VOGDB_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VOGDB EDA\n",
    "Exploring VOGs from [VOGDB](http://www.vogdb.org/download).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and view VOGDB files\n",
    "* **vog.members.tsv.gz**, Tab separated file of VOGs and the comma separated lists of their member protein ids.\n",
    "* **vog.annotations.tsv.gz**, Tab separated file of VOGs and their consensus functional annotations (preferrably from Swissprot annotations, if not available then the annotations from RefSeq were used).\n",
    "* **vog.lca.tsv.gz**, Tab separated file of VOGs and the taxonomic lineage of the last common ancestor (LCA) of member genomes. Genomes with unclassified taxonomic lineages have not been used for LCA determination, which can result in VOG without LCA (if all proteins of a VOG are from unclassified lineages). The numbers of genomes per VOG and LCA, as well as the total numbers of genomes in the LCA are given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to VOGDB files\n",
    "vogdb_files = '/home/alepfu/Desktop/VOGDB_files/'\n",
    "\n",
    "# Filenames\n",
    "vogdb_file_members = 'vog.members.tsv'\n",
    "vogdb_file_annotations = 'vog.annotations.tsv'\n",
    "vogdb_file_lca = 'vog.lca.tsv'\n",
    "\n",
    "# Load data\n",
    "vogdb_members_df = pd.read_csv(vogdb_files + vogdb_file_members, sep='\\t')\n",
    "vogdb_annotations_df = pd.read_csv(vogdb_files + vogdb_file_annotations, sep='\\t')\n",
    "vogdb_lca_df = pd.read_csv(vogdb_files + vogdb_file_lca, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of VOGs: 25400\n"
     ]
    }
   ],
   "source": [
    "n_vogs = vogdb_members_df.shape[0]\n",
    "print('Number of VOGs:', n_vogs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VOGDB members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>#GroupName</th>\n",
       "      <th>ProteinCount</th>\n",
       "      <th>SpeciesCount</th>\n",
       "      <th>FunctionalCategory</th>\n",
       "      <th>ProteinIDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VOG00001</td>\n",
       "      <td>743</td>\n",
       "      <td>36</td>\n",
       "      <td>Xh</td>\n",
       "      <td>1560514.YP_009094638.1,1720526.YP_009310212.1,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VOG00002</td>\n",
       "      <td>569</td>\n",
       "      <td>69</td>\n",
       "      <td>Xh</td>\n",
       "      <td>10506.NP_049038.1,1269028.YP_007354781.1,10245...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VOG00003</td>\n",
       "      <td>704</td>\n",
       "      <td>161</td>\n",
       "      <td>Xu</td>\n",
       "      <td>1675866.YP_009357223.1,320432.YP_249674.1,1592...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VOG00004</td>\n",
       "      <td>618</td>\n",
       "      <td>509</td>\n",
       "      <td>Xu</td>\n",
       "      <td>2041855.YP_009639449.1,2560719.YP_009639326.1,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VOG00005</td>\n",
       "      <td>877</td>\n",
       "      <td>689</td>\n",
       "      <td>XhXrXs</td>\n",
       "      <td>1922558.YP_009333546.1,1923610.YP_009336918.1,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #GroupName  ProteinCount  SpeciesCount FunctionalCategory  \\\n",
       "0   VOG00001           743            36                 Xh   \n",
       "1   VOG00002           569            69                 Xh   \n",
       "2   VOG00003           704           161                 Xu   \n",
       "3   VOG00004           618           509                 Xu   \n",
       "4   VOG00005           877           689             XhXrXs   \n",
       "\n",
       "                                          ProteinIDs  \n",
       "0  1560514.YP_009094638.1,1720526.YP_009310212.1,...  \n",
       "1  10506.NP_049038.1,1269028.YP_007354781.1,10245...  \n",
       "2  1675866.YP_009357223.1,320432.YP_249674.1,1592...  \n",
       "3  2041855.YP_009639449.1,2560719.YP_009639326.1,...  \n",
       "4  1922558.YP_009333546.1,1923610.YP_009336918.1,...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_members_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaN cells: 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of NaN cells:', vogdb_members_df.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ProteinCount</th>\n",
       "      <th>SpeciesCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25400.000000</td>\n",
       "      <td>25400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.005197</td>\n",
       "      <td>11.183622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44.590497</td>\n",
       "      <td>39.074229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1835.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProteinCount  SpeciesCount\n",
       "count  25400.000000  25400.000000\n",
       "mean      12.005197     11.183622\n",
       "std       44.590497     39.074229\n",
       "min        2.000000      2.000000\n",
       "25%        2.000000      2.000000\n",
       "50%        4.000000      4.000000\n",
       "75%        8.000000      7.000000\n",
       "max     1973.000000   1835.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_members_df[['ProteinCount','SpeciesCount']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.distplot(vogdb_members_df['ProteinCount'], kde=False)\n",
    "plt.title('Protein Count Distribution')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = sns.distplot(vogdb_members_df['SpeciesCount'], kde=False)\n",
    "plt.title('Species Count Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "Most groups are small.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vogdb_members_df['Xr'] = vogdb_members_df['FunctionalCategory'].str.contains('Xr').astype(int)\n",
    "vogdb_members_df['Xs'] = vogdb_members_df['FunctionalCategory'].str.contains('Xs').astype(int)\n",
    "vogdb_members_df['Xh'] = vogdb_members_df['FunctionalCategory'].str.contains('Xh').astype(int)\n",
    "vogdb_members_df['Xp'] = vogdb_members_df['FunctionalCategory'].str.contains('Xp').astype(int)\n",
    "vogdb_members_df['Xu'] = vogdb_members_df['FunctionalCategory'].str.contains('Xu').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = vogdb_members_df[['Xr','Xs','Xh','Xp','Xu']].sum().plot.bar()\n",
    "plt.title('Functional Categories')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = vogdb_members_df[['Xr','Xs','Xh','Xp']].sum().plot.bar()\n",
    "plt.title('Functional Categories without Xu')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "Most groups are poorly characterized.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 1% VOGDB members\n",
    "\n",
    "Exploring the top 1% of VOGs having the most proteins or species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of top 1% of VOGs: 254\n"
     ]
    }
   ],
   "source": [
    "n_top_vogs = int(n_vogs / 100)\n",
    "print('Number of top 1% of VOGs:', n_top_vogs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ProteinCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>326.531496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>275.064184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>138.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>168.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>221.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>395.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1973.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProteinCount\n",
       "count    254.000000\n",
       "mean     326.531496\n",
       "std      275.064184\n",
       "min      138.000000\n",
       "25%      168.250000\n",
       "50%      221.500000\n",
       "75%      395.500000\n",
       "max     1973.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_top_proteins_members_df = vogdb_members_df.sort_values('ProteinCount', ascending=False).head(n_top_vogs)\n",
    "vogdb_top_proteins_members_df[['ProteinCount']].describe()"
   ]
  },
  {
   "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>SpeciesCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>290.224409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>233.062819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>206.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>336.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1835.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SpeciesCount\n",
       "count    254.000000\n",
       "mean     290.224409\n",
       "std      233.062819\n",
       "min      123.000000\n",
       "25%      158.000000\n",
       "50%      206.500000\n",
       "75%      336.250000\n",
       "max     1835.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_top_species_members_df = vogdb_members_df.sort_values('SpeciesCount', ascending=False).head(n_top_vogs)\n",
    "vogdb_top_species_members_df[['SpeciesCount']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.distplot(vogdb_top_proteins_members_df['ProteinCount'], kde=False)\n",
    "plt.title('Top 1% Protein Count Distribution')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = sns.distplot(vogdb_top_species_members_df['SpeciesCount'], kde=False)\n",
    "plt.title('Top 1% Species Count Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = vogdb_top_proteins_members_df[['Xr','Xs','Xh','Xp','Xu']].sum().plot.bar()\n",
    "plt.title('Functional Categories for Top 1% Protein Count')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = vogdb_top_species_members_df[['Xr','Xs','Xh','Xp','Xu']].sum().plot.bar()\n",
    "plt.title('Functional Categories for Top 1% Species Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "The characterization for large groups is better.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VOGDB annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>#GroupName</th>\n",
       "      <th>ProteinCount</th>\n",
       "      <th>SpeciesCount</th>\n",
       "      <th>FunctionalCategory</th>\n",
       "      <th>ConsensusFunctionalDescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VOG00001</td>\n",
       "      <td>743</td>\n",
       "      <td>36</td>\n",
       "      <td>Xh</td>\n",
       "      <td>sp|Q5UQJ2|YR863_MIMIV Putative ankyrin repeat ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VOG00002</td>\n",
       "      <td>569</td>\n",
       "      <td>69</td>\n",
       "      <td>Xh</td>\n",
       "      <td>sp|Q9J4Z6|V244_FOWPN Putative ankyrin repeat p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VOG00003</td>\n",
       "      <td>704</td>\n",
       "      <td>161</td>\n",
       "      <td>Xu</td>\n",
       "      <td>sp|Q91FD6|388R_IIV6 Putative MSV199 domain-con...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VOG00004</td>\n",
       "      <td>618</td>\n",
       "      <td>509</td>\n",
       "      <td>Xu</td>\n",
       "      <td>sp|P51704|RPC1_BPHC1 Repressor protein CI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VOG00005</td>\n",
       "      <td>877</td>\n",
       "      <td>689</td>\n",
       "      <td>XhXrXs</td>\n",
       "      <td>sp|P17766|POLG_PPVNA Genome polyprotein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #GroupName  ProteinCount  SpeciesCount FunctionalCategory  \\\n",
       "0   VOG00001           743            36                 Xh   \n",
       "1   VOG00002           569            69                 Xh   \n",
       "2   VOG00003           704           161                 Xu   \n",
       "3   VOG00004           618           509                 Xu   \n",
       "4   VOG00005           877           689             XhXrXs   \n",
       "\n",
       "                      ConsensusFunctionalDescription  \n",
       "0  sp|Q5UQJ2|YR863_MIMIV Putative ankyrin repeat ...  \n",
       "1  sp|Q9J4Z6|V244_FOWPN Putative ankyrin repeat p...  \n",
       "2  sp|Q91FD6|388R_IIV6 Putative MSV199 domain-con...  \n",
       "3          sp|P51704|RPC1_BPHC1 Repressor protein CI  \n",
       "4            sp|P17766|POLG_PPVNA Genome polyprotein  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_annotations_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaN cells: 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of NaN cells:', vogdb_annotations_df.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vogdb_annotations_df['SwissProt'] = vogdb_annotations_df['ConsensusFunctionalDescription'].str.startswith('sp').astype(int)\n",
    "vogdb_annotations_df['Refseq'] = vogdb_annotations_df['ConsensusFunctionalDescription'].str.startswith('REFSEQ').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = vogdb_annotations_df[['Refseq','SwissProt']].sum().plot.bar()\n",
    "plt.title('Source of functional descriptions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "Most annotations come from Refseq, which is the fallback.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VOGDB LCA"
   ]
  },
  {
   "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>#GroupName</th>\n",
       "      <th>GenomesInGroupAndLCA</th>\n",
       "      <th>GenomesTotalInLCA</th>\n",
       "      <th>LastCommonAncestor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VOG00001</td>\n",
       "      <td>20</td>\n",
       "      <td>6131</td>\n",
       "      <td>Viruses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VOG00002</td>\n",
       "      <td>59</td>\n",
       "      <td>6131</td>\n",
       "      <td>Viruses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VOG00003</td>\n",
       "      <td>153</td>\n",
       "      <td>6131</td>\n",
       "      <td>Viruses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VOG00004</td>\n",
       "      <td>505</td>\n",
       "      <td>6131</td>\n",
       "      <td>Viruses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VOG00005</td>\n",
       "      <td>342</td>\n",
       "      <td>1683</td>\n",
       "      <td>Viruses;Riboviria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #GroupName  GenomesInGroupAndLCA  GenomesTotalInLCA LastCommonAncestor\n",
       "0   VOG00001                    20               6131            Viruses\n",
       "1   VOG00002                    59               6131            Viruses\n",
       "2   VOG00003                   153               6131            Viruses\n",
       "3   VOG00004                   505               6131            Viruses\n",
       "4   VOG00005                   342               1683  Viruses;Riboviria"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_lca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>GenomesInGroupAndLCA</th>\n",
       "      <th>GenomesTotalInLCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25400.000000</td>\n",
       "      <td>25400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.719803</td>\n",
       "      <td>965.066299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.081363</td>\n",
       "      <td>1687.287043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1833.000000</td>\n",
       "      <td>6131.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GenomesInGroupAndLCA  GenomesTotalInLCA\n",
       "count          25400.000000       25400.000000\n",
       "mean              10.719803         965.066299\n",
       "std               38.081363        1687.287043\n",
       "min                0.000000           1.000000\n",
       "25%                2.000000           5.000000\n",
       "50%                3.000000          48.000000\n",
       "75%                7.000000        1343.000000\n",
       "max             1833.000000        6131.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_lca_df[['GenomesInGroupAndLCA','GenomesTotalInLCA']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaN cells: 1222\n"
     ]
    }
   ],
   "source": [
    "print('Number of NaN cells:', vogdb_lca_df.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN columns: ['LastCommonAncestor']\n"
     ]
    }
   ],
   "source": [
    "print('NaN columns:', vogdb_lca_df.columns[vogdb_lca_df.isna().any()].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is there an undefined taxonomic level count? False\n"
     ]
    }
   ],
   "source": [
    "def count_taxonomic_levels(lca):\n",
    "    \"\"\"Counts terms in LCA column to quantify taxonomic accuracy.\"\"\"\n",
    "    n_levels = -1;\n",
    "    \n",
    "    if (pd.isnull(lca)):\n",
    "        n_levels = 0\n",
    "    elif (lca == 'Viruses'):\n",
    "        n_levels = 1\n",
    "    else:\n",
    "        n_levels = lca.count(';') + 1\n",
    "        \n",
    "    return n_levels\n",
    "\n",
    "\n",
    "vogdb_lca_df['N_taxonomic_levels'] = vogdb_lca_df.apply(lambda row: count_taxonomic_levels(row['LastCommonAncestor']), axis=1)\n",
    "\n",
    "print('Is there an undefined taxonomic level count?', -1 in vogdb_lca_df['N_taxonomic_levels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25400.000000\n",
       "mean         3.269016\n",
       "std          1.260271\n",
       "min          0.000000\n",
       "25%          3.000000\n",
       "50%          3.000000\n",
       "75%          4.000000\n",
       "max          9.000000\n",
       "Name: N_taxonomic_levels, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_lca_df['N_taxonomic_levels'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.distplot(vogdb_lca_df['N_taxonomic_levels'], kde=False, bins=10)\n",
    "plt.title('LCA Taxonomic Levels Distribution')\n",
    "plt.xticks(np.arange(0, 10, 1.0))\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = vogdb_lca_df.boxplot(column='N_taxonomic_levels')\n",
    "plt.title('Boxplot LCA Taxonomic Levels')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "Most groups have only a broad lineage.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 1% VOGDB LCA\n",
    "\n",
    "Exploring the top 1% of VOGs having the most genomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>GenomesInGroupAndLCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>282.259843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>226.509943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>204.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>327.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1833.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GenomesInGroupAndLCA\n",
       "count            254.000000\n",
       "mean             282.259843\n",
       "std              226.509943\n",
       "min              121.000000\n",
       "25%              152.000000\n",
       "50%              204.500000\n",
       "75%              327.000000\n",
       "max             1833.000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_top_genomes_lca_df = vogdb_lca_df.sort_values('GenomesInGroupAndLCA', ascending=False).head(n_top_vogs)\n",
    "vogdb_top_genomes_lca_df[['GenomesInGroupAndLCA']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    254.000000\n",
       "mean       1.952756\n",
       "std        0.788838\n",
       "min        1.000000\n",
       "25%        1.250000\n",
       "50%        2.000000\n",
       "75%        2.000000\n",
       "max        7.000000\n",
       "Name: N_taxonomic_levels, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vogdb_top_genomes_lca_df['N_taxonomic_levels'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d465Xq1Yt5s+fz4cffkiHDh0YMWLELU/WrKzsrB3gr659+/YsXLiQ9PT0Aju+azIzM42irEOHDsDVE/ZSUlL48ccfad68ebHW9/rrr1OtWjW2bNlCrVq1iIqKYuHChcbjKSkpzJ07l5CQEN566y1j9NbV1ZULFy6QmZlpFMfx8fFFjiDA1VGGP5+dHR8fX+xPzH9mMplwc3Pj9OnT3HPPPaXq4+233+b++++/aZs+ffqwZs0amjVrhp2dHQ888ABwtXjs2LHjbZ15XKNGDV588UVefPFFY65ZixYtihzdveY///kPjo6ON83o5ubG7NmzMZvNHDp0iGHDhtG6desCO+w//81Fuf75uv55rlq1KpmZmcZjf/4QcKu+TSYTderU4dy5c8aHmfPnz+Pm5lZkHpE7ia2tLT169GD69OkcOXIEPz8/qlSpwvnz52nSpAlw9X11/Wt/7dq15OTk4OrqyvLly3n22WeNx1JSUgocI+Lj47n33ntvWK+rqyvnzp0zbqenp3Pp0qVSv8caNWpEvXr12LFjhzEK/meRkZHk5+czcuRI477s7GwiIiKMaVy369SpU8yYMYPVq1fTokULbGxs8Pf3LzDCuXLlSuzs7HBycmL16tXGfF5XV1fOnz9vtEtNTSUtLe22tsGft9/58+dxdHSkdu3aN/xKVHHUq1ePvXv3lnj5a33c6njj6elJ7dq12bdvH1u2bKFfv35A0ce4PxfHDz30EA899BAZGRnMmzePl19+udgDOX91GjG2oJycHLKysox/ubm5BAUF4e7uzujRo/n555+NT3FLly5lz5497Ny5E1tbW6KiooiIiCAiIoKtW7fi7e1dopPwrly5QrVq1XBycuL8+fM3vOBfeeUVfHx8mDlzJq1btzbOqr7nnnto1qwZCxcuJDs7mx9++IGIiIgCv39bGG9vb/Lz81m1ahW5ubl89dVXfPXVV/j7+xc7/5+FhITw5ptvcubMGcxmM8eOHSv0pAKgwPbPysrCbDbzyCOP8MYbbxjFYHJyMrt27TKW6datGydPnmTp0qUEBAQYxV/37t05duwYW7duJScnh+zsbL799ltOnTp1w3qjo6ONjE5OTtjY2GBra1vk33fx4kU+++wzZs2axciRI3FycrqhzdatW0lMTMRkMhnTSGxtbbG3t6dGjRoF5nLfrg8//JCkpCQuXLjAsmXLjBEsDw8Pjh07xsmTJ8nIyGDJkiUFlnNxcbnl+vr06cOSJUu4ePEiycnJLF269LZeQyJ3ErPZzM6dO0lJSaFx48bY2tri7+/Pm2++SVpaGufOnWPlypXGa//UqVMsXLiQefPm8frrr7N8+fIbpo+9/fbbZGdnG+cS3Gz/GRgYyMaNG4mLiyM7O5sFCxYYI9Fw9duiovYHZrP5hv2kyWRi0qRJvPPOO2zYsIG0tDTy8/M5fPgw06ZNA65+s/nPf/7TOE5FRESwaNEidu/efUPxVZT09HRsbGxwdnYmPz+fjz/+uMCUqhMnTvDuu+8yb9485s2bx5IlS/jpp5+Aq/uYdevWGdMB5s2bR9u2bYucsnht2RUrVnD+/HnS0tJYuHAhffr0ua3BhFvp3r07v/32Gx999BHZ2dmkpaVx9OjRQtvn5eUV2P45OTm3dby5lv+///0vPXr0MO4v6hh3TWJiIrt37yYjIwN7e3uqVatW4Bs8uUojxhZ0/VmzACNGjGDs2LGsWrWKRYsW8dRTT5GSkoKLiwvdunWjRYsWjB8/nv79+xf4agiunmE8c+ZMxo8fj53d7T9NY8aMYdKkSXh7e/P3v/8df39/41cXtm7dyjfffGPMe5s2bRp9+/bl888/p2fPnixcuJCXX36ZDh06GCc03M4JBA4ODrz33nu88sorvP3229SvX58333yzVFMgrhk+fDi5ubkMGTKEixcv0qRJE959992bts3Ly7thlHrt2rU8/fTTmEwmhgwZwu+//06dOnUICgrC19cXuDpK6uvry5YtW5g8ebKxbK1atfjggw+YM2cOr7zyCnC1cJw6deoN6/7555+ZMWMGly5dolatWjz11FO0atWq0L/L398fk8mEvb09zZs356WXXir0ZJnY2FhmzZrFlStXqFu3Lq+++qoxOhIWFsaYMWPIzs5m7ty5RU6huKZ3794MHjyY5ORkevTowTPPPANc/R3Up59+mscff5xq1arx/PPPs3HjRmO5hx9+mLFjx+Lt7U2nTp1uuPBAWFgYc+bMMQ42AQEBPP3007eVSaSiGzFihPGBt0GDBsyZM8cY2Z02bRqvvfYa3bt3x8HBgdDQUAYMGEBubi4TJkzgmWeeMb4BHDt2LBMnTjROzqtTpw41a9akU6dOVK1alZdffvmmcz/btWvHmDFjGD16NCkpKXh5eRV4D/7zn/9k0qRJZGZm8uqrr950nxIbG3vDfvKHH37A39+fatWqsXTpUmbMmIGDgwP33nsvw4YN49tvv+XcuXM8/vjjxpQ/uDqocM899xAVFcUTTzxx29vR09OTRx55hAEDBmBra8uAAQOM0c7s7GwmTJjAqFGjjNH3UaNGMXHiRP7973/TtWtXhg8fzsiRI0lNTcXb25vXX3/9ttb76KOP8vvvv/PII4+Qk5NDly5dCuzzS6pWrVqsXLmSmTNnsmDBAhwdHXn66acL/dZ08eLFLF682Ljdrl07Vq1aVeTxpk+fPrz99tv06NGDGjVqGPcXdYy7Ji8vj/fee4/w8HBsbGzw9PQ01iX/YzIXNjtbREREytTBgweZMGECMTEx1o4iImgqhYiIiIgIoMJYRERERATQVAoREREREUAjxiIiIiIiQAX5VYpvvvmGqlWrWjsGWVlZxb7csHL89TMoh3LcTo5b/QrJX823335bIba7/HVVlPe2/DXdap9dIQpjk8mEh4eHtWMQFxenHBUsR0XIoBzKcTs5KhMHB4cKsd3lr6uivLflr+lW+2xNpRARERERQYWxiIiIiAigwlhEREREBFBhLCIiIiICqDAWEREREQFUGIuIVHq//PILQUFBxr8HHniAVatWWTuWVEKjR4/G0dGR++67D0dHR0aPHm3tSFLJVIifaxMREetp1KgRkZGRAOTl5dG5c2f8/PysnEoqm9GjR7N06VLmzp1Lly5d2LNnDy+88AIAb7/9tpXTSWWhEWMRETHs37+fhg0b0qBBA2tHkUrm/fffZ+7cuYSHh1OtWjXCw8OZO3cu77//vrWjSSWiEWMRETFERUXRp0+fW7bJysqqdBc1kbKXlZVFly5diIuLIzMzk7i4OLp06aLXm5QrFcZl6HJ6NqlZucVeLtvOibMX02/6WA0HO2pVsy9tNBGRG2RnZ7Nr1y7GjRt3y3a68p2UBQcHB/bs2UN4eLhx5bsFCxbo9SYWd6sPWiqMy1BqVi4xJ/4o9nLxCfHUczff9LHOTeuoMBaRMhETE4Onpyd16tSxdhSphJ555hljTnGXLl1YsGABL7zwAiNGjLByMqlMVBiLiAhwdRpFQECAtWNIJXXtBLspU6aQlZWFg4MDI0aM0Il3Uq508p2IiJCRkcG+ffvo0aOHtaNIJfb222+TmZnJsWPHyMzMVFEs5U4jxiIiQtWqVTl48KC1Y4iIWJVGjEVEREREuI3CePLkybRr1+6mP9/zwQcf0KxZMy5cuACA2WxmxowZ+Pn5ERgYyA8//GD5xCIiIiIiZaDIwrh///4sX778hvvj4+PZt28f9evXN+6LiYnh119/ZceOHbz22mu8/PLLFg0rIiIiIlJWiiyMW7duTa1atW64f/bs2UyYMAGTyWTcFx0dTXBwMCaTiVatWpGSkkJSUpJlE4uIiIiIlIESzTGOjo7G1dWV5s2bF7g/MTERd3d347a7uzuJiYmlSygiIiIiUg6K/asUGRkZLF26lBUrVtzwmNl840Uprh9RLkx+fn6FuNzjtUtQWkq2nRPxCfHFXi43J6fQ5ZKdTaQm/FbaaLfF0tvjTs2gHMohIiKVQ7EL49OnT3P27FmCgoIASEhIoH///qxbtw53d3cSEhKMtgkJCbi6uhbZp42NTYW43OO1S1BaytmL6YVewe5Wrl75rt5NH3Op48LddzUsbbTbYuntcadmUA7luJ0cIiJy5yt2YdysWTP2799v3Pb19WX9+vU4Ozvj6+vLv/71LwICAvjuu++oUaPGbRXGIiIiIiLWVmRhHB4ezqFDh7h48SKdO3dm9OjRhIaG3rRtly5d2LNnD35+flStWpVZs2ZZPLCIiIiISFkosjBesGDBLR/ftWuX8X+TycRLL71U+lQiIiIiIuVMV74TEREREUGFsYiIiIgIoMJYRERERARQYSwiIiIiAqgwFhEREREBVBiLiIiIiAAqjEVEREREABXGIiIiIiKACmMREREREUCFsYiIiIgIoMJYRERERARQYSwiIiIiAqgwFhEREREBVBiLiIiIiAAqjEVEBEhJSSEsLAx/f3969epFbGystSOJiJQ7O2sHEBER65s5cyadOnVi0aJFZGdnk5mZae1IIiLlTiPGIiKVXFpaGl9//TUhISEA2NvbU7NmTSunEhEpfxoxFhGp5M6cOYOzszOTJ0/mxx9/xNPTk6lTp1KtWrWbts/KyiIuLq6cU0plkpmZqdeYWIUKYxGRSi43N5djx44xbdo0WrZsyYwZM1i2bBnPP//8Tds7ODjg4eFRzimlMomLi9NrTMrMrT50aSqFiEgl5+7ujru7Oy1btgTA39+fY8eOWTmViEj5K7Iwnjx5Mu3ataNPnz7GfXPnzsXf35/AwEBGjRpFSkqK8dh7772Hn58fPXv2ZO/evWWTWkRELKZu3bq4u7vzyy+/ALB//34aN25s5VQiIuWvyMK4f//+LF++vMB9HTp0YMuWLWzevJn/+7//47333gPgp59+IioqiqioKJYvX84rr7xCXl5e2SQXERGLmTZtGuPHjycwMJC4uDhGjBhh7UgiIuWuyDnGrVu35uzZswXu69ixo/H/Vq1asX37dgCio6MJCAjA3t6ehg0bcs8993D06FG8vLwsHFtERCzJw8ODjRs3WjuGiIhVlfrkuw0bNtCrVy8AEhMTjTlqAG5ubiQmJhbZR35+foU4+9TSZ8Fm2zkRnxBf7OVyc3IKXS7Z2URqwm+ljXZbKsJZwRUhg3Ioh4iIVA6lKozfffddbG1t6du3LwBms/mGNiaTqch+bGxsKsTZp5Y+C/bsxXTqud+4TYoSnxBPPfd6N33MpY4Ld9/VsLTRbktFOCu4ImRQDuW4nRwiInLnK3Fh/Nlnn7F7925WrVplFL/u7u4kJCQYbRITE3F1dS19ShERERGRMlain2uLiYnh/fff591336Vq1arG/b6+vkRFRZGdnc2ZM2f49ddfadGihcXCioiIiIiUlSJHjMPDwzl06BAXL16kc+fOjB49mmXLlpGdnc3QoUMBaNmyJa+++ir33nsvvXr1onfv3tja2jJ9+nRsbW3L/I8QERERESmtIgvjBQsW3HBfaGhooe1HjhzJyJEjS5dKRERERKSc6cp3IiIiIiKoMBYRERERAVQYi4iIiIgAKoxFRERERAAVxiIiIiIigApjERERERFAhbGIiIiICKDCWEREREQEUGEsIiIiIgKoMBYRERERAVQYi4iIiIgAKoxFRERERAAVxiIiIiIigApjERERERFAhbGIiIiICKDCWEREREQEUGEsIiIiIgKoMBYRERERAcDO2gFERMT6fH19qV69OjY2Ntja2rJx40ZrRxIRKXdFFsaTJ09m9+7duLi4sGXLFgAuXbrE2LFjOXfuHA0aNGDhwoXUqlULs9nMzJkz2bNnD46OjsyZMwdPT88y/yNERKT0Vq9ejbOzs7VjiIhYTZFTKfr378/y5csL3Lds2TLatWvHjh07aNeuHcuWLQMgJiaGX3/9lR07dvDaa6/x8ssvl0loERERERFLK3LEuHXr1pw9e7bAfdHR0Xz44YcABAcHM2jQICZMmEB0dDTBwcGYTCZatWpFSkoKSUlJuLq6lk16ERGxmGHDhmEymXj44Yd5+OGHC22XlZVFXFxcOSaTyiYzM1OvMbGKEs0xTk5ONopdV1dXLly4AEBiYiLu7u5GO3d3dxITE4ssjOP3qXMAACAASURBVPPz8yvEG8DSb8RsOyfiE+KLvVxuTk6hyyU7m0hN+K200W5LRdgxVYQMyqEclcHHH3+Mm5sbycnJDB06lEaNGtG6deubtnVwcMDDw6OcE0plEhcXp9eYlJlbHTcsevKd2Wy+4T6TyVTkcjY2NhXiDWDpN+LZi+nUc79xmxQlPiGeeu71bvqYSx0X7r6rYWmj3ZaKsGOqCBmUQzluJ8edzs3NDQAXFxf8/Pw4evRooYWxiMhfVYl+rs3FxYWkpCQAkpKSjJM13N3dSUhIMNolJCRoGoWISAWXnp5OWlqa8f+vvvqKe++918qpRETKX4kKY19fXyIiIgCIiIigW7duBe43m818++231KhRQ4WxiEgFl5yczGOPPUbfvn0JDQ2lS5cudO7c2dqxRETKXZFTKcLDwzl06BAXL16kc+fOjB49muHDh/P888+zfv166tWrx1tvvQVAly5d2LNnD35+flStWpVZs2aV+R8gIiKl07BhQzZt2mTtGCIiVldkYbxgwYKb3r969eob7jOZTLz00kulTyUiIiIiUs50SWgREREREVQYi4iIiIgAKoxFRERERAAVxiIiIiIigApjERERERFAhbGIiIiICKDCWEREREQEUGEsIiIiIgKoMBYRERERAVQYi4iIiIgAKoxFRERERAAVxiIiIiIigApjERERERFAhbGIiIiICKDCWEREREQEUGEsIiIiIgKoMBYRERERAVQYi4iIiIgAKoxFRERERACwK83Cq1atYt26dZhMJpo2bcrs2bNJSkoiPDycy5cvc9999/H6669jb29vqbwiIiIiImWixCPGiYmJrFmzhg0bNrBlyxby8vKIiopi/vz5PPnkk+zYsYOaNWuyfv16S+YVERERESkTpZpKkZeXR2ZmJrm5uWRmZlK3bl0OHDhAz549AejXrx/R0dEWCSoiIiIiUpZKPJXCzc2Np556iq5du+Lg4ECHDh3w9PSkZs2a2Nld7dbd3Z3ExMQi+8rPzycuLq6kUSwmMzPTojmy7ZyIT4gv9nK5OTmFLpfsbCI14bfSRrstlt4ed2oG5VAOERGpHEpcGF++fJno6Giio6OpUaMGY8aMISYm5oZ2JpOpyL5sbGzw8PAoaRSLiYuLs2iOsxfTqeduLvZy8Qnx1HOvd9PHXOq4cPddDUsb7bZYenvcqRmUQzluJ8dfQV5eHgMGDMDNzY333nvP2nGkEvr444+ZOXOm8d6eOnUqjz76qLVjSSVS4sJ437593H333Tg7OwPQo0cPYmNjSUlJITc3Fzs7OxISEnB1dbVYWBERKTtr1qyhcePGpKWlWTuKVEIff/wxU6dO5YMPPsDFxYXk5GSGDRsGoOJYyk2J5xjXr1+f7777joyMDMxmM/v376dJkyb4+Pjw+eefA/DZZ5/h6+trsbAiIlI2EhIS2L17NyEhIdaOIpXUzJkz+eCDD+jatStVqlSha9eufPDBB8ycOdPa0aQSKfGIccuWLenZsyf9+vXDzs4ODw8PHn74YR566CHGjh3LwoUL8fDwIDQ01JJ5RUSkDMyaNYsJEyZw5cqVIttmZWX9ZaaPSMURFxeHi4sLcXFxxvkD127r9SblpVS/YxwWFkZYWFiB+xo2bKifaBMRuYN88cUXODs7c//993Pw4MEi2zs4OFSIud3y1+Lh4UFycjJdu3Y15hh/8cUXeHh46PUmFnWrD1q68p2ISCX3zTffsGvXLnx9fQkPD+fAgQOMHz/e2rGkkpk6dSrDhg3jiy++ICcnhy+++IJhw4YxdepUa0eTSqRUI8YiInLnGzduHOPGjQPg4MGDrFixgvnz51s5lVQ2106wGz16tDFiPHPmTJ14J+VKhbGIiIhUCI8++iiPPvpohfkpRql8VBiLiIjBx8cHHx8fa8cQEbEKzTEWEREREUGFsYiIiIgIoMJYRERERARQYSwiIiIiAqgwFhEREREBVBiLiIiIiAAqjEVEREREABXGIiIiIiKACmMREREREUCFsYiIiIgIoMJYRERERARQYSwiIiIiAqgwFhEREREBwM7aASoSG4fqnL2YbrH+snLyLNaXiIiIiJQtFcbXycwzceTEHxbrz+tvtS3Wl4iIiIiUrVJNpUhJSSEsLAx/f3969epFbGwsly5dYujQofTo0YOhQ4dy+fJlS2UVERERESkzpSqMZ86cSadOndi+fTuRkZE0btyYZcuW0a5dO3bs2EG7du1YtmyZpbKKiIiIiJSZEhfGaWlpfP3114SEhABgb29PzZo1iY6OJjg4GIDg4GB27txpmaQiIiIiImWoxHOMz5w5g7OzM5MnT+bHH3/E09OTqVOnkpycjKurKwCurq5cuHChyL7y8/OJi4sraRSLycWB+ITfLdZf8zr2xCfEFz9HTk6hyyU7m0hN+K200W5LZmam1Z+XipBBOZRDREQqhxIXxrm5uRw7doxp06bRsmVLZsyYUeJpEzY2Nnh4eJQ0isV8d/IM9dzrWay/qtWqlai/+IT4QpdzqePC3Xc1LG202xIXF2f156UiZFAO5bidHCIicucr8VQKd3d33N3dadmyJQD+/v4cO3YMFxcXkpKSAEhKSsLZ2dkySUVEREREylCJC+O6devi7u7OL7/8AsD+/ftp3Lgxvr6+REREABAREUG3bt0sk1REREREpAyV6neMp02bxvjx48nJyaFhw4bMnj2b/Px8nn/+edavX0+9evV46623LJVVRERERKTMlKow9vDwYOPGjTfcv3r16tJ0KyIiIiJS7nTlOxGRSi4rK4vHH3+c7Oxs8vLy6NmzJ2FhYdaOJSJS7lQYi4hUcvb29qxevZrq1auTk5PDY489RufOnWnVqpW1o4mIlKtSXflORETufCaTierVqwNXf4ozNzcXk8lk5VQiIuVPI8YiIkJeXh79+/fn9OnTPPbYY8ZPcd5MVlaWfrtZiqVv37789NNPZdJ3kyZN2LRpU5n0LZWPCmMREcHW1pbIyEhSUlIYNWoUJ06coGnTpjdt6+DgUCEurCJ3jpMnTxar/f9NiuLXOQFllEYqu1t9sNdUChERMdSsWRMfHx/27t1r7SgiIuVOhbGISCV34cIFUlJSAMjMzGTfvn00atTIyqlERMqfplKIiFRySUlJTJo0iby8PMxmM/7+/nTt2tXasUREyp0KYxGRSq558+ZERERYO4aIiNVpKoWIiIiICCqMRUREREQAFcYiIiIiIoAKYxERERERQIWxiIiIiAigwlhEREREBFBhLCIiIiICqDAWEREREQFUGIuIiIiIACqMRUREREQACxTGeXl5BAcH8+yzzwJw5swZQkND6dGjB88//zzZ2dmlDikiIiIiUtZKXRivWbOGxo0bG7fnz5/Pk08+yY4dO6hZsybr168v7SpERERERMpcqQrjhIQEdu/eTUhICABms5kDBw7Qs2dPAPr160d0dHTpU4qIiIiIlLFSFcazZs1iwoQJ2Nhc7ebixYvUrFkTOzs7ANzd3UlMTCx9ShERERGRMmZX0gW/+OILnJ2duf/++zl48GCh7UwmU5F95efnExcXV9IoFpOLA/EJv1usv+Z17IlPiC9+jpycQpdLdjaRmvBbaaMZbByqk5l38+coFwe+O3mmWP052prJz7piiWgAZGZmVojXhnIoh4iI/PWVuDD+5ptv2LVrFzExMWRlZZGWlsbMmTNJSUkhNzcXOzs7EhIScHV1LbIvGxsbPDw8ShrFYr47eYZ67vUs1l/VatVK1F98Qnyhy7nUceHuuxqWNprh7MV0jpz4o5Acvxc7f+emdbj7rr9ZIhoAcXFxFeK1oRzKUVQOERG585V4KsW4ceOIiYlh165dLFiwgLZt2/LGG2/g4+PD559/DsBnn32Gr6+vxcKKiIiIiJQVi/+O8YQJE1i5ciV+fn5cunSJ0NBQS69CRERERMTiSjyV4no+Pj74+PgA0LBhQ/1Em4iIiIjccXTlOxERERERVBiLiIiIiAAqjEVEREREABXGIiIiIiKACmMREREREcBCv0ohIiJ3rvj4eCZOnMgff/yBjY0NAwcOZMiQIdaOJSJS7lQYi4hUcra2tkyaNAlPT0/S0tIYMGAAHTp0oEmTJtaOJiJSrjSVQkSkknN1dcXT0xMAJycnGjVqRGJiopVTiYiUP40Yi4iI4ezZs8TFxdGyZctC22RlZREXF1eOqaQiCv1yNCb7svkAVcMD/rF6ksX7NWe7sa7j2xbvV/46VBiLiAgAV65cISwsjClTpuDk5FRoOwcHBzw8PMoxmVREaavH8uucgDLpOy4urkxeY/83KQqPZ/Tarexu9cFeUylERIScnBzCwsIIDAykR48e1o4jImIVKoxFRCo5s9nM1KlTadSoEUOHDrV2HBERq1FhLCJSyR05coTIyEgOHDhAUFAQQUFB7Nmzx9qxRETKneYYi4hUct7e3hw/ftzaMURErE4jxiIiIiIiqDAWEREREQFUGIuIiIiIACqMRUREREQAFcYiIiIiIkApCuP4+HgGDRpEr169CAgIYPXq1QBcunSJoUOH0qNHD4YOHcrly5ctFlZEREREpKyUuDC2tbVl0qRJbNu2jX//+9989NFH/PTTTyxbtox27dqxY8cO2rVrx7JlyyyZV0RERESkTJS4MHZ1dcXT0xMAJycnGjVqRGJiItHR0QQHBwMQHBzMzp07LZNURERERKQMWWSO8dmzZ4mLi6Nly5YkJyfj6uoKXC2eL1y4YIlViIiIiIiUqVJf+e7KlSuEhYUxZcoUnJycStRHfn4+cXFxpY1Sark4EJ/wu8X6a17HnviE+OLnyMkpdLlkZxOpCb+VNpoh286p0HXdKkdhLJ0vMzOzQrw2lEM5RETkr69UhXFOTg5hYWEEBgbSo0cPAFxcXEhKSsLV1ZWkpCScnZ2L7MfGxgYPD4/SRLGI706eoZ57PYv1V7VatRL1F58QX+hyLnVcuPuuhqWNZjh7MZ167uZi5yiMpfPFxcVViNeGcihHUTlEROTOV+KpFGazmalTp9KoUSOGDh1q3O/r60tERAQAERERdOvWrfQpRURERETKWIlHjI8cOUJkZCRNmzYlKCgIgPDwcIYPH87zzz/P+vXrqVevHm+99ZbFwoqIiIiIlJUSF8be3t4cP378po9d+01jEREREZE7ha58JyIiIiKCCmMREREREUCFsYiIiIgIoMJYRERERARQYSwiIiIiAljgynciIiJSOf3fpKgy7P0Xi/dYq2oVi/cpfy0qjEVERKTYfp0TUGZ9/9+kqDLtX6QwmkohIiIiIoIKYxERERERQIWxiIiIiAigwlhEpNKbPHky7dq1o0+fPtaOIiJiVSqMRUQquf79+7N8+XJrxxARsToVxiIilVzr1q2pVauWtWOIiFidfq5NRESKJSsri7i4OGvHkDtI3759+emnn4q1jGnu7bVr0qQJmzZtKkEqkRupMBYRkWJxcHDAw8PD2jHkDnLy5MlitY+Li9NrTMrMrT7YayqFiIiIiAgqjEVEREREABXGIiKVXnh4OI888ginTp2ic+fOrFu3ztqRRESsQnOMRUQquQULFlg7gohIhaARYxERERERynDEOCYmhpkzZ5Kfn09oaCjDhw8vq1WJSBEup2eTmpVrsf5sHKpbrC8REZGKokwK47y8PF599VVWrlyJm5sbISEh+Pr60qRJk7JYnfyF2ThU5+zFdIv2WcPBjlrV7C3aZ0WXmpVLzIk/LNafp7PJYn2JiIhUFGVSGB89epR77rmHhg0bAhAQEEB0dLQKYym2zDwTRyxY0AF0blqn0hXGIiIiUrQyKYwTExNxd3c3bru5uXH06NFC25tMpgpxFSV7wKumBTu8dKVE/XnVrAlcueljqQlXiEsoXawb11f8HIWxdD6LPyeUPGNFeI1CyXNYdDvm3vnbw5KysrKsHaFc6cp3Uh70GpOycqt9dpkUxmaz+Yb7TKbCv3pt1apVWcQQEZEyoH22iPxVlcmvUri7u5OQ8L8hucTERFxdXctiVSIiIiIiFlEmhfE//vEPfv31V86cOUN2djZRUVH4+vqWxapERERERCyiTKZS2NnZMX36dJ5++mny8vIYMGAA9957b1msSkRERETEIkzmm00IFhERERGpZHTlOxERERERVBiLiIiIiABleEno21URLh09efJkdu/ejYuLC1u2bCn39QPEx8czceJE/vjjD2xsbBg4cCBDhgwp9xxZWVk8/vjjZGdnk5eXR8+ePQkLCyv3HNdcm6Pu5ubGe++9Z5UMvr6+VK9eHRsbG2xtbdm4caNVcqSkpPDiiy9y4sQJTCYTs2bNwsvLq1wz/PLLL4wdO9a4febMGcLCwnjyySfLNceqVatYt24dJpOJpk2bMnv2bBwcHMo1A8Dq1atZt24dZrOZ0NDQct8OIiJiYWYrys3NNXfr1s18+vRpc1ZWljkwMNB88uTJcs9x6NAh8/fff28OCAgo93Vfk5iYaP7+++/NZrPZnJqaau7Ro4dVtkV+fr45LS3NbDabzdnZ2eaQkBBzbGxsuee4ZsWKFebw8HDz8OHDrZaha9eu5uTkZKut/5qJEyeaP/30U7PZbDZnZWWZL1++bNU8ubm55vbt25vPnj1brutNSEgwd+3a1ZyRkWE2m83msLAw84YNG8o1g9lsNh8/ftwcEBBgTk9PN+fk5JiHDBliPnXqVLnnEPmzpk2bmmfPnm3cXr58uXnRokWFtv/Pf/5jleNNSS1cuND81VdfFWuZAwcOWPw4UhZ9ivVZdSrF9ZeOtre3Ny4dXd5at25NrVq1yn2913N1dcXT0xMAJycnGjVqRGJiYrnnMJlMVK9eHYDc3Fxyc3NveXGWspSQkMDu3bsJCQmxyvorkrS0NL7++mtjW9jb21OzpoUvCVhM+/fvp2HDhjRo0KDc152Xl0dmZia5ublkZmZa5XfSf/75Z1q2bEnVqlWxs7OjdevW/Oc//yn3HCJ/Zm9vz44dO7hw4cJttd+5cyc//fRTGaeynDFjxtC+fXtrx5C/KKsWxje7dLQ1isGK5uzZs8TFxdGyZUurrD8vL4+goCDat29P+/btrZZj1qxZTJgwARsb60+FHzZsGP379+ff//63VdZ/5swZnJ2dmTx5MsHBwUydOpX09HSrZLkmKiqKPn36lPt63dzceOqpp+jatSsdO3bEycmJjh07lnuOpk2bcvjwYS5evEhGRgYxMTEFLmwkYi12dnY8/PDDrF69usi233zzDbt27eL1118nKCiI06dP8+mnnzJgwAD69u3L6NGjycjIAGDkyJFEREQA8MknnzBu3Djg6qWbBw4cSGBgIKNGjeLy5csADBo0iHnz5hESEkLPnj05fPgwcHXK3uTJkwkMDCQ4OJgDBw4AsHHjRp577jlGjBiBr68v//rXv1i5ciXBwcEMHDiQS5cuATBp0iS2b98OXB1ge+SRR+jbty8hISGkpaUV+Tenp6czefJkBgwYQHBwMDt37gQgNDSUkydPGu0GDRrE999/X2j76x06dIigoCCCgoIIDg6+rRxSMVm14jAX89LRlcGVK1cICwtjypQpODk5WSWDra0tkZGR7Nmzh6NHj3LixIlyz/DFF1/g7OzM/fffX+7r/rOPP/6Yzz77jPfff5+1a9fy9ddfl3uG3Nxcjh07xqOPPkpERARVq1Zl2bJl5Z7jmuzsbHbt2oW/v3+5r/vy5ctER0cTHR3N3r17ycjIIDIystxzNG7cmKeffpqnnnqKp59+mmbNmmFra1vuOURu5vHHH2fz5s2kpqbest0DDzyAr68vEydOJDIykr/97W/4+fmxYcMGNm3aRKNGjVi/fj0Ar732GkuWLOHw4cOsXLmSadOmATBx4kTGjx/P5s2badq0KYsXLzb6z8vLY/369UyZMsW4f+3atQBs3ryZN954g0mTJpGVlQXAyZMnmT9/PuvXr+fNN9/E0dGRiIgIWrVqZRTl12RnZzN27FimTJnCpk2bWLVqFY6OjkVum6VLl9K2bVs2bNjAmjVrmDdvHunp6QQEBLBt2zYAkpKSSEpK4v777y+0/fVWrFjB9OnTiYyMZO3atbeVQyomqxbGunR0QTk5OYSFhREYGEiPHj2sHYeaNWvi4+PD3r17y33d10YxfH19CQ8P58CBA4wfP77cc8DVEUoAFxcX/Pz8OHr0aLlncHd3x93d3Ri99/f359ixY+We45qYmBg8PT2pU6dOua9737593H333Tg7O1OlShV69OhBbGxsueeAqyNMn332GWvXrqV27drcc889Vskh8mdOTk4EBQWxZs2aYi978uRJHnvsMQIDA9m8ebMxilqnTh3CwsIYPHgwL7zwArVr1yY1NZXU1FTatGkDQL9+/YyRYQA/Pz8APD09OXfuHABHjhyhb9++wNUPmPXr1+fUqVMA+Pj44OTkhLOzMzVq1DCumtu0aVNj+WtOnTpF3bp1adGihfE329kV/ZsCX375Je+//z5BQUEMGjSIrKws4uPj6dWrlzESvW3bNuODf2Htr/fAAw8wZ84c1qxZQ2pq6m3lkIrJqs/c9ZeOdnNzIyoqijfeeMOakazGbDYzdepUGjVqxNChQ62W48KFC9jZ2VGzZk0yMzPZt28fzzzzTLnnGDdunPE13cGDB1mxYgXz588v9xzp6enk5+fj5OREeno6X331Fc8991y556hbty7u7u788ssvNGrUiP3799O4ceNyz3FNVFQUAQEBVll3/fr1+e6778jIyMDR0ZH9+/db7ZuF5ORkXFxcOH/+PDt27LDaVBuRmxkyZAj9+/enf//+xVpu0qRJvPPOOzRv3pyNGzdy6NAh47ETJ05Qu3ZtkpKSbqsve3t7AGxsbMjLywNu/m3xn9tfW6ZKlSo3LH+N2Wwu8bfMixYtolGjRjfcX7t2bX788Ue2bdvGK6+8csv2f/zxh/H/4cOH06VLF/bs2cPAgQNZuXKlVffRUnJWHTG+/tLRvXv3plevXla5dHR4eDiPPPIIp06donPnzqxbt67cMxw5coTIyEgOHDhgzFPas2dPuedISkpi8ODBBAYGEhISQvv27enatWu556gokpOTeeyxx+jbty+hoaF06dKFzp07WyXLtGnTGD9+PIGBgcTFxTFixAir5MjIyGDfvn1W+1ajZcuW9OzZk379+hEYGEh+fj4PP/ywVbKMHj2a3r17M2LECF566SWrn8Qrcr3atWvj7+9vTIUoTPXq1bly5Ypx+8qVK9StW5ecnBw2b95s3H/06FFiYmL47LPPWLFiBWfOnKFGjRrUrFnTGCWOjIykdevWt1xf69atjX5PnTpFfHz8TYvUojRq1IikpCTjW7y0tDRyc3OLXK5jx47861//Mgr06799CwgIYPny5aSmptKsWbMi219z+vRpmjVrxvDhw7n//vuNEXC581h9rL9Lly506dLFqhkWLFhg1fUDeHt7c/z4cWvHoHnz5jfM47I2Hx8ffHx8rLLuhg0bsmnTJqus+888PDys9hvK16tatSoHDx60aoawsDCr/r72NR999JG1I4jc0lNPPWXM6S1M7969mTZtGh9++CGLFi1izJgxhIaG0qBBA5o2bcqVK1fIzs7mxRdfZPbs2bi5ufHCCy8wZcoU1qxZw9y5c3nppZfIyMigYcOGzJ49+5bre+yxx3jppZcIDAzE1taW2bNnFxgpvl329va8+eabzJgxg8zMTBwdHVm5cmWR0xiee+45Zs2aRd++fTGbzTRo0MD4nfyePXsyc+bMAt8M3qr9NatXr+bgwYPY2NjQpEkTqw2gSOmZzLf6TkNEREREpJKw/u9giYiIiIhUAFafSiEiIiJl79133zV+deEaf39/Ro4caaVElrd3794bTtS+++67WbJkiZUSyZ1GUylERERERNBUChERERERQIWxiIiIiAigwlhuQ7NmzZgzZ45x+4MPPuDtt98utP3OnTv56aefyiOaRbz11lvs27evWMscPHiQZ5991qI5yqJPERERuX0qjKVI9vb27NixgwsXLtxW+zutMB4zZgzt27e3dgwRERGxMhXGUiQ7OzsefvhhVq9eXWTbb775hl27dvH6668TFBTE6dOn+fTTTxkwYAB9+/Zl9OjRZGRkADBy5EjjYiKffPKJcQnouLg4Bg4cSGBgIKNGjeLy5csADBo0iHnz5hESEkLPnj2NKy1lZWUxefJkAgMDCQ4O5sCBAwBs3LiR5557jhEjRuDr68u//vUvVq5cSXBwMAMHDuTSpUvA1cufXjtT++jRozzyyCP07duXkJAQ0tLSivyb09PTmTx5MgMGDCA4OJidO3cCEBoaysmTJ412gwYN4vvvvy+0/fUOHTpkXAExODj4tnKIiIhI6agwltvy+OOPs3nzZlJTU2/Z7oEHHsDX15eJEycSGRnJ3/72N/z8/NiwYQObNm2iUaNGxuVJX3vtNZYsWcLhw4dZuXIl06ZNA2DixImMHz+ezZs307RpUxYvXmz0n5eXx/r165kyZYpx/7WrOm3evJk33niDSZMmkZWVBcDJkyeZP38+69ev580338TR0ZGIiAhatWp1wxX+srOzGTt2LFOmTGHTpk2sWrUKR0fHIrfN0qVLadu2LRs2bGDNmjXMmzeP9PR0AgIC2LZtG3D1UttJSUncf//9hba/3ooVK5g+fTqRkZGsXbv2tnKIiIhI6eh3jOW2ODk5ERQUxJo1a4pdpJ08eZKFCxeSmprKlStX6NixIwB16tQhLCyMwYMHs3jxYmrXrk1qaiqpqam0adMGgH79+jFmzBijLz8/PwA8PT05d+4cAEeOHOGJJ54AoHHjxtSvX9+4Tr2Pjw9OTk4A1Pj/9u4epJEtDOP4X8EghEiaYGMVwViIhZBOFCx0oplBAlrYiFMEVFCsFLERxC8EUVAbjQjWigimE/yoLFOo2ARiEQiChabRaLZYMty97sZxDcNkwwAAAopJREFU7122eX7VzDnvmTmnezm8c8bno6OjA4CGhoYPv+BOp9MEAgGam5udNbtxeXnJ6ekpiUQC+L6Dnc1miUQiDA0NMTY2RjKZxDCMsvH/1NLSwuLiIqZp0tnZidfrdTUXERER+X1KjMW1wcFBYrEYsVjsS+OmpqbY3NyksbGRg4MDrq6unL67uzv8fj+5XM7VszweDwCVlZW8vb0BUO4o7lJ8aUxVVdWH8SXFYpGKigp3i/qX9fV1gsHgh3a/38/t7S3JZJLZ2dmy8Q8PD851PB6nvb2ds7Mz+vv72d3dpb6+/rfmJiIiIu6olEJc8/v9GIbhlEL8itfrJZ/PO/f5fJ5AIMDr6yvHx8dOeyqV4vz8nMPDQxKJBPf39/h8Pmpqapz64aOjI8LhcNn3hcNh57npdJpsNvvTJPUzwWCQXC5HKpUC4Pn5mUKh8Om41tZW9vf3nQT9+vra6evp6WF7e5unpydCodCn8SWZTIZQKEQ8HqepqcnZARcREZE/R4mxfIlt2zw+PpaN6e7uZmdnh97eXjKZDOPj4/T19WHbtpOwvry8MDMzw/z8PLW1tUxOTjI9PU2xWGRpaYnl5WVM0+Tm5obR0dGy7xsYGOD9/R3TNJmYmGBhYeGHnWK3PB4Pq6urzM3NYVkWtm07tcrljIyMUCgUsCyLaDTK2tqa09fV1cXJyQmRSMRVfMne3h7RaBTLsqiurqatre3L6xEREZGv0S+hRURERETQjrGIiIiICKCP7+Q/2Nracs7/LTEMg+Hh4b80o//fxcUFKysrP7TV1dWxsbHxl2YkIiIif4pKKUREREREUCmFiIiIiAigxFhEREREBFBiLCIiIiICKDEWEREREQHgGx2sMSgGZG2QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "ax = sns.distplot(vogdb_top_genomes_lca_df['N_taxonomic_levels'], kde=False, bins=10)\n",
    "plt.title('LCA Taxonomic Levels Distribution')\n",
    "plt.xticks(np.arange(0, 10, 1.0))\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "ax = vogdb_top_genomes_lca_df.boxplot(column='N_taxonomic_levels')\n",
    "plt.title('Boxplot LCA Taxonomic Levels')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:darkgreen;font-weight:bold;\">\n",
    "For larger groups, the resolution for the linage drops.\n",
    "</span>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}