Compare_BleTIES_PacBio_Nanopore_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tetrahymena BleTIES predictions comparison\n",
    "\n",
    "Compare BleTIES predictions from PacBio and Nanopore data, for benchmarking of BleTIES on real-world data.\n",
    "\n",
    "Both were run in 'subreads' mode with BleTIES MILRAA v0.1.9.\n",
    "\n",
    "Aims:\n",
    "\n",
    " * Compare IES predictions from PacBio vs. Nanopore\n",
    "   * Coordinates of prediction\n",
    "   * Sequence identity between PacBio and Nanopore\n",
    " * Compare IES predictions vs. previously curated sequences from published MIC genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio import pairwise2\n",
    "import re\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pybedtools as pbt\n",
    "import math\n",
    "import numpy as np\n",
    "import datetime\n",
    "import glob\n",
    "\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "# -----------\n",
    "# IES annotations\n",
    "pb = pbt.BedTool(\"tthe_pb_clr.milraa_subreads.comb.milraa_ies.gff3\")\n",
    "ont = pbt.BedTool(\"tthe_ont.milraa_subreads.comb.milraa_ies.gff3\")\n",
    "# IES sequences predicted by BleTIES\n",
    "pb_seq = SeqIO.to_dict(SeqIO.parse(\"tthe_pb_clr.milraa_subreads.comb.milraa_ies.fasta\", \"fasta\"))\n",
    "ont_seq = SeqIO.to_dict(SeqIO.parse(\"tthe_ont.milraa_subreads.comb.milraa_ies.fasta\", \"fasta\"))\n",
    "# Reference MAC genome and annotation\n",
    "mac_ref = SeqIO.to_dict(SeqIO.parse(\"ref/1-upd-Genome-assembly.fasta\", \"fasta\"))\n",
    "mac_gff = pbt.BedTool(\"ref/2-upd-Genome-GFF3-latest-2.gff3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ID': 'BREAK_POINTS_SUBREADS_chr_001_7588_2410',\n",
       " 'IES_length': '2410',\n",
       " 'IES_subread_coverage': '71',\n",
       " 'IES_zmw_coverage': '71',\n",
       " 'average_subread_coverage': '144',\n",
       " 'average_zmw_coverage': '144'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb[0].attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IES stats from BleTIES predictions\n",
    "# ----------------------------------\n",
    "# IESs from PacBio\n",
    "pb_stats = []\n",
    "for i in pb:\n",
    "    pb_stats.append({'id' : i.name,\n",
    "                     'score' : float(i.score),\n",
    "                     'len' : int(i.attrs['IES_length']),\n",
    "                     'iescov' : int(i.attrs['IES_subread_coverage'])})\n",
    "# IESs from Nanopore\n",
    "ont_stats = []\n",
    "for i in ont:\n",
    "    ont_stats.append({'id' : i.name,\n",
    "                      'score' : float(i.score),\n",
    "                      'len' : int(i.attrs['IES_length']),\n",
    "                      'iescov' : int(i.attrs['IES_subread_coverage'])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12,4), constrained_layout=True)\n",
    "\n",
    "fig.suptitle(\"T. thermophila, BleTIES IES reconstructions from PacBio CLR library\")\n",
    "\n",
    "axs[0].hist2d(pd.DataFrame(pb_stats)['score'],\n",
    "              pd.DataFrame(pb_stats)['len'], bins=50)\n",
    "axs[0].set_title(\"IES length vs. retention score\")\n",
    "axs[0].set_xlabel(\"IES retention score\")\n",
    "axs[0].set_ylabel(\"Reconstructed IES length (bp)\")\n",
    "\n",
    "\n",
    "axs[1].hist(pd.DataFrame(pb_stats)['score'], bins=50)\n",
    "axs[1].set_title(\"Retention score histogram\")\n",
    "axs[1].set_xlabel(\"IES retention score\")\n",
    "axs[1].set_ylabel(\"Counts\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_retention_scores.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_retention_scores.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12,4), constrained_layout=True)\n",
    "\n",
    "fig.suptitle(\"T. thermophila, BleTIES IES reconstructions from Nanopore library\")\n",
    "\n",
    "axs[0].hist2d(pd.DataFrame(ont_stats)['score'],\n",
    "              pd.DataFrame(ont_stats)['len'], bins=50)\n",
    "axs[0].set_title(\"IES length vs. retention score\")\n",
    "axs[0].set_xlabel(\"IES retention score\")\n",
    "axs[0].set_ylabel(\"Reconstructed IES length (bp)\")\n",
    "\n",
    "\n",
    "axs[1].hist(pd.DataFrame(ont_stats)['score'], bins=50)\n",
    "axs[1].set_title(\"Retention score histogram\")\n",
    "axs[1].set_xlabel(\"IES retention score\")\n",
    "axs[1].set_ylabel(\"Counts\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_retention_scores.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_retention_scores.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12,8), constrained_layout=True, sharey=True)\n",
    "\n",
    "fig.suptitle(\"T. thermophila, BleTIES IES reconstructions from PacBio CLR library\")\n",
    "\n",
    "axs[0][0].hist(pd.DataFrame(pb_stats)['len'], bins=50)\n",
    "axs[0][0].set_title(\"All IESs\")\n",
    "axs[0][0].set_xlabel(\"Reconstructed IES length (bp)\")\n",
    "axs[0][0].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[0][1].hist([math.log2(i) for i in pd.DataFrame(pb_stats)['len']], bins=50)\n",
    "axs[0][1].set_title(\"Lengths log scaled\")\n",
    "axs[0][1].set_xlabel(\"log2 reconstructed IES length\")\n",
    "axs[0][1].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[1][0].hist(pd.DataFrame(pb_stats).query('score>0.1')['len'], bins=50)\n",
    "axs[1][0].set_title(\"Retention score >0.1\")\n",
    "axs[1][0].set_xlabel(\"Reconstructed IES length (bp)\")\n",
    "axs[1][0].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[1][1].hist([math.log2(i) for i in pd.DataFrame(pb_stats).query('score>0.1')['len']], bins=50)\n",
    "axs[1][1].set_title(\"Retention score >0.1, lengths log scaled\")\n",
    "axs[1][1].set_xlabel(\"log2 reconstructed IES length\")\n",
    "axs[1][1].set_ylabel(\"Counts\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_len_histograms.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_len_histograms.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XJFl3NfL448WwXTR+n+SGgeHagWX3TnJmkuuTXJnky0l2mIt9kSSturkKjFZxm3+8CSipPwzQpAWWZDnw50ABT5zDrD9eVRsMDMva9nYEjgZeCmwM7AD8J3DzHG5bkiRJs2CAJi28/YDTgCOB/edhezsD51fVKdX5ZVV9sqp+DpDkQUlOb61rlyV52zyUSZI0hSSPb70erk3yzST3GZh3QZKXJTkryXVJPp5kvYH5r0hySZJfJDloordFkoOBZwCvaL0sPjuwyZ0nyy/JZklObOW4OsnXk8z4XTLJxkmOTnJFkguT/OPEeknWSvJvrTfH+Ule2Mq49hR5vTLJxUl+meScJLsP5PPqJD9r885Ism2b944kF7Xr2hlJ/nyasj6k1fG1SX6QZNeBeTsk+WrL/2Rgs5n2XZoNAzRp4e0HHNOGxybZYszb+x5w9yRvT/KoJBsMzX8H8I6q2gi4K3DsmMsjSZpCkvsBHwSeC9wJeC9wwlCX+KcCj6PrEXEf4IC27uOAlwCPBnYEdp1YoaqOoLvuvKX1snjCTPnR9bxYCWwObAG8mq73x0z+g67Hxl2Av6C77j2rzXsOsAfdzcP7A0+api7uBrwQeGBVbQg8FrigzX4J8HRgT2Aj4NnAjW3ed1v+mwIfAT4xGMQO5L818DngjW3ZlwGfTLJ5W+QjwBl0gdkbmJ+bqlqCDNCkBZTkEcD2wLFVdQbwM+Bv5ij7p7Y7gBPDqQBVdR7dRXpruuDryvYcwkSg9ntgxySbVdUNVXXaHJVHkrTqDgbeW1Xfrqqbq+oo4LfAQwaWeWdV/aKqrgY+SxeMQBdo/XdV/biqbgQOG3GbU+X3e2BLYPuq+n1Vfb2qpg3QkqwF7Au8qvXYuAD4N+BvB8r4jqpaWVXXAIdPk93NwLrAPZPcvqouqKqftXkHAf9YVee03iE/qKqrAKrqw1V1VVXdVFX/1vK42yT5PxP4fFV9vqr+UFUnA6cDeybZDngg8Jqq+m1Vfa3VjTTnDNCkhbU/cFJVXdmmP8Lc3ZE7tqqWDQyPmphRVadV1VOranO6598eCRzaZh8I/Cnw0yTfTfL4OSqPJGnVbQ+8dPCGG7AtsNXAMpcOjN8ITNxw2wq4aGDe4Ph0psrvrcAK4KQk5yU5ZIS8NgNuD1w4kHYh3U3CVSpjVa0AXkwXaF6e5GNJJuphW7qbnLfRuoCe3bpsXkvXmjdZ98Ttgb8equtH0AWlWwHXVNWvhvZDmnMGaNICSbI+3Z3Dv0hyaZJLgX8A7pvkvvNVjqr6LvAp4N5t+tyqejpwZ+DNwHFJ7jhf5ZEk3cpFwJuGbrjdoao+OsK6lwDbDExvOzR/lO6JtyzctYC9tKruQvdSq5dMPAM2jSvpWt62H0jbDrh4xDIOl+EjVTXR+6TorlPQ1dNdh5dvz5u9gu56u0l7YdZ1QCbJ/iLgQ0N1fceqOryVc5Oh6+F205VVmi0DNGnhPImuu8Y96bqP7AzcA/g6Xf/8sUjyiCTPSXLnNn13ugvtaW36mUk2r6o/ABOv5v/DuMojSfqj2ydZb2BYG3gf8P+SPDidOybZK8mGI+R3LPCsJPdIcgfgNUPzL6N7Lmwk6V5WsmOS0AU5NzPD9aGqbm7leFOSDZNsT/e82MTvhx0LvCjJ1kmWAa+cZvt3S7Jbe/7uN8CvB7b/fuANSXZq9XSfJHcCNgRuAq4A1k7yWrpn1CbzYeAJSR7bXjqyXrrfIN2mqi6k6+74+iTrtEcUnjBFPtJqMUCTFs7+dM8G/LyqLp0YgHcBz5jqDVar4Gm59e+g3dCCsmvpArIfJrkB+CJwPPCWtt7jgB+3ee8A9q2qX69mWSRJM/s8XdAxMRxWVafTvUjjXcA1dF0MDxgls6r6AvBO4NS23sQzxb9tfz9A9zzXtUk+PUKWOwH/A9wAfAv4r6o6dYT1/g74FXAe8A267vwfbPPeB5wEnAV8n64ObmLyn35Zl+4ZtSvpumHeGXhVm/c2umDvJOD6tm/rA1+iu879H12XxN8wRTfKqroI2Jvu5SdXtOVezi3fl/8GeDBwNfA6up+skeZcZni2U5IkSWuAJPcAfgSsW1U3LXR5JpNkD+A9VbX9jAtLayhb0CRJktZQSf4qybpJNqF7XuuzfQrOkqyfZM8ka7fX3L+OrleHtGQZoEmSJK25ngtcTveGw5uB5y1scW4jwOvpum9+HzgbeO2ClkhaYHZxlCRJkqSesAVNkiRJknrCAE2SJEmSemJ1X+O9oDbbbLNavnz5QhdDktRjZ5xxxpVVtfl8bc9rkyRpJtNdmxZ1gLZ8+XJOP/30hS6GJKnHklw4n9vz2iRJmsl01ya7OEqSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPLOofqpYkSZJ0W8sP+dyU8y44fK95LIlWlS1okiRJktQTBmiSJEmS1BMGaJIkSZLUEz6DJkmSJC1C0z1npsXLFjRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6glfEiJJkiT1lC8CWXpsQZMkSZKknjBAkyRJkqSeGGuAlmRZkuOS/DTJ2UkemmTTJCcnObf93aQtmyTvTLIiyVlJ7j/OskmSJElS34z7GbR3AF+sqn2SrAPcAXg1cEpVHZ7kEOAQ4JXAHsBObXgw8O72VyOaro/yBYfvNY8lkSRJkjQbYwvQkmwMPBI4AKCqfgf8LsnewK5tsaOAr9AFaHsDR1dVAae11rctq+qScZVRkiRJWmq8qd9v4+ziuANwBfDfSb6f5P1J7ghsMRB0XQps0ca3Bi4aWH9lS5MkSZKkJWGcAdrawP2Bd1fV/YBf0XVn/KPWWlarkmmSg5OcnuT0K664Ys4KK0nSbHltkiTNlXEGaCuBlVX17TZ9HF3AdlmSLQHa38vb/IuBbQfW36al3UpVHVFVu1TVLptvvvnYCi9J0qi8NkmS5srYArSquhS4KMndWtLuwE+AE4D9W9r+wGfa+AnAfu1tjg8BrvP5M0mSJElLybjf4vh3wDHtDY7nAc+iCwqPTXIgcCHw1Lbs54E9gRXAjW1ZSZIkSfNkuheIgC8RmQ9jDdCq6kxgl0lm7T7JsgW8YJzlkSRJkqQ+G+sPVUuSJEmSRmeAJkmSJEk9YYAmSZIkST1hgCZJkiRJPWGAJkmSJEk9YYAmSZIkST1hgCZJkiRJPWGAJkmSJEk9MdYfqtbsTPcL7v56uyRJkrTmsgVNkiRJknrCFrRFZrrWNUmSJEmLmy1okiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTviREkiRJWkC+BE6DDNCWiJk++P6+miRJkrTw7OIoSZIkST1hgCZJkiRJPWGAJkmSJEk9YYAmSZIkST3hS0IWiG/rkSRJkjTMFjRJkiRJ6omxBmhJLkjywyRnJjm9pW2a5OQk57a/m7T0JHlnkhVJzkpy/3GWTZIkSZL6Zj5a0B5VVTtX1S5t+hDglKraCTilTQPsAezUhoOBd89D2SRJkiSpNxaii+PewFFt/CjgSQPpR1fnNGBZki0XoHySJEmStCDG/ZKQAk5KUsB7q+oIYIuquqTNvxTYoo1vDVw0sO7KlnYJGrvpXlpyweF7zWNJJEmSpKVr3AHaI6rq4iR3Bk5O8tPBmVVVLXgbWZKD6bpAst12281dSSVJmiWvTZKWCm/qj99YuzhW1cXt7+XA8cCDgMsmui62v5e3xS8Gth1YfZuWNpznEVW1S1Xtsvnmm4+z+JIkjcRrkyRprowtQEtyxyQbTowDjwF+BJwA7N8W2x/4TBs/Adivvc3xIcB1A10hJUmSJGmNN84ujlsAxyeZ2M5HquqLSb4LHJvkQOBC4Klt+c8DewIrgBuBZ42xbJIkSZLUO2ML0KrqPOC+k6RfBew+SXoBLxhXeSRJkqSFMN1zW9KwhXjNviRJkiRpEgZokiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTBmiSJEmS1BNrL3QB1H/LD/nclPMuOHyveSyJJEmStGazBU2SJEmSesIATZIkSZJ6wgBNkiRJknrCZ9AkSZIkrTbfWzA3bEGTJEmSpJ4wQJMkSZKknjBAkyRJkqSe8Bk0SZIkaTVN9/yVtCrG3oKWZK0k309yYpveIcm3k6xI8vEk67T0ddv0ijZ/+bjLJkmSJEl9Mh9dHF8EnD0w/Wbg7VW1I3ANcGBLPxC4pqW/vS0nSZIkSUvGWAO0JNsAewHvb9MBdgOOa4scBTypje/dpmnzd2/LS5IkSdKSMO5n0P4deAWwYZu+E3BtVd3UplcCW7fxrYGLAKrqpiTXteWvHMwwycHAwQDbbbfdWAuvmfl7F5LktUmSNHfG1oKW5PHA5VV1xlzmW1VHVNUuVbXL5ptvPpdZS5I0K16bJElzZZwtaA8HnphkT2A9YCPgHcCyJGu3VrRtgIvb8hcD2wIrk6wNbAxcNcbyjZ1v85EkSZK0KsbWglZVr6qqbapqObAv8OWqegZwKrBPW2x/4DNt/IQ2TZv/5aqqcZVPkiRJkvpmlQO0JJskuc9qbPOVwEuSrKB7xuwDLf0DwJ1a+kuAQ1ZjG5Ikzbk5uAZKkjStkbo4JvkK8MS2/BnA5Un+t6peMsr6VfUV4Ctt/DzgQZMs8xvgr0fJT5Kk+bK610BJklbFqC1oG1fV9cCTgaOr6sHAo8dXLEmSesNroCRp3owaoK2dZEvgqcCJYyyPJEl94zVQkjRvRg3QXg98CVhRVd9Nchfg3PEVS5Kk3vAaKEmaN6O+Zv+SqvrjQ9FVdV6St42pTJIk9YnXQEnSvBk1QPsP4P4jpEl/NN3vwF1w+F7zWBJJWi1eAyVJ82baAC3JQ4GHAZsnGXxb1UbAWuMsmCRJC8lroCRpIczUgrYOsEFbbsOB9Ou55cemJUlaE3kNlCTNu2kDtKr6KvDVJEdW1YXzVCZJkhac10BJ0kIY9Rm0dZMcASwfXKeqdhtHoSRJ6hGvgZKkeTNqgPYJ4D3A+4Gbx1ccSZJ6x2ugJGnejBqg3VRV7x5rSSRJ6ievgZKkeTPqD1V/Nsnzk2yZZNOJYawlkySpH7wGSpLmzagtaPu3vy8fSCvgLnNbHEmSesdroCRp3owUoFXVDuMuyGI03Q8xS5LWDF4DJUnzaaQALcl+k6VX1dFzWxxJkvrFa6AkaT6N2sXxgQPj6wG7A98DvDhJktZ0XgMlSfNm1C6Ofzc4nWQZ8LGxlEiSpB7xGihJmk+jtqAN+xVgn3xJ0lLkNVBaonz/wOxNV3cXHL7XPJak/0Z9Bu2zdG+sAlgLuAdw7LgKJUlSX3gNlCTNp1Fb0P51YPwm4MKqWjmG8miJ8C6KpEXEa6Akad6M9EPVVfVV4KfAhsAmwO/GWShJkvrCa6AkaT6NFKAleSrwHeCvgacC306yzzgLJklSH3gNlCTNp1G7OB4KPLCqLgdIsjnwP8Bx4yqYJEk94TVQkjRvRmpBA243cWFqrppp3STrJflOkh8k+XGS17f0HZJ8O8mKJB9Psk5LX7dNr2jzl89ifyRJmmurfA2UJGm2Rr3AfDHJl5IckOQA4HPA52dY57fAblV1X2Bn4HFJHgK8GXh7Ve0IXAMc2JY/ELimpb+9LSdJ0kKbzTVQkqRZmakVbMckD6+qlwPvBe7Thm8BR0y3bnVuaJO3b0MBu3FLt5CjgCe18b3bNG3+7kmyarsjSdLcWJ1roCRJszVTC9q/A9cDVNWnquolVfUS4Pg2b1pJ1kpyJnA5cDLwM+DaqrqpLbIS2LqNbw1c1LZ1E3AdcKdV2x1JkubMal0DJUmajZkCtC2q6ofDiS1t+UyZV9XNVbUzsA3wIODusynkoCQHJzk9yelXXHHF6mYnSdJURr4Gem2SJM2Vmd7iuGyaeeuPupGqujbJqcBDgWVJ1m6tZNsAF7fFLga2BVYmWRvYmO5B7OG8jqB1Ldlll11q1DJo8ZjuR6zBH7KWNG9GvgZ6bZIkzZWZWhTiibwAACAASURBVNBOT/Kc4cQkBwFnTLdiks2TLGvj6wN/CZwNnApM/H7M/sBn2vgJbZo2/8tV5UVOkrRQZn0NlCRptmZqQXsxcHySZ3DLxWgXYB3gr2ZYd0vgqCRr0QWCx1bViUl+AnwsyRuB7wMfaMt/APhQkhXA1cC+q7w3kiTNndW5BkqSNCvTBmhVdRnwsCSPAu7dkj9XVV+eKeOqOgu43yTp59E9jzac/hvgr0cptCRJ47Y610BJkmZrphY0AKrqVLquiZIkLSleA6WlZaZn4aVxG/WHqiVJkiRJY2aAJkmSJEk9YYAmSZIkST1hgCZJkiRJPWGAJkmSJEk9YYAmSZIkST1hgCZJkiRJPWGAJkmSJEk9MdIPVUt9Mt0PSF5w+F7zWBJJkiStLr/b3ZotaJIkSZLUEwZokiRJktQTdnGcwXRNrpIkSZI0l2xBkyRJkqSeMECTJEmSpJ4wQJMkSZKknjBAkyRJkqSeMECTJEmSpJ4wQJMkSZKknjBAkyRJkqSeMECTJEmSpJ4wQJMkSZKknjBAkyRJkqSeGFuAlmTbJKcm+UmSHyd5UUvfNMnJSc5tfzdp6UnyziQrkpyV5P7jKpskSZIk9dHaY8z7JuClVfW9JBsCZyQ5GTgAOKWqDk9yCHAI8EpgD2CnNjwYeHf7O3bLD/ncfGxG82C6/+UFh+81jyWRJEmSVt3YWtCq6pKq+l4b/yVwNrA1sDdwVFvsKOBJbXxv4OjqnAYsS7LluMonSZIkSX0zL8+gJVkO3A/4NrBFVV3SZl0KbNHGtwYuGlhtZUuTJEmSpCVh7AFakg2ATwIvrqrrB+dVVQG1ivkdnOT0JKdfccUVc1hSSZJmx2uTJGmujPMZNJLcni44O6aqPtWSL0uyZVVd0rowXt7SLwa2HVh9m5Z2K1V1BHAEwC677LJKwZ0kSePgtUlaXHz/gPpsnG9xDPAB4OyqetvArBOA/dv4/sBnBtL3a29zfAhw3UBXSEmSJEla442zBe3hwN8CP0xyZkt7NXA4cGySA4ELgae2eZ8H9gRWADcCzxpj2SRJkiSpd8YWoFXVN4BMMXv3SZYv4AXjKo8kSZKkxWWm7qhr4s8ozctbHCVJkiRJMxvrS0KkPpntA8Fr4p0ZSZIk9ZMtaJIkSZLUEwZokiRJktQTBmiSJEmS1BMGaJIkSZLUEwZokiRJktQTvsVRkiRJa5zZvr1ZWmi2oEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk/4mn1JkiRJi9J0P6dwweF7zWNJ5o4BmjSDmX5HZbF++CVJktQ/dnGUJEmSpJ4wQJMkSZKknrCLoyRJkhadmR5BkBYrAzRpNa2JD6dKkiRpYdjFUZIkSZJ6wgBNkiRJknrCAE2SJEmSesJn0CRJktRLvghES9HYWtCSfDDJ5Ul+NJC2aZKTk5zb/m7S0pPknUlWJDkryf3HVS5JkiRJ6qtxtqAdCbwLOHog7RDglKo6PMkhbfqVwB7ATm14MPDu9lda1HzDoyRJklbF2AK0qvpakuVDyXsDu7bxo4Cv0AVoewNHV1UBpyVZlmTLqrpkXOWTFprBmyRJkobN90tCthgIui4FtmjjWwMXDSy3sqXdRpKDk5ye5PQrrrhifCWVJGlEXpskSXNlwd7i2FrLahbrHVFVu1TVLptvvvkYSiZJ0qrx2iRJmivzHaBdlmRLgPb38pZ+MbDtwHLbtDRJkiRJWjLm+zX7JwD7A4e3v58ZSH9hko/RvRzkOp8/kyRJWjP43LU0urEFaEk+SvdCkM2SrAReRxeYHZvkQOBC4Klt8c8DewIrgBuBZ42rXNJi4IVMkiRpaRrnWxyfPsWs3SdZtoAXjKsskiRJ6id/jFq6tfnu4ihJkiRJY7dYeyQt2FscJUmSJEm3ZoAmSZIkST1hgCZJkiRJPWGAJkmSJEk9YYAmSZIkST3hWxylRWZ1Xkfc5zcWSZIkyRY0SZIkSeoNW9CkJWSx/h6IJEnSUmELmiRJkiT1hAGaJEmSJPWEXRwlAbN/+YhdIyVpaVidl1RJGp0taJIkSZLUE7agSRqbme622vomSZJ0awZoknrJN05KkqSlyC6OkiRJktQTtqBJWi2r89C4D5xLkqSF0OeeOragSZIkSVJPGKBJkiRJUk/YxVGSpEWoz91z1G92L5f6zQBN0hplXF88/MKrpcCgT5IWngGapEXHu7/S7Pn5kaR+M0CTJEkzWp3WtXG0zNnaNz0DcWn2Fvr80qsALcnjgHcAawHvr6rDF7hIkiRpCVnoL2aS1JsALclawH8CfwmsBL6b5ISq+snClkySxnM3erove6uzPb9Ear5bT/r2e4h9az0aV9DXt/2UNDd6E6ABDwJWVNV5AEk+BuwNGKBJWiMtxJer2W5zXEGfrRVaTJZCMClp4fUpQNsauGhgeiXw4OGFkhwMHNwmb0hyzhxsezPgyjnIZ01gXdzCuriFdXFrva6PvHle8xxbXczhfmw/ZzlNYUzXplXV6+OyR6yn0VhPo7GeRrPG1NN8XJv6FKCNpKqOAI6YyzyTnF5Vu8xlnouVdXEL6+IW1sWtWR+3sC4647g2rSr/F6OxnkZjPY3GehqN9bRqbrfQBRhwMbDtwPQ2LU2SJEmSloQ+BWjfBXZKskOSdYB9gRMWuEySJEmSNG9608Wxqm5K8kLgS3Sv2f9gVf14nja/oN1Sesa6uIV1cQvr4tasj1tYF/3h/2I01tNorKfRWE+jsZ5WQapqocsgSZIkSaJfXRwlSZIkaUkzQJMkSZKknljSAVqSxyU5J8mKJIcsdHnGIcm2SU5N8pMkP07yopa+aZKTk5zb/m7S0pPkna1Ozkpy/4G89m/Ln5tk/4Xap9WVZK0k309yYpveIcm32z5/vL2khiTrtukVbf7ygTxe1dLPSfLYhdmT1ZdkWZLjkvw0ydlJHrpUj40k/9A+Iz9K8tEk6y2VYyPJB5NcnuRHA2lzdhwkeUCSH7Z13pkk87uHa77h85pua7Lz3UKXqY8mOxcudJn6YlXOlUvZFPX01vbZOyvJ8UmWLWQZ+27JBmhJ1gL+E9gDuCfw9CT3XNhSjcVNwEur6p7AQ4AXtP08BDilqnYCTmnT0NXHTm04GHg3dCcg4HV0Px7+IOB1i/gk9CLg7IHpNwNvr6odgWuAA1v6gcA1Lf3tbTla/e0L3At4HPBf7XhajN4BfLGq7g7cl65eltyxkWRr4O+BXarq3nQvKtqXpXNsHElX3kFzeRy8G3jOwHrD29LqGz6v6bYmO99pwDTnQnWOZPRz5VJ2JLetp5OBe1fVfYD/A14134VaTJZsgEb3BWJFVZ1XVb8DPgbsvcBlmnNVdUlVfa+N/5LugrQ13b4e1RY7CnhSG98bOLo6pwHLkmwJPBY4uaqurqpr6D5oi+5LVpJtgL2A97fpALsBx7VFhutioo6OA3Zvy+8NfKyqfltV5wMr6I6nRSXJxsAjgQ8AVNXvqupaluixQfdW2/WTrA3cAbiEJXJsVNXXgKuHkufkOGjzNqqq06p7K9XRA3lpDgyf13Rb05zvdFvD58JfLHB5emMVz5VL1mT1VFUnVdVNbfI0ut871hSWcoC2NXDRwPTKlrbGat2w7gd8G9iiqi5psy4FtmjjU9XLmlJf/w68AvhDm74TcO3ASWNwv/64z23+dW35NaUudgCuAP67dY16f5I7sgSPjaq6GPhX4Od0gdl1wBks3WMD5u442LqND6dr7gyf13RbU53vNGCyc2FVnbSwpeq9qc6VmtqzgS8sdCH6bCkHaEtKkg2ATwIvrqrrB+e1u9pr/O8tJHk8cHlVnbHQZemJtYH7A++uqvsBv2Koa8YSOjY2obsLugOwFXBHFmcr4FgsleNgMfK8NrIZz3ea/FyY5JkLW6rFw3PlzJIcSvf4zTELXZY+W8oB2sXAtgPT27S0NU6S29MFZ8dU1ada8mWt6xHt7+Utfap6WRPq6+HAE5NcQNeldTe6ZxKWta4ccOv9+uM+t/kbA1exZtQFdC0ZK6vq2236OLovMEvx2Hg0cH5VXVFVvwc+RXe8LNVjA+buOLiYW3dlWcx10ke3Oa8l+fDCFqmXpjrf6dYmOxc+bIHL1HdTnSs1JMkBwOOBZ5Q/xDytpRygfRfYKd1b2tahewj2hAUu05xrz8V8ADi7qt42MOsEYOIta/sDnxlI36+9qe0hdN0bLgG+BDwmySbtDttjWtqiUVWvqqptqmo53f/7y1X1DOBUYJ+22HBdTNTRPm35aun7pnuT3w50Lz34zjztxpypqkuBi5LcrSXtDvyEJXhs0HXneUiSO7TPzERdLMljo5mT46DNuz7JQ1rd7jeQl1bTFOc1WzyGTHO+061Ndi70ZSrTm+pcqQFJHkfXFfuJVXXjQpen96pqyQ7AnnRvkvkZcOhCl2dM+/gIuub2s4Az27An3fMypwDnAv8DbNqWD93bLX8G/JDuTU4TeT2b7qUHK4BnLfS+rWa97Aqc2MbvQvclegXwCWDdlr5em17R5t9lYP1DWx2dA+yx0PuzGvWwM3B6Oz4+DWyyVI8N4PXAT4EfAR8C1l0qxwbwUbrnTX5P19Jw4FweB8AurV5/BrwLyELv85o4DJ7XHCatn9uc7xa6TH0cJjsXLnSZ+jKsyrlyKQ9T1NMKuueUJ76Lvmehy9nnIa0iJUmSJEkLbCl3cZQkSZKkXjFAkyRJkqSeMECTJEmSpJ4wQJMkSZKknjBAkyRJkqSeMEDTopHk5iRnJvlRks8mWbbA5Xn1HOa1LMnzZ7HeYUleNl16kiOTnN/q7swk32zpWyQ5MckPkvwkyeen2MYNq1quEcq9c5I9Z9qPSdZLki8n2SjJ8iQ/WsXtvjDJs2dTZkmayuqcJ5Mck+Scdm37YJLbz2XZ5pLXvdmb7XVPS5MBmhaTX1fVzlV1b+Bq4AULXJ5JL1QtiFjVz9YyYJUvVKvg5a3udq6qh7W0fwJOrqr7VtU9gUPGuP1hO9P9Ht+q2hP4QVVdP8vtfhD4u1muK0njcAxwd+DPgPWBg6ZbOMna81GoKXjdm73ZXve0BBmgabH6FrD1xESSlyf5bpKzkrx+IH2/lvaDJB9qactbK8xZSU5Jsl1LPzLJO5N8M8l5SfZp6Vsm+dpA692fJzkcWL+lHdPyPCfJ0XQ/7rnt4B24JPskObKNb5Hk+FamHyR5GHA4cNeW31tn2KdDk/xfkm8Ad1uNOtyS7gckAaiqs2ZaYbIytX0/O8n7kvw4yUlJ1m/zHtiWPTPJW1v9rUN3kXxaS39ay/6eSb7S6v7vpyjCM4DPDEyv3er/7CTHJblD2+4FSd6S5IdJvpNkx7aPNwIXJHnQqlWVJM2sBSoT57ofTpzfktwuyX8l+WmSk5N8fuIaU1Wfr4buh++3mSTfA5KckOTLdD+K7HVvdhbjdU9L0UL/UraDw6gDcEP7uxbwCeBxbfoxwBFA6G46nAg8ErgX8H/AZm25TdvfzwL7t/FnA59u40e2fG8H3BNY0dJfChw6sO0NB8vTxpcDfwAeMlzeNr4PcGQb/zjw4oH8Nm7r/2hg+an26QHAD4E7ABsBK4CXTVJXh02kt/06HzizDce09McC1wKnAocCW81Q71OVaTlwE7BzW+5Y4Jlt/EfAQ9v44RP7CBwAvGuovN8E1gU2A64Cbj9JWS4cqP/lQAEPb9MfHNjnCwb+Z/sBJw7kcSjw0oU+nh0cHNacYeA8+RTg5HZu3wL4OV1QsA/w+Xbu/BPgGmCfoTxuD3wP+PNJ8j+ALrCYuI553bttHR3GGnjdc1iaw0I2k0urav0kZ9K1nJ1NdxGE7gT6GOD7bXoDYCfgvsAnqupKgKq6us1/KPDkNv4h4C0D2/h0Vf0B+EmSLVrad4GJ5wI+XVVnTlG+C6vqtBH2Yze6oIGquhm4LskmQ8tMtU8bAsdX1xJEkhNG2B50XT2OG0yoqi8luQvwOGAP4PtJ7l1VV0yRx1Rl+jlw/kC9nAEsT/eM4IZV9a2W/hHg8dOU8XNV9Vvgt0kup/tys3JomU2r6pcD0xdV1f+28Q8Dfw/8a5v+6MDftw+sczlddyJJmmuPAD7azu2XJfkq8MCW/ol2fbk0yamTrPtfwNeq6utT5H3ywHXM697M1pTrnpYguzhqMfl1Ve0MbE93N2viGbQA/1K39DXfsao+MMtt/HZgPABV9TW6O2YXA0cm2W+KdX81NF0D4+utYjnmcp+mVFVXV9VHqupv6S7Ij5xlmQbr7WaY1c2fUfK4Kbd+zqGG5tcI4+sBv55F+SRpLJK8DtgceMk0iw1eY7zuzdIivO5pCTJA06LT7qL9PfDSdA9Lfwl4dpINAJJsneTOwJeBv05yp5a+acvim8C+bfwZwFR3K2nrbQ9cVlXvA94P3L/N+n2mf9vWZUnu0QKKvxpIPwV4Xst7rSQbA7+ku0s4Yap9+hrwpCTrJ9kQeMJ0ZZ9hv3bLLc9sbQjcle6u4FSmKtOkqupa4JdJHtyS9h2YPby/ozoHuMvA9HZJHtrG/wb4xsC8pw38/dZA+p/SdUGRpLn2dbrnjNZKsjndl//vAP8LPKU9i7YFsOvECkkOout69/TWkjUKr3uzsEive1qCjNS1KFXV95OcRXdB+1CSewDfSgJwA11f8B8neRPw1SQ303VROIDuLX7/neTlwBXAs2bY3K7Ay5P8vuU9cSfxCOCsJN+j68s+7BC6/upXAKfTdY0AeBFwRJID6e6YPa+qvpXkf9O9Nv4LVfXyKfbpe0k+DvyArqved0essrcm+ceB6QfR9et/V5Kb6G7WvL+qpsyvqk6arExtH6ZyIPC+JH8Avgpc19JPBQ5pXVb/ZcR9APgc3f9jRZs+B3hBkg8CPwHePbDsJu0Y+S3w9IH0h9P1/ZekuXY8XXfCH9C1Jr2iqi5N8klgd7rz1EV0z5pNnA/fQ/d87cS59VNV9U/TbWSq87HXvVtZU657WoJSNdxDSJLmRpINquqGNn4IsGVVvWg18tsSOLqq/nKG5S4Adpl4DmMg/X7AS1rXFkmaNxPnw9a69R26FxxdutDl0tya6+ueliZb0CSN015JXkV3rrmQ7k7urFXVJelea7xRze630DYDXrM6ZZCkWTqxvURiHeANBmdrrDm97mlpsgVNkiRJknrCl4RIkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSQsgyReS7L/Q5ZAkzcxz9sJJ8pUkBy3QtivJjquZx/KWz9pzVa5xSXJkkjfO97q6LQM0rZGSXJDk10luSHJpO3FsMOK6uyZZOYdlOSzJhwfTqmqPqjpqrraxJkiye5KfJrkxyalJtp9m2Tck+WGSm5IcNo/FlDQGnrMXnzXtnL2QgaA0zABNa7InVNUGwM7A/YBXLXB5Fr1VvQOYZN0kG4+w3GbAp4DXAJsCpwMfn2aVFcArgM+tSnkk9Zrn7DnmOVtanAzQtMarqkuBL9Fd9IE/XoT+NcnPk1yW5D1J1k9yR+ALwFbtTu4NSbZKcrskhyT5WZKrkhybZNOW10T3hf1bflcmObTNexzwauBpLa8ftPQ/3qlref9jkguTXJ7k6IkL5HR5TybJnkl+kuSXSS5O8rKBeXsnOTPJ9W0/HtfSt0pyQpKrk6xI8pyBdQ5LclySDye5HjhgurqYxGbARUmOSfLoJFOdc54M/LiqPlFVvwEOA+6b5O5T/E+PqqovAL+cqi4kLU6es/84z3P2JJI8O8nZSa5J8qUMtNy1uv9/Sc5Ncm2S/0ySNm+tJP/W/ifnJ3lhW37tJG8C/hx4V/u/v2tgk4+eIr8dk3w1yXUtz+kC1MHyT/f/Wz/JUW3fzk7yikzROpzO29sxeH26Fsp7D+Tzb+0YvS7JN5Ks3+Z9Il0r9XVJvpbkXtOU9fHtGLw2yTeT3Gdg3v2SfK8dux8H1htl/zUaAzSt8ZJsA+xBdwdvwuHAn9J9AdgR2Bp4bVX9qi37i6raoA2/AP4OeBLwF8BWwDXAfw5t6hHA3YDdgdcmuUdVfRH4Z+DjLa/7TlLEA9rwKOAuwAbAu4aWuU3eU+zuB4DnVtWGwL2BL7c6eBBwNPByYBnwSOCCts7HgJVtv/YB/jnJbgN57g0c19Y7ZsS6AKCqLqar5+8DbwfOT/JPSe4ytOi9gB8MrPcr4GctXdIS4jnbc/ZUkuxNF0A/Gdgc+Drw0aHFHg88ELgP8FTgsS39OXTHys7A/enqBICqOrTl9cL2f3/hCPm9ATgJ2ATYBviPEXdjuv/f64DldMfVXwLPnCafx9AdF38KbNzKdlWb96/AA4CH0bVwvgL4Q5v3BWAn4M7A9+iOkdtIcj/gg8BzgTsB7wVOSHezZB3g08CHWv6fAJ4y4v5rFFXl4LDGDXQXshvo7tYVcAqwrM0L8CvgrgPLPxQ4v43vCqwcyu9sYPeB6S2B3wNr051MC9hmYP53gH3b+GHAh4fy+wpwUBs/BXj+wLy7jZr3JPv9c7qT6UZD6e8F3j7J8tsCNwMbDqT9C3DkQNm/NmpdjPB/eQDwTuDyVgf3bekfAA4fWvZ/gQNmyO/DwGELfbw5ODis3uA523P2NOsM1v0XgAMH5t0OuBHYvk0X8IiB+ccCh7TxL9MFwxPzHt2WX3t4OwPLTJff0cARg//rKco/cUysPcL/7zzgsQPzDmLo2B6Ytxvwf8BDgNsN1cmvJ/5XM5RtWSvbxm36SOCNbfzdwBuGlj+HLtB/JPALIAPzvjmxrsPqD7agaU32pOruSu4K3J2u6wZ0d93uAJzRmu2vBb7Y0qeyPXD8wPJn051ktxhY5tKB8Rvp7qqOYivgwoHpC+lO5LPJ+ynAnsCFrevFQ1v6tnR3Nyfb9tVVNdjt5EK6u9P/v737j7asrO87/v4IIqjAgNApDpAhkcYQW5GMCppaI4kRSYM1qBiroKSspPirGOIQ22rSujLWrCjWBDsKAVKCIpGIQqMEUJsIyA8RQTROcQhD+DEqPzQmKvrtH/u54XC9P869c885+977fq111tn72fvs8+znnruf893PjzPljmmvGaYsZvNVuruuW+j+Jmta+reBPabtuwd2YZRWE6/ZXrPn82PA6QPn8k26AH7w/Gcr+yfyyLKZXk6zme14v9Xe+3NJbknymiGONd/fb+g8VtUVdC23fwjcm2Rzkj3o/m92ZYbPT+vmuSldd9cHebhVdp/p+9KV9ZumyrqV9wEtj08E7qwWmQ2ch5aIAZpWvKr6NN1dod9vSV+nu7v001W1pj32rG5wOnR3k6a7AzhqYP81VbVrdd1B5s3CPNv/ju5COOVA4CHgniGO/cg3qrq2qo6h67rw53R3+6DL/0/M8t57J9l92vsPntf0/C+oLFqFcFSS8+nuFh9Nd8dw//a3AbgFeOrAax7X8nvL/GctaSXxmg14zZ7NHXStYIPnsltVfXaI195F1xVxygHTts/3d3/kzlV3V9V/qKon0rWC/lHmn5J/vr/ffHmcnof3VNXPAIfQdXU8le7/5R+Z+fPzq3RdYH+erlvk+paeGfa9A3j7tLJ+bFWd3/K5bmo83sB5aIkYoGm1eDfwC0meWlU/BN4PvCvJPwNIsi7JVL/ye4An5JEzWb0PeHvaYOQk+7a+8MO4B1if2Qdbnw/8pyQHpZtWemr8w0MLOcEkuyR5RZI9q+r7wIM83Of8TODV6aZFflQ73ydX1R103RJ+L8mubQDwiXTdUGYzdFm08t3Wzulq4ElV9eKq+ti087sIeEqSX0myK/BfgZuq6suzHPfRbb9HATu3vO80TDlJWha8Zq+Sa3Yenlhl/Xxl1s7ltLSJLZLsmeQlQ7wOuuD3Da0s1wBvnrb9HrqxX0NJ8pJ04yWhG9dXPPz3m9EQf78L6M5vryTrgNfOciiSPD3JM5M8mq4L8D8CP2z/L2cBf5BuQpKdkhyR5DHA7sB36caqPZbu7zyb9wO/3t4jSR6X5OgWXF5Fd1Pi9e1v+2LgGXOduxbGAE2rQlVtp+sv/l9b0pvpumxc3Zr5/5JuHAGtgjkfuK016z8ROB24GPhkkm/RVVzPHPLtP9yev5Hkhhm2n0U30PYzwNfoLrKvW9gZ/pNXAlvbOf068AqAqvoc8Gq6Qd8PAJ/m4TvAL6e7i/Z3dJXuW6vqL+d4j4WUxXeAF1TV06rq9Kr6+kw7tb/PrwBvp6vongkcN7U93Yxt7xt4yfvp7qi/HHhLW37lHHmWtIx4zV5V1+wD6LrHzdu6WVUXAe8APtjK7Ga6iT+G8X66ST1uopsE5VK6IOMHbfvpwLHpZlB8zxDHezpwTZJv05XvG6rqtiFeN9ff73fpAuSv0X3GL6QLqGayRzun++jK7xvAO9u23wS+CFxL1w30HXTf+c/l4bL+Et1nYUZVdR3dxCrvbe+xhW5yHKrqe3QTtZzQjv8yup9d0BLJI7uPSpIkSeOR5D8D26vqf435fY8C3ldVs/7A9qQl+Q26CWb+zaTzovEyQJMkSdKKlu53wH6OrhVtLfBnwNVV9caJZmxAkv3oulleRTcV/iXAe6vq3RPNmMbOAE2SJEkrWpLH0nUVfTJdF8tL6LolPjjRjA1o4wQvAQ4C7qf7zbTTWpdCrSIGaJIkSZLUE04SIkmSJEk9sfOkM7Aj9tlnn1q/fv2ksyFJ6rHrr7/+61U1148aLynrJknSfOaqm5Z1gLZ+/Xquu+66SWdDktRjSW4f5/tZN0mS5jNX3WQXR0mSJEnqCQM0SZIkSeoJAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6gkD3pkFowAAH2dJREFUNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6omRBmhJ1iS5MMmXk9ya5Igkeye5LMlX2/Nebd8keU+SLUluSnLYKPMmSZIkSX0z6ha004G/qKonA08FbgU2ApdX1cHA5W0d4Cjg4PY4CThjxHmTJEmSpF4ZWYCWZE/gOcCZAFX1vaq6HzgGOKftdg7worZ8DHBuda4G1iTZb1T5kyRJkqS+GWUL2kHAduCPk3w+yQeSPA5YW1V3tX3uBta25XXAHQOv39bSHiHJSUmuS3Ld9u3bR5h9SZKGY90kSVoqO4/42IcBr6uqa5KczsPdGQGoqkpSCzloVW0GNgNs2LBhQa+VJGkUrJskLSfrN14y67atm44eY040k1EGaNuAbVV1TVu/kC5AuyfJflV1V+vCeG/bfidwwMDr929pkiRJkqYx0FqZRtbFsaruBu5I8pMt6UjgS8DFwPEt7Xjgo235YuBVbTbHw4EHBrpCSpIkSdKKN8oWNIDXAecl2QW4DXg1XVB4QZITgduBl7Z9LwVeCGwBvtP2lSRJkrRAc7Wuqd9GGqBV1Y3Ahhk2HTnDvgWcPMr8SJIkSVKfjfp30CRJkiRJQzJAkyRJkqSeGPUYNEmSJEmL5Fiy1ccWNEmSJEnqCVvQJEmSJAHzt9j5+2qjZwuaJEmSJPWEAZokSZIk9YQBmiRJkiT1hAGaJEmSJPWEAZokSZIk9YQBmiRJkiT1hAGaJEmSJPWEAZokSZIk9YQBmiRJkiT1hAGaJEmSJPWEAZokSZIk9cTOk86AJEmSpOVh/cZLZt22ddPRY8zJymULmiRJkiT1hAGaJEmSJPWEAZokSZIk9YQBmiRJkiT1hAGaJEmSJPWEsziuEnPNuAPOuiNJkiT1wUhb0JJsTfLFJDcmua6l7Z3ksiRfbc97tfQkeU+SLUluSnLYKPMmSZIkSX0zji6OP1dVh1bVhra+Ebi8qg4GLm/rAEcBB7fHScAZY8ibJEmSJPXGJMagHQOc05bPAV40kH5uda4G1iTZbwL5kyRJkqSJGHWAVsAnk1yf5KSWtraq7mrLdwNr2/I64I6B125raY+Q5KQk1yW5bvv27aPKtyRJQ7NukiQtlVEHaD9bVYfRdV88OclzBjdWVdEFcUOrqs1VtaGqNuy7775LmFVJkhbHukmStFRGOotjVd3Znu9NchHwDOCeJPtV1V2tC+O9bfc7gQMGXr5/S5MkSZJWrPlm29bqMrIWtCSPS7L71DLwfOBm4GLg+Lbb8cBH2/LFwKvabI6HAw8MdIWUJEmSpBVvlC1oa4GLkky9z59W1V8kuRa4IMmJwO3AS9v+lwIvBLYA3wFePcK8SZIkSVLvjCxAq6rbgKfOkP4N4MgZ0gs4eVT5kSRJkqS+m8Q0+5IkSZKkGYx0khCNlwNMJUmSpOXNAK2H5gq0tm46eow5kSRJkjROBmgCDAolSZKkPnAMmiRJkiT1hAGaJEmSJPWEXRyXGScCkSRJklYuAzRJkiRJO8w5DZaGXRwlSZIkqScM0CRJkiSpJwzQJEmSJKknDNAkSZIkqSecJETzcsCnJEmSNB62oEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElSTxigSZIkSVJPGKBJkiRJUk8YoEmSJElST4w8QEuyU5LPJ/l4Wz8oyTVJtiT5UJJdWvpj2vqWtn39qPMmSZIkSX0yjha0NwC3Dqy/A3hXVT0JuA84saWfCNzX0t/V9pMkSZKkVWPnUR48yf7A0cDbgVOSBHge8Kttl3OAtwFnAMe0ZYALgfcmSVXVKPOo0Vm/8ZJZt23ddPQYcyJJkiQtD6NuQXs38FvAD9v6E4D7q+qhtr4NWNeW1wF3ALTtD7T9JUmSJGlVGFkLWpJfAu6tquuTPHcJj3sScBLAgQceuFSHHbu5WpckScvLSqmbJI2G3/u0EKPs4vhs4JeTvBDYFdgDOB1Yk2Tn1kq2P3Bn2/9O4ABgW5KdgT2Bb0w/aFVtBjYDbNiwwe6PE+YFR5KsmyRJS2dkXRyr6rSq2r+q1gPHAVdU1SuAK4Fj227HAx9tyxe3ddr2Kxx/JkmSJGk1mcTvoL2ZbsKQLXRjzM5s6WcCT2jppwAbJ5A3SZIkSZqYBXdxTLIXcEBV3TTsa6rqU8Cn2vJtwDNm2OcfgZcsND+SJI3LYupASZIWYqgWtCSfSrJHkr2BG4D3J/mD0WZNkqTJsw6UJI3TsF0c96yqB4EXA+dW1TOBnx9dtiRJ6g3rQEnS2AwboO2cZD/gpcDHR5gfSZL6xjpQkjQ2wwZovwN8AthSVdcm+XHgq6PLliRJvWEdKEkam2EnCbmrqv7V1EpV3Wb/e0nSKmEdKGle/jaslsqwAdr/BA4bIk2SpJXGOlCSdtBcAezWTUePMSf9N2eAluQI4FnAvklOGdi0B7DTKDMmSdIkWQdKkiZhvha0XYDHt/12H0h/EDh2VJmSJKkHrAMlSWM3Z4BWVZ8GPp3k7Kq6fUx5kiRp4qwDJUmTMOwYtMck2QysH3xNVT1vFJmSJKlHrAMlSWMzbID2YeB9wAeAH4wuO5Ik9Y51oCRpbIYN0B6qqjNGmhNJkvrJOlCSNDbD/lD1x5L8xyT7Jdl76jHSnEmS1A/WgZKksRm2Be349nzqQFoBP7602ZEkqXesAyVJYzNUgFZVB406I5Ik9ZF1oCRpnIYK0JK8aqb0qjp3abMjSVK/WAdKksZp2C6OTx9Y3hU4ErgBsHKSJK101oGSpLEZtovj6wbXk6wBPjiSHEmS1CPWgZKkcRp2Fsfp/h6wT74kaTWyDpQkjcywY9A+RjdjFcBOwE8BF4wqU5Ik9YV1oCRpnIYdg/b7A8sPAbdX1bYR5EeSpL6xDpQEwPqNl0w6C1oFhuriWFWfBr4M7A7sBXxvlJmSJKkvrAMlSeM0bBfHlwLvBD4FBPifSU6tqgtHmDetYHPdgdq66egx5kSS5mYdKEkap2G7OL4FeHpV3QuQZF/gL4FZK6ckuwKfAR7T3ufCqnprkoPoZr96AnA98Mqq+l6Sx9BNWfwzwDeAl1XV1kWdVU/YDL4485WbAZykMVtwHShJ0mINO4vjo6YqpuYbQ7z2u8DzquqpwKHAC5IcDrwDeFdVPQm4Dzix7X8icF9Lf1fbT5KkSVtMHShJ0qIMW8H8RZJPJDkhyQnAJcClc72gOt9uq49ujwKex8N3Hc8BXtSWj2nrtO1HJsmQ+ZMkaVQWXAdKkrRYc3ZxTPIkYG1VnZrkxcDPtk1XAefNd/AkO9F1Y3wS8IfA/wPur6qH2i7bgHVteR1wB0BVPZTkAbpukF9f0BlJkrQEdrQOlCRpMeZrQXs38CBAVX2kqk6pqlOAi9q2OVXVD6rqUGB/4BnAk3cwvyQ5Kcl1Sa7bvn37jh5OkqTZDF0HWjdJkpbKfAHa2qr64vTElrZ+2DepqvuBK4EjgDVJplru9gfubMt3AgcAtO170vXzn36szVW1oao27LvvvsNmQZKkhRq6DrRukiQtlflmcVwzx7bd5nphm+Xq+1V1f5LdgF+gm/jjSuBYupkcjwc+2l5ycVu/qm2/oqpq3jOYIGdplKQVbdF1oCRJizVfC9p1Sf7D9MQkv0Y3tmwu+wFXJrkJuBa4rKo+DrwZOCXJFroxZme2/c8EntDSTwE2Dn8akiQtuR2pAyVJWpT5WtDeCFyU5BU8XBltAHYB/t1cL6yqm4CnzZB+G914tOnp/wi8ZIg8S5I0DouuAyVJWqw5A7Squgd4VpKfA57Ski+pqitGnjNJkibIOlCSxmOuYUNbNx09xpz0w3wtaABU1ZV0Y8ckSVpVrAMlSeM07A9VS5IkSZJGzABNkiRJknrCAE2SJEmSemKoMWjScuEgU0mSJC1nBmhadvyBcEmSJK1UdnGUJEmSpJ4wQJMkSZKknjBAkyRJkqSeMECTJEmSpJ4wQJMkSZKknjBAkyRJkqSeMECTJEmSpJ7wd9AkSZKkxt9b1aTZgiZJkiRJPWGAJkmSJEk9YYAmSZIkST1hgCZJkiRJPeEkIZIkSZJ6ab5JW7ZuOnpMORkfW9AkSZIkqScM0CRJkiSpJwzQJEmSJKknDNAkSZIkqSdGNklIkgOAc4G1QAGbq+r0JHsDHwLWA1uBl1bVfUkCnA68EPgOcEJV3TCq/Gn1mWuQ6UocYCpJkqTlZ5QtaA8Bb6qqQ4DDgZOTHAJsBC6vqoOBy9s6wFHAwe1xEnDGCPMmSZIkSb0zsgCtqu6aagGrqm8BtwLrgGOAc9pu5wAvasvHAOdW52pgTZL9RpU/SZIkSeqbsYxBS7IeeBpwDbC2qu5qm+6m6wIJXfB2x8DLtrW06cc6Kcl1Sa7bvn37yPIsSdKwrJskSUtl5D9UneTxwJ8Bb6yqB7uhZp2qqiS1kONV1WZgM8CGDRsW9FppNo5Pk7QjrJskSUtlpC1oSR5NF5ydV1Ufacn3THVdbM/3tvQ7gQMGXr5/S5MkSZKkVWFkAVqblfFM4Naq+oOBTRcDx7fl44GPDqS/Kp3DgQcGukJKkiRJ0oo3yi6OzwZeCXwxyY0t7beBTcAFSU4Ebgde2rZdSjfF/ha6afZfPcK8SZIkSVLvjCxAq6q/AjLL5iNn2L+Ak0eVH0mSJEnqu7HM4ihJkiRJmp8BmiRJkiT1hAGaJEmSJPXEyH8Hbbmb6/extDrM9xnwd9IkSZK0VAzQJEmStKp4A159ZhdHSZIkSeoJAzRJkiRJ6gkDNEmSJEnqCcegSTtorn7sTiAiSZKkhbAFTZIkSZJ6whY0SZIkrTjO1Lg6rMSeTLagSZIkSVJPGKBJkiRJUk/YxVEaoZXY7C5JkqTRsQVNkiRJknrCAE2SJEmSesIATZIkSZJ6wgBNkiRJknrCAE2SJEmSesJZHPGHDCVJkpYbv79ppbIFTZIkSZJ6wgBNkiRJknrCAE2SJEmSemJkAVqSs5Lcm+TmgbS9k1yW5Kvtea+WniTvSbIlyU1JDhtVviRJkiSpr0Y5ScjZwHuBcwfSNgKXV9WmJBvb+puBo4CD2+OZwBntWVqx5hrcvHXT0WPMiSRJkvpiZAFaVX0myfppyccAz23L5wCfogvQjgHOraoCrk6yJsl+VXXXqPInSZKkfnOmRu2I5XozfNxj0NYOBF13A2vb8jrgjoH9trU0SZIkSVo1JvY7aFVVSWqhr0tyEnASwIEHHrjk+ZL6YLne8ZFWK+smSdJSGXcL2j1J9gNoz/e29DuBAwb227+l/Yiq2lxVG6pqw7777jvSzEqSNAzrJknSUhl3C9rFwPHApvb80YH01yb5IN3kIA84/kyama1rkiRJK9fIArQk59NNCLJPkm3AW+kCswuSnAjcDry07X4p8EJgC/Ad4NWjypckSZLGy5uL0vBGOYvjy2fZdOQM+xZw8qjyIq0W8812ZSUoSZLUbxObJETS+HkHU5Ikqd8M0CRJkjQx/taZ9EjjnsVRkiRJkjQLAzRJkiRJ6gkDNEmSJEnqCQM0SZIkSeoJAzRJkiRJ6glncZQEOAW/JElSH9iCJkmSJEk9YQuapHnZuiZJkjQeBmiSdojBmyRJ0tKxi6MkSZIk9YQtaJJ6yZY5SeqXua7L4LVZy0ufv2cYoEkaGStzSVo95rvmSxqOXRwlSZIkqScM0CRJkiSpJwzQJEmSJKknHIMmaWJGMV6hz4N+JUmS5mOAJmnVMHiTJEl9Z4AmadkZd8sbGMBJWjm8WSX1mwGaJEmSAKfKl/rAAE2ShuAdZ0nLiYGWtHwZoEnSCBnYqW/8TEpSv/UqQEvyAuB0YCfgA1W1acJZkqSJ8Eu0doStJ5K0eJOug3sToCXZCfhD4BeAbcC1SS6uqi9NNmeSNLfFfhn2S7Tmslomrlns/8Gozn/SX8wkqTcBGvAMYEtV3QaQ5IPAMYABmiQNWC1f3DW3Scxmulij+EzuSCA17psjowr6vMkjrUx9CtDWAXcMrG8DnjmhvEjSirScvtAZaK4cfQqI+nbc5fQ/KWk8+hSgDSXJScBJbfXbSb6yg4fcB/j6Dh5jNbLcFs4yWzjLbOH2yTtWRpnlHUt2qB9bsiPNYgR100z8f1g4y2xxLLeFs8wWblmW2TjqplTVkr3LjkhyBPC2qvrFtn4aQFX93ojf97qq2jDK91iJLLeFs8wWzjJbOMts5fJvu3CW2eJYbgtnmS2cZTa7R006AwOuBQ5OclCSXYDjgIsnnCdJkiRJGpvedHGsqoeSvBb4BN00+2dV1S0TzpYkSZIkjU1vAjSAqroUuHTMb7t5zO+3UlhuC2eZLZxltnCW2crl33bhLLPFsdwWzjJbOMtsFr0ZgyZJkiRJq12fxqBJkiRJ0qq2qgO0JC9I8pUkW5JsnHR+JinJAUmuTPKlJLckeUNL3zvJZUm+2p73aulJ8p5WdjclOWzgWMe3/b+a5PhJndO4JNkpyeeTfLytH5TkmlY2H2qT3pDkMW19S9u+fuAYp7X0ryT5xcmcyXgkWZPkwiRfTnJrkiP8nM0tyX9q/5c3Jzk/ya5+zlaX6dcZzW+ma82k89R3M11rJp2nPkpyVpJ7k9w8kDZjPabOLGX2zvb/eVOSi5KsmWQe+2TVBmhJdgL+EDgKOAR4eZJDJpuriXoIeFNVHQIcDpzcymMjcHlVHQxc3tahK7eD2+Mk4AzoLlDAW+l+ZPwZwFtXwUXqDcCtA+vvAN5VVU8C7gNObOknAve19He1/WjlfBzw08ALgD9qn8+V6nTgL6rqycBT6crOz9kskqwDXg9sqKqn0E2idBx+zlab6dcZzW+ma41mMce1Rj/qbLrr6KDZ6jF1zuZHy+wy4ClV9a+AvwFOG3em+mrVBmh0X+q2VNVtVfU94IPAMRPO08RU1V1VdUNb/hZdRbaOrkzOabudA7yoLR8DnFudq4E1SfYDfhG4rKq+WVX30f3zTf+HXDGS7A8cDXygrQd4HnBh22V6mU2V5YXAkW3/Y4APVtV3q+prwBa6z+eKk2RP4DnAmQBV9b2quh8/Z/PZGdgtyc7AY4G78HO2aky/zmh+c1xrNLfp15q/m3B+eqmqPgN8c1rybPWYmLnMquqTVfVQW70a2H/sGeup1RygrQPuGFjf1tJWvdYl6mnANcDaqrqrbbobWNuWZyu/1Vau7wZ+C/hhW38CcP/ABWfw/P+pbNr2B9r+q6nMDgK2A3/cumt9IMnj8HM2q6q6E/h94G/pArMHgOvxc7aaTL/OaH6zXWs0i5muNVX1ycnmalmZrR7TcF4D/J9JZ6IvVnOAphkkeTzwZ8Abq+rBwW3VTfnptJ9Nkl8C7q2q6yedl2VkZ+Aw4Iyqehrw90zrBuLn7JFa181j6L5wPhF4HCu7tVADvM4s2rzXGj3STNeaJP9+srlanqzHFibJW+iG2pw36bz0xWoO0O4EDhhY37+lrVpJHk0XnJ1XVR9pyfe0LmW053tb+mzlt5rK9dnALyfZStdF9nl0Yx7WtO4h8Mjz/6eyadv3BL7B6iqzbcC2qrqmrV9I9yXKz9nsfh74WlVtr6rvAx+h++z5OVsdfuQ6k+R/TzZLy8Js1xrNbqZrzbMmnKflZLZ6THNIcgLwS8Aryt/++ierOUC7Fji4zYS2C91A2IsnnKeJaWNUzgRurao/GNh0MTA1Q97xwEcH0l/VZtk7nK4rxF3AJ4DnJ9mr3Y17fktbcarqtKrav6rW031+rqiqVwBXAse23aaX2VRZHtv2r5Z+XJt97yC6CTE+N6bTGKuquhu4I8lPtqQjgS/h52wufwscnuSx7f90qsz8nK0Cs1xnbNWYxxzXGs1upmuNE6sMb7Z6TLNI8gK67tu/XFXfmXR++mTn+XdZmarqoSSvpftStxNwVlXdMuFsTdKzgVcCX0xyY0v7bWATcEGSE4HbgZe2bZcCL6SbaOA7wKsBquqbSf4bXQAM8LtVNX0g7Ur3ZuCDSf478HnaIPX2/CdJttANlD0OoKpuSXIB3ZeHh4CTq+oH48/22LwOOK/dGLmN7rPzKPyczaiqrklyIXAD3efj88Bm4BL8nElzmelao1nMca3RNEnOB54L7JNkG92swrN9XxKzltlpwGOAy7p7AlxdVb8+sUz2SGxNlCRJkqR+WM1dHCVJkiSpVwzQJEmSJKknDNAkSZIkqScM0CRJkiSpJwzQJEmSJKknDNC0bCT5QZIbk9yc5GNJ1kw4P7+9hMdak+Q/LuJ1b0vym3OlJzk7ydda2d2Y5LMtfW2Sjyf5QpIvJbl0lvf49kLzNUS+D03ywvnOY4bXJckVSfZIsj7JzQt839cmec1i8ixJs9mR62SS85J8pdVtZyV59FLmbSlZ7y3eYus9rU4GaFpO/qGqDq2qp9D9vtPJE87PjBVVCyIW+r+1BlhwRbUAp7ayO7SqntXSfhe4rKqeWlWHABtH+P7THUr3+2YL9ULgC1X14CLf9yy630aSpL44D3gy8C+B3YBfm2vnJJP8DVvrvcVbbL2nVcgATcvVVcC6qZUkpya5NslNSX5nIP1VLe0LSf6kpa1vrTA3Jbk8yYEt/ewk70ny2SS3JTm2pe+X5DMDrXf/OskmYLeWdl475leSnAvcDBwweAcuybFJzm7La5Nc1PL0hSTPovuBy59ox3vnPOf0liR/k+SvgJ/cgTLcD9g2tVJVN833gpny1M791iTvT3JLkk8m2a1te3rb98Yk72zltwtdJfmylv6ydvhDknyqlf3rZ8nCK4CPDqzv3Mr/1iQXJnlse9+tSf5Hki8m+VySJ7Vz/A6wNckzFlZUkjS/FqhMXeu+OHV9S/KoJH+U5MtJLkty6VQdU1WXVgN8Dth/huOekOTiJFcAl7c0672FW471nlajqvLhY1k8gG+3552ADwMvaOvPBzYDobvp8HHgOcBPA38D7NP227s9fww4vi2/Bvjztnx2O+6jgEOALS39TcBbBt5798H8tOX1wA+Bw6fnty0fC5zdlj8EvHHgeHu21988sP9s5/QzwBeBxwJ7AFuA35yhrN42ld7O62vAje1xXkv/ReB+4ErgLcAT5yn32fK0HngIOLTtdwHw79vyzcARbXnT1DkCJwDvnZbfzwKPAfYBvgE8eoa83D5Q/uuBAp7d1s8aOOetA3+zVwEfHzjGW4A3Tfrz7MOHj5XzGLhO/gpwWbu2rwX+li4oOBa4tF07/zlwH3DstGM8GrgB+NczHP8EusBiqh6z3vvRMnobK7De87E6H5NsJpcWarckN9K1nN1KVwlCdwF9PvD5tv544GDgqcCHq+rrAFX1zbb9CODFbflPgP8x8B5/XlU/BL6UZG1LuxaYGhfw51V14yz5u72qrh7iPJ5HFzRQVT8AHkiy17R9Zjun3YGLqmsJIsnFQ7wfdF09LhxMqKpPJPlx4AXAUcDnkzylqrbPcozZ8vS3wNcGyuV6YH26MYK7V9VVLf1PgV+aI4+XVNV3ge8muZfuy822afvsXVXfGli/o6r+ui3/b+D1wO+39fMHnt818Jp76boTSdJS+1ng/HZtvyfJp4Gnt/QPt/rl7iRXzvDaPwI+U1X/d5ZjXzZQj1nvzW+l1HtaheziqOXkH6rqUODH6O5mTY1BC/B79XBf8ydV1ZmLfI/vDiwHoKo+Q3fH7E7g7CSvmuW1fz9tvQaWd11gPpbynGZVVd+sqj+tqlfSVcjPWWSeBsvtB7Comz/DHOOhPHKcQ03bXkMs7wr8wyLyJ0kjkeStwL7AKXPsNljHWO8t0jKs97QKGaBp2Wl30V4PvCndYOlPAK9J8niAJOuS/DPgCuAlSZ7Q0vduh/gscFxbfgUw291K2ut+DLinqt4PfAA4rG36fuaebeueJD/VAop/N5B+OfAb7dg7JdkT+BbdXcIps53TZ4AXJdktye7Av50r7/Oc1/Py8Jit3YGfoLsrOJvZ8jSjqrof+FaSZ7ak4wY2Tz/fYX0F+PGB9QOTHNGWfxX4q4FtLxt4vmog/V/QdUGRpKX2f+nGGe2UZF+6L/+fA/4a+JU2Fm0t8NypFyT5Nbqudy9vLVnDsN5bhGVa72kVMlLXslRVn09yE12F9idJfgq4KgnAt+n6gt+S5O3Ap5P8gK6Lwgl0s/j9cZJTge3Aq+d5u+cCpyb5fjv21J3EzcBNSW6g68s+3Ua6/urbgevoukYAvAHYnOREujtmv1FVVyX563TTxv+fqjp1lnO6IcmHgC/QddW7dsgie2eS/zyw/gy6fv3vTfIQ3c2aD1TVrMerqk/OlKd2DrM5EXh/kh8CnwYeaOlXAhtbl9XfG/IcAC6h+3tsaetfAU5OchbwJeCMgX33ap+R7wIvH0h/Nl3ff0laahfRdSf8Al1r0m9V1d1J/gw4ku46dQfdWLOp6+H76MbXTl1bP1JVvzvXm8x2Pbbee4SVUu9pFUrV9B5CkrQ0kjy+qr7dljcC+1XVG3bgePsB51bVL8yz31Zgw9Q4jIH0pwGntK4tkjQ2U9fD1rr1OboJju6edL60tJa63tPqZAuapFE6OslpdNea2+nu5C5aVd2VblrjPWpxv4W2D/BfdiQPkrRIH2+TSOwC/DeDsxVrSes9rU62oEmSJElSTzhJiCRJkiT1hAGaJEmSJPWEAZokSZIk9YQBmiRJkiT1hAGaJEmSJPWEAZokSZIk9cT/B4c6vgHSb7yXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12,8), constrained_layout=True, sharey=True)\n",
    "\n",
    "fig.suptitle(\"T. thermophila, BleTIES IES reconstructions from Nanopore library\")\n",
    "\n",
    "axs[0][0].hist(pd.DataFrame(ont_stats)['len'], bins=50)\n",
    "axs[0][0].set_title(\"All IESs\")\n",
    "axs[0][0].set_xlabel(\"Reconstructed IES length (bp)\")\n",
    "axs[0][0].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[0][1].hist([math.log2(i) for i in pd.DataFrame(ont_stats)['len']], bins=50)\n",
    "axs[0][1].set_title(\"Lengths log scaled\")\n",
    "axs[0][1].set_xlabel(\"log2 reconstructed IES length\")\n",
    "axs[0][1].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[1][0].hist(pd.DataFrame(ont_stats).query('score>0.1')['len'], bins=50)\n",
    "axs[1][0].set_title(\"Retention score >0.1\")\n",
    "axs[1][0].set_xlabel(\"Reconstructed IES length (bp)\")\n",
    "axs[1][0].set_ylabel(\"Counts\")\n",
    "\n",
    "axs[1][1].hist([math.log2(i) for i in pd.DataFrame(ont_stats).query('score>0.1')['len']], bins=50)\n",
    "axs[1][1].set_title(\"Retention score >0.1, lengths log scaled\")\n",
    "axs[1][1].set_xlabel(\"log2 reconstructed IES length\")\n",
    "axs[1][1].set_ylabel(\"Counts\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_len_histograms.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_len_histograms.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(8,8), constrained_layout=True, sharex=True)\n",
    "\n",
    "fig.suptitle(\"T. thermophila, BleTIES IES read coverage\")\n",
    "\n",
    "axs[0].hist(pd.DataFrame(pb_stats)['iescov'], bins=[i*10 for i in range(140)])\n",
    "axs[0].set_title(\"PacBio\")\n",
    "axs[0].set_xlabel(\"IES read coverage\")\n",
    "axs[0].set_ylabel(\"Counts\")\n",
    "axs[0].set_xlim(-20,250)\n",
    "\n",
    "axs[1].hist(pd.DataFrame(ont_stats)['iescov'], bins=[i*10 for i in range(140)])\n",
    "axs[1].set_title(\"Nanopore\")\n",
    "axs[1].set_xlabel(\"IES read coverage\")\n",
    "axs[1].set_ylabel(\"Counts\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_ies_readcov.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_ies_readcov.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Joint plot for PacBio IESs length vs. retention score\n",
    "g = sns.JointGrid(\n",
    "    data=pd.DataFrame(pb_stats),\n",
    "    x='score', y='len')\n",
    "g.plot_joint(sns.histplot)\n",
    "g.plot_marginals(sns.histplot, linewidth=0)\n",
    "g.set_axis_labels(xlabel=\"Retention score\", ylabel=\"Length (bp)\")\n",
    "g.fig.tight_layout()\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_len_score_jointplot.png\")\n",
    "plt.savefig(\"plots/tthe_milraa_pb_ies_len_score_jointplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Joint plot for Nanopore IESs length vs. retention score\n",
    "g = sns.JointGrid(\n",
    "    data=pd.DataFrame(ont_stats),\n",
    "    x='score', y='len')\n",
    "g.plot_joint(sns.histplot)\n",
    "g.plot_marginals(sns.histplot, linewidth=0)\n",
    "g.set_axis_labels(xlabel=\"Retention score\", ylabel=\"Length (bp)\")\n",
    "g.fig.tight_layout()\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_len_score_jointplot.png\")\n",
    "plt.savefig(\"plots/tthe_milraa_ont_ies_len_score_jointplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantile</th>\n",
       "      <th>pb_score</th>\n",
       "      <th>ont_score</th>\n",
       "      <th>pb_len</th>\n",
       "      <th>ont_len</th>\n",
       "      <th>pb_cov</th>\n",
       "      <th>ont_cov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15468</td>\n",
       "      <td>0.160460</td>\n",
       "      <td>550.80</td>\n",
       "      <td>670.60</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.31520</td>\n",
       "      <td>0.273500</td>\n",
       "      <td>1177.00</td>\n",
       "      <td>1215.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.47520</td>\n",
       "      <td>0.401300</td>\n",
       "      <td>1969.00</td>\n",
       "      <td>1996.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.60625</td>\n",
       "      <td>0.514900</td>\n",
       "      <td>3081.50</td>\n",
       "      <td>3123.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.71038</td>\n",
       "      <td>0.607680</td>\n",
       "      <td>4498.00</td>\n",
       "      <td>4549.00</td>\n",
       "      <td>101.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.86610</td>\n",
       "      <td>0.757992</td>\n",
       "      <td>7438.98</td>\n",
       "      <td>7621.36</td>\n",
       "      <td>129.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96850</td>\n",
       "      <td>0.840900</td>\n",
       "      <td>11311.00</td>\n",
       "      <td>10684.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1510.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantile  pb_score  ont_score    pb_len   ont_len  pb_cov  ont_cov\n",
       "0      0.00   0.00080   0.000900     14.00     13.00     1.0      3.0\n",
       "1      0.10   0.15468   0.160460    550.80    670.60    20.0     21.0\n",
       "2      0.25   0.31520   0.273500   1177.00   1215.00    40.0     35.0\n",
       "3      0.50   0.47520   0.401300   1969.00   1996.00    63.0     52.0\n",
       "4      0.75   0.60625   0.514900   3081.50   3123.00    85.0     68.0\n",
       "5      0.90   0.71038   0.607680   4498.00   4549.00   101.0     81.0\n",
       "6      0.99   0.86610   0.757992   7438.98   7621.36   129.0    103.0\n",
       "7      1.00   0.96850   0.840900  11311.00  10684.00   562.0   1510.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for MILRAA IES scores and lengths\n",
    "# (not filtered)\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1]\n",
    "pd.DataFrame(\n",
    "    zip(quants, \n",
    "        np.quantile(pd.DataFrame(pb_stats)['score'],q=quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats)['score'],q=quants),\n",
    "        np.quantile(pd.DataFrame(pb_stats)['len'],q=quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats)['len'],q=quants),\n",
    "        np.quantile(pd.DataFrame(pb_stats)['iescov'],q=quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats)['iescov'],q=quants)),\n",
    "    columns=['quantile','pb_score','ont_score','pb_len','ont_len','pb_cov','ont_cov']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare IESs predicted from both PacBio and Nanopore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ONT predicted IESs:\n",
      "8237\n",
      "Number of ONT predictions with score > 0.1\n",
      "7928\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of ONT predicted IESs:\")\n",
    "print(len(ont))\n",
    "print(\"Number of ONT predictions with score > 0.1\")\n",
    "print(len([i for i in ont_stats if i['score'] > 0.1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of PacBio predicted IESs:\n",
      "8459\n",
      "Number of PacBio predictions with score > 0.1\n",
      "7930\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of PacBio predicted IESs:\")\n",
    "print(len(pb))\n",
    "print(\"Number of PacBio predictions with score > 0.1\")\n",
    "print(len([i for i in pb_stats if i['score'] > 0.1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index IESs by coordinate\n",
    "# Keeping only IESs with score > 0.1\n",
    "# ------------------------\n",
    "# PacBio\n",
    "pb_pos_dict = defaultdict(dict)\n",
    "for i in pb:\n",
    "    if float(i.score) > 0.1:\n",
    "        pb_pos_dict[i.chrom][int(i.start)] = i\n",
    "# Nanopore\n",
    "ont_pos_dict = defaultdict(dict)\n",
    "for i in ont:\n",
    "    if float(i.score) > 0.1:\n",
    "        ont_pos_dict[i.chrom][int(i.start)] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joint dict\n",
    "joint = []\n",
    "# Compare coordinates\n",
    "for chrom in pb_pos_dict:\n",
    "    for start in pb_pos_dict[chrom]:\n",
    "        if chrom in ont_pos_dict:\n",
    "            # +/- 5 bp\n",
    "            for x in range(start - 50, start + 50):\n",
    "                if x in ont_pos_dict[chrom]:\n",
    "                    distdiff = start - x\n",
    "                    joint.append({\"pb\" : pb_pos_dict[chrom][start],\n",
    "                                  \"ont\" : ont_pos_dict[chrom][x],\n",
    "                                  \"distdiff\" : distdiff})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of IESs within 50 bp of each other\n",
      "7574\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of IESs within 50 bp of each other\")\n",
    "print(len(joint))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pairwise alignment comparison to get percentage IDs\n",
    "joint_stats = []\n",
    "for i in joint:\n",
    "    i_pb_seq = pb_seq[i['pb'].name]\n",
    "    i_ont_seq = ont_seq[i['ont'].name]\n",
    "    alnscore = (pairwise2.align.globalxx(\n",
    "        i_pb_seq.seq,\n",
    "        i_ont_seq.seq,\n",
    "        score_only=True))\n",
    "    joint_stats.append({'pb' : i['pb'].name,\n",
    "                        'ont' : i['ont'].name,\n",
    "                        'pb_len' : int(i['pb'].attrs['IES_length']),\n",
    "                        'ont_len' : int(i['ont'].attrs['IES_length']),\n",
    "                        'alnscore' : alnscore,\n",
    "                        'distdiff' : i['distdiff']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pb</th>\n",
       "      <th>ont</th>\n",
       "      <th>pb_len</th>\n",
       "      <th>ont_len</th>\n",
       "      <th>alnscore</th>\n",
       "      <th>distdiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_7588_2410</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_7588_2399</td>\n",
       "      <td>2410</td>\n",
       "      <td>2399</td>\n",
       "      <td>2381.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_33899_1348</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_33899_1346</td>\n",
       "      <td>1348</td>\n",
       "      <td>1346</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_35277_539</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_35280_494</td>\n",
       "      <td>539</td>\n",
       "      <td>494</td>\n",
       "      <td>488.0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_37584_2103</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_37584_2079</td>\n",
       "      <td>2103</td>\n",
       "      <td>2079</td>\n",
       "      <td>2074.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_44621_3016</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_44622_2974</td>\n",
       "      <td>3016</td>\n",
       "      <td>2974</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         pb  \\\n",
       "0   BREAK_POINTS_SUBREADS_chr_001_7588_2410   \n",
       "1  BREAK_POINTS_SUBREADS_chr_001_33899_1348   \n",
       "2   BREAK_POINTS_SUBREADS_chr_001_35277_539   \n",
       "3  BREAK_POINTS_SUBREADS_chr_001_37584_2103   \n",
       "4  BREAK_POINTS_SUBREADS_chr_001_44621_3016   \n",
       "\n",
       "                                        ont  pb_len  ont_len  alnscore  \\\n",
       "0   BREAK_POINTS_SUBREADS_chr_001_7588_2399    2410     2399    2381.0   \n",
       "1  BREAK_POINTS_SUBREADS_chr_001_33899_1346    1348     1346    1325.0   \n",
       "2   BREAK_POINTS_SUBREADS_chr_001_35280_494     539      494     488.0   \n",
       "3  BREAK_POINTS_SUBREADS_chr_001_37584_2079    2103     2079    2074.0   \n",
       "4  BREAK_POINTS_SUBREADS_chr_001_44622_2974    3016     2974    2950.0   \n",
       "\n",
       "   distdiff  \n",
       "0         0  \n",
       "1         0  \n",
       "2        -3  \n",
       "3         0  \n",
       "4        -1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(joint_stats).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save comparison stats as TSV to avoid re-running pairwise alignments\n",
    "pd.DataFrame(joint_stats).to_csv(\"tthe_milraa_ies_comparison.tsv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in joint_stats:\n",
    "    # Calculate percentage ID as alnscore (number of matches) / longer sequence\n",
    "    longest = max([i['pb_len'], i['ont_len']])\n",
    "    i['pid'] = i['alnscore'] / longest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in joint_stats:\n",
    "    # Number mismatches is the difference between shortest of the two \n",
    "    # original sequences and the number of matching bases\n",
    "    shortest = min([i['pb_len'], i['ont_len']])\n",
    "    i['mm'] = shortest - i['alnscore']\n",
    "    # Number of indels is difference in two sequence lengths\n",
    "    i['indel'] = i['pb_len'] - i['ont_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12,8), constrained_layout=True)\n",
    "fig.suptitle(\"Comparison of BleTIES IES predictions from PacBio vs. Nanopore data\")\n",
    "\n",
    "axs[0][0].hist([i['pid'] for i in joint_stats], bins=[i/100 for i in range(1,101)])\n",
    "axs[0][0].set_title(\"Sequence identity\")\n",
    "axs[0][0].set_ylabel(\"Count\")\n",
    "\n",
    "axs[0][1].hist([i['mm'] for i in joint_stats], bins=100)\n",
    "axs[0][1].set_title(\"Number of mismatches\")\n",
    "axs[0][1].set_ylabel(\"Count\")\n",
    "\n",
    "axs[1][0].hist([i['indel'] for i in joint_stats if i['indel'] > -200 and i['indel'] < 200], bins=100)\n",
    "axs[1][0].set_title(\"Length difference (relative to Nanopore)\")\n",
    "axs[1][0].set_ylabel(\"Count\")\n",
    "\n",
    "axs[1][1].hist([i['distdiff'] for i in joint_stats], bins=50)\n",
    "axs[1][1].set_title(\"Difference in predicted insert position\")\n",
    "axs[1][1].set_ylabel(\"Count\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_pb_vs_ont_ies_comparison.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_pb_vs_ont_ies_comparison.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantiles</th>\n",
       "      <th>pid</th>\n",
       "      <th>indel</th>\n",
       "      <th>distdiff</th>\n",
       "      <th>distdiff_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010811</td>\n",
       "      <td>-3514.00</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.913733</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.977907</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.983797</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.987308</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.989786</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.993784</td>\n",
       "      <td>1080.08</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3646.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantiles       pid    indel  distdiff  distdiff_abs\n",
       "0       0.00  0.010811 -3514.00     -34.0           0.0\n",
       "1       0.10  0.913733     1.00      -1.0           0.0\n",
       "2       0.25  0.977907     8.00       0.0           0.0\n",
       "3       0.50  0.983797    17.00       0.0           0.0\n",
       "4       0.75  0.987308    30.00       0.0           0.0\n",
       "5       0.90  0.989786    68.00       0.0           4.0\n",
       "6       0.99  0.993784  1080.08       9.0          11.0\n",
       "7       1.00  1.000000  3646.00      20.0          34.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for comparison of Pacbio and Nanopore MILRAA IESs\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]\n",
    "pd.DataFrame(\n",
    "    zip(quants,\n",
    "        np.quantile(pd.DataFrame(joint_stats)['pid'], quants),\n",
    "        np.quantile(pd.DataFrame(joint_stats)['indel'], quants),\n",
    "        np.quantile(pd.DataFrame(joint_stats)['distdiff'], quants),\n",
    "        np.quantile(pd.DataFrame(joint_stats)['distdiff'].apply(math.fabs), quants)),\n",
    "    columns=['quantiles','pid','indel','distdiff','distdiff_abs']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get coordinates of published IESs in latest MAC assembly\n",
    "\n",
    "MAC assembly used for BleTIES predictions was published 2020, and MIC assembly from 2016, both available on ciliate.org. IESs are annotated in the MIC assembly, but a mapping of IES coordinates in the MAC reference is not available.\n",
    "\n",
    "To compare our MILRAA IES predictions to the published IESs, we need to make our own mapping.\n",
    "\n",
    "Procedure:\n",
    "\n",
    " * Extract flanking +/- 500 bp from each IES in the MIC assembly, and concatenate\n",
    " * Align these IES flanking regions against the MAC genome with blat\n",
    " * Pick the best scoring hit for each query:\n",
    "   * If match is in one \"block\", and the block has length 1000, pick first tstart + 500 as coordinate\n",
    "   * If match is in two \"blocks\", pick second tstart as IES coordinate\n",
    "   * If match is in three or four blocks:\n",
    "     * If first block is closer in length to 500 bp than last block, pick second tstart as coordinate\n",
    "     * Else if last block is closer in length to 500 bp than first block, pick last tstart as coordinate\n",
    "\n",
    "Because the IES excision is imprecise in Tetrahymena, the concatenated flanking regions of each IES would often align in two \"blocks\" in the MAC reference, because the excision boundary is not exactly the same each time so there is an indel relative to the reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MIC reference genome and IES GFF\n",
    "ies_gff = pbt.BedTool(\"ref/mic/mic.ies.gff\")\n",
    "mic_ref = SeqIO.to_dict(SeqIO.parse(\"ref/mic/2016_mic.genome.fasta\", \"fasta\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get flanking 500 bp on either side of each IES in the MIC assembly\n",
    "with open(\"ref/mic/ies_flanking_500bp.fasta\", \"w\") as fh:\n",
    "    for i in ies_gff:\n",
    "        if re.match(\"IES\", i.name):\n",
    "            # Flanking 500 bp around the IES\n",
    "            seg1 = mic_ref[i.chrom][i.start-500:i.start]\n",
    "            seg2 = mic_ref[i.chrom][i.end:i.end+500]\n",
    "            fh.write(\">\" + i.name + \".flank\")\n",
    "            fh.write(\"\\n\")\n",
    "            fh.write(str(seg1.seq + seg2.seq))\n",
    "            fh.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7544"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write IES sequences\n",
    "ies_seqs = []\n",
    "for i in ies_gff:\n",
    "    if re.match(\"IES\", i.name):\n",
    "        ies_seq = mic_ref[i.chrom][i.start:i.end]\n",
    "        ies_seq.name = i.name\n",
    "        ies_seq.id = i.name\n",
    "        ies_seq.description = \"\"\n",
    "        ies_seqs.append(ies_seq)\n",
    "SeqIO.write(ies_seqs, \"ref/mic/ies_seqs.fasta\", \"fasta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7544\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "grep -c '>' ref/mic/ies_flanking_500bp.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 103345795 letters in 181 sequences\n",
      "Searched 7544000 bases in 7544 sequences\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# Align the IES flanks against the MAC reference to find corresponding\n",
    "# coordinate for each IES in the MAC\n",
    "blat ref/1-upd-Genome-assembly.fasta ref/mic/ies_flanking_500bp.fasta \\\n",
    "    -fastMap -minScore=500 ref/mic/ies_flanking_500bp.mac_asm.blat.psl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse blat output and retain only best-scoring hit for each query\n",
    "pslhdr = ['match', 'mismatch', 'repmatch', 'ns',\n",
    "          'qgapcount', 'qgapbases', 'tgapcount', 'tgapbases','strand',\n",
    "          'qname','qsize','qstart','qend',\n",
    "          'tname','tsize','tstart','tend',\n",
    "          'blockcount','blocksizes','qstarts','tstarts']\n",
    "psldict = defaultdict(dict)\n",
    "fh = open(\"ref/mic/ies_flanking_500bp.mac_asm.blat.psl\", \"r\")\n",
    "for i in range(5):\n",
    "    fh.readline() # Strip header lines\n",
    "for line in fh:\n",
    "    spl = line.rstrip().split(\"\\t\")\n",
    "    rec = dict(zip(pslhdr, spl))\n",
    "    # Replace lower-scoring hit if already present\n",
    "    if psldict[rec['qname']]:\n",
    "        if int(rec['match']) > int(psldict[rec['qname']]['match']):\n",
    "            psldict[rec['qname']] = rec\n",
    "    else:\n",
    "        psldict[rec['qname']] = rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Find IES coordinate in MAC based on the alignment blocks\n",
    "# Also record the orientation of the MIC->MAC hit because the IES sequence will need to be \n",
    "# reverse complemented to match the orientation in the MAC reference\n",
    "ies_mac_coords = []\n",
    "edge_cases = []\n",
    "for rec in list(psldict.values()):\n",
    "    # Initialize/clear variable to hold coordinate\n",
    "    coord = None\n",
    "    blocks = [int(i) for i in rec['blocksizes'].rstrip(',').split(',')]\n",
    "    tstarts = [int(i) for i in rec['tstarts'].rstrip(',').split(',')]\n",
    "    # Calculate offset for queries that do not match along entire length (e.g. Ns)\n",
    "    if rec['strand'] == '+':\n",
    "        start_offset = int(rec['qstart'])\n",
    "        end_offset = 1000 - int(rec['qend'])\n",
    "    else:\n",
    "        start_offset = 1000 - int(rec['qend'])\n",
    "        end_offset = int(rec['qstart'])\n",
    "    # Find the likely insert position based on mapped blocks:\n",
    "    #\n",
    "    # If aligns in a single block\n",
    "    if len(blocks) == 1:\n",
    "        # if block is total less than 10 bp from 1000 bp (including offsets)\n",
    "        if 1000 - (blocks[0] + start_offset + end_offset) < 10:\n",
    "            coord = tstarts[0] + (500 - start_offset)\n",
    "        else:\n",
    "            edge_cases.append(rec)\n",
    "            #print(\"\\t\".join([rec[i] for i in ['strand','qstart','qend','qname','blocksizes','qstarts','tstarts']]))\n",
    "    # If splits neatly into two blocks\n",
    "    elif len(blocks) == 2:\n",
    "        coord = tstarts[1]\n",
    "    # If three or four blocks\n",
    "    elif len(blocks) == 3 or len(blocks) == 4:\n",
    "        # check if the first or last block is closer to 500 bp, and > 480 bp\n",
    "        blocks[0] += start_offset\n",
    "        blocks[2] += end_offset\n",
    "        # last block is closer to 500 bp\n",
    "        if (500 - blocks[-1]) <= (500 - blocks[0]) and (500 - blocks[-1]) < 20:\n",
    "            coord = tstarts[-1]\n",
    "        elif (500 - blocks[0]) < (500 - blocks[-1]) and (500 - blocks[0]) < 20:\n",
    "            coord = tstarts[1]\n",
    "        else:\n",
    "            edge_cases.append(rec)\n",
    "            #print(\"\\t\".join([rec[i] for i in ['strand','qstart','qend','qname','blocksizes','qstarts','tstarts']]))\n",
    "    else:\n",
    "        edge_cases.append(rec)\n",
    "        #print(\"\\t\".join([rec[i] for i in ['strand','qstart','qend','qname','blocksizes','qstarts','tstarts']]))\n",
    "    if coord:\n",
    "        ies_mac_coords.append({'ies' : rec['qname'].split('.')[0],\n",
    "                               'chrom' : rec['tname'],\n",
    "                               'strand' : rec['strand'],\n",
    "                               'coord' : coord})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write BED file of IES coordinates in the MAC genome\n",
    "with open(\"ref/mic/ies_relative_to_mac.bed\", \"w\") as fh:\n",
    "    for i in ies_mac_coords:\n",
    "        fh.write(\"\\t\".join([str(j) for j in [i['chrom'], i['coord'], i['coord'], i['ies']]]))\n",
    "        fh.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ies</th>\n",
       "      <th>chrom</th>\n",
       "      <th>strand</th>\n",
       "      <th>coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>IES-00310-r17</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>33898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>IES-00311-r18</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>44621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>IES-00312-r11</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>82108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>IES-00313-r16</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>91467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>IES-00314-r16</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>94128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ies    chrom strand  coord\n",
       "123  IES-00310-r17  chr_001      -  33898\n",
       "122  IES-00311-r18  chr_001      -  44621\n",
       "121  IES-00312-r11  chr_001      -  82108\n",
       "120  IES-00313-r16  chr_001      -  91467\n",
       "119  IES-00314-r16  chr_001      -  94128"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(ies_mac_coords).query('chrom==\"chr_001\"').sort_values('coord').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of published IES annotations in MIC genome:\n",
      "7544\n",
      "IESs with corresponding MAC coordinates identified\n",
      "7468\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of published IES annotations in MIC genome:\")\n",
    "print(len(ies_gff) - 5) # first five features in GFF file are actually chromosome lengths\n",
    "print(\"IESs with corresponding MAC coordinates identified\")\n",
    "print(len(ies_mac_coords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find closest matching published IES to the MILRAA IESs\n",
    "# (only MILRAA IESs with score > 0.1)\n",
    "# ------------------------------------------------------\n",
    "# Nanopore\n",
    "annot_ies_ont_besthit = []\n",
    "annot_ies_ont_nohit = []\n",
    "for i in ies_mac_coords:\n",
    "    if i['chrom'] in ont_pos_dict:\n",
    "        hit = 0\n",
    "        for x in range(i['coord'] - 10, i['coord'] + 10):\n",
    "            if x in ont_pos_dict[i['chrom']]:\n",
    "                distdiff = i['coord'] - x\n",
    "                annot_ies_ont_besthit.append({'ont' : ont_pos_dict[i['chrom']][x],\n",
    "                                              'ies_name' : i['ies'],\n",
    "                                              'chrom' : i['chrom'],\n",
    "                                              'ies_coord' : i['coord'],\n",
    "                                              'strand' : i['strand'],\n",
    "                                              'distdiff' : distdiff\n",
    "                                             })\n",
    "                hit += 1\n",
    "        if hit == 0:\n",
    "            annot_ies_ont_nohit.append(i)\n",
    "# PacBio\n",
    "annot_ies_pb_besthit = []\n",
    "annot_ies_pb_nohit = []\n",
    "for i in ies_mac_coords:\n",
    "    if i['chrom'] in pb_pos_dict:\n",
    "        hit = 0\n",
    "        for x in range(i['coord'] - 10, i['coord'] + 10):\n",
    "            if x in pb_pos_dict[i['chrom']]:\n",
    "                distdiff = i['coord'] - x\n",
    "                annot_ies_pb_besthit.append({'pb' : pb_pos_dict[i['chrom']][x],\n",
    "                                              'ies_name' : i['ies'],\n",
    "                                              'chrom' : i['chrom'],\n",
    "                                              'ies_coord' : i['coord'],\n",
    "                                              'strand' : i['strand'],\n",
    "                                              'distdiff' : distdiff\n",
    "                                             })\n",
    "                hit += 1\n",
    "        if hit == 0:\n",
    "            annot_ies_pb_nohit.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ies</th>\n",
       "      <th>chrom</th>\n",
       "      <th>strand</th>\n",
       "      <th>coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IES-04342-r12</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>208408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IES-04359-r15</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>433256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IES-04363-r12</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>467330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>IES-04371-r14</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>594645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>IES-04377-r10</td>\n",
       "      <td>chr_001</td>\n",
       "      <td>-</td>\n",
       "      <td>660176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ies    chrom strand   coord\n",
       "35  IES-04342-r12  chr_001      -  208408\n",
       "34  IES-04359-r15  chr_001      -  433256\n",
       "33  IES-04363-r12  chr_001      -  467330\n",
       "32  IES-04371-r14  chr_001      -  594645\n",
       "31  IES-04377-r10  chr_001      -  660176"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(annot_ies_pb_nohit).query('chrom == \"chr_001\"').sort_values('coord').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary stats for published vs. MILRAA IESs\n",
    "\n",
    "Eyeballing the existing annotated IESs that were not predicted by BleTIES, it appears that the mapper called these as clips rather than inserts. It is possible that the IESs were simply too long to be spanned by the reads, a limiting case for BleTIES, however MILCOR can be used to bin those reads out for separate assembly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "annot_ies_pb_nohit_list = [i['ies'] for i in annot_ies_pb_nohit]\n",
    "annot_ies_ont_nohit_list = [i['ies'] for i in annot_ies_ont_nohit]\n",
    "# Dicts mapping IES name in MIC annotation to best hit in BleTIES IES predictions\n",
    "annot_ies_pb_besthit_dict = {i['ies_name'] : \n",
    "                                 {'pb_name' : i['pb'].name,\n",
    "                                  'strand' : i['strand']} \n",
    "                             for i in annot_ies_pb_besthit}\n",
    "annot_ies_ont_besthit_dict = {i['ies_name'] : \n",
    "                                 {'ont_name' : i['ont'].name,\n",
    "                                  'strand' : i['strand']}\n",
    "                              for i in annot_ies_ont_besthit}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BREAK_POINTS_SUBREADS_chr_023_3325_3904'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_ies_pb_besthit[0]['pb'].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gather stats from comparing MILRAA IESs to published IESs\n",
    "annot_ies_stats = []\n",
    "for i in ies_gff:\n",
    "    if re.match(\"IES\", i.name):\n",
    "        annot_ies_stats.append({'ies' : i.name,\n",
    "                                'chrom' : i.chrom,\n",
    "                                'start' : int(i.start),\n",
    "                                'end' : int(i.end),\n",
    "                                'len' : int(i.end) - int(i.start),\n",
    "                                'in_pb' : i.name in annot_ies_pb_besthit_dict,\n",
    "                                'pb_name' : annot_ies_pb_besthit_dict[i.name]['pb_name'] if i.name in annot_ies_pb_besthit_dict else None,\n",
    "                                'pb_strand' : annot_ies_pb_besthit_dict[i.name]['strand'] if i.name in annot_ies_pb_besthit_dict else None,\n",
    "                                'in_ont' : i.name in annot_ies_ont_besthit_dict,\n",
    "                                'ont_name' : annot_ies_ont_besthit_dict[i.name]['ont_name'] if i.name in annot_ies_ont_besthit_dict else None,\n",
    "                                'ont_strand' : annot_ies_ont_besthit_dict[i.name]['strand'] if i.name in annot_ies_ont_besthit_dict else None\n",
    "                               })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Compare published IES sequences extracted from 2016 MIC assembly version\n",
    "# to MILRAA reconstructions, pairwise alignment and %id etc.\n",
    "\n",
    "# Long running step, if rerunning notebook load saved file instead\n",
    "\n",
    "ies_seqs_dict = {i.id : i for i in ies_seqs}\n",
    "\n",
    "for i in annot_ies_stats:\n",
    "    if i['pb_name']:\n",
    "        ies_seq = ies_seqs_dict[i['ies']]\n",
    "        # Reverse complement MIC IES sequence if match on\n",
    "        # MAC is on - strand\n",
    "        if i['pb_strand'] == '-':\n",
    "            ies_seq = ies_seq.reverse_complement()\n",
    "        ies_pb_seq = pb_seq[i['pb_name']]\n",
    "        alnscore = (pairwise2.align.globalxx(\n",
    "            ies_seq.seq,\n",
    "            ies_pb_seq.seq,\n",
    "            score_only=True))\n",
    "        i['pb_alnscore'] = alnscore\n",
    "        i['pb_len'] = len(ies_pb_seq)\n",
    "    else:\n",
    "        i['pb_alnscore'] = None\n",
    "        i['pb_len'] = None\n",
    "    # Check for ONT IES\n",
    "    if i['ont_name']:\n",
    "        ies_seq = ies_seqs_dict[i['ies']]\n",
    "        # Reverse complement MIC IES sequence if its match on\n",
    "        # MAC is on - strand\n",
    "        if i['ont_strand'] == '-':\n",
    "            ies_seq = ies_seq.reverse_complement()\n",
    "        ies_ont_seq = ont_seq[i['ont_name']]\n",
    "        alnscore = (pairwise2.align.globalxx(\n",
    "            ies_seq.seq,\n",
    "            ies_ont_seq.seq,\n",
    "            score_only=True))\n",
    "        i['ont_alnscore'] = alnscore\n",
    "        i['ont_len'] = len(ies_ont_seq)\n",
    "    else:\n",
    "        i['ont_alnscore'] = None\n",
    "        i['ont_len'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate percent ID, mismatch bp, and indel bp\n",
    "for i in annot_ies_stats:\n",
    "    if i['pb_alnscore']:\n",
    "        i['pb_pid'] = i['pb_alnscore']/max([i['len'],i['pb_len']])\n",
    "        i['pb_mm'] = i['len'] - i['pb_alnscore']\n",
    "        i['pb_indel'] = i['pb_len'] - i['len']\n",
    "    else:\n",
    "        i['pb_pid'] = None\n",
    "        i['pb_mm'] = None\n",
    "        i['pb_indel'] = None\n",
    "    if i['ont_alnscore']:\n",
    "        i['ont_pid'] = i['ont_alnscore']/max([i['len'],i['ont_len']])\n",
    "        i['ont_mm'] = i['len'] - i['ont_alnscore']\n",
    "        i['ont_indel'] = i['ont_len'] - i['len']\n",
    "    else:\n",
    "        i['ont_pid'] = None\n",
    "        i['ont_mm'] = None\n",
    "        i['ont_indel'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to file to avoid recalculation if rerunning notebook\n",
    "annot_ies_stats_df = pd.DataFrame(annot_ies_stats)\n",
    "# Transform log2 lengths\n",
    "annot_ies_stats_df['log2_len'] = annot_ies_stats_df['len'].apply(math.log2)\n",
    "annot_ies_stats_df.to_csv(\"tthe_published_vs_milraa_ies.tsv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ont_ies_annot_nohit = [rec for rec in ont_stats \n",
    "                       if rec['id'] not in set([i['ont'].name for i in annot_ies_ont_besthit]) \n",
    "                       and rec['score'] > 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_ies_annot_nohit = [rec for rec in pb_stats \n",
    "                      if rec['id'] not in set([i['pb'].name for i in annot_ies_pb_besthit])\n",
    "                      and rec['score'] > 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NB: MILRAA IESs counting only those with score > 0.1\n",
      "\n",
      "PacBio:\n",
      "Total MILRAA IESs predicted: 8459\n",
      "MILRAA IESs with retention score >0.1: 7930\n",
      "MILRAA IESs with published IESs within 10 bp: 5105\n",
      "MILRAA IESs no published IES within 10 bp: 2825\n",
      "Published IESs with MILRAA IES within 10 bp: 5105\n",
      "Published IESs with no MILRAA IES within 10 bp: 2363\n",
      "\n",
      "Nanopore\n",
      "Total MILRAA IESs predicted: 8237\n",
      "MILRAA IESs with retention score >0.1: 7928\n",
      "MILRAA IESs with published IESs within 10 bp: 5064\n",
      "MILRAA IESs no published IES within 10 bp: 2864\n",
      "Published IESs with MILRAA IES within 10 bp: 5056\n",
      "Published IESs with no MILRAA IES within 10 bp: 2412\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "NB: MILRAA IESs counting only those with score > 0.1\n",
    "\n",
    "PacBio:\n",
    "Total MILRAA IESs predicted: {str(len(pb_stats))}\n",
    "MILRAA IESs with retention score >0.1: {str(len([i for i in pb_stats if i['score'] > 0.1]))}\n",
    "MILRAA IESs with published IESs within 10 bp: {str(len(set([i['pb'].name for i in annot_ies_pb_besthit])))}\n",
    "MILRAA IESs no published IES within 10 bp: {str(len(pb_ies_annot_nohit))}\n",
    "Published IESs with MILRAA IES within 10 bp: {str(len(annot_ies_pb_besthit_dict))}\n",
    "Published IESs with no MILRAA IES within 10 bp: {str(len(annot_ies_pb_nohit))}\n",
    "\n",
    "Nanopore\n",
    "Total MILRAA IESs predicted: {str(len(ont_stats))}\n",
    "MILRAA IESs with retention score >0.1: {str(len([i for i in ont_stats if i['score'] > 0.1]))}\n",
    "MILRAA IESs with published IESs within 10 bp: {str(len(set([i['ont'].name for i in annot_ies_ont_besthit])))}\n",
    "MILRAA IESs no published IES within 10 bp: {str(len(ont_ies_annot_nohit))}\n",
    "Published IESs with MILRAA IES within 10 bp: {str(len(annot_ies_ont_besthit_dict))}\n",
    "Published IESs with no MILRAA IES within 10 bp: {str(len(annot_ies_ont_nohit))}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nanopore predictions have two numbers that do not match up:\n",
    "# MILRAA IESs with published IESs within 10 bp: 5064\n",
    "# Published IESs with MILRAA IES within 10 bp: 5056\n",
    "\n",
    "# Check which published IES has more than one MILRAA IES in the vicinity\n",
    "check_dups_ont = defaultdict(list)\n",
    "for i in annot_ies_ont_besthit:\n",
    "    check_dups_ont[i['ies_name']].append(i['ont'].name)\n",
    "dupslist = []\n",
    "for i in check_dups_ont:\n",
    "    if len(check_dups_ont[i]) > 1:\n",
    "        dupslist.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dupslist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'ont': Interval(chr_025:240877-240878),\n",
       "   'ies_name': 'IES-00550-r15',\n",
       "   'chrom': 'chr_025',\n",
       "   'ies_coord': 240881,\n",
       "   'strand': '-',\n",
       "   'distdiff': 4},\n",
       "  'BREAK_POINTS_SUBREADS_chr_025_240878_918'],\n",
       " [{'ont': Interval(chr_025:240887-240888),\n",
       "   'ies_name': 'IES-00550-r15',\n",
       "   'chrom': 'chr_025',\n",
       "   'ies_coord': 240881,\n",
       "   'strand': '-',\n",
       "   'distdiff': -6},\n",
       "  'BREAK_POINTS_SUBREADS_chr_025_240888_1334'],\n",
       " [{'ont': Interval(chr_068:396228-396229),\n",
       "   'ies_name': 'IES-03651-r18',\n",
       "   'chrom': 'chr_068',\n",
       "   'ies_coord': 396229,\n",
       "   'strand': '-',\n",
       "   'distdiff': 1},\n",
       "  'BREAK_POINTS_SUBREADS_chr_068_396229_2514'],\n",
       " [{'ont': Interval(chr_068:396234-396235),\n",
       "   'ies_name': 'IES-03651-r18',\n",
       "   'chrom': 'chr_068',\n",
       "   'ies_coord': 396229,\n",
       "   'strand': '-',\n",
       "   'distdiff': -5},\n",
       "  'BREAK_POINTS_SUBREADS_chr_068_396235_2514'],\n",
       " [{'ont': Interval(chr_071:444112-444113),\n",
       "   'ies_name': 'IES-03404-r5',\n",
       "   'chrom': 'chr_071',\n",
       "   'ies_coord': 444120,\n",
       "   'strand': '+',\n",
       "   'distdiff': 8},\n",
       "  'BREAK_POINTS_SUBREADS_chr_071_444113_1562'],\n",
       " [{'ont': Interval(chr_071:444119-444120),\n",
       "   'ies_name': 'IES-03404-r5',\n",
       "   'chrom': 'chr_071',\n",
       "   'ies_coord': 444120,\n",
       "   'strand': '+',\n",
       "   'distdiff': 1},\n",
       "  'BREAK_POINTS_SUBREADS_chr_071_444120_2575'],\n",
       " [{'ont': Interval(chr_099:2036853-2036854),\n",
       "   'ies_name': 'IES-05760-r11',\n",
       "   'chrom': 'chr_099',\n",
       "   'ies_coord': 2036859,\n",
       "   'strand': '-',\n",
       "   'distdiff': 6},\n",
       "  'BREAK_POINTS_SUBREADS_chr_099_2036854_1165'],\n",
       " [{'ont': Interval(chr_099:2036858-2036859),\n",
       "   'ies_name': 'IES-05760-r11',\n",
       "   'chrom': 'chr_099',\n",
       "   'ies_coord': 2036859,\n",
       "   'strand': '-',\n",
       "   'distdiff': 1},\n",
       "  'BREAK_POINTS_SUBREADS_chr_099_2036859_2130'],\n",
       " [{'ont': Interval(chr_105:2050048-2050049),\n",
       "   'ies_name': 'IES-01287-r9',\n",
       "   'chrom': 'chr_105',\n",
       "   'ies_coord': 2050057,\n",
       "   'strand': '-',\n",
       "   'distdiff': 9},\n",
       "  'BREAK_POINTS_SUBREADS_chr_105_2050049_1553'],\n",
       " [{'ont': Interval(chr_105:2050056-2050057),\n",
       "   'ies_name': 'IES-01287-r9',\n",
       "   'chrom': 'chr_105',\n",
       "   'ies_coord': 2050057,\n",
       "   'strand': '-',\n",
       "   'distdiff': 1},\n",
       "  'BREAK_POINTS_SUBREADS_chr_105_2050057_1558'],\n",
       " [{'ont': Interval(chr_166:2486-2487),\n",
       "   'ies_name': 'IES-01993-r12',\n",
       "   'chrom': 'chr_166',\n",
       "   'ies_coord': 2480,\n",
       "   'strand': '+',\n",
       "   'distdiff': -6},\n",
       "  'BREAK_POINTS_SUBREADS_chr_166_2487_1850'],\n",
       " [{'ont': Interval(chr_166:2488-2489),\n",
       "   'ies_name': 'IES-01993-r12',\n",
       "   'chrom': 'chr_166',\n",
       "   'ies_coord': 2480,\n",
       "   'strand': '+',\n",
       "   'distdiff': -8},\n",
       "  'BREAK_POINTS_SUBREADS_chr_166_2489_1855'],\n",
       " [{'ont': Interval(chr_174:1039746-1039747),\n",
       "   'ies_name': 'IES-04752-r13',\n",
       "   'chrom': 'chr_174',\n",
       "   'ies_coord': 1039741,\n",
       "   'strand': '+',\n",
       "   'distdiff': -5},\n",
       "  'BREAK_POINTS_SUBREADS_chr_174_1039747_401'],\n",
       " [{'ont': Interval(chr_174:1039749-1039750),\n",
       "   'ies_name': 'IES-04752-r13',\n",
       "   'chrom': 'chr_174',\n",
       "   'ies_coord': 1039741,\n",
       "   'strand': '+',\n",
       "   'distdiff': -8},\n",
       "  'BREAK_POINTS_SUBREADS_chr_174_1039750_393'],\n",
       " [{'ont': Interval(chr_179:2908872-2908873),\n",
       "   'ies_name': 'IES-05258-r13',\n",
       "   'chrom': 'chr_179',\n",
       "   'ies_coord': 2908881,\n",
       "   'strand': '+',\n",
       "   'distdiff': 9},\n",
       "  'BREAK_POINTS_SUBREADS_chr_179_2908873_1950'],\n",
       " [{'ont': Interval(chr_179:2908880-2908881),\n",
       "   'ies_name': 'IES-05258-r13',\n",
       "   'chrom': 'chr_179',\n",
       "   'ies_coord': 2908881,\n",
       "   'strand': '+',\n",
       "   'distdiff': 1},\n",
       "  'BREAK_POINTS_SUBREADS_chr_179_2908881_1714']]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[i, i['ont'].name] for i in annot_ies_ont_besthit if i['ies_name'] in dupslist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lengths of published vs. MILRAA IESs\n",
    "\n",
    "We hypothesize that longer IESs (>10 kbp) may not be assembled well by MILRAA because the reads are not sufficient to span them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2, figsize=(12,8), constrained_layout=True, sharey=True)\n",
    "\n",
    "fig.suptitle(\"Length histograms of published IESs\")\n",
    "\n",
    "p1 = sns.histplot(data=annot_ies_stats_df,\n",
    "                 x='len', hue='in_pb', multiple='dodge',\n",
    "                 binwidth=500, linewidth=0,\n",
    "                 ax=axs[0][0])\n",
    "p1.set_title(\"Compared with PacBio IES predictions\")\n",
    "p1.legend(title = \"In MILRAA PacBio predictions\", labels=['True','False'])\n",
    "\n",
    "p2 = sns.histplot(data=annot_ies_stats_df,\n",
    "                  x='len', hue='in_ont', multiple='dodge',\n",
    "                  binwidth=500, linewidth=0,\n",
    "                  ax=axs[0][1])\n",
    "p2.set_title(\"Compared with Nanopore IES predictions\")\n",
    "p2.legend(title = \"In MILRAA Nanopore predictions\", labels=['True','False'])\n",
    "\n",
    "# Log scaled\n",
    "p3 = sns.histplot(data=annot_ies_stats_df,\n",
    "                  x='log2_len', hue='in_pb', multiple='dodge',\n",
    "                  binwidth=0.2, linewidth=0,\n",
    "                  ax=axs[1][0])\n",
    "p3.legend(title = \"In MILRAA PacBio predictions\", labels=['True','False'])\n",
    "\n",
    "p4 = sns.histplot(data=annot_ies_stats_df,\n",
    "                  x='log2_len', hue='in_ont', multiple='dodge',\n",
    "                  binwidth=0.2, linewidth=0,\n",
    "                  ax=axs[1][1])\n",
    "p4.legend(title = \"In MILRAA Nanopore predictions\", labels=['True','False'])\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_vs_published_ies_len_histograms.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_vs_published_ies_len_histograms.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantiles</th>\n",
       "      <th>len_ont</th>\n",
       "      <th>len_notont</th>\n",
       "      <th>len_pb</th>\n",
       "      <th>len_notpb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>131.00</td>\n",
       "      <td>388.00</td>\n",
       "      <td>131.00</td>\n",
       "      <td>352.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>832.00</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>833.70</td>\n",
       "      <td>2434.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1330.50</td>\n",
       "      <td>3363.00</td>\n",
       "      <td>1334.75</td>\n",
       "      <td>3547.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>2113.00</td>\n",
       "      <td>5330.00</td>\n",
       "      <td>2095.00</td>\n",
       "      <td>5564.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>3265.50</td>\n",
       "      <td>8351.00</td>\n",
       "      <td>3204.00</td>\n",
       "      <td>8453.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>4553.60</td>\n",
       "      <td>12004.80</td>\n",
       "      <td>4483.20</td>\n",
       "      <td>12108.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>7144.42</td>\n",
       "      <td>22999.04</td>\n",
       "      <td>6993.62</td>\n",
       "      <td>23020.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>9860.00</td>\n",
       "      <td>43428.00</td>\n",
       "      <td>9860.00</td>\n",
       "      <td>43428.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantiles  len_ont  len_notont   len_pb  len_notpb\n",
       "0       0.00   131.00      388.00   131.00     352.00\n",
       "1       0.10   832.00     2230.00   833.70    2434.00\n",
       "2       0.25  1330.50     3363.00  1334.75    3547.75\n",
       "3       0.50  2113.00     5330.00  2095.00    5564.00\n",
       "4       0.75  3265.50     8351.00  3204.00    8453.75\n",
       "5       0.90  4553.60    12004.80  4483.20   12108.00\n",
       "6       0.99  7144.42    22999.04  6993.62   23020.60\n",
       "7       1.00  9860.00    43428.00  9860.00   43428.00"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for lengths of published IESs predicted vs. not predicted by MILRAA\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]\n",
    "pd.DataFrame(\n",
    "    zip(quants,\n",
    "        np.quantile(annot_ies_stats_df.query('in_ont')['len'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('not in_ont')['len'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_pb')['len'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('not in_pb')['len'],quants),\n",
    "       ),\n",
    "    columns=['quantiles',\n",
    "             'len_ont','len_notont',\n",
    "             'len_pb','len_notpb']\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ies</th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>len</th>\n",
       "      <th>in_pb</th>\n",
       "      <th>pb_name</th>\n",
       "      <th>pb_strand</th>\n",
       "      <th>in_ont</th>\n",
       "      <th>ont_name</th>\n",
       "      <th>...</th>\n",
       "      <th>pb_len</th>\n",
       "      <th>ont_alnscore</th>\n",
       "      <th>ont_len</th>\n",
       "      <th>pb_pid</th>\n",
       "      <th>pb_mm</th>\n",
       "      <th>pb_indel</th>\n",
       "      <th>ont_pid</th>\n",
       "      <th>ont_mm</th>\n",
       "      <th>ont_indel</th>\n",
       "      <th>log2_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IES-04625-r7</td>\n",
       "      <td>chr1</td>\n",
       "      <td>29183</td>\n",
       "      <td>32709</td>\n",
       "      <td>3526</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.783817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IES-04624-r11</td>\n",
       "      <td>chr1</td>\n",
       "      <td>62448</td>\n",
       "      <td>66339</td>\n",
       "      <td>3891</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_023_3325_3904</td>\n",
       "      <td>-</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_023_3325_3864</td>\n",
       "      <td>...</td>\n",
       "      <td>3904.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>3864.0</td>\n",
       "      <td>0.993084</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.988178</td>\n",
       "      <td>46.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>11.925925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IES-05548-r12</td>\n",
       "      <td>chr1</td>\n",
       "      <td>100522</td>\n",
       "      <td>102612</td>\n",
       "      <td>2090</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_1449001_2036</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.950239</td>\n",
       "      <td>104.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.029287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IES-05549-r13</td>\n",
       "      <td>chr1</td>\n",
       "      <td>110531</td>\n",
       "      <td>115929</td>\n",
       "      <td>5398</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_1444281_5313</td>\n",
       "      <td>-</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_1444281_5280</td>\n",
       "      <td>...</td>\n",
       "      <td>5313.0</td>\n",
       "      <td>5260.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>0.979807</td>\n",
       "      <td>109.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>0.974435</td>\n",
       "      <td>138.0</td>\n",
       "      <td>-118.0</td>\n",
       "      <td>12.398209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IES-05550-r10</td>\n",
       "      <td>chr1</td>\n",
       "      <td>122262</td>\n",
       "      <td>126432</td>\n",
       "      <td>4170</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_1437950_4172</td>\n",
       "      <td>-</td>\n",
       "      <td>True</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_chr_001_1437950_4158</td>\n",
       "      <td>...</td>\n",
       "      <td>4172.0</td>\n",
       "      <td>4133.0</td>\n",
       "      <td>4158.0</td>\n",
       "      <td>0.997363</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.991127</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>12.025832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ies chrom   start     end   len  in_pb  \\\n",
       "0   IES-04625-r7  chr1   29183   32709  3526  False   \n",
       "1  IES-04624-r11  chr1   62448   66339  3891   True   \n",
       "2  IES-05548-r12  chr1  100522  102612  2090   True   \n",
       "3  IES-05549-r13  chr1  110531  115929  5398   True   \n",
       "4  IES-05550-r10  chr1  122262  126432  4170   True   \n",
       "\n",
       "                                      pb_name pb_strand  in_ont  \\\n",
       "0                                        None      None   False   \n",
       "1     BREAK_POINTS_SUBREADS_chr_023_3325_3904         -    True   \n",
       "2  BREAK_POINTS_SUBREADS_chr_001_1449001_2036         -   False   \n",
       "3  BREAK_POINTS_SUBREADS_chr_001_1444281_5313         -    True   \n",
       "4  BREAK_POINTS_SUBREADS_chr_001_1437950_4172         -    True   \n",
       "\n",
       "                                     ont_name  ...  pb_len  ont_alnscore  \\\n",
       "0                                        None  ...     NaN           NaN   \n",
       "1     BREAK_POINTS_SUBREADS_chr_023_3325_3864  ...  3904.0        3845.0   \n",
       "2                                        None  ...  2036.0           NaN   \n",
       "3  BREAK_POINTS_SUBREADS_chr_001_1444281_5280  ...  5313.0        5260.0   \n",
       "4  BREAK_POINTS_SUBREADS_chr_001_1437950_4158  ...  4172.0        4133.0   \n",
       "\n",
       "   ont_len    pb_pid  pb_mm  pb_indel   ont_pid  ont_mm  ont_indel   log2_len  \n",
       "0      NaN       NaN    NaN       NaN       NaN     NaN        NaN  11.783817  \n",
       "1   3864.0  0.993084   14.0      13.0  0.988178    46.0      -27.0  11.925925  \n",
       "2      NaN  0.950239  104.0     -54.0       NaN     NaN        NaN  11.029287  \n",
       "3   5280.0  0.979807  109.0     -85.0  0.974435   138.0     -118.0  12.398209  \n",
       "4   4158.0  0.997363    9.0       2.0  0.991127    37.0      -12.0  12.025832  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_ies_stats_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantiles</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>131.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>996.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1638.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>2775.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>4673.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>7746.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>16976.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>43428.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantiles       len\n",
       "0       0.00    131.00\n",
       "1       0.10    996.30\n",
       "2       0.25   1638.75\n",
       "3       0.50   2775.00\n",
       "4       0.75   4673.00\n",
       "5       0.90   7746.00\n",
       "6       0.99  16976.50\n",
       "7       1.00  43428.00"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for lengths of published IESs predicted vs. not predicted by MILRAA\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]\n",
    "pd.DataFrame(\n",
    "    zip(quants,\n",
    "        np.quantile(annot_ies_stats_df['len'],quants)\n",
    "       ),\n",
    "    columns=['quantiles',\n",
    "             'len']\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retention scores of published vs. MILRAA IESs\n",
    "\n",
    "We compare the retention scores and lengths of MILRAA IESs that are not present in the published set, to those that are, to evaluate whether they are likely to be artefacts. If they are artefacts we expect them to have lower scores and be shorter. We already applied a score cutoff, discarding MILRAA IESs with scores <0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ont_stats_overcutoff = [rec for rec in ont_stats if rec['score'] > 0.1]\n",
    "for rec in ont_stats_overcutoff:\n",
    "    if rec['id'] in [i['ont_name'] for i in annot_ies_stats]:\n",
    "        rec['in_pub'] = True\n",
    "    else:\n",
    "        rec['in_pub'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_stats_overcutoff = [rec for rec in pb_stats if rec['score'] > 0.1]\n",
    "for rec in pb_stats_overcutoff:\n",
    "    if rec['id'] in [i['pb_name'] for i in annot_ies_stats]:\n",
    "        rec['in_pub'] = True\n",
    "    else:\n",
    "        rec['in_pub'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7930"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pb_stats_overcutoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5056"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([i['in_pub'] for i in ont_stats_overcutoff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5105"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([i['in_pub'] for i in pb_stats_overcutoff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'BREAK_POINTS_SUBREADS_chr_001_7588_2399',\n",
       " 'score': 0.3504,\n",
       " 'len': 2399,\n",
       " 'iescov': 41,\n",
       " 'in_pub': False}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ont_stats_overcutoff[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2, figsize=(12,8), constrained_layout=True, sharey=True)\n",
    "\n",
    "fig.suptitle(\"Length and score histograms of MILRAA IESs\")\n",
    "\n",
    "p1 = sns.histplot(data=pd.DataFrame(pb_stats_overcutoff),\n",
    "                 x='len', hue='in_pub', multiple='dodge',\n",
    "                 binwidth=250, linewidth=0,\n",
    "                 ax=axs[0][0])\n",
    "p1.set_title(\"PacBio - lengths\")\n",
    "p1.legend(title = \"In published predictions\", labels=['True','False'])\n",
    "\n",
    "p2 = sns.histplot(data=pd.DataFrame(ont_stats_overcutoff),\n",
    "                  x='len', hue='in_pub', multiple='dodge',\n",
    "                  binwidth=250, linewidth=0,\n",
    "                  ax=axs[0][1])\n",
    "p2.set_title(\"Nanopore - lengths\")\n",
    "p2.legend(title = \"In published predictions\", labels=['True','False'])\n",
    "\n",
    "# Log scaled\n",
    "p3 = sns.histplot(data=pd.DataFrame(pb_stats_overcutoff),\n",
    "                  x='score', hue='in_pub', multiple='dodge',\n",
    "                  binwidth=0.025, linewidth=0,\n",
    "                  ax=axs[1][0])\n",
    "p3.set_title(\"PacBio - retention scores\")\n",
    "p3.legend(title = \"In published predictions\", labels=['True','False'])\n",
    "\n",
    "p4 = sns.histplot(data=pd.DataFrame(ont_stats_overcutoff),\n",
    "                  x='score', hue='in_pub', multiple='dodge',\n",
    "                  binwidth=0.025, linewidth=0,\n",
    "                  ax=axs[1][1])\n",
    "p4.set_title(\"Nanopore - retention scores\")\n",
    "p4.legend(title = \"In published predictions\", labels=['True','False'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantiles</th>\n",
       "      <th>pb_len_inpub</th>\n",
       "      <th>pb_len_notpub</th>\n",
       "      <th>ont_len_inpub</th>\n",
       "      <th>ont_len_notinpub</th>\n",
       "      <th>pb_score_inpub</th>\n",
       "      <th>pb_score_notpub</th>\n",
       "      <th>ont_score_inpub</th>\n",
       "      <th>ont_score_notinpub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>102.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>133.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.100800</td>\n",
       "      <td>0.10070</td>\n",
       "      <td>0.10070</td>\n",
       "      <td>0.100600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>835.4</td>\n",
       "      <td>621.8</td>\n",
       "      <td>803.00</td>\n",
       "      <td>525.00</td>\n",
       "      <td>0.232040</td>\n",
       "      <td>0.20924</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.177120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>1287.00</td>\n",
       "      <td>1096.50</td>\n",
       "      <td>0.355900</td>\n",
       "      <td>0.34310</td>\n",
       "      <td>0.29660</td>\n",
       "      <td>0.279375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>2058.00</td>\n",
       "      <td>1846.00</td>\n",
       "      <td>0.492500</td>\n",
       "      <td>0.49310</td>\n",
       "      <td>0.41410</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>2994.0</td>\n",
       "      <td>3148.00</td>\n",
       "      <td>2919.25</td>\n",
       "      <td>0.609600</td>\n",
       "      <td>0.62120</td>\n",
       "      <td>0.52210</td>\n",
       "      <td>0.516600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>4426.4</td>\n",
       "      <td>4245.6</td>\n",
       "      <td>4439.00</td>\n",
       "      <td>4171.90</td>\n",
       "      <td>0.710020</td>\n",
       "      <td>0.73080</td>\n",
       "      <td>0.60835</td>\n",
       "      <td>0.618070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>6737.0</td>\n",
       "      <td>6939.4</td>\n",
       "      <td>6854.45</td>\n",
       "      <td>6956.00</td>\n",
       "      <td>0.866196</td>\n",
       "      <td>0.87196</td>\n",
       "      <td>0.75081</td>\n",
       "      <td>0.768419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>8955.0</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>9316.00</td>\n",
       "      <td>8979.00</td>\n",
       "      <td>0.935700</td>\n",
       "      <td>0.96850</td>\n",
       "      <td>0.82190</td>\n",
       "      <td>0.840900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantiles  pb_len_inpub  pb_len_notpub  ont_len_inpub  ont_len_notinpub  \\\n",
       "0       0.00         102.0           15.0         133.00             15.00   \n",
       "1       0.10         835.4          621.8         803.00            525.00   \n",
       "2       0.25        1331.0         1162.0        1287.00           1096.50   \n",
       "3       0.50        2082.0         1906.0        2058.00           1846.00   \n",
       "4       0.75        3161.0         2994.0        3148.00           2919.25   \n",
       "5       0.90        4426.4         4245.6        4439.00           4171.90   \n",
       "6       0.99        6737.0         6939.4        6854.45           6956.00   \n",
       "7       1.00        8955.0         8600.0        9316.00           8979.00   \n",
       "\n",
       "   pb_score_inpub  pb_score_notpub  ont_score_inpub  ont_score_notinpub  \n",
       "0        0.100800          0.10070          0.10070            0.100600  \n",
       "1        0.232040          0.20924          0.20000            0.177120  \n",
       "2        0.355900          0.34310          0.29660            0.279375  \n",
       "3        0.492500          0.49310          0.41410            0.400000  \n",
       "4        0.609600          0.62120          0.52210            0.516600  \n",
       "5        0.710020          0.73080          0.60835            0.618070  \n",
       "6        0.866196          0.87196          0.75081            0.768419  \n",
       "7        0.935700          0.96850          0.82190            0.840900  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for lengths and scores of MILRAA IESs overlapping vs. not overlapping with published set\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]\n",
    "pd.DataFrame(\n",
    "    zip(quants,\n",
    "        np.quantile(pd.DataFrame(pb_stats_overcutoff).query('in_pub')['len'],quants),\n",
    "        np.quantile(pd.DataFrame(pb_stats_overcutoff).query('not in_pub')['len'],quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats_overcutoff).query('in_pub')['len'],quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats_overcutoff).query('not in_pub')['len'],quants),\n",
    "        np.quantile(pd.DataFrame(pb_stats_overcutoff).query('in_pub')['score'],quants),\n",
    "        np.quantile(pd.DataFrame(pb_stats_overcutoff).query('not in_pub')['score'],quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats_overcutoff).query('in_pub')['score'],quants),\n",
    "        np.quantile(pd.DataFrame(ont_stats_overcutoff).query('not in_pub')['score'],quants),\n",
    "       ),\n",
    "    columns=['quantiles',\n",
    "             'pb_len_inpub','pb_len_notpub','ont_len_inpub','ont_len_notinpub',\n",
    "             'pb_score_inpub','pb_score_notpub','ont_score_inpub','ont_score_notinpub']\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f83f939e048>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=pd.DataFrame(ont_stats_overcutoff), row='in_pub')\n",
    "g.map_dataframe(sns.histplot, x='len', y='score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f83f8f567f0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=pd.DataFrame(pb_stats_overcutoff), row='in_pub')\n",
    "g.map_dataframe(sns.histplot, x='len', y='score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence comparisons between MILRAA and published IESs\n",
    "\n",
    "How well does the MILRAA reconstruction work? For this we compare the sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(12,12), sharey=True, constrained_layout=True)\n",
    "\n",
    "# Percent ID\n",
    "axs[0][0].hist(annot_ies_stats_df['pb_pid'].dropna(), bins=50)\n",
    "axs[0][0].set_title(\"PacBio: Identity to published IES\")\n",
    "axs[0][0].set_ylabel(\"Count\")\n",
    "\n",
    "axs[0][1].hist(annot_ies_stats_df['ont_pid'].dropna(), bins=50)\n",
    "axs[0][1].set_title(\"Nanopore: Identity to published IES\")\n",
    "\n",
    "# Mismatches\n",
    "axs[1][0].hist(annot_ies_stats_df['pb_mm'].dropna(), bins=50)\n",
    "axs[1][0].set_title(\"PacBio: Mismatches to published IES\")\n",
    "axs[1][0].set_ylabel(\"Count\")\n",
    "\n",
    "axs[1][1].hist(annot_ies_stats_df['ont_mm'].dropna(), bins=50)\n",
    "axs[1][1].set_title(\"Nanopore: Mismatches to published IES\")\n",
    "\n",
    "# Indels\n",
    "axs[2][0].hist(annot_ies_stats_df['pb_indel'].dropna(), bins=50)\n",
    "axs[2][0].set_title(\"PacBio: Length difference to published IES\")\n",
    "axs[2][0].set_ylabel(\"Count\")\n",
    "\n",
    "axs[2][1].hist(annot_ies_stats_df['ont_indel'].dropna(), bins=50)\n",
    "axs[2][1].set_title(\"Nanopore: Length difference to published IES\")\n",
    "\n",
    "plt.savefig(\"plots/tthe_milraa_vs_published_ies_seq_comparisons.pdf\")\n",
    "plt.savefig(\"plots/tthe_milraa_vs_published_ies_seq_comparisons.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantiles</th>\n",
       "      <th>pb_pid</th>\n",
       "      <th>pb_mm</th>\n",
       "      <th>pb_indel</th>\n",
       "      <th>pb_indel_abs</th>\n",
       "      <th>ont_pid</th>\n",
       "      <th>ont_mm</th>\n",
       "      <th>ont_indel</th>\n",
       "      <th>ont_indel_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.077024</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4472.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.037930</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5428.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.886909</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-119.3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.820501</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-218.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.979259</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.965934</td>\n",
       "      <td>14.00</td>\n",
       "      <td>-23.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.989873</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.987596</td>\n",
       "      <td>24.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.993222</td>\n",
       "      <td>15.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.990872</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.994878</td>\n",
       "      <td>251.60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>172.00</td>\n",
       "      <td>0.992802</td>\n",
       "      <td>436.20</td>\n",
       "      <td>11.00</td>\n",
       "      <td>251.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.997125</td>\n",
       "      <td>2473.23</td>\n",
       "      <td>292.3</td>\n",
       "      <td>1186.74</td>\n",
       "      <td>0.995312</td>\n",
       "      <td>3086.24</td>\n",
       "      <td>273.78</td>\n",
       "      <td>1470.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>6640.00</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>4472.00</td>\n",
       "      <td>0.999219</td>\n",
       "      <td>6643.00</td>\n",
       "      <td>4803.00</td>\n",
       "      <td>5428.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantiles    pb_pid    pb_mm  pb_indel  pb_indel_abs   ont_pid   ont_mm  \\\n",
       "0       0.00  0.077024     0.00   -4472.0          0.00  0.037930     0.00   \n",
       "1       0.10  0.886909     2.00    -119.3          3.00  0.820501     8.00   \n",
       "2       0.25  0.979259     4.00       3.0          7.00  0.965934    14.00   \n",
       "3       0.50  0.989873     8.00       9.0         13.00  0.987596    24.00   \n",
       "4       0.75  0.993222    15.00      16.0         27.00  0.990872    64.00   \n",
       "5       0.90  0.994878   251.60      29.0        172.00  0.992802   436.20   \n",
       "6       0.99  0.997125  2473.23     292.3       1186.74  0.995312  3086.24   \n",
       "7       1.00  0.998667  6640.00    2296.0       4472.00  0.999219  6643.00   \n",
       "\n",
       "   ont_indel  ont_indel_abs  \n",
       "0   -5428.00           0.00  \n",
       "1    -218.00           2.00  \n",
       "2     -23.00           5.00  \n",
       "3      -8.00          11.00  \n",
       "4       0.00          39.00  \n",
       "5      11.00         251.40  \n",
       "6     273.78        1470.78  \n",
       "7    4803.00        5428.00  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantile summary for sequence comparison of published vs. MILRAA IESs\n",
    "quants = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]\n",
    "pd.DataFrame(\n",
    "    zip(quants,\n",
    "        np.quantile(annot_ies_stats_df.query('in_pb')['pb_pid'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_pb')['pb_mm'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_pb')['pb_indel'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_pb')['pb_indel'].apply(math.fabs),quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_ont')['ont_pid'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_ont')['ont_mm'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_ont')['ont_indel'],quants),\n",
    "        np.quantile(annot_ies_stats_df.query('in_ont')['ont_indel'].apply(math.fabs),quants),),\n",
    "    columns=['quantiles',\n",
    "             'pb_pid','pb_mm','pb_indel','pb_indel_abs',\n",
    "             'ont_pid','ont_mm','ont_indel','ont_indel_abs',\n",
    "             ]\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sum up total processing time for PacBio data\n",
    "starttimes = []\n",
    "finishtimes = []\n",
    "for logfile in glob.glob('bleties_pb/logs/*.log'):\n",
    "    with open(logfile, \"r\") as fh:\n",
    "        for line in fh:\n",
    "            started = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Started BleTIES MILRAA\", line)\n",
    "            finished = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Finished MILRAA\", line)\n",
    "            if started:\n",
    "                starttime = datetime.datetime.fromisoformat(started.group(1).replace(' ','T').replace(',', '.'))\n",
    "                starttimes.append(starttime)\n",
    "            elif re.search(r\"Finished\", line):\n",
    "                finishtime = datetime.datetime.fromisoformat(finished.group(1).replace(' ','T').replace(',', '.'))\n",
    "                finishtimes.append(finishtime)\n",
    "pb_totaltime = datetime.timedelta(0)\n",
    "# Have to add in a roundabout way because list sum doesn't work\n",
    "timings = [i[1] - i[0] for i in zip(starttimes, finishtimes)]\n",
    "for i in timings:\n",
    "    pb_totaltime += i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sum up total processing time for Nanopore data\n",
    "starttimes = []\n",
    "finishtimes = []\n",
    "for logfile in glob.glob('bleties_ont/logs/*.log'):\n",
    "    with open(logfile, \"r\") as fh:\n",
    "        for line in fh:\n",
    "            started = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Started BleTIES MILRAA\", line)\n",
    "            finished = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Finished MILRAA\", line)\n",
    "            if started:\n",
    "                starttime = datetime.datetime.fromisoformat(started.group(1).replace(' ','T').replace(',', '.'))\n",
    "                starttimes.append(starttime)\n",
    "            elif re.search(r\"Finished\", line):\n",
    "                finishtime = datetime.datetime.fromisoformat(finished.group(1).replace(' ','T').replace(',', '.'))\n",
    "                finishtimes.append(finishtime)\n",
    "ont_totaltime = datetime.timedelta(0)\n",
    "# Have to add in a roundabout way because list sum doesn't work\n",
    "timings = [i[1] - i[0] for i in zip(starttimes, finishtimes)]\n",
    "for i in timings:\n",
    "    ont_totaltime += i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall clock time, PacBio dataset:\n",
      "1 day, 2:43:41.485000\n",
      "Total wall clock time, Nanopore dataset:\n",
      "21:27:39.232000\n"
     ]
    }
   ],
   "source": [
    "print(\"Total wall clock time, PacBio dataset:\")\n",
    "print(pb_totaltime)\n",
    "print(\"Total wall clock time, Nanopore dataset:\")\n",
    "print(ont_totaltime)"
   ]
  }
 ],
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