(0))\n",
    "impact_vep = vep_df.groupby(['Impact']).size()\n",
    "impact_vep.name = \"VEP\"\n",
    "impact_snpeff = snpeff_df.groupby(['Impact']).size()\n",
    "impact_snpeff.name = \"SnpEff\"\n",
    "impact_df = pd.DataFrame([impact_vep, impact_snpeff])\n",
    "print(\"\\nImpacts\")\n",
    "print(impact_df.transpose())\n",
    "counts_vep = vep_df.count()\n",
    "counts_vep.name = 'VEP'\n",
    "counts_snpeff = snpeff_df.count()\n",
    "counts_snpeff.name = 'SnpEff'\n",
    "counts_df = pd.DataFrame([counts_vep, counts_snpeff])\n",
    "print(\"\\nCounts\")\n",
    "print(counts_df.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This example is interesting. Vep is usually a pretty general `protein_altering_variant` annotation. Snpeff breaks it down to `disruptive_inframe_insertion`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>Feature_id</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Annotation</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Hgvsc</th>\n",
       "      <th>Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19511</th>\n",
       "      <td>117120152</td>\n",
       "      <td>C</td>\n",
       "      <td>CCGA</td>\n",
       "      <td>ENST00000003084</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>protein_altering_variant</td>\n",
       "      <td>MODERATE</td>\n",
       "      <td>ENST00000003084.6:c.4_5insCGA</td>\n",
       "      <td>protein_altering_variant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             POS REF   ALT       Feature_id         Biotype  \\\n",
       "19511  117120152   C  CCGA  ENST00000003084  protein_coding   \n",
       "\n",
       "                     Annotation    Impact                          Hgvsc  \\\n",
       "19511  protein_altering_variant  MODERATE  ENST00000003084.6:c.4_5insCGA   \n",
       "\n",
       "                         Effect  \n",
       "19511  protein_altering_variant  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df[vep_df['Effect'].str.contains('protein_altering_variant')][:1]"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
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       "      <td>intron_variant&amp;non_coding_transcript_variant</td>\n",
       "      <td>MODIFIER</td>\n",
       "      <td>ENST00000546407.1:n.166+4290_166+4291insCGA</td>\n",
       "      <td>intron_variant</td>\n",
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       "             POS REF   ALT       Feature_id               Biotype  \\\n",
       "19511  117120152   C  CCGA  ENST00000003084        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000426809        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000446805        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000454343        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000546407  processed_transcript   \n",
       "\n",
       "                                         Annotation    Impact  \\\n",
       "19511                      protein_altering_variant  MODERATE   \n",
       "19511                      protein_altering_variant  MODERATE   \n",
       "19511                                intron_variant  MODIFIER   \n",
       "19511                      protein_altering_variant  MODERATE   \n",
       "19511  intron_variant&non_coding_transcript_variant  MODIFIER   \n",
       "\n",
       "                                             Hgvsc                    Effect  \n",
       "19511                ENST00000003084.6:c.4_5insCGA  protein_altering_variant  \n",
       "19511                ENST00000426809.1:c.4_5insCGA  protein_altering_variant  \n",
       "19511  ENST00000446805.1:c.-191+404_-191+405insCGA            intron_variant  \n",
       "19511                ENST00000454343.1:c.4_5insCGA  protein_altering_variant  \n",
       "19511  ENST00000546407.1:n.166+4290_166+4291insCGA            intron_variant  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df.ix[19511]"
   ]
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   "cell_type": "code",
   "execution_count": 11,
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    {
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       "      <td>c.-191+404_-191+405insCGA</td>\n",
       "      <td>intron_variant</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "             POS REF   ALT       Feature_id               Biotype  \\\n",
       "19511  117120152   C  CCGA  ENST00000003084        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000454343        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000426809        protein_coding   \n",
       "19511  117120152   C  CCGA  ENST00000546407  processed_transcript   \n",
       "19511  117120152   C  CCGA  ENST00000446805        protein_coding   \n",
       "\n",
       "                         Annotation    Impact                      Hgvsc  \\\n",
       "19511  disruptive_inframe_insertion  MODERATE                c.4_5insCGA   \n",
       "19511  disruptive_inframe_insertion  MODERATE                c.4_5insCGA   \n",
       "19511  disruptive_inframe_insertion  MODERATE                c.4_5insCGA   \n",
       "19511                intron_variant  MODIFIER  n.166+4290_166+4291insCGA   \n",
       "19511                intron_variant  MODIFIER  c.-191+404_-191+405insCGA   \n",
       "\n",
       "                             Effect  \n",
       "19511  disruptive_inframe_insertion  \n",
       "19511  disruptive_inframe_insertion  \n",
       "19511  disruptive_inframe_insertion  \n",
       "19511                intron_variant  \n",
       "19511                intron_variant  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df.ix[19511]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So this example is interesting. It shows two things:\n",
    "1. SnpEff is providing these \"structural_interaction_variant\" annotations, which vep does not provide\n",
    "2. SnpEff is a bit more granular annotating inframe variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>POS</th>\n",
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       "    <tr>\n",
       "      <th>58085</th>\n",
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       "      <td>T</td>\n",
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       "      <td>protein_coding</td>\n",
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      ],
      "text/plain": [
       "             POS   REF ALT                        Feature_id         Biotype  \\\n",
       "58085  117182112  TTAA   T  2PZG:B_388-B_567:ENST00000003084  protein_coding   \n",
       "\n",
       "                           Annotation Impact              Hgvsc  \\\n",
       "58085  structural_interaction_variant   HIGH  c.1160_1162delTAA   \n",
       "\n",
       "                               Effect  \n",
       "58085  structural_interaction_variant  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df[snpeff_df['Effect'].str.contains('structural_interaction_variant')][:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "deletable": true,
    "editable": true
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   "outputs": [
    {
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       "             POS   REF ALT       Feature_id         Biotype        Annotation  \\\n",
       "58085  117182112  TTAA   T  ENST00000003084  protein_coding  inframe_deletion   \n",
       "58085  117182112  TTAA   T  ENST00000426809  protein_coding  inframe_deletion   \n",
       "58085  117182112  TTAA   T  ENST00000454343  protein_coding  inframe_deletion   \n",
       "\n",
       "         Impact                                Hgvsc            Effect  \n",
       "58085  MODERATE  ENST00000003084.6:c.1160_1162delTAA  inframe_deletion  \n",
       "58085  MODERATE  ENST00000426809.1:c.1070_1072delTAA  inframe_deletion  \n",
       "58085  MODERATE  ENST00000454343.1:c.1160_1162delTAA  inframe_deletion  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df.ix[58085]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
    {
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       "      <td>MODERATE</td>\n",
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       "      <td>disruptive_inframe_deletion</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             POS   REF ALT                        Feature_id         Biotype  \\\n",
       "58085  117182112  TTAA   T  2PZG:B_388-B_567:ENST00000003084  protein_coding   \n",
       "58085  117182112  TTAA   T                   ENST00000003084  protein_coding   \n",
       "58085  117182112  TTAA   T                   ENST00000454343  protein_coding   \n",
       "58085  117182112  TTAA   T                   ENST00000426809  protein_coding   \n",
       "\n",
       "                           Annotation    Impact              Hgvsc  \\\n",
       "58085  structural_interaction_variant      HIGH  c.1160_1162delTAA   \n",
       "58085     disruptive_inframe_deletion  MODERATE  c.1160_1162delTAA   \n",
       "58085     disruptive_inframe_deletion  MODERATE  c.1160_1162delTAA   \n",
       "58085     disruptive_inframe_deletion  MODERATE  c.1070_1072delTAA   \n",
       "\n",
       "                               Effect  \n",
       "58085  structural_interaction_variant  \n",
       "58085     disruptive_inframe_deletion  \n",
       "58085     disruptive_inframe_deletion  \n",
       "58085     disruptive_inframe_deletion  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df.ix[58085]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Similarly to the structural interaction variant example, Snpeff provides an additional annotation called `sequence_feature`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "      <td>117120148</td>\n",
       "      <td>C</td>\n",
       "      <td>CA</td>\n",
       "      <td>ENST00000003084</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>sequence_feature</td>\n",
       "      <td>LOW</td>\n",
       "      <td>c.-1_1insA</td>\n",
       "      <td>sequence_feature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             POS REF ALT       Feature_id         Biotype        Annotation  \\\n",
       "19449  117120148   C  CA  ENST00000003084  protein_coding  sequence_feature   \n",
       "\n",
       "      Impact       Hgvsc            Effect  \n",
       "19449    LOW  c.-1_1insA  sequence_feature  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df[snpeff_df['Effect'].str.contains('sequence_feature')][3:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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      ],
      "text/plain": [
       "             POS REF ALT       Feature_id               Biotype  \\\n",
       "19449  117120148   C  CA  ENST00000003084        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000426809        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000446805        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000454343        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000546407  processed_transcript   \n",
       "\n",
       "                                         Annotation    Impact  \\\n",
       "19449                           5_prime_UTR_variant  MODIFIER   \n",
       "19449                         upstream_gene_variant  MODIFIER   \n",
       "19449                                intron_variant  MODIFIER   \n",
       "19449                           5_prime_UTR_variant  MODIFIER   \n",
       "19449  intron_variant&non_coding_transcript_variant  MODIFIER   \n",
       "\n",
       "                                  Hgvsc                 Effect  \n",
       "19449         ENST00000003084.6:c.1dupA    5_prime_UTR_variant  \n",
       "19449                                    upstream_gene_variant  \n",
       "19449  ENST00000446805.1:c.-191+401dupA         intron_variant  \n",
       "19449         ENST00000454343.1:c.1dupA    5_prime_UTR_variant  \n",
       "19449  ENST00000546407.1:n.166+4287dupA         intron_variant  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df.ix[19449]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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    "deletable": true,
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   "outputs": [
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      ],
      "text/plain": [
       "             POS REF ALT       Feature_id               Biotype  \\\n",
       "19449  117120148   C  CA  ENST00000003084        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000454343        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000426809        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000003084        protein_coding   \n",
       "19449  117120148   C  CA  ENST00000546407  processed_transcript   \n",
       "19449  117120148   C  CA  ENST00000446805        protein_coding   \n",
       "\n",
       "                          Annotation    Impact           Hgvsc  \\\n",
       "19449  frameshift_variant&start_lost      HIGH         c.1dupA   \n",
       "19449  frameshift_variant&start_lost      HIGH         c.1dupA   \n",
       "19449  frameshift_variant&start_lost      HIGH         c.1dupA   \n",
       "19449               sequence_feature       LOW      c.-1_1insA   \n",
       "19449                 intron_variant  MODIFIER  n.166+4287dupA   \n",
       "19449                 intron_variant  MODIFIER  c.-191+401dupA   \n",
       "\n",
       "                   Effect  \n",
       "19449  frameshift_variant  \n",
       "19449  frameshift_variant  \n",
       "19449  frameshift_variant  \n",
       "19449    sequence_feature  \n",
       "19449      intron_variant  \n",
       "19449      intron_variant  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df.ix[19449]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here's a consequential mismatch. Vep annotates this deletion of a T on ENST00000446805 as `coding_sequence_variant` with a `LOW` impact, whereas SnpEff annotates it as a frameshift with a `HIGH` impact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>32771</th>\n",
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       "      <td>LOW</td>\n",
       "      <td>ENST00000446805.1:c.109T&gt;C</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             POS REF ALT       Feature_id         Biotype  \\\n",
       "32771  117171030  CT   C  ENST00000446805  protein_coding   \n",
       "32774  117171031   T   A  ENST00000446805  protein_coding   \n",
       "32775  117171031   T   G  ENST00000446805  protein_coding   \n",
       "32776  117171031   T   C  ENST00000446805  protein_coding   \n",
       "\n",
       "                                              Annotation Impact  \\\n",
       "32771  incomplete_terminal_codon_variant&coding_seque...    LOW   \n",
       "32774  incomplete_terminal_codon_variant&coding_seque...    LOW   \n",
       "32775  incomplete_terminal_codon_variant&coding_seque...    LOW   \n",
       "32776  incomplete_terminal_codon_variant&coding_seque...    LOW   \n",
       "\n",
       "                             Hgvsc                   Effect  \n",
       "32771  ENST00000446805.1:c.109delT  coding_sequence_variant  \n",
       "32774   ENST00000446805.1:c.109T>A  coding_sequence_variant  \n",
       "32775   ENST00000446805.1:c.109T>G  coding_sequence_variant  \n",
       "32776   ENST00000446805.1:c.109T>C  coding_sequence_variant  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df[vep_df['Effect'].str.contains('coding_sequence_variant')][:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    "deletable": true,
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   },
   "outputs": [
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      ],
      "text/plain": [
       "             POS REF ALT       Feature_id         Biotype  \\\n",
       "32771  117171030  CT   C  ENST00000003084  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000426809  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000446805  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000454343  protein_coding   \n",
       "\n",
       "                                              Annotation Impact  \\\n",
       "32771                                 frameshift_variant   HIGH   \n",
       "32771                                 frameshift_variant   HIGH   \n",
       "32771  incomplete_terminal_codon_variant&coding_seque...    LOW   \n",
       "32771                                 frameshift_variant   HIGH   \n",
       "\n",
       "                             Hgvsc                   Effect  \n",
       "32771  ENST00000003084.6:c.352delT       frameshift_variant  \n",
       "32771  ENST00000426809.1:c.352delT       frameshift_variant  \n",
       "32771  ENST00000446805.1:c.109delT  coding_sequence_variant  \n",
       "32771  ENST00000454343.1:c.352delT       frameshift_variant  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vep_df.ix[32771]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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       "      <th>32771</th>\n",
       "      <td>117171030</td>\n",
       "      <td>CT</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>frameshift_variant&amp;splice_region_variant</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>c.109delT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32771</th>\n",
       "      <td>117171030</td>\n",
       "      <td>CT</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000454343</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>c.352delT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32771</th>\n",
       "      <td>117171030</td>\n",
       "      <td>CT</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000426809</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>c.352delT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32771</th>\n",
       "      <td>117171030</td>\n",
       "      <td>CT</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000003084</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>sequence_feature</td>\n",
       "      <td>LOW</td>\n",
       "      <td>c.352delT</td>\n",
       "      <td>sequence_feature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             POS REF ALT       Feature_id         Biotype  \\\n",
       "32771  117171030  CT   C  ENST00000003084  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000446805  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000454343  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000426809  protein_coding   \n",
       "32771  117171030  CT   C  ENST00000003084  protein_coding   \n",
       "\n",
       "                                     Annotation Impact      Hgvsc  \\\n",
       "32771                        frameshift_variant   HIGH  c.352delT   \n",
       "32771  frameshift_variant&splice_region_variant   HIGH  c.109delT   \n",
       "32771                        frameshift_variant   HIGH  c.352delT   \n",
       "32771                        frameshift_variant   HIGH  c.352delT   \n",
       "32771                          sequence_feature    LOW  c.352delT   \n",
       "\n",
       "                   Effect  \n",
       "32771  frameshift_variant  \n",
       "32771  frameshift_variant  \n",
       "32771  frameshift_variant  \n",
       "32771  frameshift_variant  \n",
       "32771    sequence_feature  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snpeff_df.ix[32771]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So we'll remove some of this inconsistances by dropping rows that are annotated as `structural_interaction_variant` or `sequence_feature` by Snpeff and normalizing SnpEff more granular *inframe* annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "snpeff_df = snpeff_df[~snpeff_df['Effect'].str.contains('structural_interaction_variant|sequence_feature')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "collapse_map = {\n",
    "'3_prime_UTR_variant': '3_prime_UTR_variant', \n",
    "'5_prime_UTR_premature_start_codon_gain_variant': '5_prime_UTR_premature_start_codon_gain_variant',\n",
    "'5_prime_UTR_variant': '5_prime_UTR_variant',\n",
    "'coding_sequence_variant': 'coding_sequence_variant',\n",
    "'conservative_inframe_deletion': 'inframe_deletion',\n",
    "'conservative_inframe_insertion': 'inframe_insertion',\n",
    "'disruptive_inframe_deletion': 'inframe_deletion',\n",
    "'disruptive_inframe_insertion': 'inframe_insertion',\n",
    "'downstream_gene_variant': 'downstream_gene_variant',\n",
    "'exon_loss_variant': 'exon_loss_variant',\n",
    "'frameshift_variant': 'frameshift_variant',\n",
    "'incomplete_terminal_codon_variant': 'incomplete_terminal_codon_variant',\n",
    "'inframe_deletion': 'inframe_deletion',\n",
    "'inframe_insertion': 'inframe_insertion', \n",
    "'initiator_codon_variant': 'initiator_codon_variant',\n",
    "'intergenic_region': 'intergenic_region',\n",
    "'intron_variant': 'intron_variant',\n",
    "'missense_variant': 'missense_variant',\n",
    "'non_coding_transcript_exon_variant': 'non_coding_transcript_exon_variant',\n",
    "'non_coding_transcript_variant': 'non_coding_transcript_variant',\n",
    "'protein_altering_variant': 'inframe_insertion',\n",
    "'splice_acceptor_variant': 'splice_acceptor_variant',\n",
    "'splice_donor_variant': 'splice_donor_variant',\n",
    "'splice_region_variant': 'splice_region_variant',\n",
    "'start_lost': 'start_lost',\n",
    "'stop_gained': 'stop_gained',\n",
    "'stop_lost': 'stop_lost', \n",
    "'stop_retained_variant': 'stop_retained_variant',\n",
    "'synonymous_variant': 'synonymous_variant',\n",
    "'upstream_gene_variant': 'upstream_gene_variant'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "vep_df['Normalized_effect'] = vep_df['Effect'].apply(lambda eff: collapse_map[eff])\n",
    "snpeff_df['Normalized_effect'] = snpeff_df['Effect'].apply(lambda eff: collapse_map[eff])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Effect and Impact Condorance\n",
    "\n",
    "First, let's look at the effects numbers again, but this time normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotations\n",
      "\n",
      "                                                     VEP    SnpEff\n",
      "3_prime_UTR_variant                               6654.0    6680.0\n",
      "5_prime_UTR_premature_start_codon_gain_variant       0.0     286.0\n",
      "5_prime_UTR_variant                               9987.0   10259.0\n",
      "coding_sequence_variant                             28.0       0.0\n",
      "downstream_gene_variant                          99177.0   99185.0\n",
      "exon_loss_variant                                    0.0       7.0\n",
      "frameshift_variant                              113706.0  113693.0\n",
      "incomplete_terminal_codon_variant                    8.0       0.0\n",
      "inframe_deletion                                 13604.0   13609.0\n",
      "inframe_insertion                                25795.0   25861.0\n",
      "initiator_codon_variant                              0.0       8.0\n",
      "intergenic_region                                    0.0    1403.0\n",
      "intron_variant                                  417918.0  416946.0\n",
      "missense_variant                                 30988.0   30988.0\n",
      "non_coding_transcript_exon_variant               29794.0   29567.0\n",
      "non_coding_transcript_variant                        0.0      42.0\n",
      "splice_acceptor_variant                           2208.0    2776.0\n",
      "splice_donor_variant                              2208.0    2869.0\n",
      "splice_region_variant                            17865.0   18005.0\n",
      "start_lost                                          55.0      65.0\n",
      "stop_gained                                       4877.0    4867.0\n",
      "stop_lost                                           55.0      63.0\n",
      "stop_retained_variant                                5.0       5.0\n",
      "synonymous_variant                                9056.0    9052.0\n",
      "upstream_gene_variant                            83853.0   83809.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd01af1bb38>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pazkUMzMzMzMzs+V42aOZmZmZmZmZmbVYLn6ZmZmZmZmZmVmL5eKXmZmZmZmZ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\nMATT/zPpfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd01af2e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vc_vep = vep_df.groupby(['Normalized_effect']).size()\n",
    "vc_vep.name = \"VEP\"\n",
    "vc_snpeff = snpeff_df.groupby(['Normalized_effect']).size()\n",
    "vc_snpeff.name = \"SnpEff\"\n",
    "vc_df = pd.DataFrame([vc_vep, vc_snpeff])\n",
    "print(\"Annotations\\n\")\n",
    "print(vc_df.transpose().fillna(0))\n",
    "vc_df.transpose().plot(kind=\"barh\", fontsize=13, figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we join the VEP and SnpEff dataframes together and count number of matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df = pd.merge(vep_df, snpeff_df, on=['POS', 'REF', 'ALT', \"Feature_id\" ], how='outer', suffixes=('_vep','_snpeff'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df['Impact_match'] = effect_df.apply(lambda row: row['Impact_vep'] == row['Impact_snpeff'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df['Effect_match'] = effect_df.apply(lambda row: row['Effect_vep'] == row['Effect_snpeff'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df['Normalized_Effect_match'] = effect_df.apply(lambda row: row['Normalized_effect_vep'] == row['Normalized_effect_snpeff'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     99.29\n",
       "False     0.71\n",
       "Name: Impact_match, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(effect_df['Impact_match'].value_counts()/effect_df['Impact_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We find a 99% match between calculated impacts between tools. And below we examine the 4x4 grid of categorization matches. There are a suprising number (almost 1600) of *HIGH-LOW* disagreements. These deserve to be examined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Impact_vep  Impact_snpeff\n",
       "HIGH        HIGH             122728\n",
       "            LOW                 307\n",
       "            MODERATE             64\n",
       "            MODIFIER             55\n",
       "LOW         HIGH               1286\n",
       "            LOW               24976\n",
       "            MODERATE             19\n",
       "            MODIFIER            633\n",
       "MODERATE    HIGH                243\n",
       "            LOW                  19\n",
       "            MODERATE          70377\n",
       "            MODIFIER              3\n",
       "MODIFIER    HIGH                 76\n",
       "            LOW                2054\n",
       "            MODERATE              5\n",
       "            MODIFIER         645789\n",
       "Name: Impact_match, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effect_df.groupby(['Impact_vep', 'Impact_snpeff'])['Impact_match'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O4FUAvgfg3QD+bBhGqODnHgRwJoCrAUwA+C/DMD55lNf9jfX4/OWlR3nsK2c99vIj3H8B\ngK0AvmwYxlVHeT4iIteyihK/AjAN4I0A3gmgA8BfDcM4C8DDAFIATrUeHwBwsvXjp1u3dQBoAXBf\nKWMnmg9WkKkUHoKZYEat/18E4FGYCeeFMBPXr0kpP23df4dhGEkANwJ4A4CfWrdPSCkfBADDMH4H\n4AkA1xuG8d0jvO5g/vHzsEVKOTCf+w3DGIZZ7T4FwM3zfH4iIlcwDMMH4EsAegBcKKVMWbffA2Af\ngC9IKc83DONRAKdZP7YBQCXMxPl067ZTrOt7SxU70Xyxgkyl8FcApxqG0WoYxvEA2gHcZd13nnX9\ny1k/8wvr+vy5nlBKmQXwMwBVADYd4XWFYRiBgov/KDH6Zz129mcjf38lgCut2544yvMREbnVWgAL\nAfwunxwDgJSyD8D9ADYbhhGEWRleaBhGO8ykOAHgfwAcZxhGFQ4nyKwgk+MwQaZSuBdm3/BFMFst\n9gLYY93XaF0PzvqZ/P8XHOV5x6zr+iPc/06YrRz5y9ES2p5Zj/3cEe6fgdlz9w2YLSFERF5zpHE7\nf1sQQC0OV4ZPg1nI2AIzGfZbt50C4ICUsqeo0RK9AGyxoFJIwqwYvxxm1eH3BfcNW9eLABwsuL3V\nup5rAM5rt66HYA7Gs90C8zBgXvwoz3UhgJGC//cd4f4vwmwJ+a5VxSYi8prCcXu2Vpi9xxMwq8kK\nZiK8CcCfADwO8zvhVOvyx2IHS/RCMEGmUrkNZg9yHYCvwkyUATNxvhJmr3Fhv/AV1vWdcz2Z1S5x\nBYBxAI8AWDnHw4aklI/OM74nnqcH+Qkp5YBhGJ+G2T/9RStmIiKv2QXzqNqlhmF8SEqZAADDMNpg\nrlLxFyllBsCYYRg7YM43WQvgi1LKlGEYT8CckN0M9h+TQzFBplL5PYDvw2y1uBuHk8s/wawgfMAw\njLj1/5NgJqAP4Nm9yXWGYZwBoAHAPwBYA+CDUsqEYRgvNr6TDcMYnXXbltkPklI+ZhjG/wK4wjCM\nG48hASciKldnG4aRKfh/vtXsJgB/NAzjGzArxf8Ec7m3Txc89j6Y7W4+mBO2YV2/r+B+Isdhgkwl\nIaXsNgzjSQDdsxJaBeC1AG6AuVTQRwEcgrnU2/VWFSLvDAB/gzkA7wJwrZTyRzaFeNsct3Uc4bGd\nAF4NsxI+5yRCIiIXuRjPXqrzkJRyoWEYMQCfgrmaTxpmS8U1UsrHCh57L8xlO4eklPn16R8C8H6Y\nbWtPFTt4ohdCKKWe/1FERERERB7BVSyIiIiIiAowQSYiIiIiKsAEmYiIiIioABNkIiIiIqICTJCJ\niIiIiAowQSYiIiIiKsAEmYiIiIioABNkIiIiIqICTJCJiIiIiAowQSYiIiIiKsAEmYiIiIioABNk\nIiIiIqICTJCJiIiIiAowQSYiIiIiKsAEmYiIiIioABNkIiIiIqICTJCJiIiIiAowQSYiIiIiKsAE\nmYiIiIioABNkIiIiIqICTJCJiIiIiAowQSYiIiIiKsAEmYiIiIioABNkIiIiIqICTJCJiIiIiAow\nQSYiIiIiKsAEmYiIiIioQEB3AORsIip8AITqVFndsbiBEPADUEohpzsWIioPIioEzIKWUp2KY8eL\nJAQOv58ci+kIhFJKdwxUAiIqqgDUAagFUAmgwrpEAIStSxDmoJG/iFlPkwGQnnWdBDADIGZdP3NR\nnSpR1I3STAgEAVQDqIL5nlYVXCIw38+Adcn/u/A9VQByBZcszPczCSABIA7zfY0DmAIwAWBKKfBD\nS1SGRFT4YY7BdTDHjgrMPRb78exxuHDcyMEce/PjcH4sjuPZY3B+TJ52e4FDiOeMv4VjcgjPHoOD\nMN/fQrlZlzQOj8P5sTj//k4CmFAKrv5+IybIriOioh5AM4AGmINwPinWcbQgAWCs4DIKYKzcEmcr\nEW4ouDRa11UawsnhcLI8AWAcwBCAUVZCiJxBREUAQBPMsbiu4FKF5xYeik3BHDNG8eyxeKLcEmch\nUI3njsP1MJPeUkvh8Dg8AfM9HVIK0xpioSJgglzGrKpwC8xBuAXmgBzSGtT8zAAYKLiMqk7n/CEK\ngVoACwsu9XojmpcsgBEAgzAT5iGlMK43JCL3s9ofFuDwWJwvUJQ6ET5WOZhJ3TNjsepUMb0hHWa1\nozXj8DjcCrO67nQxWGOwdRlUCkm9IdELwQS5jIioCAJoA9BuXer0RmSbFMzErh9At+pUw6V8cSFQ\nA2ApgEUwB+KKUr5+EcUA9OQvPCRIZA8RFbU4PA63oTwKE/MxCTNZ7gPQVcqjfULAB3P8bYc5Fjfh\nua0Q5UgBGMbhsfgQj/aVBybIDme1TCwD0AFzD9oLK4/MADgIoAtAr92HAa0JGq0AlsBMjBvsfH4H\nyw/SXUphQHcwROXCmqzcDnPMaIfZtuZ2Cmbh4iDMZHnU7hcQAmGY321LrWu37GgcTRrmDkgPgP1K\nwTFVe3o2JsgOZFUnVlqXRs3h6JYB0AtgL4ADqlNlXsiTWEnxYgCrYH7JRWyLsDxNA9gHYK9SGNId\nDJHTWElxG8xxeDm8kbwdzRSAAwD2qE71gscMIRCB+Z6ugHeKPkeiYFbs9wLYx6N8zsIE2SFEVFQC\nWA1z4GjSHI5TpWAmdbtVp+qfzw8IgQYAa2C+t5VFjK2cTSL/viqM6Q6GSCcRFYtg7kgvB3ekj2QM\nwG6YY/HM8z3Yap9YAnMsXgJvJ8VHomAWg/KFi7TmeDyPCbJmIioWA1gP8xATB435m4I5QD+tOtWz\nZg0LgRDMgXgNuLNxrPoB7IR56I99cuQJIiryY8Z6lMekXKfIJ3W7AOybvUazEGgCYMAs/HBnY/7S\nML/fdioF21tbaH6YIGvAwdhWCmaP3HbcoOIANsCsFvMkOC9OHMDTAJ7iskXkViIqFgA4DmbFmGPG\nixMH8BQG1z+F7+1YCHMsbtUckxsMwCxa7GPRorSYIJeQiIpqACfCTI45GNtl+pQERt6oMHSiwM5V\nOexvr4DyOX2JpXKhYPYdblUKJV1dhKhYRFQsgTkWL9Qdi2vkIjmMvjaO0UuC6F6VwbY1AYzXeb1v\n205xADsAbFcKKd3BeAET5BKwJt2dBLOyyTYKu0y8NIaRN/iRbnv22piJUBpyeRpPr4gg5+f7bZ8u\nAFuUwqDuQIheCBEVy2GOxWy9skumNouRNyUw8bIIVPjZy7IN18fxxFqBwSa2V9gnBTNR3sZJfcXF\nBLmIrCXaTobZf8WKpl2mNyUweK14TmI8WyKUxo7VGexaFgEE33/79MJMlOc1UZJIJ+tEHithJsZe\nWdKx+HKRHIbfmMD4KyNQoaMXIoYa4nh0g58VZVulYbZePKkU4rqDcSMmyEVgneHuNJgVYyZmdomt\nS2LwXQrJlcdWjYhFUnh8XRYHF7vlBCBO0Qfgb0phRHcgRHOxWinOAOd62Ef5FUZfF8fI5WGoyvmf\nyENBob85jkc3BDFTpePU0G6VAbANwONc+cJeTJBtJKIiAOAE68IeY7skl6Zw6F1ZxI9/cQnuRHUS\nW45TGGjm4T77KJgz2B/hgvfkFCIqGmEmxu26Y3ENJRTGL4xj5I1BZOtfeIKbg0LX4ji2rg8hEeb3\npH1iAB4FIJUCEzsbMEG2iYiKNQA2gWvt2idbmcWh9yYxdXYFYOOku+H6OB44KcAqhq3SAB6HebjP\n1jMfEs2XiIoKAKcCWAsevbNP7LgE+j/sQ6bVvhaJrMjh6ZVxbFtTyUnVthqBeWSvT3cg5Y4J8osk\noqIZwGYAzbpjcZWJ8+MYfEcQueriVBiyIoenVsWxfTUHZ3tNA3hAKRzQHQh5h9VnvAHAKeAZ7+zz\nTJHi3OIVfqYrUvjbSQrDjUefU0LH6gCA+5XC857IhebGBPkFstopToM5KDPBskuqNY3+j2WQWFua\nfuHpyiT+diI4ONvuAID72HZBxWatZXwOWKSw18RLYxh8Z6hoRYpCCgoH2+J49Pgw0sH59zXT80kD\neBjmCUeY7B0jJsgvgIiKNgDnAqjRHYtrKL/CyBUxjLy+AgiWdmk2BYWDi+N4dAMHZ3ulADyoFJ7W\nHQi5j4gKH8yK8Qng8pn2STen0fexDBLrSz+pORXI4LENaRxo54Rqex0C8FelMK47kHLCBPkYiKgI\nAjgd5hnwyC7JjhR6P6ued9m2oscRzOBvJ2XQ38JJfPbqBnAPD/WRXazWtvPAZdvsNXppDENvjpS8\nSDHbocY47js1hFSIBQv7ZAE8AnP9ZCZ+88AEeZ5EVDQBuABAre5YXGXs4jgG3x7WPiDnKSjsXhbD\nlvXsTbZXCsDd7E2mF0tExYkwJ+I5Y8xwg2xlFn3XJRE7yTmTzJPBDO4/OYNDXHXIZv0A/sz2t+fH\nBHkeRFQcB3PJIO7N2iUXyaHvkwnMnOqcAbnQRHUCd2/yI1bJlS7stR1m20VOdyBUXkRURAC8FECH\n7lhcJbYuib5P+17U0m3FoqAgl8ewdX0lT/ZkqwSAu5RCj+5AnIwJ8lGIqAjB7DVerjsWV4mvTqL3\neh+yDc4bkAtlfFk8fEKKJxix3TCAO5XCpO5AqDyIqFgE4GXgMpr2Gvr7GYxeVgn4nZ18jtUm8NfT\nAohXcN1kez0O4FEWLObGBPkIRFS0wByQORHPTsNXzmDkKucPyIUOtMXw0AkR5Pw8pGufFMy+5H26\nAyHnspZvOwnmZLzyGTOcLlOfQe/1aSTWlM/Of9qfxYMnptCzqHxiLg8DMKvJ07oDcRomyHMQUbEa\n5rJBbKmwi/Ir9F0Xx/QZ5VkBGq9J4K4zgkiG+Tdhr8eVwsO6gyDnsZbSPB/AMs2huEtieQo9XxDI\n1jn7CN6RbF89g21Gle4wXCYO4I9KYVB3IE7CBHkWERWnwaxYkF0ytVn0fCGN5IrynmwRD6dx1xkK\nkzU8EYG99gP4i1LI6A6EnEFERRWACwEs0B2Lq0ydHkf/x0NQZb6j37UwhgdOruBEaltlYS4Ft0d3\nIE7BBNliVSvOA7BCcyjuklycQveXhOP7jecr48vivlPTXArOdsMA/sCZ1WQt4fYKsN/YXiOXxTB8\nTQXgkqRytDaBu84Mcu162z2mFB7THYQTMEEGIKKiEuaAzDMx2WnmpAR6Px2EirhrAMtB4fH1ccgV\n/AK31wzMw3zDugMhPURULIe5UgUnY9lFCYWBD8Uxeb77xqtYJIU/nykwXeWOAoxz7IFZTc7qDkQn\nzyfIIipqAbwKQLXuWFxl7KIYBt9VUVaT8Y7V3o4ZPHwCe+HslYGZJPfqDoRKS0TFBgAv0R2Hq+TC\nOXR/PqnlrHilkvZncc9paQw28aievQZgHtVL6Q5EF08nyCIqGgC8EjyUZ6+Ry2IYfos33tOuhTHc\n79C1nMtXFsCflEKX7kCoNKyTf2zSHYer5CI5HPxaCqll7k8csyKHezalMMCTithsCMDvlUJSdyA6\neDZBts6MdzEAfqDsNPL6GIav8VbC2L0whvtOqeBC9rbKwVx6iMvAuZyIilMBnKw7DlfJVmbR9bU0\nUku88/2WFTnce1qK80NsNwrgNqUQ1x1IqXkyQbbWOL4YAFcjsNPwVTMYeaM3Ww56W2K45zQmyfZS\nME9PvVt3IFQcIirOALBRdxyukq3MouvGDFIdYd2hlFxO5HDvqUn0tbq3pUSPcZhJ8ozuQErJcwmy\ndUamCwGwqd9OQ38/g9ErvJkc5/U1x/DXTUyS7XevUnhKdxBkLxEVmwGs1x2Hq2Srszj49QzSbd5L\njvNyIof7T07yhCK2mwJwq5dOKOKpM4NZbRVMju02+FYmxwDQNlSJlz4Uh8h5a6+z+M4WAqt0B0H2\nsSrHTI7tlKllcgwAPuXDWY9F0NHnuZaAIqsB8Coh4JkdD88kyCIq6mG2VTA5ttPwlTMYex2T47yF\nw5U4+1EOzPY7Twgs0R0EvXjWhDy2VdgpF86h66tMjvN8EHjJlggWDSZ0h+IytQAuFsIb7ameSJBF\nVFSDE/LsN/HSGEauZnI82+LBSpyy3VO9WiXgA3CBEFioOxB64URUrAVXq7CXEgrdn08ivZjJcSEf\nBDY/GkT9hGeXKSuSBQAuFML9a5W7PkEWURGBmRxznWM7xY5PYOAD3OE4kjUHqmDs41nh7BWAOTA3\n6g6Ejp2zSw5YAAAgAElEQVR1EpCzdcfhOn2fjLt6neMXI5Dz4/wHfaiI8zT29loI4GVCuDuHdPXG\nWaePvghAve5YXCXZkULP54JAwNV/Py/aSTsr0N7Pdgt7hWAe4uMObxkRUdEG4GUAOIHVToNviWH6\nLG8tq3mswukALnggh2Da02eFK4KlAM7RHUQxuT3BORc8fbS9Mo0ZdP+TcN3po4tBQOAlW8JYMObJ\nRdaLqBLAK7xwiM8NrLOV/h3c/31TWmMXxTB2GZPj+aiOh3D+39KcQG27NULgBN1BFItrByxrIshK\n3XG4Si6cQ9eXssjWc6LjfPmVD+c95EfVTFp3KC6zAMB5uoOgoxNREQTwcgDsj7XT9CkJDL6LbRXH\nonEygnMe4RE9+20SAu26gygGVybIIiqWADhNdxyu03ddgrOkX4BQJoDzH8rBl83pDsVlVgjBM7A5\n3EsB9ozbKt2cRt91QcDPdpVj1TZUieN2cQK1vQTMCdR1ugOxm+sSZGs5t/PBXjd7jb42hplTeTjv\nhaqOhXHmVi45ZL9ThcBS3UHQc4moOAXAMt1xuIryK/REc2xxexE27KpE8wjHYnuFYLa9uWr5N1cl\nyCIqQgBeAZ5C2l7x1UkMXcMVK16sJQOVWHmQK1vY73whOBHXSURULANwiuYw3Gfg/XFPnkLaTj4I\nbH7Mj3CSk/bslS9OuoarEmQAmwH3lfm1ylZm0Xu9jytW2OSU7RHUTXJdTnsFYS45xKqaA1jrzp+r\nOw7XmXhpDJMv41E8O0RSQZzzCMdh+y0RAsfrDsIurkl6RFSsAng6Wtv1fjaFbAMn5dnFr3w472HA\nn2E/sr0WgCeg0E5EhYDZd8wqp52Si1M49D6+p3ZqGq/AiTt5RM9+m4TAAt1B2MEVCbKIihqY1WOy\n0/BVM4gfz5nSdqtMhLD5MfbA2e94t86mLiMnAlikOwhXyQVz6L1BQYV5hMRua/dVYOEQx2J7+WG2\nvZX9MpxlnyCLqPDB7Hth37GdEstTGLmSh/OKpW2oEiu6WL2w33lC8JTyOoioaAH7ju136D1xpBey\nelwM5lr1AZ5ExHYNAM7QHcSLVfYJMoCTAbTqDsJVlF+h7zrFZYSK7JQdYVQkeApUe1WC6yOXnLXe\n8flwx3eKc8SOT7DvuMjC6QBOf4Inc7Lf+nJfYaisBzMRFQsAnKQ7DtcZuibG9Y5LIJD148wtPIGI\n/ZYIgTW6g/CYTQBqdQfhKrlwDn0f9QE+FiqKrWOgEu39PImI/c4p56XfyjZBtiaDnAOud2yvxPIU\nxi5lxaJUWkcrsLybrRb2O4OtFqVhtVas1x2H6wy+M47sgrJNLsrOpieDCHDytM0qAJyuO4gXqmwT\nZADHAWjWHYSrKKHQ/3G2VpTayTtCCKXYA2evCFzQA+d01hyQs8FChb3iq5OYuICFilIKpwM4bRsn\n7NlvnRBYqDuIF6IsE2QRFVXgqaTtN/o6LkKvQygTwKYn2QNnvzVCoE13EC63AXDHkk6OoYRC/8fA\n1goNlvZW8Cx7RXG2EOWXb5ZdwJazYJ4cgOySqc9g5Comx7p0DFSiZZgDs/028wQixWGdEORU3XG4\nzujlcc4B0URA4IzHfYBSukNxmQYAJ+gO4liVXYIsomIJgGW643CdwXemuM6mZqduZ8XIfvUw1+Yl\n+50FlP9ap46Srcxi5AomxzpVx0NYc4AT9ux3shDlNZG3rBJkq9/tTN1xuE6yI4Wps3hCEN3qpsOc\nsFcUJwgB9nPaSETFYqC8l3BypKFrE1ARFip0O16GeLZT2/lRZmc7LasEGcA6AHW6g3CdgX/Ist/N\nIU58KgiR4+E9ewXAOQu2sVYQ4gRIu6Va05g4n4UKJwhlAtgoWUW23wohyue8FWWTIIuoCIFnabLf\nzEkJJNZzUHaKSCqI43azimy/NUKgUXcQLrEanJhnv8H3ZIBA2Xwnu97qAxU8kVNRlM3OdTl9GDcC\nXNfUdofezcqx06zbF+Gyb7YTKLPDe04kosIPTsyzX9xIYuYUFiqcxK98OHl7SncYLtRaLmfYK4sE\nWURFBMDxuuNwnfGXc7a0EwWyfpz4FJd9s9+Scl2P00HWAajWHYTrHHov26qcqGOgAnWTTJLtd5oQ\nzl87vSwSZJiz0Lmsm52UUBi+mjPQnWp5T4SH94qCvcgvkIiKAICTdMfhOtOnxZFcwaOjTiQgcPIO\nHs2zXyOAlbqDeD6OT5BFVIRhVi3ITpPnxZFt4E6HU/mUD8ftZhXZfouEQIvuIMqUAfPUsWSn4asc\nX0nztNaRCKpn0rrDcCHHr4vs+AQZ5imlmcjZbeQNXErI6ZZ3RxBMs3phP66LfIyslSs26o7DdeKr\nk0iuZvXYyQQENj7NBNl+C4RAh+4gjsbRCbI1IWSD7jhcZ/rkBHuPy0Ag58fafawi22+pEKjXHUSZ\nWQmgRncQrjN8NdfaLQcdAxFEkmx5s5+jq8iOTpBhHtLj3rXdRt7ECSHlYvWBENdFth2rocfO0V9k\nZSm1KI3Yifx+KwdseSuWNiHQrDuII3FsgsxDekWSWJlEYg37CMtFOB3AqoNcsN5+a3h2vfkRUdEO\nrntsv+E3pXmCpjKyojuCAM+uVwSObXlzbIIMYBlQXuftLgvDV7Ontdys38vVRuznA9u35ouFCrtl\narOYegmrx+UkkPVj7b6E7jBcaJkQzsz1nJwgc+UKu2XqM5g5idXjclOZCGHRIAdm+60RwtFjoHYi\nKmoAtOuOw3XGLk0AQf7tlZtVB7lggP0EzHZax3HkB1RERTWAxbrjcJ3xC1OAn4f0ytGa/Ty0Z79K\nAEt0B+FwjvziKnuT5zPRKkcVySBah9mLbD/DiScOcWSCDGAt4Lw3q+xNXMBD9eWqdZhLvhXHWt0B\nOJU1D4QJst1i65PINIV0h0Ev0OoDHIft58hiheMSZGtQXqM7DteJr04i08pBuVz5lQ/Le1i5sF+H\nEKjSHYRDdQB8b2w3/iomWOWsbTACPyfrFYHjihWOS5Bh9rtV6w7CdcYv4aBc7lYddOLntdyxSnpk\njvvCKnu5cA7Tm7gGfTnz53xY3sM5IfbrcNrKQk78wuWXld2UX2HqTA7K5a5uOoLaqZTuMFyIR6xm\nEVERgQMPeZa9yXMSUGGexbTcreri79B+PgCrdQdRyFEJsoiKADgo22/ynDhUhB9oN1hzgKc8tV+t\nEGjSHYTDLIfDvh9cYfwivqdu0DAZRtUMx2L7rdAdQCGnfVjbAXAimd0mX8YJj27R0c8+8uJYrjsA\nh+H7YbdMfQbJlTyS5xarung0z37NQjinxdZpCTIHZbspv0J8HQdlt4ikgqif4MBsP449FhEVIQBt\nuuNwnamX8Mx5brL4EI/KFscy3QHkOSZBFlHhA7BUdxyuM3NyAirkmN8z2WBJPw/t2a9eCNTrDsIh\nlsJB3w2uMf0S3RGQnWqnwwgnOfndfo5ps3DSILgYAA8f221qs9IdAtls8SG2IRUHq8gmvg9245E8\n9xEQ6Bjg0Tz7tQoBR5zx10kJ8jLdAbjSzMk8Y5Pb1E2FWLkoimW6A9DNmijNU0vbbeakJI/kuVBH\nPwtQ9hNwSDeBkz6wHJTtllyaQraeCbLbCAi0D/CkIfZrFgIR3UFothCcKG2/qc08sYQbNY2GIXJM\nku3niHzQEQmyiIoaADW643CdybMzukOgIuno52Sf4likOwDNODmvGHgkz50COT9aR1issJ8jxiFH\nJMhwyJvhOjOnOeX3S3ZrHg0BipUL+3l9LPL69tsvuTiFbAMTZLda0sd2N/tFhECj7iCckkBxULab\n8iskOzjp0a0COT/qJ7mahf08OxaJqAgCPGGK7WIn8kiemzWNcbm34tA+FjNBdqvEqjQQdMrvl4qh\ndYSVC/s1OGUGtQYL4ZzvBPeIb2A7lJvVzITYh1wU2vNC7YOhiIpaAFW643Cd2AZWLdyueZSDcnF4\ntQ9Z+xeSK8VXs8LoZj7lw4JxLvdmv0VCQOvOpfYEGWbVguwW36A7Aiq2BeNcbaA4vDomeXW7iycX\nySHTzP5jt+PRvGIIA3pP3uSEBHmB7gBcKbGSg7LbVSZCCKY5MNvPc2OSiAoB6J8U4zqx9SmeXtoD\nmkd1R+BWWsdiJshulKnNcta0RzSP8tCe/bw4JtUC4Jhht/jx3IH1goYJfnaKQ+ukYSckyJw1bbf4\n8UyavKJ1mCcgsF9ICNTqDqLEvLhTUHyx9U74jqVii6SCqEhw3o/9vFtBtk4QwqXI7JZYxaTJK+on\n+QVcHF5LGFmoKIb0Ys4T8IqGCS67aT/vJsjw3pdQaaTa2fPmFdUxzpAvDq+NTV7b3uLLBXPI1vHQ\nu1fUTXFVIftFhNC3yhkTZDdKten+vVKpVCRYoSoOr41NXtve4ku1s//YS+qmdUfgVtrGJt2JlNf6\n/Eoj3cSqhVf4lQ+VMR7as59nxiYRFQEAlbrjcJ1UO3tSvaRmmkdui0PbWKw7Qa7R/Pruk63MQlXy\nsLuX1E2zUmU/L41N1boDcKXkUh5y95KqOI/mFYe2sVh3gsyB2W6pDlYtvKZuipMy7RcQAhHdQZSI\nl3YGSifVrjsCKqVIMsBTTheF9xJkERU+8BTT9ksuYTXRa2qnOSgXh1cSR69sZ2mlF+kuQFEpCQhU\ns92tCLyXIMOsHrNnx26pdiZLXlMzwy/i4vBK4sgjecWQbuYhd6+pm2KByn6eTZDJbtl67nR4TTjF\n33lxeCVB9sp2llaOc0E8J5Jkgcp+ISH0nC9DZ4LMWdPFkPXM5HvKC2ZYQS4Or4xRXtnO0slFcoCf\nO65eE04xQS4OLWOUzi/WsMbXdq9sLQdlrwmmWakqDq+c5ZNjsd2yNTzU7kU8mlcsnqsgc1AuhmwV\nq4leE8jyd14cXhmjvLIjUDqZeq4s40XhlO4I3ErLWKzzi5WDcjHkKpkseY2AQDDNipX9vDJGeWVH\noHSytTzU7kWsIBeL5yrIXvnyKS0myN4UTrFiZT/Xj1EiKgQAnnnTbtlafh69KJRmglwcnqsgs2ph\nN+VXUGH2o3oRE+Ri8MIY5fqdAC2y9bojIB2YIBeLlnFK5zqN2gfm9jvbLwtNhVZBwT+5fPL24ZOG\nty68f+H5NQdrLt//mv0fzlRmkoWPX3rb0rcrn8p0XdT1k5aHWs6sPVh7aTaUHQKAZENyZ9+5fbfr\n2RJLLpwDUNYJckvXLZuqxx5/BSByE81n/m6k7cJtANDYd8f6pv4/fnDXKV9/FwCsfuxj38sE6/bm\nf+7Ahuv+edG+n14YjvWtN29RwpdL1e098R+vX7j/pvMqp/acriBymXDjwW7jH37hT02E2nd//62+\nbLJWiUCyf8XVP0lWLZnUsc22CGa0H9Jtb7/mslBo/yog55+cvPT24eFPbF248CPn19T8/vL9++/+\ncCazMAkALS2f31Rd/ccLAJGbmTn33kOHvni/EHFfR8dV1/j9o81C5Pyjo+/41fj4m/do3iTtY1QJ\nOGIbaw7WtLU83PLembaZOwfOGri7urt6YfNjzVcDQDaSPdR9QfdNKqCe2QksHItFWviX/GnJW3xJ\nXyME1KHTD/0ktig2rG9rAOQqtX8e7dIuv3dZKDm0Ckr5Jxeccvtw+yVbgeeOya37bz67alKerYQv\nM9V44p+G21+9tW3Pf1w015hcMbW3YeH+m94uVC6QDjV0da/74E36ttBGDpkPMnssTqcXTzQ2/uAy\nwJdVKpDp6/vOj1KptdOLF7/1NaHQ3jVCKBGPb9za3//dO4LBrorFi6+9VohkhVKhZF/fD36YSq2M\nad4kzyXIWv+QGnY2GIFYoG3fa/d9JTQeqlpyx5LP+tK+sD/pr1UBNTH78Y3bG9f5k/7mTEWmP39b\nojHxaM8FPb8qbeRHocp7XfpQfKCqZnTLJQfXffQfA5nJSEvXby4Zabtwmy8zE6gbfuiinC/0zO9F\nCX98/8brv174832rrr0dwO0A0HLwl2f6MzM1geRopGp8x8v3nXDDZ5UvlFu+7YsfrBu8f3nl9N4V\n2WDt0MHj3vOD+kN/XdXadcuru9Z96Gcl3mT7+PSe4rSh4YdGINDftm/fvV8JhXZXLVlyxWd9vpmw\n3z9Wq1TlM783v38wVFNz66u6un79pVyuKrNs2cWfHht729b6+h+fqFQotX//PV+rrr59UUvLP75l\nfPzNX9K5TdA8RpWI9h1qf8wfatra9IZ0Tfrp/G1Njze9bnLF5O0jG0d2tN3d9sqmx5tOHTp16GHg\nuWNx89bmTblALnbw4oP/0bi9cX3zlubXHnzlwX/XtT0AAOWOP52Ggb8YgfR4276NnV8JxQeqljz9\nrc8Ot1+ydfaYHI711lRPbH/5/g2fjgLAsp03fnSs9fztc43JANDS9evXTzWe/Kfh9lc93vH0t66q\nmNrTGK9ZNaptQ+0i9O8XzTUWp9OL9h869MUfx2JnDy9e/I5XNTV98+ypqVc9EQ7vMfbtu/crQFqs\nXHnWDeHwtgebmr51TjJp7Orv/84dra3Xnd3SEr2wp+ent2jeLC0fKJ2fYq2HIsaN8V3dF3T/AADS\nNekYcgiPrRt7ovf83t8qqGf9lftSvkDdnrpXTqyYuE1PtPPl0//pfBHqhh9cl4o0P5UJNyQTVUsn\n8gnrov03XTzdsPFuBZGZz/OIXMpXPbHz3MEll/8l5w9nIHzZQGoiLHIpH1Q2lAnVzwRSY62JysX7\nAWC89dw9weTIqmJuW9FpHpjHx/9+V3f3TebnKb0kBmTCY2PXPtHb++PfKoVngqut/e3yTGbRgXR6\nSTybXZBOpTr21tT8btXQ0Kce6u39wS/Mn186LUTCCaeh98LhUu3bmAvlMl0Xdn07G84e3pFK+lum\nlkwdAICZxTM7KoYq1gNzj8WR4cjamcUzWwFgdP3oU8GZoP7PcpkXK/LGWzbv6l7zPvNzHV4Qg8qG\nRS4tZo/JkZmuBdlA9UAuUJXJBaoy6VB9d+3II8vzz1M4JkNlRTA1umqk7RVPAED32g/c7Irk2CHm\nGou7un7z77HY2cNAFn7/WH0m0zKWybTEgWzA55sI+P3jQUCobLYhFQrtWjs5eflWAJiYuOrJUGjP\nOr1bBEBTrurZCrLyK5WtzKYAoOXhls2p2tT2dE06PtdjF92/6KLp9um7c+FcovD20GRozbLfLfuA\nUMI/cvzIryZXTHaXIvYjK++qRTA1tkDksqFl27/0PpFLV443n3VrqmLhaDA52t67+p2/qx1+6LL8\nY4XKBZfuvPFaXya2IFG1dEv/yjffmb+vqff2k5IVi3Zkg9VpAJhqPPnWpTtv/CcIXypR2f7ITP1x\ngzVjT/RWTu09HsDWhkN3r/ZlEws0bLJ9NCfISoVVNttifp5aOjenUiu2p9NLnvN5CgQG6rLZ6un8\n/3O52slA4FCdUlVZpcycuKnpqy9LJE54uGTBH1l5f6DmR3uCrAIqlw1kn9VDn6nI9Nbvqj9+cNPg\ng1W9Vcf50r5aYO6x2J/216Yr0+bflA8KgPKlfP5cKKdxZRd3/OkoX1BlQ3Xm57rrV5tTkZbtlZO7\nmmePyfHqFYNNPbcuDsX7q3P+inQwObIykJ7YlX+ewjE5PNNTo0Qg2b7r+1cEUmNL0pGWPT1r3v0b\nXdtoK6H0f57mGIuVCqvGxu8c19Dwn2/IZhv7Bwc/+xAQVInECY+tWHHOlwDlm56+4P8ymfaEzxer\nS6VWTANAMrl20ueL1endIgCaxil3fIpfhKbHm06o6q86q//s/pvnur+qt6olOBVcOnTq0KOFt8fa\nYvsnVk3ceuDVB741um70f5u3Nr+1NBG7mFLCl0tWHVz/0X89tPT1P1kw8Oc3N/f87xWDHZf+cvZD\npxpP+lXvqrf/rGvtB/+lYnrf6bXDDy7N31c1vn3zWOt5DwBAIDkaqRndenHXug9dv2/j5z4dTI4s\nrxl5rH1wyWvvU8KXWf7k5z9eOfH0cTlfcKqUm+pWTU1fP6Gq6r6z+vu/PefnaQ7PGvgWLvzYecFg\nz5K+vm87/GgNFdPgaYO/qhyoPHX5b5d/RCghgCOPxXPQnqS4TVPvbSdUTcqz+le++ea5xuRURWts\nsun0X7fv/rf3Ld79g7dmg7V9OHzg6FljMpATvmyyfmTRBXcd2PCpGwPJ0Y4FfX84vqQbVCwOaLHI\nmz0Wj47+w469ex+8PpNpHWhr+8CFlZX3NkUiT560f/+dnzl48P8+W1V1/7nh8M5Zp53PefqzpLOC\nrH3WfeO2xvW1+2ov7nlZzzePVD2u3V97vC/ta1zxmxXXiayIiKyoWXTPopf3n9N/x3TH9AAATKyZ\n2Ne8tblGZIVQfqXxE6L9LX1RMsHqSQB7lS+Ui9WtG1KA8qen2xYe/MW1AODLpeqWP/mFj+3feP2N\nA8vfeE/+51KR1qcjM12LJ5vOOOhPTYT82URDrHbNCABUTT69KBuoHEpVLJoGgHSkZU/l1N4lUwtO\n6ele+8GfA0AgNR5euvPrJ2jYZPvoL1ygsfG762trb7m4p+en35yregwAmczi8crKR545H7rfP1af\nSGzcBwCtrZ85KxJ5fGNX1y+/p1SVE9Z1Lu8P1Pw45xu9QLwlPnbg1Qe+A5jjtC/lqzvSWJwNZSeC\n08FaABBp4QcAvdVjwE1/Oo19d6yvHX7k4p417/mmPz0V9mdmFs41Jg91XPrYUMeljwHA0h1fe3sq\n3DICALPH5FTFwumcPzISq1tnTnCvbHs6FO9fBGCbpk20T84ZbY6zx+Kmpq+dODz88ccBP6amLt7S\n0PAfl1RV3TOUTnfsz2abU9kskMm09FRW3teWy1WNh0K7atPpJfFI5In6XK5qXPf2QNM4pbOCrPUP\nKTgVrKjfVX9573m930nVpY44Q7N/c/+f91227/P7Xrvvy6PHjf48VZva1n9O/x2L7ln08uZHm08D\nzBnYKqCm9CbHQLnv7U0uOG1nON6/FiorQvGBKgGIvSd+4TP7NnZ+ed/Gzi/nfKGJ/Ruvv7FqfHvr\n0p03XguVg8ilfMHk8MpkZUcfANSMb2vPBKsH8s+ZqFwy7M9ML/Knp4MAEEyOLE1WtA4u6PvDhsW7\nf/BqAGjq/f3pyYpF2/VstU00J8jBYFdFff1/Xd7b++/fOdqM58nJ1+wPBPqXBYMHKgKBgXAw2LNq\ncvJ1eyor722qqrr73O7um7+fyzXMq9e8BBzxZVdkjtzGxX9ZfMmCJxccDwA1XTVnxRbFnjzSWBxv\nju+s7q0+BQAWbF+wMV2dlnqjB+Y5XcLxgsnhivqh+y7vXf2O76QqWmOJ6uXje0/84mdnj8kil/It\nf/ILH/VlZgLhma7aQHqiY7LptIPAc8dk5QvlsoHK4arxnS0AEEoMLU1HWg7p2ka3mWssrq399SU1\nNb9tB4CKioeXZzLNh5JJYzAY7F0KpIUQM/5AYHBxMrlhOJlct7Ou7penAEB9/c9PTiaNHTq3x6Jl\nj9OzFeTG7Y2n+jK+6rZ72t6Zvy1Vl5KhiZDhy/rqOv7Y8YF0TXpfzwU9v57r58fWjj286IFFb6vu\nqT4XCr7hjcM/LV30R1Dmg3Kievl4rHrFYyue/MJ1ADDWsvlmCP9zvsBn6jccahi8d2zFk5//NARy\nicqOJyaazzwAAIHUaF3OH3lmubZkVfvUdN2GPy596usfVRDZdLhp33jruXv86elgx+iW81Y8Gb0u\n5wvO9K5+5w9LtqHFkPNpzZAbG793qs+XqG5re+/hz1NqtQyFdhs+X7yuo+PKD6TTS/f19Pz01xMT\nl9/S0XH1BwFgauqSW9PpJfGWls+9QohE1ZIlr39//ucPHrz1X3K5Gp2VQPeUAY9Me6W+dm/tkgXb\nFrzel/ItgEB2+W+XnzJ63OgtC7YteEPdnrpLUjWp3SMbR45YXRw+afiRjjs61i3/zfKPw4fMwJkD\nPylh+HMT7vjTaez/06m+XLq6bc+PnvlcDyy/6sezJ9UpXygXr1nx2LIdX70OgBptfenNyhfKAc8d\nkwFgqOPS/2k5+Ku3iC7lSwfreofbLnyyJBtUbI44kvfcsXh09F3/3dz89Tc1NX0jq1Qg3d//7R8l\nk+unamr+sHPFivM/AQCx2Jn3xWIvGUmlVtzV3v6Wty1ffu7HlQrHent/+CN9W/MMLR8ooTQVPUVU\n/B2A5c/7QJo/5VfY9Vv9n1AqvTvOSmKkwQsntiilKaUw317qsiSiIgLgGt1xuM7oa2MYelul7jCo\nxKYqk/i/8zkO2+9hpfB4qV9UZ4tFSuNru5PICoik9ooQaZAMeX7CbRF4YYxKPv9D6Jj5J1io8KJU\n0JEtSy6gZSzW+aXKgbkYfHF3HNujY8MEuRhcP0apTqUApHXH4TpMkL0pHeT3b3F4LkH2QnWm9Hwz\n/IB6jYJCOqj9jGgu5JUxyivbWTr+SSbIXpRwxJnb3UhLsYIJstv4mSB7TsbP33lxeGWMcn2lvOQC\n4zyi40WpEFssisNzFWQOysXgn+QH1Gt4WK9YvDJGeWVHoHT8Uzyi40XJEI8cFIfnKshHXCuVXgQ/\nTwjnOekAJ2YWh1fGKK9sZ+n4Ej4gy2KF17DFoli0jFE6E2RmcsXgH+Og7DVJHtYrEq+MUV7ZztLy\nxbnj6jWJMCvI9ksp5b0Wi2k49CxOZS3Uww+o10xXssWiOLySOHplO0srMFzeZ26iYzdZzdYa+2kb\nn7QlyKpT5cBDe/YL9fAD6jUTNdwpKg6vJI5e2c7SCvVzx9VLFBSmqoK6w3Ah7yXIFg7Mdgt36zx9\nOOkwUaP7c+xGGaWQ0B1EiXAcLoZQN4+QekkinIHysVhhPybIZBP/tB8ixt43L5ngYb0i8NLYNK07\nAFcKd+n+fqVSmqng925xeDZBntT8+u4UHOaZsbwiJ3KIVfKwnv08MzapTpUB293sF+rijquXTFWz\npaY4tI3FuhPkUc2v706hPn5QvSIW4USg4vDa2OS17S2+UB/b3bxkskp3BG6lbWzSnSAPa359dwr2\nsZFU5TMAACAASURBVPfNK3hYr1i8NjZ5bXuLz5f0wT/Bo3lewcnSxZBUSl8LmNYEWXWqKfAsTvar\n2KV7x4dKZbyWRwuKY0R3ACXmte0tjWAvj/B4xWgdW93sp3VcckIixYHZbhU7+EH1ikNNTvgMu01K\nKe/0IFs4DhdDxdPcgfWCRCiNeAVbauyn9ciWE75cOTDbLTAegH+MlQsvGGrkuU3t58V+3AkAHDPs\nVvkkJ+p5wXgtW2mKw/MVZPa+FUNkL1tX3C4WSSEV4hew/Tw3JqlOpcBihf0qngoCOc4JcbvBRv6O\ni8PzCfIh3QG4UsVO3RFQsY3WseJXHF4dk7y63cXjj/kRGGJ10e0GF7C9wn5JAGM6A9CeIKtONQFg\nRnccrsNDe+43uEB3BG7VpzsATby63cUV2cMdWTfLQWGknvN+7NevFLRW5rUnyBYOzHaL7AkBGU4Q\ncTNWLYphTCnEdQehyQCg9wvJlSq3c/kvN5uqTiHnd0ou5Sba80Kn/FL7dQfgOiIrEOphH7JbZXxZ\njNWyamE/z45FqlOlAAzpjsN1Kp/g0Tw3G27gEYLi0D4WOyVB1r6n4ErVD/MkEm413JgEBCtT9vP6\nWOT17bdfuDsE/zj7kN2qexF3gOyXUEr/pGFHJMiqU00C+s6W4lo197HC6FbdC3VH4FZeTxC9vv3F\nUbWFCbIbZX05DDSFdYfhQtqrx4BDEmRLj+4AXCeyP8RTnbqQgkL3Ig7K9htWCgndQWg2AK6HbL+a\n+3i0x42GGpNQPv5u7eeIfNBJCfIB3QG4UuVWJshuM1mdQjLMw3r2O6A7AN1Up8rAIV9OrlL5eBgi\nxUnTbtO9kJNa7acAHNQdBOCsBLkHACeV2Y2VC/fpbWWFrzj26w7AIQ7oDsB1fGkfIjKpOwyykYJC\nzyKeydR+h5RCTHcQgIMSZNWpcgC6dMfhOlVbwkCalQs36Wpjb7n9xpXSuyi9gxwEwDHDbjUP6I6A\n7DRZnUIizKU27eeYQoVjEmSLY94Y1/Clfah4mpULt0iE0hirY9XCfgd0B+AUqlMlwcl69qt5gDu2\nbtLXwiN5xeGYPNBpCXI3OEHEfnV36o6A7NKzkG1IxeGYQdkhDugOwHUCowGE93p9Eqh77FnKQoX9\nhpVyzopmjkqQrQki3brjcJ2ae8MQCa6J7AZyOatQ9ptSiifImGU/eFY9+9XfztYVNxivSWC6imOx\n/fbpDqCQoxJki9QdgOv40j5UP8Q2i3I3UZ3EZA2rFvbjmDOL6lRxcE6I/WrvjkAkWawod3uWcEfH\nfgrALt1BFHJigtwNYEZ3EK7TcCuXBSt3+zr4xWo/BSbIR/K07gBcx5f0oepRFivKWVbksL8jojsM\nF+p2yuoVeY5LkFWnctxehCtUyDACg+xfLVc5kcPeJTw5iP16lOIO+RF0Ac76wnKFhtsc971Lx6C/\nJYFMgL9D+zluh9ypv2THvVGuUHcnTxpSrgaaEkgHeRTAfhxrjoDFiiKp3BaBf4TFinK1e5lT86Zy\nFoMDW7oc+YtWnWoKQK/uOFyn/vYwkOXEm3K0exlP+GK/OBxyxiYH4w5EMdT9mcWKcpQIpTHQzPYK\n++1SynlrrzsyQbY8pTsA1wmMB1D5RFx3GHSMYpEU+lo4KNvPkYOyk6hONQkWK+zXcGsYyPBvr9zs\nXsrKf3E4ckfcyQnyfgBTuoNwnaaf8TB9uXlqZQYQrCDbKwdgu+4gysQ23QG4TmA8gJoHuCZyOcn4\nsnh6ZYXuMFzogFKY1B3EXBybIFv9bxyY7VaxO4zwbg7M5SIVyGDPElaP7beHk/PmR3WqLgCjuuNw\nnaafBYEcW97Kxf4OTs4rjsd1B3AkTv9lPw2AyZzdmm7moFwudi9LIud3+ue0HDl2UHaoJ3QH4Dqh\n/iBb3spETuSwYzVXEbLfgFIY1B3EkTj6i9c6s94O3XG4TvUjFQj2cS1Op8v4snhqJavH9juoFMZ1\nB1Fm9gLOOQWsazTdxJa3ctDTmkA8EtAdhgs5ulDh6ATZsgNARncQrtP4S04QcboD7Uku7VYUrIYe\nI9WpcgCe1B2H61TIMMJ7eZTUyRQUthk8rbT9RpVy3tJuhRyfIKtOlYBDZziWtbo/R+Af51JDTpWD\nwvbVPK20/QaUwoDuIMoUW96KYQFb3hxtsDGByRomyPZzfKHC8QmyZStYRbaXUAILfs4E2akOtscR\nr+AhPfs9ojuAcmW1vDn6kGhZqnmoAqED3PFwIgWFLcfxKJ79xgDs0R3E8ymLBFl1qji4ooX96v9Q\ngeAAe5GdJuPLYus6TgixX7dS6NcdRJnbAXD1D9u1fo/LODpRz8I4xut4JM9+jygFxx85KYsE2fIE\nACZzdhJKoOX7jv8j9ZynVyaQDLNqYT9Wj18k1am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ES49YS9p1MFGkAnkemJTpxm+9WI8K\n5WBYYxm/bobhNyZ+pVDOJQo8vTbLk+taKDrYMi669gMPWctB14GInKryWpGz8HOxFlUHpdBdYPCt\nWSavacE2PjffDndleHQTHFqkBXjByQNPAo9ay4zrYKJMBfI8MinTiT+KsQH1KAdn8tIZht9kGD43\nxlPrCuxcrRHjYO3FL4wPuw5EJAjl1outwCK3kURIsbXIyG9kGH1FI/v78jyWjGvEOFBZ4AngcWvJ\nuA6mHqhAdsCkTDP+rhdno964M2WBfcDj/LGdAc7FH6nXB5AzkwE8/AUfE66DEamG8hn5NuOvF1HO\nODMZYDvD65/iS88sxc/F+gBy5g7jjxjvtJai62DqiQpkx0zKrMJP0KtR+8WpmMbfPWG73WafU8AZ\nQzP+Nk8b0VTqqRrAT8a7lIylXpiUacIftNiMBi1O1QHgaWCn3WafkzOMYTH+67oO0MK8k1cAdgBP\nWsuQ62DqlQrkkCjvfLEBP5GoqJtbAdiNn4wP2G0n/s9rDL34r+t6tEf18UwBu4BnrGXYdTAiLpmU\nWYGfL/oAtQjMbRx/gOJpu82e8GyZxhAH1uAPWqxCg0FzsfgLoHcBO7QjhXsqkEOovKhvXflS76uu\ni/gjFDuB3XabzZ/OLzGGGLASv1hehUYz0viJeKe1DLgORiRsyrtfrMTPw31Ag9OA3JsG9gDP2G32\ntHOGMbTg5+G1wGJULA/gH992aTeKcFGBHHLlreLW4ifqekkmM/gLw/YA/XabDfTMbOVieSn+iMYa\n6mdKdQTox39tD2ozeZGTY1Imjv/BejV+Lm53G9G8GaSci+02G/hUf7lYXoWfh1dSHx9CCvgjxf3A\nbms54Qi8uKECuYaYlGkEVuAnkpVAh9uIAlPAX4hwCNhrt9l53S3BGLrxD3zLgSVEZ1p1Bn9rtn6g\nX6MTIsEoz/LN5uHlQFTOCDeFn4cP4OfiecsZ5YGL5fgF81JgIRCVbTqHKedh4JDWd9QGFcg1zKRM\nB/6o8qLy115qI1HP4CfhgfLXIbvNltyGdJQxLMBP0EvKX2thhLmEP0J8GH/UZ9BaRtyGJBJ95VaM\nXo7m4kXURmucBUbxc/Ah4NDJ9BPPF2NI4L+mSzmaj2thhDlDOQeXL4e1X3FtUoEcIeWN8BfgJ+gF\nQFf50ombT+I5/ARceRmZz1GJIBhDE/7ruQB/AeXsv10s+rP4ozzj5csYfhIe1qiESDiUZ/t68XNx\nV8XF1enup/A/QB/Jw8BY0O1r1WQMBn/W9Nhc3I2bLfoKHM3D4/iv6aC2xYwOFch1oFw4t3O0WG7F\nL+5agGb8loIm/E/nsfJlrl7nEn5SyFd8zeEv3viVS60VwqeqXDi3A20Vl9bydbOvZ6Li61xJ3OK/\nriX8BYnZ8iWDP9KeLn+dxE/CE9YSmtF2ETl5JmUSHC2W2zmah4/NxXGO5uK5FPjVXDyDn3vT+AVx\nuvz91Okubq4F5cK57XkuDRzNwbP5+HjHt9lLnqN5eDYXz76+E8C4WtaiTwWyHFd56nD2UghTG0Qt\nKifyBEeLYquFciLyfMoDHJXFcuFktriU4ytvOxenXBBr0EHmogJZRERERKRCVFaIioiIiIgEQgWy\niIiIiEgFFcgiIiIiIhVUIIuIiIiIVFCBLCIiIiJSQQWyiIiIiEgFFcgiIiIiIhVUIIuIiIiIVFCB\nLCIiIiJSIeE6ADkzyWTyp8DXPc/762Oufy/wVuBKYCdQOOZH3+p53n3JZNJW3B4DxoGPeJ53e9WD\nr3PJZPJC4IvACvzXfhj4sOd5Pz3N33cnsBGYOOamL3ue9+VkMvlbwKeALwH/DPwQmPI874LTewYi\nMku5uHYpF8tcVCDXvq8C7wT++pjrf7N825XAtZ7n9T/P7zhyezKZvBK4JZlMJj3PGww+XAFIJpMG\nuAV4j+d53y1f91rgW8lkcpXneenT/NV/6Hne145z228AH/c87++SyeRvAgc9z7v6NB9HRJ7rqygX\n1xzlYjkeFci179+Av0wmk2d5nrcLIJlM9gFbgVcAf3Mqv8zzvJ8lk8kdwOXAtwOOVY7qBZYB985e\n4XneN5LJ5H3AJclk8vPAncCvA83A2z3PuyuZTH4VGAUuwB+heBC46URJPJlMfhH/b3p2Mpl8A3Ae\n0JlMJh/xPO/8oJ+cSB1SLq5NysUyJ/Ug1zjP8yaA/wJurrj6LcA3y7edjgYge6axyfMaAu4H7kgm\nk+9KJpNrASpGl7YC93qedzb+iNQnKn72NcDrgFVAF/CeEz2Y53l/CNyHP6pxA/BR4OdKyCLBUC6u\nWcrFMieNIEfDV/HfuH9S/v5m4Hcqbr8zmUxW9r0NHm86J5lMvgxYCvysCnFKmed5NplMXg98CPgg\n8LfJZPJJ4JPACDDped63ynd/CHh3xY9/y/O8YYBkMvlN4ArgL8u3fTGZTFYmcICXeZ63u0pPRUSO\n+irKxTVFuViORwVyNPwYaE4mk5cCRaCtfN2sE/W9zSbtGPAs/pt4qlrBis/zvHFgG7AtmUwuAd4O\n/At+kh6vuGsRiFd8P1Lx71FgQcX3z9f3JiLVpVxcg5SLZS4qkCPA87xSMpn8R+BN+G/gf/Q8r3QK\nv+JESVsClkwmVwJ9s6ukPc8bAP603JN2ogU5vRX/7uG5SVpEHFEurj3KxXI8KpCj46v4W8WUgBvd\nhiInYRXwzWQyeYPneQ8CJJPJi4HV+KNOz+fGZDLZDUziLxz516pGKiKn4qsoF9cS5WKZkwrkiPA8\nb0cymTww++9jbj627w3K+zHOT3RyLM/zfl7eH/V/JpPJLvxpu0PAG/EPrM/nduAbwNn4iz3+vuK2\nufre7vM8763BRC4iz0e5uLYoF8vxGGut6xhE5CSVtxba4XneZ1zHIiJSr5SLo0/bvImIiIiIVFCB\nLCIiIiJSQS0WIiIiIiIVNIIsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJB\nBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhU\nUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIi\nFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiI\nSAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIi\nIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iI\niIhUUIEsIiIiIlJBBbKIiIiISAUVyCIiIiIiFVQgi4iIiIhUUIEsIiIiIlJBBbKIiIiISAUVyCIi\nIiIiFVQgi4iIiIhUSLgOQORYyWTyWuAO4KOe532h4vpngQxwL/A2YJnneYeSyWQn8MfAa4FlwChw\nF/Bxz/N2lH/WAj/wPO/Git/31crfU+3nJSJSTRW5E+ASz/PuL1//GuAb5evXAoeBFPB6YHn5+/8E\ntnmeN1b+GVvxq9PAY8CXPc/7Wvn2twP/cJxQVgHXHXO7BQ4C3wP+wPO88Yq43wJ8DZgBFnmeFIW7\nRwAAIABJREFUN12+/k7gmuM8xjuAtx/n9ts9z7vuOD8nclI0giw1LZlMxoHvAx/AT7CvAP4EuB64\nJ5lMLnQYnoiIC3ng5RXfv6x8HfjH/e8AHwK+DrwS+GvgfcDtyWSyseLn7gUuB24GxoH/m0wm/+iY\nx/pk+T6Vl8Nz3H4V8BXg3cAXj/kdbwDuBhrK8cz6rfLPvqL8/fcqHuO75etyczz+7/zKKyJyijSC\nLLXulfgJ8bOe532ifN1tyWTyEaAbmHIWmYiIG7/ALypT5e9fBjyAnytvBF4E/JnneR8r3/7DZDKZ\nBf4bcBPwj+Xrxz3PuxcgmUx+G3gE+GQymfwfFY+1Y/Y+lZLJ5Fy335NMJj8AXFhxv07gBuB3gSJ+\nsfyvAJ7nPVm+z9Ly3QcrH6v8GHauxxc5UyqQJcxiyWTyRP9Hryp/nZ0+pPwzvyh/W6q4rznm95kz\nD1FEJHTuAj6WTCaXAIuBlcD/wS+Qry3f59+P+Zl/wy+QX8zRAvkIz/OKyWTya8DngUsqbjo2T1vP\n84pz3B4DXgIsBL5dcfur8UeOv1n++qfJZLJtts3iZMxxnCh6nmfnvLPISVKLhYTZZ/GnBWcva+a4\nT2f56xBAMplcf8zPfKrivi895ra3ViVqERG3foLfN/wy/FaLncCO8m095a+Hj/mZ2e+fry1ttPy1\nu+K6f+K5efX2Y35m9vYsfovE94GPVNz+evyR6QT+KHcLz22zOJGmYx4/j7+2ROSMaARZwuzL+KMe\ns749x332l78uAfYC+4CL8RfrHXv/n+P3Ks/axqklYhGRWpAFfow/KLAUvzCdNVT+ugzYU3H9kvLX\nYwvnSivLXwc5OjjxceCHFfeZPOZnZm9/LfBR4F88zxsESCaTXeUYm/Bz96wjbRYnIQdcecx1u0/y\nZ0WOSwWyhNl+z/MemP0mmUzm5rjP7O1vBO73PC8LPJBMJjfMcd+JY37fcKDRioiEx3fxe5C78BfF\nzfbx/hg/X96Evwhv1hvKX2+b65eVF0S/ARgD7gfWlW/aVZlX57DL87wHksnkk8A7gT9JJpP/Ws7V\nr8Yvjt+PP8AB8Gbg10+hzcKe4PFFTosKZKl13wfuBH6vXEDfjj/K8Yfl21UEi0g9+h7+rhFp/Bx5\nU/n6HwE/AD6QTCZnyt9vxW9pu4fn9iZ3JZPJy4AFwG8DG4EPep6XqViEt758n0o7jvkez/PSyWTy\nC8D/X/5df45fcI8AX/E8rwRQXiz4JvzZvZMZRTZzPL71PO8Xc95b5CSpB1lqWjmpvgr4K/ytiG4F\n/gx/NOL1nud9yWF4IiJOeJ63D3gUuMPzvEzFTRZ4DX6efDP+IMPv4m/1doPneYWK+16G35r2baAP\neJfneX91zEN9unyfysuNzO0rwAH8BYRr8LfjvH22OC77Cf5+yG+Y4+fn0jjH4//sJH9W5LiMtVro\nKSIiIiIySyPIIiIiIiIVVCCLiIiIiFRQgSwiIiIiUkEFsoiIiIhIBRXIIiIiIiIVVCCLiIiIiFRQ\ngSwiIiIiUkEFsoiIiIhIBRXIIiIiIiIVVCCLiIiIiFRQgSwiIiIiUkEFsoiIiIhIBRXIIiIiIiIV\nVCCLiIiIiFRQgSwiIiIiUkEFsoiIiIhIBRXIIiIiIiIVVCCLiIiIiFRQgSwiIiIiUkEFsoiIiIhI\nBRXIIiIiIiIVVCCLiIiIiFRQgSwiIiIiUkEFsoiIiIhIBRXIIiIiIiIVVCCLiIiIiFRQgSwiIiIi\nUiHhOgBxy6RMDGgqXxL4H5qOvRigCBSA/LFf7TZr5z/ycDOGBvzX89ivCcACpYqvs5cCkAWy1lJ0\nELaIOGJSJgE0A41AnLlzscXPE7+Si+02W3AQdqgZQ4y5c3ADR1/P0hyXPJABctai41udMla1TaSZ\nlGkFOoGu8qUTaAVaypfGM3yIEjADTM9xGQPGopa4y0m3C+gG2oG2iktr+Wv8DB+mgP+6pstfJ4Hx\nisu0ErdIbSgPRHTy3FzcztE83MKZ54w8ft5NA1Plr9P4uWPUbrOTZ/j7Q8cYGoEe/Ne1bY5Lyxk+\nhMUftJjh6HFugopcbC25M3wMCSkVyBFhUiYOLAQWA4uABfhJuMFlXPgJZhIYLV9GgCG7zY45jeok\nGUM70IufhBeUv3bhvj2piJ+ox4DB2YuStYhb5UGJRRWXLqADfybOpQLPzcOjwGG7zWadRnUSyoMS\nC8uXBRzNxa0u4yrL4BfLI/h5+DAwqgGM2qcCuUaZlGkHVnC0IO7BfdF2KjLAAHCofBm022zJZUDG\nYPBfxyXA0vKl3WVMp6GyYD5gLSOO4xGJLJMyhqP5YjYXtzkN6tSNcTQPH7Lb7ITjeGZb1Crz8GJq\nqyW0AAzh5+EBYL+1hP6DiDyXCuQaUe5PWw6sLF+63UYUuCL+J+99wB67zY7Ox4MaQxuwBliNn4jP\ntOUkbNJA/+zFWjKO4xGpaSZlOoBV+Hl4OdHMGQeAPcA+u81WfVaqPDixGD8Xr8QfKXY94h4ki18w\nz+biAWtxOiAkJ6YCOcTKU3VnAX34xVstjRCfqQlgL36SPhjk6LIxLMJPxGvwE3E9GcJ/XXdYS020\nuYi4ZlJmKX4uXo3f71ovSvgjy3vxBy7Gg/rF5VHiVfiv6Wr8BYr1Io//IWQ38Kxa48JJBXLImJRp\nxk/E6/CL4ih9ij5dOeBZ4Gm7zR44nV9gDD3ARmA94ehbC4MRYCew01qcT6uKhIlJmUX4efgsaq/V\nqlrGgGeAZ+w2O3WqP2wMcfyBiQ34xXE9DfocTxF/5nQnsMdaIrWovZapQA6B8gK7s/ALuOWoKH4+\nU/gJ+ukTjWYYQwt+It5A/Y0Un6oh4GngaY1mSL0qr+04G78wrqeR4lNlgYP4OWPXiXYqMoYl+Me3\ns/C3FJW5FfBH67dbS7/rYOqdCmSHyr1sm4Ek9TW9FJTDwFPADrvNFuFIL9sq/Nd1JRqhOFUF/JGM\nJ61l0HUwItVWXmg3mzNWoQGKU1UAdgFP2G32SM4whmZgE/7xrctRbLVsAngS8LTAzw0VyPOsIhmf\ng1/AKRmfuQyFBU/ylR1phnrOQyM/QRnET9A7Ne0nUVNuZ9uEP2Lc4TicqDhM/1ue4O//zzJK8Q2c\n+d7O4rdgzA5aHHYdTD1RgTxPyhvFrwe2ok/TwSn0FBh6U5aJFzVTbIpxYMkMj21MMNYVtZXlLmWA\nR4EnrCXvOhiRM2FSpg04H78wVgEXlKkLMwy/2ZLZ2EKmMc8za3JsX9dCIaFZvOAcAh5S+8X8UIFc\nZeXCOAlcgEYpgpNflOfwu/JMXdYC8V8dhR9cMMND58QY6Va/W3CywOPAY+pTllpTbmm7AD8fq2gL\nyuQVGQbfZsgv/9VcW4gV2bk6w2PJZvIN+jASnMPAw9ayx3UgUaYCuUrKC+/Oxh+pqLWN48Or0Flk\n8O0ZJl7UAicxMnGwd4YHz40z2a4R5eDk8FsvHtW+yhJ2JmU6gRfgz+CpMA5KekuGgfdCru/E62fy\n8SLbz8rw5PoWSnH9DYIzjD+ivNt1IFGkArkKTMqsAy5FWwMFp9RcYujNGcZe0YxtPLUEW8Kyb9kM\nD5/TyExzLZ2NKexywEPA49r0XsLGpEwTcCH+4jsVZUHJrM0x8L4imc0tp/6zjXme2FDg6b5mMFp/\nE5zDwM+tZcB1IFGiAjlAJmUWA5fjnyJTgmDjlpHXzjD8uiZs65lN0RVNid2rZnhkUzO5Rk33BWcC\n+IVGMSQMym1t5+CPGqvFKii5ZXkOvzfP9EVnvo98ujnHL88usmfFqRfZ8nx24efiSdeBRIEK5ACU\n9868BH8KT4IyfX6GQ78bo9AbbHtEPl7kl5tz7Fij5Bysg/ijGEOuA5H6ZFJmDXAZWggdHBu3DL41\nzeivnVxb26kY6cxwzwtiaoELVBF4DL9HWYuqz4AK5DNQHqk4D3+kQlP3QSm2Fzn0gSxTl1f3jHfD\nXRnueUGcqbaGqj5O/XkKfxRDC/lkXpQX4F2Nv3WmBKVagxSVSqaEt3aGRza1YmNquwhOGrjHWna5\nDqRWqUA+TeXTkL4QnaEtWGM3pDn8zjNvpzhZJVNi+1kzPJpUcg5WGviptTzrOhCJrvK+8luAi9Ag\nRXCK7UUO/XaWqSurO0hRabo5x70XlDjcq5NmBWsvfi4+5VOD1zsVyKeovDvFhfi7U6igCkpuWZ6D\nf1Ags9FN24OSc7Xswk/O2u1CAmVSphu4FljsOJRoGbs+zeC7Gim1ufnAsXdpmvvPa9I6kUDlgHut\nZbvrQGqJCuRTYFKmF3gRsMB1LJEy+qo0h9/RDA3uV5o/s2aaB8/RaHKwZoCfaDRZglAeNT4Pf9RY\nRVRQiu1FDnwkR/p892szsg0FfvaCAgOLNGARrH7gLmuZdh1ILVCBfJJMymzB37rNfREXFcXWIgf+\nKEv6BfM3jXcyxtuz3HVxjGn1JgfsSfxFfEXXgUhtMinTArwYWOE6lkhJn5PhwEfjFLvCk/Mslqf7\n0jx0Tqu2hAtUBrjTWva6DiTsVCCfQHkvzWuBNY5DiZaZZJb9H49RXBCehFypECty/3lZnl0ZruK9\n9g0Dt1nLuOtApLaYlFmBP4On92RQrLEMvTXNyGta5zwjaRiMdWS485IEMy3qMQ/Wo8B92sP++FQg\nPw+TMkuAl6ATfgRr6E3TDL8xvAm50p5laX5xfjPFgLc3qm95/JaLHa4DkfArt1RcCGxF6z6CU+gp\n0P+JAtkN4W9jKMSL3Ht+jn3L3bd/RMth4Hbtmzw3FcjHYVLmfOBi1FIRnGJrkf5Ujsym2kpyUy15\n7rzUaq/OwHn4C/jUciFzMinTij9Iscx1LJEyvTXDgT9KOFuId7p2r0xz7/ktarkIVA64w1r2uA4k\nbFQgH6O8t/E1wAbXsURKblmefZ+zVd1Ps5oK8SJ3X5TXopHADQA/tJYZ14FIuJiU6QFuRDN4wRp9\nVZrD72qpiRm8uQx3z3DHpY3kG7RAM1j3WcsvXQcRJiqQK5iUaQZeCix1HUukpLdk6P9Ew7ztbVwt\nJSwPnjvDjj71QAZrEvi+tYy6DkTCwaTMavyR43CuUahF1lgGfjvN+EvbXIdyxqabc9x2hSHdqv8f\nwfLw29/Ul4wK5CPKe2reCHS6jiVSxq5PM/BbzYGfotSlp9ekeXCLiuRg5fB74fa5DkTcKu8YdBnq\nNw5OqalE/7YsM1tqq73t+eQSBe66pMhQT5PrUCLmIP6sXtZ1IK6pQObI6ujrgdqc/g+rgfdMM/Zr\ntT9aMZdDvWnuvkiL94Jl8U+N+oTrQGT+ldvbrgTOdh1LpBR6Cuz9fJH88ugVkiVT4hfnZ7TbUOAm\ngFvrfbehui+QTcqchb+vpgqdoNi4Zf8nZpi+KNpJa7w9y21XJHTGp8A9ZC0PuA5C5k+5OL4O6HMc\nSrRk1+TY+/kYpY7aWox3qh7fMM1jyWgOxriTAb5rLcOuA3GlrgtkkzIb8RfkaSovKDZu6U9lQnE2\npvkw1Zrlh1cmyDapSA7Wo9Zyr+sgpPpMyiTw136sdB1LpGTW5tj7hXjNr/04WU+dNc0vN6tIDlYO\n+J61HHYdiAt1WyCblNkMXOU6jkixccu+T2ci1ed2MqZacvzwqriK5MA9aS0/dR2EVI9JmQb8tR/a\nxi1ImXVZ9n4uUTfF8Syvb5qHzlWRHKw8/iLqg64DmW91WSCblDkPfxGIBKXUUGLfZ7JkNtdXcTxr\nujnHD6+OkWmK9lTm/HsauMta6i9RRZxJmUbg5cBi17FEysyGLPs+l8A211dxPOuZNdM8sEVFcrAK\n+Av3+l0HMp/qrkA2KXMBcInrOCKl1FBi3+eyNXcCkKClm3P88KoYM80qkoO1E/ixiuToKBfHrwR6\nXccSKemzs/R/OoGt89msnaumue98FcnBKuEXyXtdBzJf6mphWrmtQsVxkEoNJfZ9QcUxQGumkZf+\npETLTMF1KBGzDrVDRUa55/hGVBwHS8XxUev2tXHpL6ddhxExMeA6Y+rnPBF1UyCXd6u40nUckWKN\nZf+2LJmNKo5ntWYbue6eEg15nT45WGcbow+3ta5it4q6OcjOi+yqHP1/ouK40ln9bVzwZNp1GBGT\nAG40hoWuA5kPdVEgm5RZib+Vm3arCNKhD87UzW4Vp6J9ppEX/zyPKaklIFgXGMN5roOQM3ItsNp1\nEJFS6Cmw73OmbnuOn8/Zu1pZ/6yK5GA1Ai83JvonVYt8gWxSZjH+FkKRf67zauhN00y8JNr7HJ+J\nnolmrn5gxnUYEXSZMWx0HYScOpMyVwDrXccRKaXmEns/X6TYrVMuH8+Fj7ew7HDGdRgR0wK8whgi\nXQNEumg0KdOJ3+umRVNBGn9RmuE3awHEiaw43MpFj6kPLngvNEZ75tYSkzLnA+e6jiNS/G01s5E8\nQ16QYhiueqCB7vGc61AipgN/JDmy9VVkC+Ty/po3AM2uY4mU9JYMhz6g1/RkbdjTxqadmuIL1uxi\nkS7XgciJmZRZjRZHB+/Ah2e0OPokJUpxXnxvjNZ03nUoEdMDvMh1ENUS2QIZ/4+2wHUQkZJdkaP/\nUw2QiPL/m+Bd8FQLKw+q3SJYjcANxtDoOhA5PpMy3Wj9R/AG3zbN1JWRnt4OXFM+wUt+bkkUSq5D\niZi1xvAC10FUQyQLHZMyFwF9ruOIlFJTif4UWghyGgyGy3/ZqNGLwHUDLzZGxVcYlfc6vgH0ISZQ\nUxdmGHmtiuPT0T7TyFUPqh85eBcZE72aK3IFcnk7t0h+mnHq4O9nKCzRge50JYpxrr2vpJ0tArca\nuMh1EPJcJmUM8BJQG0ygCj0FDn44ATF9KDxdywZb1fZWFS8yhh7XQQQpUgVyeTrvWtdxRM7oy9JM\nXa4RizPVNdXEpY+q1SJ4W6M4elHjLgJWuQ4iUmzcsi9VoNQW2UVR8+b87c30jGVdhxExDcBLjSEy\nO6pEpkA2KRPHH7FQ8ghSZm2Ow+/RorygrO1vpa9foxfBu8YYtLNKCJiUWQ5c4DqOyBl4X5pcn3Jx\nEGI2xjX3xXRCp8B1EqGznkamQMZfJV0XZ3eZN6XmEv3bgIYo/T9x75JHm+mY0pZDwWrCn+LT1LND\nJmWa8BdI6+8QpMkrZxi/UR8Ag9Sca+Dq+5WHg7fBmGjsdx6Jwqd8prwtruOInAN/lKG4UH3HQYuX\nYlz7C4gVtZo6WMuB810HUedeCBrJD1R+UZ6Dv6s8XA1LRlo419Ne9cG7yhg6XAdxpmq+QDYp04z6\njoM3/pI00xep77ha2mcaufAJ9SMH7yJjWOQ6iHpkUmYTsNZ1HJGz/6NF7R5URefsaKFrQiPJwWok\nAjsM1XyBDFwD0T7d4bwrdBcYeK/OzlRt6/a20juihSLBiuEnZq1FmEcmZbqAK1zHETkjr0mT3aC+\n42qK2RhXPWjBaoehYC2hxncUq+kC2aTMemCN6zgi5+Af5LGtGrGoNoPhyoeMWi0C14W2fptvV6MF\n0sHKLckzdLOK4/nQOd3EeZ4WTwdvqzG1e8K2mi2Qy4tBLncdR+RMXDND+nydvnS+tGYa2fqUWi2C\nd64xWrQ7H0zKbMTv/5YgHfzDAraxZo/RNefsnS1aPB24GP6H55pUy2++SwEVckEqthYZeF9k9jCs\nGeufbaV7XIk5WDHg6lrvgQu78hqQy1zHETlj16fJbNTxbT7FbIwrH9JsXvCWGsPZroM4HTVZIJuU\nWQpsch1H5Ay8P0upXdOk8y2G4YqH1QMXvMXAZtdBRNxlgNoAglToLHL43VoD4sKCiWY27larRfAu\nMab2BjRrrkA2KVPTQ/ahNbMhy+QLtdjRla6pJjY+q1aL4F1sjBbxVkP5hCAbXccROYffm9UaEIfO\n396kE4gEriZbYmuuQMbf77hmm75Da+D9Gr10bYvXSLygKb5gNVKDiTnsygMVkTljVmhkV+WYvLrm\nRtoiJVGMs/XJjOswImi9MbW1VqGmCuRyv9tW13FEzuQVGbLrNE3qWmMhwZanlZiDt84YFrsOImLO\nBrpdBxE5A+8vQUx9866t7W+hbTrvOowIuryW1oXUVIEMXIg/IiRBscZy+D219v8gujbubqYlU3Ad\nRgRpFDkgJmUa8XOxBGl6a4aZczRQEQYxG+Oix1UgB28hsMF1ECerZgojkzKdUJsrIUNt9NUzFHr1\noSMs4jbGC55QYg7eEmPocx1ERJyPFuYFb+B9NTOyVheWD7aycFQncgrexcZQEz32NVMg42/8X0vx\nhl+pucTwTSqOw2bVwWad+rQqLq6l6b0wMinTir8ORII0dn2a/HLtXBE2Fz+mtTnBawPOcR3EyaiJ\ngtOkzEJgves4ImfwN2cotWlbt7AxGC5+TKuog7cA7bpwpraiM+YFq9RQYuht2n8+jBZMNLPqgHYX\nCt4FxoS/XbYmCmTU7xa8YmuR8Rs0TRpWi0Zb6BnT9F7wXqBR5NNjUqYNtbkFb+xlWYpdKpDD6jyv\nVuqkWtJMDYwih/4Pb1KmG9Q7GLjR12SwTTXRB1S3zvM0ihy8DuAs10HUqC3UwDGjplhjGXmt8nCY\ndU43sXRQuwsF79yw9yLXQrI7z3UAkWPjltFXqt8t7JYOttCurYaq4ALXAdSa8s4VGj0O2uQLMxQX\nhn6que6d56kXOXgtQNJ1EM8n1AVyeUGIegaDNvayjE4pXQMMhi2eCuTgLTSGla6DqDHnAGoDCNrw\nG0N9DJayhWMtLFDLWxWcF+aWt7C/OTWlVw0jvxHqaQ2psOpgM01ZtVoE73zXAdQKkzJx4FzXcUTO\n9PkZcqs0k1cr1PJWDZ3AWtdBHE9oi09N6VXJxNXa97iWxG2Mc55R/1vwVhhDr+sgasRG/OlQCdLQ\nW1xHIKdi6aDOrlcdoW15C22BjN+bokIuaMM3hXY6Q45j3b5m4oWS6zAiSPv5nhy9TkHLrM2ROVu7\nCNWSGIYtz6hADl6vMSx1HcRcwlwgb3IdQORk1ubIrVZSrjWJYpy1/RpFDt7aWtiL0yWTMkuBbtdx\nRM7oq3U6+Vq06kATsaIGK4IXynovlAWySZkl+Jv6S5DGXqlPv7Vq3d5QvldrXALY4DqIkAvlgaum\n2bhl8nL1HteiRCnOmgMarAjeWWEcrAjrQTfUW3/UJBu3TF6ppFyreiaa1f9WFSoAj6O8DkR7Rgdt\n8qoMtlULpWvVhj1qUwxeghCeLTl0BbJJmQZgnes4ImfqsoxOK13jNj6bcx1CBC3UYr3jWodOKx28\n0Ze7jkDORM9YMy0zapEJXugGK0JXIOMnZe23GTQl5drXtz90U1AREbrEHBJ6XYJW6CmQ2aR1ILXM\nYDRYURW9xrDQdRCVwlgg68QgQSt0Fpk5R0m51jXnGnTK06pYb0woc6EzJmUWAItcxxE5Yy/LQkxT\n9LWur18zK9URqvovVAeF8pnzlriOI3LGb8hCXEk5Cjbu1grq4DUCK1wHETKh3by/po2/WLOjUdCa\nbWTxkAYrgheqvBOqAhnog/CedrBmTbwwbH9nOV1LhpowJes6jAgKVWIOAb0eQcuuyVNYrDapqFjb\nrzPrBa/dmPDMXIWtcOpzHUDkFNuL5FZr94qoSJTiLBnOug4jgvqM0YdzAJMyHRCuXsBImLxKfatR\nsmxQH3aqIzQfzkNTIJuUaQKWu44jciYvV89b1Kw+oDaL4DVDOM/m5EBoDlCRMnWp+lajpCXbQOek\ntt4MXp/rAGaFpkAG1hCueKJh8irXEUjQlh3WgbY6VBj69DoErdheJLtGI45Rs/qAZgWC121MOE4U\nF6aCtM91AJFj45aZc9ReETWt2UY6ppSYg9fnOgDXTMq0AItdxxE5msmLphUDOuFLdfS5DgBCUiCb\nlDGovSJ46S05bJPewFG05oA2qg9euzF0ug7CseVooXTwpq5wHYFUQ/dEEw15LdYLXih2FQpFgYy/\nIETTT0GbvFpFVFStOBSW927U1PsH9Xp//sGzxpI+VzN5URTDsPKQZvOCt8QYnA/uheUgq6RcDdPn\na8/NqOqeaCJe0GK94NV7Lqr35x+8mU05bLPzg71UyYoBbbsZvDghaPVSgRxVpeYShUUqkKMqhmHh\nmEYugle3uah8oqYu13FETvo8zeRFWc+YjrPV4TwXOy+Qy/3H2l4paOnNOS0KibglwxpBDl6rMXVb\nJDo/IEXSzGbnx1mporZMg/qQq8J5PgrDG7cX9R8Hb2aL3rBR1zuiD0DV4TwxO1Kvz7u6suu0LWPU\nLRrRbF7wFhuD0/dOGApk530mkZTWqEXk9Yxraq866jUn1evzrp5CT4Fil96nUbdkSLN5wYsDPS4D\nCEMRpVOaVkO2T6PyUddYSNCa1pmcgld3OcmkTBzodh1H5KS36P1ZD3pHtQizOnpdPngYCmSnL0Ak\n5Vbksa16w9aDJcNaABS8HmNCkRvn0wLCcTyIlvQW7XBQD7onNEtQHU4HK5wmRJMyMQjHKQUjJb1Z\noxb1YrEW6lVBPeYlDVRUQ2aDPnTUg0QpTuek+pCDV78FMv6UnkY6g5Y9S6MW9aJzyvV7OKrqrc2i\n3p7v/Mgt1chiveie0ML44PUY4+7Mnq4Prhq1qIbcCu1uUC/aZrRCvjrqLTepQA5asb2oVrc60jWl\n2bzgJXC4NsJ1gVxv05jzI79MSbleNGcTmJJmDIJXb7nJ6WrxSMqu0vqAetI5qYGp6nCWi10XyB2O\nHz+a8gs1rVcvDIaOafWcB69ucpNJmSa0F33wcqs0olhPOtKu66mo6nT1wK7/oHVzEJo3hZ4CNLj+\nu8p86ppU71vw2l32vs0z5eFqyK5RgVxP2tJqd6uOdlcP7LqQUmIOmqb16k/XpA7EwYsBba6DmCfO\nDkCRll9eLx+wBPx96RMF5eLgOasTnRXIJmUSQLOrx4+s3Gq9QetN57QOxNVRL4WjBiqqIbdMI4r1\npnNKA1TBq78CGSXl6sgt14KtetOeVoFcHfWSo+rlec6vwgItlq437dNqdwteXbZYKCkYtYQ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G/Zsj94Z3v7bdeAjQ0N/d4/On5Koc5RUdTzeM9FsUKsffndy987e92hyw/9Qzwb78q35wdnr5tZ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\ndPn/m8oXZrMps0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd01bc2bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2)\n",
    "figure.set_size_inches(10,10)\n",
    "figure.suptitle(\"VEP-SnpEff Impact Concordance\", fontsize=14, fontweight='bold')\n",
    "for idx, level in enumerate(effect_df['Impact_snpeff'].dropna().unique()):\n",
    "    pos = (int(idx/2), idx%2)\n",
    "    vep_level = effect_df[effect_df['Impact_vep'] == level]\n",
    "    snpeff_level = effect_df[effect_df['Impact_snpeff'] == level]\n",
    "    axes[pos].set_title(level, fontsize=12, fontweight='bold')\n",
    "    venn2_unweighted([set(vep_level.index.values), set(snpeff_level.index.values)], set_labels=('VEP', 'SnpEff'), set_colors=('g', 'b'),\n",
    "                     ax=axes[pos[0]][pos[1]])\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=.95)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     94.57\n",
       "False     5.43\n",
       "Name: Effect_match, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(effect_df['Effect_match'].value_counts()/effect_df['Effect_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looking at the unnormalized effects, there is 95% rate of condodance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Effect_match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effect_vep</th>\n",
       "      <th>Effect_snpeff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">3_prime_UTR_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>6648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_lost</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">5_prime_UTR_variant</th>\n",
       "      <th>5_prime_UTR_premature_start_codon_gain_variant</th>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>9944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_insertion</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">coding_sequence_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">downstream_gene_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>99161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">frameshift_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>112937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incomplete_terminal_codon_variant</th>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">inframe_deletion</th>\n",
       "      <th>conservative_inframe_deletion</th>\n",
       "      <td>4609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disruptive_inframe_deletion</th>\n",
       "      <td>8964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">inframe_insertion</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_insertion</th>\n",
       "      <td>8624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disruptive_inframe_insertion</th>\n",
       "      <td>10130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">intron_variant</th>\n",
       "      <th>intron_variant</th>\n",
       "      <td>416576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>1342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missense_variant</th>\n",
       "      <th>missense_variant</th>\n",
       "      <td>30988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">non_coding_transcript_exon_variant</th>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>29472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">protein_altering_variant</th>\n",
       "      <th>disruptive_inframe_insertion</th>\n",
       "      <td>6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">splice_acceptor_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_deletion</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_insertion</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disruptive_inframe_deletion</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disruptive_inframe_insertion</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_acceptor_variant</th>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">splice_donor_variant</th>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">splice_region_variant</th>\n",
       "      <th>5_prime_UTR_premature_start_codon_gain_variant</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_insertion</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disruptive_inframe_insertion</th>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intron_variant</th>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_acceptor_variant</th>\n",
       "      <td>1084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>15896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">start_lost</th>\n",
       "      <th>initiator_codon_variant</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">stop_gained</th>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_lost</th>\n",
       "      <th>stop_lost</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_retained_variant</th>\n",
       "      <th>stop_retained_variant</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">synonymous_variant</th>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synonymous_variant</th>\n",
       "      <td>9052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">upstream_gene_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_inframe_insertion</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>83765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   Effect_match\n",
       "Effect_vep                         Effect_snpeff                                               \n",
       "3_prime_UTR_variant                3_prime_UTR_variant                                     6648\n",
       "                                   downstream_gene_variant                                   12\n",
       "                                   frameshift_variant                                         6\n",
       "                                   non_coding_transcript_variant                              6\n",
       "                                   splice_region_variant                                     74\n",
       "                                   stop_lost                                                  8\n",
       "5_prime_UTR_variant                5_prime_UTR_premature_start_codon_gain_variant           279\n",
       "                                   5_prime_UTR_variant                                     9944\n",
       "                                   conservative_inframe_insertion                             1\n",
       "                                   exon_loss_variant                                          3\n",
       "                                   frameshift_variant                                        34\n",
       "                                   non_coding_transcript_variant                             14\n",
       "                                   splice_region_variant                                     34\n",
       "                                   start_lost                                                15\n",
       "                                   upstream_gene_variant                                     18\n",
       "coding_sequence_variant            5_prime_UTR_variant                                       12\n",
       "                                   frameshift_variant                                         4\n",
       "                                   splice_region_variant                                     12\n",
       "downstream_gene_variant            3_prime_UTR_variant                                       16\n",
       "                                   downstream_gene_variant                                99161\n",
       "                                   splice_region_variant                                     16\n",
       "frameshift_variant                 3_prime_UTR_variant                                       14\n",
       "                                   5_prime_UTR_variant                                       14\n",
       "                                   exon_loss_variant                                          2\n",
       "                                   frameshift_variant                                    112937\n",
       "                                   splice_donor_variant                                     725\n",
       "                                   splice_region_variant                                     17\n",
       "incomplete_terminal_codon_variant  frameshift_variant                                         6\n",
       "inframe_deletion                   conservative_inframe_deletion                           4609\n",
       "                                   disruptive_inframe_deletion                             8964\n",
       "                                   exon_loss_variant                                          2\n",
       "                                   frameshift_variant                                        31\n",
       "inframe_insertion                  3_prime_UTR_variant                                        2\n",
       "                                   5_prime_UTR_variant                                        1\n",
       "                                   conservative_inframe_insertion                          8624\n",
       "                                   disruptive_inframe_insertion                           10130\n",
       "                                   frameshift_variant                                        14\n",
       "                                   splice_donor_variant                                     166\n",
       "                                   splice_region_variant                                     19\n",
       "intron_variant                     intron_variant                                        416576\n",
       "                                   splice_region_variant                                   1342\n",
       "missense_variant                   missense_variant                                       30988\n",
       "non_coding_transcript_exon_variant downstream_gene_variant                                   12\n",
       "                                   non_coding_transcript_exon_variant                     29472\n",
       "                                   splice_donor_variant                                       2\n",
       "                                   splice_region_variant                                    309\n",
       "                                   upstream_gene_variant                                     18\n",
       "protein_altering_variant           disruptive_inframe_insertion                            6832\n",
       "                                   frameshift_variant                                         2\n",
       "splice_acceptor_variant            5_prime_UTR_variant                                       10\n",
       "                                   conservative_inframe_deletion                             10\n",
       "                                   conservative_inframe_insertion                            16\n",
       "                                   disruptive_inframe_deletion                               26\n",
       "                                   disruptive_inframe_insertion                              10\n",
       "                                   frameshift_variant                                       414\n",
       "                                   non_coding_transcript_exon_variant                         1\n",
       "                                   non_coding_transcript_variant                              7\n",
       "                                   splice_acceptor_variant                                 1692\n",
       "                                   splice_region_variant                                     42\n",
       "                                   stop_gained                                                4\n",
       "splice_donor_variant               frameshift_variant                                       217\n",
       "                                   non_coding_transcript_variant                              9\n",
       "                                   splice_donor_variant                                    1760\n",
       "                                   splice_region_variant                                    240\n",
       "splice_region_variant              5_prime_UTR_premature_start_codon_gain_variant             7\n",
       "                                   5_prime_UTR_variant                                      214\n",
       "                                   conservative_inframe_insertion                             8\n",
       "                                   disruptive_inframe_insertion                             239\n",
       "                                   frameshift_variant                                        16\n",
       "                                   intron_variant                                           370\n",
       "                                   non_coding_transcript_exon_variant                        41\n",
       "                                   non_coding_transcript_variant                              2\n",
       "                                   splice_acceptor_variant                                 1084\n",
       "                                   splice_donor_variant                                     198\n",
       "                                   splice_region_variant                                  15896\n",
       "                                   stop_gained                                                4\n",
       "start_lost                         initiator_codon_variant                                    8\n",
       "                                   start_lost                                                47\n",
       "stop_gained                        splice_donor_variant                                      18\n",
       "                                   stop_gained                                             4859\n",
       "stop_lost                          stop_lost                                                 55\n",
       "stop_retained_variant              stop_retained_variant                                      5\n",
       "synonymous_variant                 splice_region_variant                                      4\n",
       "                                   synonymous_variant                                      9052\n",
       "upstream_gene_variant              5_prime_UTR_variant                                       64\n",
       "                                   conservative_inframe_insertion                             1\n",
       "                                   frameshift_variant                                        12\n",
       "                                   non_coding_transcript_exon_variant                        53\n",
       "                                   non_coding_transcript_variant                              4\n",
       "                                   start_lost                                                 3\n",
       "                                   upstream_gene_variant                                  83765"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(effect_df.groupby(['Effect_vep', 'Effect_snpeff'])['Effect_match'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     99.08\n",
       "False     0.92\n",
       "Name: Normalized_Effect_match, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(effect_df['Normalized_Effect_match'].value_counts()/effect_df['Normalized_Effect_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "And once normalized (ie inframe annotations are generalized). The condordance rate jumps to 99%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Effect_match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normalized_effect_vep</th>\n",
       "      <th>Normalized_effect_snpeff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">3_prime_UTR_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>6648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_lost</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">5_prime_UTR_variant</th>\n",
       "      <th>5_prime_UTR_premature_start_codon_gain_variant</th>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>9944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_insertion</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">coding_sequence_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">downstream_gene_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>99161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">frameshift_variant</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>112937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incomplete_terminal_codon_variant</th>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">inframe_deletion</th>\n",
       "      <th>exon_loss_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_deletion</th>\n",
       "      <td>13573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">inframe_insertion</th>\n",
       "      <th>3_prime_UTR_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_insertion</th>\n",
       "      <td>25586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">intron_variant</th>\n",
       "      <th>intron_variant</th>\n",
       "      <td>416576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>1342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missense_variant</th>\n",
       "      <th>missense_variant</th>\n",
       "      <td>30988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">non_coding_transcript_exon_variant</th>\n",
       "      <th>downstream_gene_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>29472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">splice_acceptor_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_deletion</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_insertion</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_acceptor_variant</th>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">splice_donor_variant</th>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">splice_region_variant</th>\n",
       "      <th>5_prime_UTR_premature_start_codon_gain_variant</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_insertion</th>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intron_variant</th>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_acceptor_variant</th>\n",
       "      <td>1084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>15896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">start_lost</th>\n",
       "      <th>initiator_codon_variant</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">stop_gained</th>\n",
       "      <th>splice_donor_variant</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_gained</th>\n",
       "      <td>4859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_lost</th>\n",
       "      <th>stop_lost</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_retained_variant</th>\n",
       "      <th>stop_retained_variant</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">synonymous_variant</th>\n",
       "      <th>splice_region_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synonymous_variant</th>\n",
       "      <td>9052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">upstream_gene_variant</th>\n",
       "      <th>5_prime_UTR_variant</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameshift_variant</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inframe_insertion</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_exon_variant</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_coding_transcript_variant</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lost</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upstream_gene_variant</th>\n",
       "      <td>83765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   Effect_match\n",
       "Normalized_effect_vep              Normalized_effect_snpeff                                    \n",
       "3_prime_UTR_variant                3_prime_UTR_variant                                     6648\n",
       "                                   downstream_gene_variant                                   12\n",
       "                                   frameshift_variant                                         6\n",
       "                                   non_coding_transcript_variant                              6\n",
       "                                   splice_region_variant                                     74\n",
       "                                   stop_lost                                                  8\n",
       "5_prime_UTR_variant                5_prime_UTR_premature_start_codon_gain_variant           279\n",
       "                                   5_prime_UTR_variant                                     9944\n",
       "                                   exon_loss_variant                                          3\n",
       "                                   frameshift_variant                                        34\n",
       "                                   inframe_insertion                                          1\n",
       "                                   non_coding_transcript_variant                             14\n",
       "                                   splice_region_variant                                     34\n",
       "                                   start_lost                                                15\n",
       "                                   upstream_gene_variant                                     18\n",
       "coding_sequence_variant            5_prime_UTR_variant                                       12\n",
       "                                   frameshift_variant                                         4\n",
       "                                   splice_region_variant                                     12\n",
       "downstream_gene_variant            3_prime_UTR_variant                                       16\n",
       "                                   downstream_gene_variant                                99161\n",
       "                                   splice_region_variant                                     16\n",
       "frameshift_variant                 3_prime_UTR_variant                                       14\n",
       "                                   5_prime_UTR_variant                                       14\n",
       "                                   exon_loss_variant                                          2\n",
       "                                   frameshift_variant                                    112937\n",
       "                                   splice_donor_variant                                     725\n",
       "                                   splice_region_variant                                     17\n",
       "incomplete_terminal_codon_variant  frameshift_variant                                         6\n",
       "inframe_deletion                   exon_loss_variant                                          2\n",
       "                                   frameshift_variant                                        31\n",
       "                                   inframe_deletion                                       13573\n",
       "inframe_insertion                  3_prime_UTR_variant                                        2\n",
       "                                   5_prime_UTR_variant                                        1\n",
       "                                   frameshift_variant                                        16\n",
       "                                   inframe_insertion                                      25586\n",
       "                                   splice_donor_variant                                     166\n",
       "                                   splice_region_variant                                     19\n",
       "intron_variant                     intron_variant                                        416576\n",
       "                                   splice_region_variant                                   1342\n",
       "missense_variant                   missense_variant                                       30988\n",
       "non_coding_transcript_exon_variant downstream_gene_variant                                   12\n",
       "                                   non_coding_transcript_exon_variant                     29472\n",
       "                                   splice_donor_variant                                       2\n",
       "                                   splice_region_variant                                    309\n",
       "                                   upstream_gene_variant                                     18\n",
       "splice_acceptor_variant            5_prime_UTR_variant                                       10\n",
       "                                   frameshift_variant                                       414\n",
       "                                   inframe_deletion                                          36\n",
       "                                   inframe_insertion                                         26\n",
       "                                   non_coding_transcript_exon_variant                         1\n",
       "                                   non_coding_transcript_variant                              7\n",
       "                                   splice_acceptor_variant                                 1692\n",
       "                                   splice_region_variant                                     42\n",
       "                                   stop_gained                                                4\n",
       "splice_donor_variant               frameshift_variant                                       217\n",
       "                                   non_coding_transcript_variant                              9\n",
       "                                   splice_donor_variant                                    1760\n",
       "                                   splice_region_variant                                    240\n",
       "splice_region_variant              5_prime_UTR_premature_start_codon_gain_variant             7\n",
       "                                   5_prime_UTR_variant                                      214\n",
       "                                   frameshift_variant                                        16\n",
       "                                   inframe_insertion                                        247\n",
       "                                   intron_variant                                           370\n",
       "                                   non_coding_transcript_exon_variant                        41\n",
       "                                   non_coding_transcript_variant                              2\n",
       "                                   splice_acceptor_variant                                 1084\n",
       "                                   splice_donor_variant                                     198\n",
       "                                   splice_region_variant                                  15896\n",
       "                                   stop_gained                                                4\n",
       "start_lost                         initiator_codon_variant                                    8\n",
       "                                   start_lost                                                47\n",
       "stop_gained                        splice_donor_variant                                      18\n",
       "                                   stop_gained                                             4859\n",
       "stop_lost                          stop_lost                                                 55\n",
       "stop_retained_variant              stop_retained_variant                                      5\n",
       "synonymous_variant                 splice_region_variant                                      4\n",
       "                                   synonymous_variant                                      9052\n",
       "upstream_gene_variant              5_prime_UTR_variant                                       64\n",
       "                                   frameshift_variant                                        12\n",
       "                                   inframe_insertion                                          1\n",
       "                                   non_coding_transcript_exon_variant                        53\n",
       "                                   non_coding_transcript_variant                              4\n",
       "                                   start_lost                                                 3\n",
       "                                   upstream_gene_variant                                  83765"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(effect_df.groupby(['Normalized_effect_vep', 'Normalized_effect_snpeff'])['Effect_match'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jqmJ+Ek8WV8l6OOpASokSrJB4Ld5C4FSiuzBx4fkVPps/1kvni4uf7Kyb1cMd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7ItmWH4+GDaDp5elmL9ateLrGHgT8bQFXQg1UAKrClSSdUCvBiXV2IH6LswRdd766E2uH6Coipy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\nSCq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W/53WHAg6CBFe3h5/vwIGg46lpObcUkfzt8LRiS/Eivzu3FwELuJ8ENvfO8ehtLZj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Y9wO7i/A0Vf/UAP0Av0yBgqISqHSqs6hnJYHS5vpRjKYQWG8tVgzir+OQt02mbbW/bgx0kp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AALAf2K879IQvwU+DUiSARqCp8L2RA+/v0ArpSCbfX7LwNQaMAv1Av9akfQleCHFYhQZg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd02f4330b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effect_list = sorted(set(effect_df['Normalized_effect_vep'].dropna().unique()) | set(effect_df['Normalized_effect_snpeff'].dropna().unique()), \n",
    "key=term_rank)\n",
    "\n",
    "figure, axes = plt.subplots(13, 2)\n",
    "figure.set_size_inches(10,45)\n",
    "figure.suptitle(\"VEP-SnpEff Effect Concordance\", fontsize=14, fontweight='bold')\n",
    "\n",
    "for idx, effect in enumerate(effect_list):\n",
    "    pos = (int(idx/2), idx%2)\n",
    "    vep_effect = effect_df[effect_df['Normalized_effect_vep'] == effect]\n",
    "    snpeff_effect = effect_df[effect_df['Normalized_effect_snpeff'] == effect]\n",
    "    axes[pos].set_title(effect, fontsize=12, fontweight='bold')\n",
    "    venn2_unweighted([set(vep_effect.index.values), set(snpeff_effect.index.values)], set_labels=('VEP', 'SnpEff'), set_colors=('g', 'b'),\n",
    "                     ax=axes[pos[0]][pos[1]])\n",
    "axes[12, 1].axis('off')\n",
    "\n",
    "plt.subplots_adjust(top=.95)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# HGVS cDot Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df['Hgvsc_vep'] = effect_df['Hgvsc_vep'].str.replace(r'^.*:', '').str.strip()\n",
    "effect_df['Hgvsc_snpeff'] = effect_df['Hgvsc_snpeff'].str.replace(r'^.*:', '').str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "effect_df['Hgvsc_match'] = effect_df.apply(lambda row: row['Hgvsc_vep'] == row['Hgvsc_snpeff'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, let's just see how many HGVS cDot notations are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     69.09\n",
       "False    30.91\n",
       "Name: Hgvsc_match, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(effect_df['Hgvsc_match'].value_counts()/effect_df['Hgvsc_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We see there is about a 69% match between generated HGVS identifiers.\n",
    "\n",
    "This isn's really a fair comparison since the tools don't always generate coding idenfiers for variants that are intergenic. So we'll check the concordance of variants where both tools generated a cDot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "has_hgvsc = (effect_df['Hgvsc_vep'] != '') & (effect_df['Hgvsc_snpeff'] != '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     87.53\n",
       "False    12.47\n",
       "Name: Hgvsc_match, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = effect_df[has_hgvsc]\n",
    "round(filtered['Hgvsc_match'].value_counts()/filtered['Hgvsc_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we see an almost 88% condordance between identifiers.\n",
    "\n",
    "But, let's filter down to coding transcripts since these are the transcripts that contain an open reading frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "is_coding = (effect_df['Biotype_vep'] == 'protein_coding') & (effect_df['Biotype_snpeff'] == 'protein_coding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "filtered_2 = effect_df[has_hgvsc & is_coding]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     86.33\n",
       "False    13.67\n",
       "Name: Hgvsc_match, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(filtered_2['Hgvsc_match'].value_counts()/filtered_2['Hgvsc_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the condordance drops slighty to 86%.\n",
    "\n",
    "Now let's just look at HGVS notation on the canonical transcript ENST00000003084"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    100.0\n",
       "Name: Hgvsc_match, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "on_canonical = (effect_df['Feature_id'] == \"ENST00000003084\")\n",
    "\n",
    "filtered_3 = effect_df[has_hgvsc & on_canonical]\n",
    "\n",
    "round(filtered_3['Hgvsc_match'].value_counts()/filtered_3['Hgvsc_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Wow! So on the canonical transcript, all the HGVS annotations match. Since this transcript is well chariacterized it suggests that the mismatches are being driven by differences in transcription representation (and/or parsing of the GFF file).\n",
    "\n",
    "Let's just look at a few random mismatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>Feature_id</th>\n",
       "      <th>Hgvsc_vep</th>\n",
       "      <th>Hgvsc_snpeff</th>\n",
       "      <th>Effect_vep</th>\n",
       "      <th>Effect_snpeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39739</th>\n",
       "      <td>117119455</td>\n",
       "      <td>TTTG</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+57delTTG</td>\n",
       "      <td>c.-424+57_-423-58delTTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39779</th>\n",
       "      <td>117119456</td>\n",
       "      <td>T</td>\n",
       "      <td>TTG</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58dupTG</td>\n",
       "      <td>c.-424+58_-423-58dupTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39804</th>\n",
       "      <td>117119456</td>\n",
       "      <td>TTG</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58delTG</td>\n",
       "      <td>c.-424+58_-423-58delTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39829</th>\n",
       "      <td>117119457</td>\n",
       "      <td>T</td>\n",
       "      <td>TA</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58insA</td>\n",
       "      <td>c.-424+58_-423-58insA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39839</th>\n",
       "      <td>117119457</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58insC</td>\n",
       "      <td>c.-424+58_-423-58insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39854</th>\n",
       "      <td>117119457</td>\n",
       "      <td>T</td>\n",
       "      <td>TTA</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58insTA</td>\n",
       "      <td>c.-424+58_-423-58insTA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39859</th>\n",
       "      <td>117119457</td>\n",
       "      <td>T</td>\n",
       "      <td>TTGA</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58insTGA</td>\n",
       "      <td>c.-424+58_-423-58insTGA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39864</th>\n",
       "      <td>117119457</td>\n",
       "      <td>T</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58insCGA</td>\n",
       "      <td>c.-424+58_-423-58insCGA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39924</th>\n",
       "      <td>117119458</td>\n",
       "      <td>G</td>\n",
       "      <td>GTG</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.-423-58_-424+58dupTG</td>\n",
       "      <td>c.-424+58_-423-58dupTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148045</th>\n",
       "      <td>117171029</td>\n",
       "      <td>GCT</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.108_109delCT</td>\n",
       "      <td>c.110_*1delCT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148089</th>\n",
       "      <td>117171030</td>\n",
       "      <td>C</td>\n",
       "      <td>CTAC</td>\n",
       "      <td>ENST00000446805</td>\n",
       "      <td>c.108_109insTAC</td>\n",
       "      <td>c.109_110insACT</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>disruptive_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506572</th>\n",
       "      <td>117251670</td>\n",
       "      <td>TT</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1delT</td>\n",
       "      <td>c.-2delT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506577</th>\n",
       "      <td>117251670</td>\n",
       "      <td>TTA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2delTA</td>\n",
       "      <td>c.-2_-1delTA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>exon_loss_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506582</th>\n",
       "      <td>117251670</td>\n",
       "      <td>TTAA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_3delTAA</td>\n",
       "      <td>c.-2_1delTAA</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>exon_loss_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506583</th>\n",
       "      <td>117251670</td>\n",
       "      <td>TTAA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_3delTAA</td>\n",
       "      <td>c.-2_1delTAA</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>conservative_inframe_deletion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506588</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1T&gt;A</td>\n",
       "      <td>c.-2T&gt;A</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506593</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1T&gt;G</td>\n",
       "      <td>c.-2T&gt;G</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506598</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1T&gt;C</td>\n",
       "      <td>c.-2T&gt;C</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506603</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5dupA</td>\n",
       "      <td>c.3dupA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506608</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2insG</td>\n",
       "      <td>c.-2_-1insG</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506613</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2insC</td>\n",
       "      <td>c.-2_-1insC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506618</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1dupT</td>\n",
       "      <td>c.-2dupT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506623</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TGC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2insGC</td>\n",
       "      <td>c.-2_-1insGC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506628</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TGA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2insGA</td>\n",
       "      <td>c.-2_-1insGA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506633</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TATC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insTCA</td>\n",
       "      <td>c.-1_1insTCA</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>conservative_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506638</th>\n",
       "      <td>117251671</td>\n",
       "      <td>T</td>\n",
       "      <td>TTGC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.1_2insTGC</td>\n",
       "      <td>c.-2_-1insTGC</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506643</th>\n",
       "      <td>117251671</td>\n",
       "      <td>TA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5delA</td>\n",
       "      <td>c.3delA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506648</th>\n",
       "      <td>117251671</td>\n",
       "      <td>TAA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5delAA</td>\n",
       "      <td>c.2_3delAA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506653</th>\n",
       "      <td>117251671</td>\n",
       "      <td>TAAA</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_5delAAA</td>\n",
       "      <td>c.1_3delAAA</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>conservative_inframe_deletion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506658</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2A&gt;G</td>\n",
       "      <td>c.-1A&gt;G</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506663</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2A&gt;C</td>\n",
       "      <td>c.-1A&gt;C</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506668</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2A&gt;T</td>\n",
       "      <td>c.-1A&gt;T</td>\n",
       "      <td>coding_sequence_variant</td>\n",
       "      <td>5_prime_UTR_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506673</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5dupA</td>\n",
       "      <td>c.3dupA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506678</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insG</td>\n",
       "      <td>c.-1_1insG</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506683</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insC</td>\n",
       "      <td>c.-1_1insC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506688</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insT</td>\n",
       "      <td>c.-1_1insT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506693</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AGA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insGA</td>\n",
       "      <td>c.-1_1insGA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506698</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>AGC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insGC</td>\n",
       "      <td>c.-1_1insGC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506703</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>ACGT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insCGT</td>\n",
       "      <td>c.-1_1insCGT</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>conservative_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506708</th>\n",
       "      <td>117251672</td>\n",
       "      <td>A</td>\n",
       "      <td>ACAG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.2_3insCAG</td>\n",
       "      <td>c.-1_1insCAG</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>conservative_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506713</th>\n",
       "      <td>117251672</td>\n",
       "      <td>AA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5delA</td>\n",
       "      <td>c.3delA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506718</th>\n",
       "      <td>117251672</td>\n",
       "      <td>AAA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5delAA</td>\n",
       "      <td>c.2_3delAA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506723</th>\n",
       "      <td>117251672</td>\n",
       "      <td>AAAA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_5delAAA</td>\n",
       "      <td>c.1_3delAAA</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>conservative_inframe_deletion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506728</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3A&gt;G</td>\n",
       "      <td>c.1A&gt;G</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506733</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3A&gt;C</td>\n",
       "      <td>c.1A&gt;C</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506738</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3A&gt;T</td>\n",
       "      <td>c.1A&gt;T</td>\n",
       "      <td>stop_gained</td>\n",
       "      <td>stop_gained</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506743</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5dupA</td>\n",
       "      <td>c.3dupA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506748</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>AG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insG</td>\n",
       "      <td>c.1_2insG</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506753</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insC</td>\n",
       "      <td>c.1_2insC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506758</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insT</td>\n",
       "      <td>c.1_2insT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506763</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insCA</td>\n",
       "      <td>c.1_2insCA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506768</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>AGC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insGC</td>\n",
       "      <td>c.1_2insGC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506773</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>ATAC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insTAC</td>\n",
       "      <td>c.1_2insTAC</td>\n",
       "      <td>protein_altering_variant</td>\n",
       "      <td>disruptive_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506778</th>\n",
       "      <td>117251673</td>\n",
       "      <td>A</td>\n",
       "      <td>ATGA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.3_4insTGA</td>\n",
       "      <td>c.1_2insTGA</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>disruptive_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506783</th>\n",
       "      <td>117251673</td>\n",
       "      <td>AA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5delA</td>\n",
       "      <td>c.3delA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506788</th>\n",
       "      <td>117251673</td>\n",
       "      <td>AAA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5delAA</td>\n",
       "      <td>c.2_3delAA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506793</th>\n",
       "      <td>117251673</td>\n",
       "      <td>AAAG</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_6delAAG</td>\n",
       "      <td>c.2_4delAAG</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>disruptive_inframe_deletion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506798</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4A&gt;G</td>\n",
       "      <td>c.2A&gt;G</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506803</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4A&gt;C</td>\n",
       "      <td>c.2A&gt;C</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506808</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4A&gt;T</td>\n",
       "      <td>c.2A&gt;T</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506813</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5dupA</td>\n",
       "      <td>c.3dupA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506818</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>AG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insG</td>\n",
       "      <td>c.2_3insG</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506823</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insC</td>\n",
       "      <td>c.2_3insC</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506828</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insT</td>\n",
       "      <td>c.2_3insT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506833</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>ATA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insTA</td>\n",
       "      <td>c.2_3insTA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506838</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>ACT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insCT</td>\n",
       "      <td>c.2_3insCT</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506843</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTG</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insCTG</td>\n",
       "      <td>c.2_3insCTG</td>\n",
       "      <td>stop_gained</td>\n",
       "      <td>stop_gained</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506848</th>\n",
       "      <td>117251674</td>\n",
       "      <td>A</td>\n",
       "      <td>AGAT</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.4_5insGAT</td>\n",
       "      <td>c.2_3insGAT</td>\n",
       "      <td>inframe_insertion</td>\n",
       "      <td>disruptive_inframe_insertion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506853</th>\n",
       "      <td>117251674</td>\n",
       "      <td>AA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5delA</td>\n",
       "      <td>c.3delA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506858</th>\n",
       "      <td>117251674</td>\n",
       "      <td>AAG</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5_6delAG</td>\n",
       "      <td>c.3_4delAG</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506863</th>\n",
       "      <td>117251674</td>\n",
       "      <td>AAGG</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.6_8delGGA</td>\n",
       "      <td>c.4_6delGGA</td>\n",
       "      <td>inframe_deletion</td>\n",
       "      <td>conservative_inframe_deletion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506868</th>\n",
       "      <td>117251675</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5A&gt;G</td>\n",
       "      <td>c.3A&gt;G</td>\n",
       "      <td>synonymous_variant</td>\n",
       "      <td>synonymous_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506873</th>\n",
       "      <td>117251675</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5A&gt;C</td>\n",
       "      <td>c.3A&gt;C</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506878</th>\n",
       "      <td>117251675</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5A&gt;T</td>\n",
       "      <td>c.3A&gt;T</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>missense_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506883</th>\n",
       "      <td>117251675</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000468795</td>\n",
       "      <td>c.5dupA</td>\n",
       "      <td>c.3dupA</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>frameshift_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868183</th>\n",
       "      <td>117355976</td>\n",
       "      <td>GG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2096delC</td>\n",
       "      <td>c.3207+2095delC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868191</th>\n",
       "      <td>117355976</td>\n",
       "      <td>GGA</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095delTC</td>\n",
       "      <td>c.3207+2094_3207+2095delTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868195</th>\n",
       "      <td>117355976</td>\n",
       "      <td>GGAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2095_4746+2097delCCT</td>\n",
       "      <td>c.4746+2093_4746+2095delCTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868199</th>\n",
       "      <td>117355976</td>\n",
       "      <td>GGAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2095_3208+2097delCCT</td>\n",
       "      <td>c.3207+2093_3207+2095delCTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868207</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2095C&gt;T</td>\n",
       "      <td>c.3207+2095C&gt;T</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868215</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2095C&gt;G</td>\n",
       "      <td>c.3207+2095C&gt;G</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868223</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2095C&gt;A</td>\n",
       "      <td>c.3207+2095C&gt;A</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868231</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094dupT</td>\n",
       "      <td>c.3207+2094dupT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868235</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GG</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2096dupC</td>\n",
       "      <td>c.4746+2094_4746+2095insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868239</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2096dupC</td>\n",
       "      <td>c.3207+2094_3207+2095insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868247</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insG</td>\n",
       "      <td>c.3207+2094_3207+2095insG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868255</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insA</td>\n",
       "      <td>c.3207+2094_3207+2095insA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868263</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GAC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insGT</td>\n",
       "      <td>c.3207+2094_3207+2095insGT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868271</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GCA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insTG</td>\n",
       "      <td>c.3207+2094_3207+2095insTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868279</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GGTA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insTAC</td>\n",
       "      <td>c.3207+2094_3207+2095insTAC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868283</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GTCG</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2095_4746+2096insGAC</td>\n",
       "      <td>c.4746+2094_4746+2095insCGA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868287</th>\n",
       "      <td>117355977</td>\n",
       "      <td>G</td>\n",
       "      <td>GTCG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2095_3208+2096insGAC</td>\n",
       "      <td>c.3207+2094_3207+2095insCGA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868295</th>\n",
       "      <td>117355977</td>\n",
       "      <td>GA</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094delT</td>\n",
       "      <td>c.3207+2094delT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868299</th>\n",
       "      <td>117355977</td>\n",
       "      <td>GAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2094_4746+2095delTC</td>\n",
       "      <td>c.4746+2093_4746+2094delCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868303</th>\n",
       "      <td>117355977</td>\n",
       "      <td>GAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095delTC</td>\n",
       "      <td>c.3207+2093_3207+2094delCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868311</th>\n",
       "      <td>117355977</td>\n",
       "      <td>GAGA</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2094delTCT</td>\n",
       "      <td>c.3207+2092_3207+2094delTCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868319</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094T&gt;C</td>\n",
       "      <td>c.3207+2094T&gt;C</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868327</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094T&gt;G</td>\n",
       "      <td>c.3207+2094T&gt;G</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868335</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094T&gt;A</td>\n",
       "      <td>c.3207+2094T&gt;A</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868339</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2094dupT</td>\n",
       "      <td>c.4746+2093_4746+2094insT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868343</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094dupT</td>\n",
       "      <td>c.3207+2093_3207+2094insT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868351</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093dupC</td>\n",
       "      <td>c.3207+2093dupC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868359</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insG</td>\n",
       "      <td>c.3207+2093_3207+2094insG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868367</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insA</td>\n",
       "      <td>c.3207+2093_3207+2094insA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868375</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AGC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insGC</td>\n",
       "      <td>c.3207+2093_3207+2094insGC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868383</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AAT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insAT</td>\n",
       "      <td>c.3207+2093_3207+2094insAT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868387</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AGCA</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2094_4746+2095insGCT</td>\n",
       "      <td>c.4746+2093_4746+2094insTGC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868391</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>AGCA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insGCT</td>\n",
       "      <td>c.3207+2093_3207+2094insTGC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868399</th>\n",
       "      <td>117355978</td>\n",
       "      <td>A</td>\n",
       "      <td>ATGC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insGCA</td>\n",
       "      <td>c.3207+2093_3207+2094insGCA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868407</th>\n",
       "      <td>117355978</td>\n",
       "      <td>AG</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093delC</td>\n",
       "      <td>c.3207+2093delC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868411</th>\n",
       "      <td>117355978</td>\n",
       "      <td>AGA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2094_4746+2095delTC</td>\n",
       "      <td>c.4746+2092_4746+2093delTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868415</th>\n",
       "      <td>117355978</td>\n",
       "      <td>AGA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095delTC</td>\n",
       "      <td>c.3207+2092_3207+2093delTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868419</th>\n",
       "      <td>117355978</td>\n",
       "      <td>AGAA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092_4746+2094delTCT</td>\n",
       "      <td>c.4746+2091_4746+2093delTTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868423</th>\n",
       "      <td>117355978</td>\n",
       "      <td>AGAA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2094delTCT</td>\n",
       "      <td>c.3207+2091_3207+2093delTTC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868431</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093C&gt;T</td>\n",
       "      <td>c.3207+2093C&gt;T</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868439</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093C&gt;G</td>\n",
       "      <td>c.3207+2093C&gt;G</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868447</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093C&gt;A</td>\n",
       "      <td>c.3207+2093C&gt;A</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868455</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092dupT</td>\n",
       "      <td>c.3207+2092dupT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868459</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GG</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2093dupC</td>\n",
       "      <td>c.4746+2092_4746+2093insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868463</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093dupC</td>\n",
       "      <td>c.3207+2092_3207+2093insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868471</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2093insG</td>\n",
       "      <td>c.3207+2092_3207+2093insG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868479</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2093insA</td>\n",
       "      <td>c.3207+2092_3207+2093insA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868483</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GTG</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2093_4746+2094insAC</td>\n",
       "      <td>c.4746+2092_4746+2093insCA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868487</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GTG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2093_3208+2094insAC</td>\n",
       "      <td>c.3207+2092_3207+2093insCA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868495</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GGT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2093insAC</td>\n",
       "      <td>c.3207+2092_3207+2093insAC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868503</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GCGT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2093insACG</td>\n",
       "      <td>c.3207+2092_3207+2093insACG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868507</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GTAG</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2094_4746+2095insACT</td>\n",
       "      <td>c.4746+2092_4746+2093insCTA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868511</th>\n",
       "      <td>117355979</td>\n",
       "      <td>G</td>\n",
       "      <td>GTAG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2094_3208+2095insACT</td>\n",
       "      <td>c.3207+2092_3207+2093insCTA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868519</th>\n",
       "      <td>117355979</td>\n",
       "      <td>GA</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092delT</td>\n",
       "      <td>c.3207+2092delT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868527</th>\n",
       "      <td>117355979</td>\n",
       "      <td>GAA</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092delTT</td>\n",
       "      <td>c.3207+2091_3207+2092delTT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868531</th>\n",
       "      <td>117355979</td>\n",
       "      <td>GAAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092_4746+2094delTCT</td>\n",
       "      <td>c.4746+2090_4746+2092delCTT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868535</th>\n",
       "      <td>117355979</td>\n",
       "      <td>GAAG</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2094delTCT</td>\n",
       "      <td>c.3207+2090_3207+2092delCTT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868543</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092T&gt;C</td>\n",
       "      <td>c.3207+2092T&gt;C</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868551</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092T&gt;G</td>\n",
       "      <td>c.3207+2092T&gt;G</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868559</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092T&gt;A</td>\n",
       "      <td>c.3207+2092T&gt;A</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868563</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092dupT</td>\n",
       "      <td>c.4746+2091dupT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868567</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>AA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092dupT</td>\n",
       "      <td>c.3207+2091dupT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868575</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>AG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insC</td>\n",
       "      <td>c.3207+2091_3207+2092insC</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868583</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insG</td>\n",
       "      <td>c.3207+2091_3207+2092insG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868591</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insA</td>\n",
       "      <td>c.3207+2091_3207+2092insA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868599</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>ACG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insCG</td>\n",
       "      <td>c.3207+2091_3207+2092insCG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868603</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092_4746+2093insGT</td>\n",
       "      <td>c.4746+2091_4746+2092insTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868607</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2093insGT</td>\n",
       "      <td>c.3207+2091_3207+2092insTG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868615</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insCAG</td>\n",
       "      <td>c.3207+2091_3207+2092insCAG</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868623</th>\n",
       "      <td>117355980</td>\n",
       "      <td>A</td>\n",
       "      <td>ATAG</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2091_3208+2092insCTA</td>\n",
       "      <td>c.3207+2091_3207+2092insCTA</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868627</th>\n",
       "      <td>117355980</td>\n",
       "      <td>AA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092delT</td>\n",
       "      <td>c.4746+2091delT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868631</th>\n",
       "      <td>117355980</td>\n",
       "      <td>AA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092delT</td>\n",
       "      <td>c.3207+2091delT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868639</th>\n",
       "      <td>117355980</td>\n",
       "      <td>AAG</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2090_3208+2091delCT</td>\n",
       "      <td>c.3207+2090_3207+2091delCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868643</th>\n",
       "      <td>117355980</td>\n",
       "      <td>AAGA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000160373</td>\n",
       "      <td>c.4746+2092_4746+2094delTCT</td>\n",
       "      <td>c.4746+2089_4746+2091delTCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868647</th>\n",
       "      <td>117355980</td>\n",
       "      <td>AAGA</td>\n",
       "      <td>A</td>\n",
       "      <td>ENST00000446636</td>\n",
       "      <td>c.3208+2092_3208+2094delTCT</td>\n",
       "      <td>c.3207+2089_3207+2091delTCT</td>\n",
       "      <td>intron_variant</td>\n",
       "      <td>intron_variant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73008 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              POS   REF   ALT       Feature_id                    Hgvsc_vep  \\\n",
       "39739   117119455  TTTG     T  ENST00000446805      c.-423-58_-424+57delTTG   \n",
       "39779   117119456     T   TTG  ENST00000446805       c.-423-58_-424+58dupTG   \n",
       "39804   117119456   TTG     T  ENST00000446805       c.-423-58_-424+58delTG   \n",
       "39829   117119457     T    TA  ENST00000446805        c.-423-58_-424+58insA   \n",
       "39839   117119457     T    TC  ENST00000446805        c.-423-58_-424+58insC   \n",
       "39854   117119457     T   TTA  ENST00000446805       c.-423-58_-424+58insTA   \n",
       "39859   117119457     T  TTGA  ENST00000446805      c.-423-58_-424+58insTGA   \n",
       "39864   117119457     T  TCGA  ENST00000446805      c.-423-58_-424+58insCGA   \n",
       "39924   117119458     G   GTG  ENST00000446805       c.-423-58_-424+58dupTG   \n",
       "148045  117171029   GCT     G  ENST00000446805               c.108_109delCT   \n",
       "148089  117171030     C  CTAC  ENST00000446805              c.108_109insTAC   \n",
       "506572  117251670    TT     T  ENST00000468795                      c.1delT   \n",
       "506577  117251670   TTA     T  ENST00000468795                   c.1_2delTA   \n",
       "506582  117251670  TTAA     T  ENST00000468795                  c.1_3delTAA   \n",
       "506583  117251670  TTAA     T  ENST00000468795                  c.1_3delTAA   \n",
       "506588  117251671     T     A  ENST00000468795                       c.1T>A   \n",
       "506593  117251671     T     G  ENST00000468795                       c.1T>G   \n",
       "506598  117251671     T     C  ENST00000468795                       c.1T>C   \n",
       "506603  117251671     T    TA  ENST00000468795                      c.5dupA   \n",
       "506608  117251671     T    TG  ENST00000468795                    c.1_2insG   \n",
       "506613  117251671     T    TC  ENST00000468795                    c.1_2insC   \n",
       "506618  117251671     T    TT  ENST00000468795                      c.1dupT   \n",
       "506623  117251671     T   TGC  ENST00000468795                   c.1_2insGC   \n",
       "506628  117251671     T   TGA  ENST00000468795                   c.1_2insGA   \n",
       "506633  117251671     T  TATC  ENST00000468795                  c.2_3insTCA   \n",
       "506638  117251671     T  TTGC  ENST00000468795                  c.1_2insTGC   \n",
       "506643  117251671    TA     T  ENST00000468795                      c.5delA   \n",
       "506648  117251671   TAA     T  ENST00000468795                   c.4_5delAA   \n",
       "506653  117251671  TAAA     T  ENST00000468795                  c.3_5delAAA   \n",
       "506658  117251672     A     G  ENST00000468795                       c.2A>G   \n",
       "506663  117251672     A     C  ENST00000468795                       c.2A>C   \n",
       "506668  117251672     A     T  ENST00000468795                       c.2A>T   \n",
       "506673  117251672     A    AA  ENST00000468795                      c.5dupA   \n",
       "506678  117251672     A    AG  ENST00000468795                    c.2_3insG   \n",
       "506683  117251672     A    AC  ENST00000468795                    c.2_3insC   \n",
       "506688  117251672     A    AT  ENST00000468795                    c.2_3insT   \n",
       "506693  117251672     A   AGA  ENST00000468795                   c.2_3insGA   \n",
       "506698  117251672     A   AGC  ENST00000468795                   c.2_3insGC   \n",
       "506703  117251672     A  ACGT  ENST00000468795                  c.2_3insCGT   \n",
       "506708  117251672     A  ACAG  ENST00000468795                  c.2_3insCAG   \n",
       "506713  117251672    AA     A  ENST00000468795                      c.5delA   \n",
       "506718  117251672   AAA     A  ENST00000468795                   c.4_5delAA   \n",
       "506723  117251672  AAAA     A  ENST00000468795                  c.3_5delAAA   \n",
       "506728  117251673     A     G  ENST00000468795                       c.3A>G   \n",
       "506733  117251673     A     C  ENST00000468795                       c.3A>C   \n",
       "506738  117251673     A     T  ENST00000468795                       c.3A>T   \n",
       "506743  117251673     A    AA  ENST00000468795                      c.5dupA   \n",
       "506748  117251673     A    AG  ENST00000468795                    c.3_4insG   \n",
       "506753  117251673     A    AC  ENST00000468795                    c.3_4insC   \n",
       "506758  117251673     A    AT  ENST00000468795                    c.3_4insT   \n",
       "506763  117251673     A   ACA  ENST00000468795                   c.3_4insCA   \n",
       "506768  117251673     A   AGC  ENST00000468795                   c.3_4insGC   \n",
       "506773  117251673     A  ATAC  ENST00000468795                  c.3_4insTAC   \n",
       "506778  117251673     A  ATGA  ENST00000468795                  c.3_4insTGA   \n",
       "506783  117251673    AA     A  ENST00000468795                      c.5delA   \n",
       "506788  117251673   AAA     A  ENST00000468795                   c.4_5delAA   \n",
       "506793  117251673  AAAG     A  ENST00000468795                  c.4_6delAAG   \n",
       "506798  117251674     A     G  ENST00000468795                       c.4A>G   \n",
       "506803  117251674     A     C  ENST00000468795                       c.4A>C   \n",
       "506808  117251674     A     T  ENST00000468795                       c.4A>T   \n",
       "506813  117251674     A    AA  ENST00000468795                      c.5dupA   \n",
       "506818  117251674     A    AG  ENST00000468795                    c.4_5insG   \n",
       "506823  117251674     A    AC  ENST00000468795                    c.4_5insC   \n",
       "506828  117251674     A    AT  ENST00000468795                    c.4_5insT   \n",
       "506833  117251674     A   ATA  ENST00000468795                   c.4_5insTA   \n",
       "506838  117251674     A   ACT  ENST00000468795                   c.4_5insCT   \n",
       "506843  117251674     A  ACTG  ENST00000468795                  c.4_5insCTG   \n",
       "506848  117251674     A  AGAT  ENST00000468795                  c.4_5insGAT   \n",
       "506853  117251674    AA     A  ENST00000468795                      c.5delA   \n",
       "506858  117251674   AAG     A  ENST00000468795                   c.5_6delAG   \n",
       "506863  117251674  AAGG     A  ENST00000468795                  c.6_8delGGA   \n",
       "506868  117251675     A     G  ENST00000468795                       c.5A>G   \n",
       "506873  117251675     A     C  ENST00000468795                       c.5A>C   \n",
       "506878  117251675     A     T  ENST00000468795                       c.5A>T   \n",
       "506883  117251675     A    AA  ENST00000468795                      c.5dupA   \n",
       "...           ...   ...   ...              ...                          ...   \n",
       "868183  117355976    GG     G  ENST00000446636              c.3208+2096delC   \n",
       "868191  117355976   GGA     G  ENST00000446636   c.3208+2094_3208+2095delTC   \n",
       "868195  117355976  GGAG     G  ENST00000160373  c.4746+2095_4746+2097delCCT   \n",
       "868199  117355976  GGAG     G  ENST00000446636  c.3208+2095_3208+2097delCCT   \n",
       "868207  117355977     G     A  ENST00000446636               c.3208+2095C>T   \n",
       "868215  117355977     G     C  ENST00000446636               c.3208+2095C>G   \n",
       "868223  117355977     G     T  ENST00000446636               c.3208+2095C>A   \n",
       "868231  117355977     G    GA  ENST00000446636              c.3208+2094dupT   \n",
       "868235  117355977     G    GG  ENST00000160373              c.4746+2096dupC   \n",
       "868239  117355977     G    GG  ENST00000446636              c.3208+2096dupC   \n",
       "868247  117355977     G    GC  ENST00000446636    c.3208+2094_3208+2095insG   \n",
       "868255  117355977     G    GT  ENST00000446636    c.3208+2094_3208+2095insA   \n",
       "868263  117355977     G   GAC  ENST00000446636   c.3208+2094_3208+2095insGT   \n",
       "868271  117355977     G   GCA  ENST00000446636   c.3208+2094_3208+2095insTG   \n",
       "868279  117355977     G  GGTA  ENST00000446636  c.3208+2094_3208+2095insTAC   \n",
       "868283  117355977     G  GTCG  ENST00000160373  c.4746+2095_4746+2096insGAC   \n",
       "868287  117355977     G  GTCG  ENST00000446636  c.3208+2095_3208+2096insGAC   \n",
       "868295  117355977    GA     G  ENST00000446636              c.3208+2094delT   \n",
       "868299  117355977   GAG     G  ENST00000160373   c.4746+2094_4746+2095delTC   \n",
       "868303  117355977   GAG     G  ENST00000446636   c.3208+2094_3208+2095delTC   \n",
       "868311  117355977  GAGA     G  ENST00000446636  c.3208+2092_3208+2094delTCT   \n",
       "868319  117355978     A     G  ENST00000446636               c.3208+2094T>C   \n",
       "868327  117355978     A     C  ENST00000446636               c.3208+2094T>G   \n",
       "868335  117355978     A     T  ENST00000446636               c.3208+2094T>A   \n",
       "868339  117355978     A    AA  ENST00000160373              c.4746+2094dupT   \n",
       "868343  117355978     A    AA  ENST00000446636              c.3208+2094dupT   \n",
       "868351  117355978     A    AG  ENST00000446636              c.3208+2093dupC   \n",
       "868359  117355978     A    AC  ENST00000446636    c.3208+2093_3208+2094insG   \n",
       "868367  117355978     A    AT  ENST00000446636    c.3208+2093_3208+2094insA   \n",
       "868375  117355978     A   AGC  ENST00000446636   c.3208+2093_3208+2094insGC   \n",
       "868383  117355978     A   AAT  ENST00000446636   c.3208+2093_3208+2094insAT   \n",
       "868387  117355978     A  AGCA  ENST00000160373  c.4746+2094_4746+2095insGCT   \n",
       "868391  117355978     A  AGCA  ENST00000446636  c.3208+2094_3208+2095insGCT   \n",
       "868399  117355978     A  ATGC  ENST00000446636  c.3208+2093_3208+2094insGCA   \n",
       "868407  117355978    AG     A  ENST00000446636              c.3208+2093delC   \n",
       "868411  117355978   AGA     A  ENST00000160373   c.4746+2094_4746+2095delTC   \n",
       "868415  117355978   AGA     A  ENST00000446636   c.3208+2094_3208+2095delTC   \n",
       "868419  117355978  AGAA     A  ENST00000160373  c.4746+2092_4746+2094delTCT   \n",
       "868423  117355978  AGAA     A  ENST00000446636  c.3208+2092_3208+2094delTCT   \n",
       "868431  117355979     G     A  ENST00000446636               c.3208+2093C>T   \n",
       "868439  117355979     G     C  ENST00000446636               c.3208+2093C>G   \n",
       "868447  117355979     G     T  ENST00000446636               c.3208+2093C>A   \n",
       "868455  117355979     G    GA  ENST00000446636              c.3208+2092dupT   \n",
       "868459  117355979     G    GG  ENST00000160373              c.4746+2093dupC   \n",
       "868463  117355979     G    GG  ENST00000446636              c.3208+2093dupC   \n",
       "868471  117355979     G    GC  ENST00000446636    c.3208+2092_3208+2093insG   \n",
       "868479  117355979     G    GT  ENST00000446636    c.3208+2092_3208+2093insA   \n",
       "868483  117355979     G   GTG  ENST00000160373   c.4746+2093_4746+2094insAC   \n",
       "868487  117355979     G   GTG  ENST00000446636   c.3208+2093_3208+2094insAC   \n",
       "868495  117355979     G   GGT  ENST00000446636   c.3208+2092_3208+2093insAC   \n",
       "868503  117355979     G  GCGT  ENST00000446636  c.3208+2092_3208+2093insACG   \n",
       "868507  117355979     G  GTAG  ENST00000160373  c.4746+2094_4746+2095insACT   \n",
       "868511  117355979     G  GTAG  ENST00000446636  c.3208+2094_3208+2095insACT   \n",
       "868519  117355979    GA     G  ENST00000446636              c.3208+2092delT   \n",
       "868527  117355979   GAA     G  ENST00000446636   c.3208+2091_3208+2092delTT   \n",
       "868531  117355979  GAAG     G  ENST00000160373  c.4746+2092_4746+2094delTCT   \n",
       "868535  117355979  GAAG     G  ENST00000446636  c.3208+2092_3208+2094delTCT   \n",
       "868543  117355980     A     G  ENST00000446636               c.3208+2092T>C   \n",
       "868551  117355980     A     C  ENST00000446636               c.3208+2092T>G   \n",
       "868559  117355980     A     T  ENST00000446636               c.3208+2092T>A   \n",
       "868563  117355980     A    AA  ENST00000160373              c.4746+2092dupT   \n",
       "868567  117355980     A    AA  ENST00000446636              c.3208+2092dupT   \n",
       "868575  117355980     A    AG  ENST00000446636    c.3208+2091_3208+2092insC   \n",
       "868583  117355980     A    AC  ENST00000446636    c.3208+2091_3208+2092insG   \n",
       "868591  117355980     A    AT  ENST00000446636    c.3208+2091_3208+2092insA   \n",
       "868599  117355980     A   ACG  ENST00000446636   c.3208+2091_3208+2092insCG   \n",
       "868603  117355980     A   ACA  ENST00000160373   c.4746+2092_4746+2093insGT   \n",
       "868607  117355980     A   ACA  ENST00000446636   c.3208+2092_3208+2093insGT   \n",
       "868615  117355980     A  ACTG  ENST00000446636  c.3208+2091_3208+2092insCAG   \n",
       "868623  117355980     A  ATAG  ENST00000446636  c.3208+2091_3208+2092insCTA   \n",
       "868627  117355980    AA     A  ENST00000160373              c.4746+2092delT   \n",
       "868631  117355980    AA     A  ENST00000446636              c.3208+2092delT   \n",
       "868639  117355980   AAG     A  ENST00000446636   c.3208+2090_3208+2091delCT   \n",
       "868643  117355980  AAGA     A  ENST00000160373  c.4746+2092_4746+2094delTCT   \n",
       "868647  117355980  AAGA     A  ENST00000446636  c.3208+2092_3208+2094delTCT   \n",
       "\n",
       "                       Hgvsc_snpeff                Effect_vep  \\\n",
       "39739       c.-424+57_-423-58delTTG            intron_variant   \n",
       "39779        c.-424+58_-423-58dupTG            intron_variant   \n",
       "39804        c.-424+58_-423-58delTG            intron_variant   \n",
       "39829         c.-424+58_-423-58insA            intron_variant   \n",
       "39839         c.-424+58_-423-58insC            intron_variant   \n",
       "39854        c.-424+58_-423-58insTA            intron_variant   \n",
       "39859       c.-424+58_-423-58insTGA            intron_variant   \n",
       "39864       c.-424+58_-423-58insCGA            intron_variant   \n",
       "39924        c.-424+58_-423-58dupTG            intron_variant   \n",
       "148045                c.110_*1delCT        frameshift_variant   \n",
       "148089              c.109_110insACT         inframe_insertion   \n",
       "506572                     c.-2delT        frameshift_variant   \n",
       "506577                 c.-2_-1delTA        frameshift_variant   \n",
       "506582                 c.-2_1delTAA          inframe_deletion   \n",
       "506583                 c.-2_1delTAA          inframe_deletion   \n",
       "506588                      c.-2T>A   coding_sequence_variant   \n",
       "506593                      c.-2T>G   coding_sequence_variant   \n",
       "506598                      c.-2T>C   coding_sequence_variant   \n",
       "506603                      c.3dupA        frameshift_variant   \n",
       "506608                  c.-2_-1insG        frameshift_variant   \n",
       "506613                  c.-2_-1insC        frameshift_variant   \n",
       "506618                     c.-2dupT        frameshift_variant   \n",
       "506623                 c.-2_-1insGC        frameshift_variant   \n",
       "506628                 c.-2_-1insGA        frameshift_variant   \n",
       "506633                 c.-1_1insTCA         inframe_insertion   \n",
       "506638                c.-2_-1insTGC         inframe_insertion   \n",
       "506643                      c.3delA        frameshift_variant   \n",
       "506648                   c.2_3delAA        frameshift_variant   \n",
       "506653                  c.1_3delAAA          inframe_deletion   \n",
       "506658                      c.-1A>G   coding_sequence_variant   \n",
       "506663                      c.-1A>C   coding_sequence_variant   \n",
       "506668                      c.-1A>T   coding_sequence_variant   \n",
       "506673                      c.3dupA        frameshift_variant   \n",
       "506678                   c.-1_1insG        frameshift_variant   \n",
       "506683                   c.-1_1insC        frameshift_variant   \n",
       "506688                   c.-1_1insT        frameshift_variant   \n",
       "506693                  c.-1_1insGA        frameshift_variant   \n",
       "506698                  c.-1_1insGC        frameshift_variant   \n",
       "506703                 c.-1_1insCGT         inframe_insertion   \n",
       "506708                 c.-1_1insCAG         inframe_insertion   \n",
       "506713                      c.3delA        frameshift_variant   \n",
       "506718                   c.2_3delAA        frameshift_variant   \n",
       "506723                  c.1_3delAAA          inframe_deletion   \n",
       "506728                       c.1A>G          missense_variant   \n",
       "506733                       c.1A>C          missense_variant   \n",
       "506738                       c.1A>T               stop_gained   \n",
       "506743                      c.3dupA        frameshift_variant   \n",
       "506748                    c.1_2insG        frameshift_variant   \n",
       "506753                    c.1_2insC        frameshift_variant   \n",
       "506758                    c.1_2insT        frameshift_variant   \n",
       "506763                   c.1_2insCA        frameshift_variant   \n",
       "506768                   c.1_2insGC        frameshift_variant   \n",
       "506773                  c.1_2insTAC  protein_altering_variant   \n",
       "506778                  c.1_2insTGA         inframe_insertion   \n",
       "506783                      c.3delA        frameshift_variant   \n",
       "506788                   c.2_3delAA        frameshift_variant   \n",
       "506793                  c.2_4delAAG          inframe_deletion   \n",
       "506798                       c.2A>G          missense_variant   \n",
       "506803                       c.2A>C          missense_variant   \n",
       "506808                       c.2A>T          missense_variant   \n",
       "506813                      c.3dupA        frameshift_variant   \n",
       "506818                    c.2_3insG        frameshift_variant   \n",
       "506823                    c.2_3insC        frameshift_variant   \n",
       "506828                    c.2_3insT        frameshift_variant   \n",
       "506833                   c.2_3insTA        frameshift_variant   \n",
       "506838                   c.2_3insCT        frameshift_variant   \n",
       "506843                  c.2_3insCTG               stop_gained   \n",
       "506848                  c.2_3insGAT         inframe_insertion   \n",
       "506853                      c.3delA        frameshift_variant   \n",
       "506858                   c.3_4delAG        frameshift_variant   \n",
       "506863                  c.4_6delGGA          inframe_deletion   \n",
       "506868                       c.3A>G        synonymous_variant   \n",
       "506873                       c.3A>C          missense_variant   \n",
       "506878                       c.3A>T          missense_variant   \n",
       "506883                      c.3dupA        frameshift_variant   \n",
       "...                             ...                       ...   \n",
       "868183              c.3207+2095delC            intron_variant   \n",
       "868191   c.3207+2094_3207+2095delTC            intron_variant   \n",
       "868195  c.4746+2093_4746+2095delCTC            intron_variant   \n",
       "868199  c.3207+2093_3207+2095delCTC            intron_variant   \n",
       "868207               c.3207+2095C>T            intron_variant   \n",
       "868215               c.3207+2095C>G            intron_variant   \n",
       "868223               c.3207+2095C>A            intron_variant   \n",
       "868231              c.3207+2094dupT            intron_variant   \n",
       "868235    c.4746+2094_4746+2095insC            intron_variant   \n",
       "868239    c.3207+2094_3207+2095insC            intron_variant   \n",
       "868247    c.3207+2094_3207+2095insG            intron_variant   \n",
       "868255    c.3207+2094_3207+2095insA            intron_variant   \n",
       "868263   c.3207+2094_3207+2095insGT            intron_variant   \n",
       "868271   c.3207+2094_3207+2095insTG            intron_variant   \n",
       "868279  c.3207+2094_3207+2095insTAC            intron_variant   \n",
       "868283  c.4746+2094_4746+2095insCGA            intron_variant   \n",
       "868287  c.3207+2094_3207+2095insCGA            intron_variant   \n",
       "868295              c.3207+2094delT            intron_variant   \n",
       "868299   c.4746+2093_4746+2094delCT            intron_variant   \n",
       "868303   c.3207+2093_3207+2094delCT            intron_variant   \n",
       "868311  c.3207+2092_3207+2094delTCT            intron_variant   \n",
       "868319               c.3207+2094T>C            intron_variant   \n",
       "868327               c.3207+2094T>G            intron_variant   \n",
       "868335               c.3207+2094T>A            intron_variant   \n",
       "868339    c.4746+2093_4746+2094insT            intron_variant   \n",
       "868343    c.3207+2093_3207+2094insT            intron_variant   \n",
       "868351              c.3207+2093dupC            intron_variant   \n",
       "868359    c.3207+2093_3207+2094insG            intron_variant   \n",
       "868367    c.3207+2093_3207+2094insA            intron_variant   \n",
       "868375   c.3207+2093_3207+2094insGC            intron_variant   \n",
       "868383   c.3207+2093_3207+2094insAT            intron_variant   \n",
       "868387  c.4746+2093_4746+2094insTGC            intron_variant   \n",
       "868391  c.3207+2093_3207+2094insTGC            intron_variant   \n",
       "868399  c.3207+2093_3207+2094insGCA            intron_variant   \n",
       "868407              c.3207+2093delC            intron_variant   \n",
       "868411   c.4746+2092_4746+2093delTC            intron_variant   \n",
       "868415   c.3207+2092_3207+2093delTC            intron_variant   \n",
       "868419  c.4746+2091_4746+2093delTTC            intron_variant   \n",
       "868423  c.3207+2091_3207+2093delTTC            intron_variant   \n",
       "868431               c.3207+2093C>T            intron_variant   \n",
       "868439               c.3207+2093C>G            intron_variant   \n",
       "868447               c.3207+2093C>A            intron_variant   \n",
       "868455              c.3207+2092dupT            intron_variant   \n",
       "868459    c.4746+2092_4746+2093insC            intron_variant   \n",
       "868463    c.3207+2092_3207+2093insC            intron_variant   \n",
       "868471    c.3207+2092_3207+2093insG            intron_variant   \n",
       "868479    c.3207+2092_3207+2093insA            intron_variant   \n",
       "868483   c.4746+2092_4746+2093insCA            intron_variant   \n",
       "868487   c.3207+2092_3207+2093insCA            intron_variant   \n",
       "868495   c.3207+2092_3207+2093insAC            intron_variant   \n",
       "868503  c.3207+2092_3207+2093insACG            intron_variant   \n",
       "868507  c.4746+2092_4746+2093insCTA            intron_variant   \n",
       "868511  c.3207+2092_3207+2093insCTA            intron_variant   \n",
       "868519              c.3207+2092delT            intron_variant   \n",
       "868527   c.3207+2091_3207+2092delTT            intron_variant   \n",
       "868531  c.4746+2090_4746+2092delCTT            intron_variant   \n",
       "868535  c.3207+2090_3207+2092delCTT            intron_variant   \n",
       "868543               c.3207+2092T>C            intron_variant   \n",
       "868551               c.3207+2092T>G            intron_variant   \n",
       "868559               c.3207+2092T>A            intron_variant   \n",
       "868563              c.4746+2091dupT            intron_variant   \n",
       "868567              c.3207+2091dupT            intron_variant   \n",
       "868575    c.3207+2091_3207+2092insC            intron_variant   \n",
       "868583    c.3207+2091_3207+2092insG            intron_variant   \n",
       "868591    c.3207+2091_3207+2092insA            intron_variant   \n",
       "868599   c.3207+2091_3207+2092insCG            intron_variant   \n",
       "868603   c.4746+2091_4746+2092insTG            intron_variant   \n",
       "868607   c.3207+2091_3207+2092insTG            intron_variant   \n",
       "868615  c.3207+2091_3207+2092insCAG            intron_variant   \n",
       "868623  c.3207+2091_3207+2092insCTA            intron_variant   \n",
       "868627              c.4746+2091delT            intron_variant   \n",
       "868631              c.3207+2091delT            intron_variant   \n",
       "868639   c.3207+2090_3207+2091delCT            intron_variant   \n",
       "868643  c.4746+2089_4746+2091delTCT            intron_variant   \n",
       "868647  c.3207+2089_3207+2091delTCT            intron_variant   \n",
       "\n",
       "                         Effect_snpeff  \n",
       "39739                   intron_variant  \n",
       "39779                   intron_variant  \n",
       "39804                   intron_variant  \n",
       "39829                   intron_variant  \n",
       "39839                   intron_variant  \n",
       "39854                   intron_variant  \n",
       "39859                   intron_variant  \n",
       "39864                   intron_variant  \n",
       "39924                   intron_variant  \n",
       "148045              frameshift_variant  \n",
       "148089    disruptive_inframe_insertion  \n",
       "506572             5_prime_UTR_variant  \n",
       "506577               exon_loss_variant  \n",
       "506582               exon_loss_variant  \n",
       "506583   conservative_inframe_deletion  \n",
       "506588             5_prime_UTR_variant  \n",
       "506593             5_prime_UTR_variant  \n",
       "506598             5_prime_UTR_variant  \n",
       "506603              frameshift_variant  \n",
       "506608             5_prime_UTR_variant  \n",
       "506613             5_prime_UTR_variant  \n",
       "506618             5_prime_UTR_variant  \n",
       "506623             5_prime_UTR_variant  \n",
       "506628             5_prime_UTR_variant  \n",
       "506633  conservative_inframe_insertion  \n",
       "506638             5_prime_UTR_variant  \n",
       "506643              frameshift_variant  \n",
       "506648              frameshift_variant  \n",
       "506653   conservative_inframe_deletion  \n",
       "506658             5_prime_UTR_variant  \n",
       "506663             5_prime_UTR_variant  \n",
       "506668             5_prime_UTR_variant  \n",
       "506673              frameshift_variant  \n",
       "506678              frameshift_variant  \n",
       "506683              frameshift_variant  \n",
       "506688              frameshift_variant  \n",
       "506693              frameshift_variant  \n",
       "506698              frameshift_variant  \n",
       "506703  conservative_inframe_insertion  \n",
       "506708  conservative_inframe_insertion  \n",
       "506713              frameshift_variant  \n",
       "506718              frameshift_variant  \n",
       "506723   conservative_inframe_deletion  \n",
       "506728                missense_variant  \n",
       "506733                missense_variant  \n",
       "506738                     stop_gained  \n",
       "506743              frameshift_variant  \n",
       "506748              frameshift_variant  \n",
       "506753              frameshift_variant  \n",
       "506758              frameshift_variant  \n",
       "506763              frameshift_variant  \n",
       "506768              frameshift_variant  \n",
       "506773    disruptive_inframe_insertion  \n",
       "506778    disruptive_inframe_insertion  \n",
       "506783              frameshift_variant  \n",
       "506788              frameshift_variant  \n",
       "506793     disruptive_inframe_deletion  \n",
       "506798                missense_variant  \n",
       "506803                missense_variant  \n",
       "506808                missense_variant  \n",
       "506813              frameshift_variant  \n",
       "506818              frameshift_variant  \n",
       "506823              frameshift_variant  \n",
       "506828              frameshift_variant  \n",
       "506833              frameshift_variant  \n",
       "506838              frameshift_variant  \n",
       "506843                     stop_gained  \n",
       "506848    disruptive_inframe_insertion  \n",
       "506853              frameshift_variant  \n",
       "506858              frameshift_variant  \n",
       "506863   conservative_inframe_deletion  \n",
       "506868              synonymous_variant  \n",
       "506873                missense_variant  \n",
       "506878                missense_variant  \n",
       "506883              frameshift_variant  \n",
       "...                                ...  \n",
       "868183                  intron_variant  \n",
       "868191                  intron_variant  \n",
       "868195                  intron_variant  \n",
       "868199                  intron_variant  \n",
       "868207                  intron_variant  \n",
       "868215                  intron_variant  \n",
       "868223                  intron_variant  \n",
       "868231                  intron_variant  \n",
       "868235                  intron_variant  \n",
       "868239                  intron_variant  \n",
       "868247                  intron_variant  \n",
       "868255                  intron_variant  \n",
       "868263                  intron_variant  \n",
       "868271                  intron_variant  \n",
       "868279                  intron_variant  \n",
       "868283                  intron_variant  \n",
       "868287                  intron_variant  \n",
       "868295                  intron_variant  \n",
       "868299                  intron_variant  \n",
       "868303                  intron_variant  \n",
       "868311                  intron_variant  \n",
       "868319                  intron_variant  \n",
       "868327                  intron_variant  \n",
       "868335                  intron_variant  \n",
       "868339                  intron_variant  \n",
       "868343                  intron_variant  \n",
       "868351                  intron_variant  \n",
       "868359                  intron_variant  \n",
       "868367                  intron_variant  \n",
       "868375                  intron_variant  \n",
       "868383                  intron_variant  \n",
       "868387                  intron_variant  \n",
       "868391                  intron_variant  \n",
       "868399                  intron_variant  \n",
       "868407                  intron_variant  \n",
       "868411                  intron_variant  \n",
       "868415                  intron_variant  \n",
       "868419                  intron_variant  \n",
       "868423                  intron_variant  \n",
       "868431                  intron_variant  \n",
       "868439                  intron_variant  \n",
       "868447                  intron_variant  \n",
       "868455                  intron_variant  \n",
       "868459                  intron_variant  \n",
       "868463                  intron_variant  \n",
       "868471                  intron_variant  \n",
       "868479                  intron_variant  \n",
       "868483                  intron_variant  \n",
       "868487                  intron_variant  \n",
       "868495                  intron_variant  \n",
       "868503                  intron_variant  \n",
       "868507                  intron_variant  \n",
       "868511                  intron_variant  \n",
       "868519                  intron_variant  \n",
       "868527                  intron_variant  \n",
       "868531                  intron_variant  \n",
       "868535                  intron_variant  \n",
       "868543                  intron_variant  \n",
       "868551                  intron_variant  \n",
       "868559                  intron_variant  \n",
       "868563                  intron_variant  \n",
       "868567                  intron_variant  \n",
       "868575                  intron_variant  \n",
       "868583                  intron_variant  \n",
       "868591                  intron_variant  \n",
       "868599                  intron_variant  \n",
       "868603                  intron_variant  \n",
       "868607                  intron_variant  \n",
       "868615                  intron_variant  \n",
       "868623                  intron_variant  \n",
       "868627                  intron_variant  \n",
       "868631                  intron_variant  \n",
       "868639                  intron_variant  \n",
       "868643                  intron_variant  \n",
       "868647                  intron_variant  \n",
       "\n",
       "[73008 rows x 8 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mismatch_df = filtered_2[filtered_2['Hgvsc_match'] == False]\n",
    "mismatch_df[['POS', 'REF', 'ALT', 'Feature_id', 'Hgvsc_vep', 'Hgvsc_snpeff', 'Effect_vep', 'Effect_snpeff']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     95.58\n",
       "False     4.42\n",
       "Name: Effect_match, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(mismatch_df['Effect_match'].value_counts()/mismatch_df['Effect_match'].size*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So we see a slightly higher percentage of effect mismatched among the variants that have differing cDot notations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotations\n",
      "\n",
      "                                    VEP   SnpEff\n",
      "3_prime_UTR_variant               619.0    616.0\n",
      "5_prime_UTR_variant                 0.0     27.0\n",
      "coding_sequence_variant            12.0      0.0\n",
      "conservative_inframe_deletion       0.0    361.0\n",
      "conservative_inframe_insertion      0.0    645.0\n",
      "disruptive_inframe_deletion         0.0    639.0\n",
      "disruptive_inframe_insertion        0.0   1282.0\n",
      "exon_loss_variant                   0.0      4.0\n",
      "frameshift_variant               8348.0   8323.0\n",
      "inframe_deletion                 1000.0      0.0\n",
      "inframe_insertion                1403.0      0.0\n",
      "intron_variant                  56723.0  56668.0\n",
      "missense_variant                 2288.0   2288.0\n",
      "protein_altering_variant          519.0      0.0\n",
      "splice_acceptor_variant           115.0    141.0\n",
      "splice_donor_variant              115.0    145.0\n",
      "splice_region_variant             878.0    878.0\n",
      "stop_gained                       312.0    311.0\n",
      "stop_lost                          25.0     29.0\n",
      "stop_retained_variant               3.0      3.0\n",
      "synonymous_variant                648.0    648.0\n"
     ]
    }
   ],
   "source": [
    "vc_vep = mismatch_df.groupby(['Effect_vep']).size()\n",
    "vc_vep.name = \"VEP\"\n",
    "vc_snpeff = mismatch_df.groupby(['Effect_snpeff']).size()\n",
    "vc_snpeff.name = \"SnpEff\"\n",
    "vc_df = pd.DataFrame([vc_vep, vc_snpeff])\n",
    "print(\"Annotations\\n\")\n",
    "print(vc_df.transpose().fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Finally, we see that the vast majority of Hgvs mismatches occur for intronic variants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def create_vcf_header():\n",
    "    metadata_lines = [\n",
    "        '##fileformat=VCFv4.0',\n",
    "        '##INFO=<ID=TX,Number=1,Type=String,Description=\"Corresponding Transcript\"',\n",
    "        '##INFO=<ID=VE,Number=1,Type=String,Description=\"VEP Effect\"',\n",
    "        '##INFO=<ID=VI,Number=1,Type=String,Description=\"Vep Impact\"',\n",
    "        '##INFO=<ID=VC,Number=1,Type=String,Description=\"Vep coding HGVS\"',\n",
    "        '##INFO=<ID=SE,Number=1,Type=String,Description=\"SnpEff Effect\"',\n",
    "        '##INFO=<ID=SI,Number=1,Type=String,Description=\"SnpEff Impact\"',\n",
    "        '##INFO=<ID=SC,Number=1,Type=String,Description=\"SnpEff coding HGVS\"']\n",
    "    header_fields = ['#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT']\n",
    "    metadata_lines.append('\\t'.join(header_fields))\n",
    "    vcf_header = '\\n'.join(metadata_lines)\n",
    "    return vcf_header\n",
    "\n",
    "def create_vcf_line(row):\n",
    "    line = []\n",
    "    line.append('7')\n",
    "    line.append(row['POS'])\n",
    "    line.append('.')\n",
    "    line.extend(row[['REF', 'ALT']])\n",
    "    line.append('.')\n",
    "    line.append('PASS')\n",
    "    info = 'TX={};VE={};VI={};VC={};SE={};SI={};SC={}'.format(*row[['Feature_id', 'Effect_vep', 'Impact_vep', 'Hgvsc_vep',\n",
    "                                                                    'Effect_snpeff', 'Impact_snpeff', 'Hgvsc_snpeff']])\n",
    "    line.append(info)\n",
    "    line.append('TX:VE:VI:VC:SE:SI:SC')\n",
    "    return '\\t'.join(map(str, line))\n",
    "\n",
    "def create_vcf(df, idxs, filename):\n",
    "    with open(filename, \"w\") as vcf_out:\n",
    "        vcf_out.write(create_vcf_header() + '\\n')\n",
    "        for i in idxs:\n",
    "            vcf_out.write(create_vcf_line(df.ix[i]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hgvs_mismatch = (effect_df['Hgvsc_vep'] != '') & (effect_df['Hgvsc_snpeff'] != effect_df['Hgvsc_vep'])\n",
    "hgvs_mismatch_idxs = effect_df[hgvs_mismatch].sort_values(by=['POS']).index.tolist()\n",
    "\n",
    "impact_mismatch_idxs = effect_df[~effect_df['Impact_match']].sort_values(by=['POS']).index.tolist()\n",
    "effect_mismatch_idxs = effect_df[~effect_df['Effect_match']].sort_values(by=['POS']).index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "create_vcf(effect_df, effect_mismatch_idxs, 'effect_mismatch.vcf')\n",
    "create_vcf(effect_df, impact_mismatch_idxs, 'impact_mismatch.vcf')\n",
    "create_vcf(effect_df, hgvs_mismatch_idxs, 'hgvs_mismatch.vcf')"
   ]
  }
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