Figure 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROS ALE Figure 4\n",
    "\n",
    "Generates Figure 4 from the manuscript \"Lab evolution and advanced data analytics reveal systems biology of ROS tolerance\" by Kevin Rychel et al., 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Set-Up\" data-toc-modified-id=\"Set-Up-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Set Up</a></span></li><li><span><a href=\"#Write-Special-Functions\" data-toc-modified-id=\"Write-Special-Functions-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Write Special Functions</a></span><ul class=\"toc-item\"><li><span><a href=\"#Fur\" data-toc-modified-id=\"Fur-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Fur</a></span></li><li><span><a href=\"#PitA\" data-toc-modified-id=\"PitA-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>PitA</a></span></li></ul></li><li><span><a href=\"#Generate-Figure\" data-toc-modified-id=\"Generate-Figure-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Generate Figure</a></span></li><li><span><a href=\"#Statistics\" data-toc-modified-id=\"Statistics-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Statistics</a></span><ul class=\"toc-item\"><li><span><a href=\"#Correlation-with-PQ\" data-toc-modified-id=\"Correlation-with-PQ-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Correlation with PQ</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use py file to set up workspace\n",
    "from src.ros_ale_functions import *\n",
    "\n",
    "# load data\n",
    "ica = load_json_model('input/precise1k_kr.json')\n",
    "ros_meta = pd.read_csv('input/ros_ale_p1k_metadata.csv', index_col = 0)\n",
    "\n",
    "# we'll need this function for panel E\n",
    "from matplotlib_venn import venn2\n",
    "\n",
    "# this will be needed for panel M\n",
    "gc_0_0 = pd.read_csv('input/pitA/0_0_pitA_growth_data.csv', index_col = 0)\n",
    "gc_1_0 = pd.read_csv('input/pitA/1_0_pitA_growth_data.csv', index_col = 0)\n",
    "\n",
    "# this will be needed for panel N\n",
    "pitA_ica = load_json_model('input/pitA/pitA_data.json')\n",
    "pitA_meta = pd.read_csv('input/pitA/pitA_meta.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Special Functions\n",
    "\n",
    "Any functions called more than once are in src.ros_ale_functions. This figure uses a few extra functions.\n",
    "\n",
    "### Fur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fur_fit(ica):\n",
    "    \"\"\"\n",
    "    Fits a log curve to the Fur-1 and Fur-2 activities (red line in fig 4C)\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ica: the main data object\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    x_shift, y_shift: arrays defining the curve\n",
    "    \"\"\"\n",
    "    # compute the best fit line\n",
    "    im_x = 'Fur-1'\n",
    "    im_y = 'Fur-2'\n",
    "\n",
    "    non_ros = ica.sample_table.index[ica.sample_table.project != 'ros_tale']\n",
    "    train_data = ica.A.loc[[im_x, im_y], non_ros]\n",
    "    x_adj = train_data.loc[im_x].min()-1\n",
    "    y_adj = train_data.loc[im_y].min()-1\n",
    "    train_data = train_data.sub([x_adj, y_adj], axis = 0)\n",
    "\n",
    "    def objective(x, a, b):\n",
    "        return a*np.log(x) + b #a * x / (b + x)\n",
    "\n",
    "    popt, pcov = curve_fit(objective, train_data.loc[im_x], train_data.loc[im_y])\n",
    "\n",
    "    x = np.linspace(train_data.loc[im_x].min(), train_data.loc[im_x].max(), 100)\n",
    "    y = [objective(i, popt[0], popt[1]) for i in x]\n",
    "    x_shift = np.array(x) + x_adj\n",
    "    y_shift = np.array(y) + y_adj\n",
    "    \n",
    "    return x_shift, y_shift\n",
    "\n",
    "def get_fur_distances(ica, ros_meta, x_shift, y_shift):\n",
    "    \"\"\"\n",
    "    Computes the distance for all points to the Fur curve\n",
    "    for plotting in fig 4F\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ica, ros_meta: main data object and metadata\n",
    "    x_shift, y_shift: vectors for the fur curve from get_fur_fit()\n",
    "    \"\"\"\n",
    "    # compute the distances & label points\n",
    "    res = pd.DataFrame(index = ica.sample_table.index)\n",
    "    res['Fur-1'] = ica.A.loc['Fur-1']\n",
    "    res['Fur-2'] = ica.A.loc['Fur-2']\n",
    "    mut_dict = {'0_0':'Start',\n",
    "                '1_2':'P18T',\n",
    "                '1_4':'P18T',\n",
    "                '3_4':'P18T',\n",
    "                '1_3':'H71Y',\n",
    "                '3_2':'A53G'}\n",
    "    res['Condition'] = 'Other'\n",
    "\n",
    "    for i, row in res.iterrows():\n",
    "        # add the condition\n",
    "        if i in ros_meta.index:\n",
    "            res.loc[i, 'Condition'] = mut_dict.get(ros_meta.lineage[i], 'No fur Mut')\n",
    "\n",
    "        # calculate the distance\n",
    "        dist = pd.DataFrame(index = x_shift, columns = ['dist', 'sign'])\n",
    "        for x_, y_ in zip(x_shift, y_shift):\n",
    "            dist.loc[x_, 'dist'] = np.sqrt((row['Fur-1']-x_)**2 + (row['Fur-2']-y_)**2)\n",
    "            dist.loc[x_, 'sign'] = -1 if row['Fur-2']<y_ else 1\n",
    "        res.loc[i, 'Distance'] = dist.dist.min() * dist.sign[dist.dist.astype(float).idxmin()]\n",
    "    \n",
    "    # decided not to show all the other samples\n",
    "    res = res.loc[res.Condition != 'Other']\n",
    "    \n",
    "    # sort order\n",
    "    order = ['Start', 'No fur Mut', 'P18T', 'A53G', 'H71Y']\n",
    "    res.Condition = res.Condition.astype('category')\n",
    "    res.Condition.cat.set_categories(order, ordered=True, inplace = True)\n",
    "    res = res.sort_values(by = 'Condition')\n",
    "    \n",
    "    return res\n",
    "\n",
    "def fur_venn(ica, ax):\n",
    "    \"\"\"\n",
    "    Draws a venn diagram of Fur genes for fig 4E\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ica: main data object\n",
    "    ax: axes to plot to\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    fur_gene_dict: a dictionary to help in labeling the venn\n",
    "    \"\"\"\n",
    "    fur1_genes = set(ica.view_imodulon('Fur-1').index)\n",
    "    fur2_genes = set(ica.view_imodulon('Fur-2').index)\n",
    "    overlap = fur1_genes.intersection(fur2_genes)\n",
    "    \n",
    "    ax = venn2((fur1_genes, fur2_genes), ('Fur-1', 'Fur-2'), ax = ax)\n",
    "    \n",
    "    fur_gene_dict = {'Fur-1 Only': ica.gene_table.loc[fur1_genes - overlap],\n",
    "                     'Overlap': ica.gene_table.loc[overlap],\n",
    "                     'Fur-2 Only': ica.gene_table.loc[fur2_genes - overlap]}\n",
    "    return fur_gene_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PitA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pitA_DiMA(pitA_ica, pitA_meta, ax):\n",
    "    \"\"\"\n",
    "    Draws the DiMA plot for the pitA mutation in fig 4N\n",
    "    Relies on the pymodulon function plot_dima()\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    pitA_ica: IcaData pymodulon object for pitA strains\n",
    "    pitA_meta: metadata for the pitA strains\n",
    "    ax: axes to plot to\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pitA_dima_df: dataframe of statistical results\n",
    "    \"\"\"\n",
    "    mut = pitA_meta.index[(pitA_meta.PQ > 0)& \\\n",
    "                    (pitA_meta.Strain.isin(['1_0', '0_0_pitA_mut']))]\n",
    "    no_mut = pitA_meta.index[(pitA_meta.PQ > 0)& \\\n",
    "                    (pitA_meta.Strain.isin(['0_0', '1_0_pitA_no_mut']))]\n",
    "\n",
    "    ax, pita_dima_df = plot_dima(pitA_ica, no_mut, mut, ax = ax, table = True,\n",
    "                                 scatter_kwargs = {'s': 12},\n",
    "                                 label_font_kwargs = {'fontsize': 7})\n",
    "    \n",
    "    ax.set_xlabel('No PitA Mutation', fontsize = 7)\n",
    "    ax.set_ylabel('With PitA Frameshift', fontsize = 7)\n",
    "    ax.tick_params(labelsize = 7)\n",
    "    \n",
    "    return pita_dima_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Figure\n",
    "\n",
    "Final rearrangement is performed and panels K and O are generated in Abode Illustrator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 845.975x926.25 with 19 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# layout\n",
    "scale = 0.95 # used to add space between panels in illustrator\n",
    "fig = plt.figure(figsize = (6.85*scale, 7.5*scale), dpi = 130)\n",
    "subfigs = fig.subfigures(2, 1, height_ratios = [2, .8])\n",
    "top_subfigs = subfigs[0].subfigures(2, 3, width_ratios = [1, 1.66, 1])\n",
    "btm_subfigs = subfigs[1].subfigures(1, 5, width_ratios = [.75, 1, 1, 1.5, .75])\n",
    "\n",
    "panel_a = top_subfigs[0][0].subplots()\n",
    "panel_e = top_subfigs[0][2].subplots()\n",
    "panel_fg = top_subfigs[1][0].subplots(2, 1, sharex = True)\n",
    "\n",
    "panel_l = btm_subfigs[0].subplots()\n",
    "panel_m = btm_subfigs[1].subplots()\n",
    "panel_n = btm_subfigs[2].subplots(4, 1, sharey = True, sharex = True,\n",
    "                                  gridspec_kw = {'hspace': 0.01})\n",
    "panel_o = btm_subfigs[3].subplots()\n",
    "\n",
    "\n",
    "# oxyR - panel A\n",
    "oxyR_dict = {'Start': ['0_0'],\n",
    "             'No oxyR mut': ['1_0','1_1','1_2','1_4',\n",
    "                         '2_0','2_1','2_2','2_3',\n",
    "                         '3_0','3_1','3_2','3_3','3_4'],\n",
    "             'oxyR P99L': ['1_3'],\n",
    "             'oxyR G197V': ['2_1'],\n",
    "             'oxyR A213T':['1_2']}\n",
    "im_bar_swarm(ica, ros_meta, 'OxyR', strain_dict = oxyR_dict, \n",
    "              ax = panel_a)\n",
    "\n",
    "# fur - main panels BCD\n",
    "fur_dict = {'Start':['0_0'],\n",
    "            'fur P18T':['1_2','1_4', '3_4'],\n",
    "            'fur H71Y':['1_3'],\n",
    "            'fur A53G':['3_2'],\n",
    "            'No fur mut':['1_0', '1_1', '2_0', '2_1',\n",
    "                           '2_2', '2_3', '3_0', '3_1',\n",
    "                           '3_3']}\n",
    "fur_colors = {'Start':'k',\n",
    "              'fur P18T':'tab:blue',\n",
    "              'fur H71Y':'tab:orange',\n",
    "              'fur A53G':'tab:green',\n",
    "              'No fur mut':'tab:gray'}\n",
    "fur_axs = scatter_swarm(ica, ros_meta, 'Fur-1', 'Fur-2',\n",
    "                        strain_dict = fur_dict, colors = fur_colors,\n",
    "                        fig = top_subfigs[0][1])\n",
    "# fur - curve in panel C\n",
    "x_shift, y_shift = get_fur_fit(ica)\n",
    "fur_axs[1][0].plot(x_shift, y_shift, color = 'r', linewidth = 1,\n",
    "                   linestyle = '--', label = 'Trendline')\n",
    "\n",
    "# fur - panel E\n",
    "fur_gene_dict = fur_venn(ica, panel_e)\n",
    "\n",
    "# fur - distances to curve, panel F\n",
    "fur_dist_data = get_fur_distances(ica, ros_meta, x_shift, y_shift)\n",
    "special_bar_swarm(ica, ros_meta, fur_dist_data, 'Distance',\n",
    "                  strain_dict = fur_dict, ax = panel_fg[0],\n",
    "                  dodge = False, zerolinewidth = 0)\n",
    "panel_fg[0].axhline(color = 'r', linewidth = 1, linestyle = '--')\n",
    "panel_fg[0].set_ylabel('Distance to\\nTrendline', fontsize = 7)\n",
    "\n",
    "# fur distances - panel G\n",
    "gene_bar_swarm(ica, ros_meta, ica.name2num('feoA'), \n",
    "               strain_dict = fur_dict, ax = panel_fg[1],\n",
    "               dodge = False)\n",
    "\n",
    "# iscR\n",
    "iscr_dict = {'Start':['0_0'],\n",
    "             'iscR V55L':['1_0', '1_1', '1_2', '1_3', '1_4'],\n",
    "             'iscR C104S':['2_0', '2_1', '2_2', '2_3'],\n",
    "             'iscR T106P':['3_1'],\n",
    "             'iscR V87A':['3_2'],\n",
    "             'iscR C98G':['3_3'],\n",
    "             'No iscR mut':['3_0', '3_4']}\n",
    "iscr_colors = {'Start':'k',\n",
    "         'iscR V55L':'tab:blue',\n",
    "         'iscR C104S':'tab:orange',\n",
    "         'iscR T106P':'tab:green',\n",
    "         'iscR V87A':'tab:red',\n",
    "         'iscR C98G':'tab:purple',\n",
    "         'No iscR mut':'tab:gray'}\n",
    "iscr_axs = scatter_swarm(ica, ros_meta, 'Isc System', 'Suf System',\n",
    "                    strain_dict = iscr_dict, colors = iscr_colors,\n",
    "                         fig = top_subfigs[1][1])\n",
    "isc_name, suf_name = 'Isc iModulon Activity', 'Suf iModulon Activity'\n",
    "iscr_axs[1][0].set_xlabel(isc_name)\n",
    "iscr_axs[0][0].set_xlabel(isc_name)\n",
    "iscr_axs[1][0].set_ylabel(suf_name)\n",
    "iscr_axs[1][1].set_ylabel(suf_name)\n",
    "\n",
    "# soxS\n",
    "im_bar_swarm(ica, ros_meta, 'Superoxide', ax = panel_l)\n",
    "panel_l.set_ylabel('SoxS iModulon Activity')\n",
    "\n",
    "# pitA & FliA\n",
    "pitA_dict = {'Start':['0_0'],\n",
    "             'pitA* (1_X)':['1_0', '1_1', '1_2', '1_3', '1_4'],\n",
    "             'ΔpitA (3_X)':['3_0', '3_1', '3_2', '3_3', '3_4'],\n",
    "             'No pitA mut (2_X)':['2_0', '2_1', '2_2', '2_3']}\n",
    "im_bar_swarm(ica, ros_meta, 'FliA', strain_dict = pitA_dict,\n",
    "             ax = panel_m, dodge = False)\n",
    "\n",
    "# pitA mutant growth curves\n",
    "gc_plot(gc_0_0, 0, ax = panel_n[0])\n",
    "gc_plot(gc_0_0, 20, ax = panel_n[1])\n",
    "gc_plot(gc_1_0, 0, ax = panel_n[2])\n",
    "gc_plot(gc_1_0, 600, ax = panel_n[3])\n",
    "\n",
    "# pitA DiMA\n",
    "pita_dima_df = pitA_DiMA(pitA_ica, pitA_meta, panel_o)\n",
    "\n",
    "plt.savefig('output/fig4_raw.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fur-1 Only\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>gene_name</th>\n",
       "      <th>synonyms</th>\n",
       "      <th>gene_product</th>\n",
       "      <th>COG</th>\n",
       "      <th>regulator</th>\n",
       "      <th>uniprot</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>essential</th>\n",
       "      <th>...</th>\n",
       "      <th>y_ome</th>\n",
       "      <th>k_eff</th>\n",
       "      <th>schmidt_prot_med</th>\n",
       "      <th>heckmann_prot_med</th>\n",
       "      <th>proteomics</th>\n",
       "      <th>p1k</th>\n",
       "      <th>p1k_ctrl_log_tpm</th>\n",
       "      <th>p1k_median_log_tpm</th>\n",
       "      <th>p1k_mad_log_tpm</th>\n",
       "      <th>in_iM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b0583</th>\n",
       "      <td>entD</td>\n",
       "      <td>entD</td>\n",
       "      <td>phosphopantetheinyl transferase EntD</td>\n",
       "      <td>Coenzyme transport and metabolism</td>\n",
       "      <td>RpoD,Crp,Fur</td>\n",
       "      <td>P19925</td>\n",
       "      <td>609459</td>\n",
       "      <td>610079</td>\n",
       "      <td>-</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.528960</td>\n",
       "      <td>3.216326</td>\n",
       "      <td>1.226081</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1656</th>\n",
       "      <td>sodB</td>\n",
       "      <td>sodB</td>\n",
       "      <td>superoxide dismutase (Fe)</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>RpoD,ryhB,fnrS,IHF,Crp,NsrR</td>\n",
       "      <td>P0AGD3</td>\n",
       "      <td>1735378</td>\n",
       "      <td>1735959</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4451.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9.475278</td>\n",
       "      <td>9.512928</td>\n",
       "      <td>1.160733</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1682</th>\n",
       "      <td>sufC</td>\n",
       "      <td>sufC;ynhD</td>\n",
       "      <td>Fe-S cluster scaffold complex subunit SufC</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp</td>\n",
       "      <td>P77499</td>\n",
       "      <td>1761766</td>\n",
       "      <td>1762512</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>545.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.565601</td>\n",
       "      <td>6.213937</td>\n",
       "      <td>1.234267</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2675</th>\n",
       "      <td>nrdE</td>\n",
       "      <td>nrdE</td>\n",
       "      <td>ribonucleoside-diphosphate reductase 2 subunit...</td>\n",
       "      <td>Nucleotide transport and metabolism</td>\n",
       "      <td>RpoD,Fur,IscR,ppGpp,NrdR</td>\n",
       "      <td>P39452</td>\n",
       "      <td>2801348</td>\n",
       "      <td>2803492</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.605689</td>\n",
       "      <td>4.881500</td>\n",
       "      <td>1.877694</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0805</th>\n",
       "      <td>fiu</td>\n",
       "      <td>fiu;ybiL</td>\n",
       "      <td>putative iron siderophore outer membrane trans...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur,Crp,rybB</td>\n",
       "      <td>P75780</td>\n",
       "      <td>839249</td>\n",
       "      <td>841531</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>129.5</td>\n",
       "      <td>514.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.383400</td>\n",
       "      <td>4.592012</td>\n",
       "      <td>2.194376</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0586</th>\n",
       "      <td>entF</td>\n",
       "      <td>entF</td>\n",
       "      <td>apo-serine activating enzyme</td>\n",
       "      <td>Secondary metabolites biosynthesis, transport,...</td>\n",
       "      <td>RpoD,YbaQ,H-NS,Fnr,Fur</td>\n",
       "      <td>P11454</td>\n",
       "      <td>614157</td>\n",
       "      <td>618038</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>32.5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.468597</td>\n",
       "      <td>4.647339</td>\n",
       "      <td>1.572830</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1681</th>\n",
       "      <td>sufD</td>\n",
       "      <td>sufD;ynhC</td>\n",
       "      <td>Fe-S cluster scaffold complex subunit SufD</td>\n",
       "      <td>Post-translational modification, protein turno...</td>\n",
       "      <td>RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp</td>\n",
       "      <td>P77689</td>\n",
       "      <td>1760520</td>\n",
       "      <td>1761791</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>218.5</td>\n",
       "      <td>179.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.049283</td>\n",
       "      <td>6.244747</td>\n",
       "      <td>1.300103</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0803</th>\n",
       "      <td>ybiI</td>\n",
       "      <td>ybiI</td>\n",
       "      <td>zinc finger domain-containing protein YbiI</td>\n",
       "      <td>Signal transduction mechanisms</td>\n",
       "      <td>RpoS,Lrp</td>\n",
       "      <td>P41039</td>\n",
       "      <td>838190</td>\n",
       "      <td>838456</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.223423</td>\n",
       "      <td>4.695483</td>\n",
       "      <td>1.306778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0595</th>\n",
       "      <td>entB</td>\n",
       "      <td>entB;entG</td>\n",
       "      <td>enterobactin synthase component B</td>\n",
       "      <td>Secondary metabolites biosynthesis, transport,...</td>\n",
       "      <td>RpoD,ryhB,Crp,Fur</td>\n",
       "      <td>P0ADI4</td>\n",
       "      <td>627694</td>\n",
       "      <td>628551</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>161.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.947376</td>\n",
       "      <td>5.332772</td>\n",
       "      <td>1.641641</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2155</th>\n",
       "      <td>cirA</td>\n",
       "      <td>cirA;cir;feuA</td>\n",
       "      <td>ferric dihyroxybenzoylserine outer membrane tr...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,omrB,omrA,Crp,Fur,Hfq,ryhB</td>\n",
       "      <td>P17315</td>\n",
       "      <td>2244778</td>\n",
       "      <td>2246769</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>427.5</td>\n",
       "      <td>586.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.420057</td>\n",
       "      <td>6.077157</td>\n",
       "      <td>2.096917</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1102</th>\n",
       "      <td>fhuE</td>\n",
       "      <td>fhuE</td>\n",
       "      <td>ferric coprogen/ferric rhodotorulic acid outer...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>Fur,GlaR</td>\n",
       "      <td>P16869</td>\n",
       "      <td>1159362</td>\n",
       "      <td>1161551</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.121129</td>\n",
       "      <td>3.924689</td>\n",
       "      <td>1.540126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1680</th>\n",
       "      <td>sufS</td>\n",
       "      <td>sufS;csdB;ynhB</td>\n",
       "      <td>L-cysteine desulfurase</td>\n",
       "      <td>Coenzyme transport and metabolism</td>\n",
       "      <td>RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp</td>\n",
       "      <td>P77444</td>\n",
       "      <td>1759303</td>\n",
       "      <td>1760523</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>366.5</td>\n",
       "      <td>262.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.452534</td>\n",
       "      <td>6.089898</td>\n",
       "      <td>1.272178</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1679</th>\n",
       "      <td>sufE</td>\n",
       "      <td>sufE;ynhA</td>\n",
       "      <td>sulfur acceptor for SufS cysteine desulfurase</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>RpoD,NsrR,IscR,IHF,OxyR,Fur,ppGpp</td>\n",
       "      <td>P76194</td>\n",
       "      <td>1758874</td>\n",
       "      <td>1759290</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.542428</td>\n",
       "      <td>5.686782</td>\n",
       "      <td>1.145125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0594</th>\n",
       "      <td>entE</td>\n",
       "      <td>entE</td>\n",
       "      <td>2%2C3-dihydroxybenzoate-AMP ligase</td>\n",
       "      <td>Secondary metabolites biosynthesis, transport,...</td>\n",
       "      <td>RpoD,ryhB,Crp,Fur</td>\n",
       "      <td>P10378</td>\n",
       "      <td>626070</td>\n",
       "      <td>627680</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>80.5</td>\n",
       "      <td>135.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.271929</td>\n",
       "      <td>5.024975</td>\n",
       "      <td>1.894910</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0584</th>\n",
       "      <td>fepA</td>\n",
       "      <td>fepA;fep;feuB</td>\n",
       "      <td>ferric enterobactin outer membrane transporter</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,omrA,YfeD,Crp,Fur,omrB</td>\n",
       "      <td>P05825</td>\n",
       "      <td>610254</td>\n",
       "      <td>612494</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>217.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.745851</td>\n",
       "      <td>5.043457</td>\n",
       "      <td>1.751003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2676</th>\n",
       "      <td>nrdF</td>\n",
       "      <td>nrdF;ygaD</td>\n",
       "      <td>ribonucleoside-diphosphate reductase 2 subunit...</td>\n",
       "      <td>Nucleotide transport and metabolism</td>\n",
       "      <td>RpoD,Fur,IscR,ppGpp,NrdR</td>\n",
       "      <td>P37146</td>\n",
       "      <td>2803502</td>\n",
       "      <td>2804461</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>29.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.079753</td>\n",
       "      <td>4.575434</td>\n",
       "      <td>1.764918</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4567</th>\n",
       "      <td>yjjZ</td>\n",
       "      <td>yjjZ</td>\n",
       "      <td>protein YjjZ</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>RpoD,ppGpp,OxyR,Fur</td>\n",
       "      <td>P55914</td>\n",
       "      <td>4605804</td>\n",
       "      <td>4606040</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.369153</td>\n",
       "      <td>3.432278</td>\n",
       "      <td>2.032587</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gene_name        synonyms  \\\n",
       "b0583      entD            entD   \n",
       "b1656      sodB            sodB   \n",
       "b1682      sufC       sufC;ynhD   \n",
       "b2675      nrdE            nrdE   \n",
       "b0805       fiu        fiu;ybiL   \n",
       "b0586      entF            entF   \n",
       "b1681      sufD       sufD;ynhC   \n",
       "b0803      ybiI            ybiI   \n",
       "b0595      entB       entB;entG   \n",
       "b2155      cirA   cirA;cir;feuA   \n",
       "b1102      fhuE            fhuE   \n",
       "b1680      sufS  sufS;csdB;ynhB   \n",
       "b1679      sufE       sufE;ynhA   \n",
       "b0594      entE            entE   \n",
       "b0584      fepA   fepA;fep;feuB   \n",
       "b2676      nrdF       nrdF;ygaD   \n",
       "b4567      yjjZ            yjjZ   \n",
       "\n",
       "                                            gene_product  \\\n",
       "b0583               phosphopantetheinyl transferase EntD   \n",
       "b1656                          superoxide dismutase (Fe)   \n",
       "b1682         Fe-S cluster scaffold complex subunit SufC   \n",
       "b2675  ribonucleoside-diphosphate reductase 2 subunit...   \n",
       "b0805  putative iron siderophore outer membrane trans...   \n",
       "b0586                       apo-serine activating enzyme   \n",
       "b1681         Fe-S cluster scaffold complex subunit SufD   \n",
       "b0803         zinc finger domain-containing protein YbiI   \n",
       "b0595                  enterobactin synthase component B   \n",
       "b2155  ferric dihyroxybenzoylserine outer membrane tr...   \n",
       "b1102  ferric coprogen/ferric rhodotorulic acid outer...   \n",
       "b1680                             L-cysteine desulfurase   \n",
       "b1679      sulfur acceptor for SufS cysteine desulfurase   \n",
       "b0594                 2%2C3-dihydroxybenzoate-AMP ligase   \n",
       "b0584     ferric enterobactin outer membrane transporter   \n",
       "b2676  ribonucleoside-diphosphate reductase 2 subunit...   \n",
       "b4567                                       protein YjjZ   \n",
       "\n",
       "                                                     COG  \\\n",
       "b0583                  Coenzyme transport and metabolism   \n",
       "b1656                   Energy production and conversion   \n",
       "b1682             Inorganic ion transport and metabolism   \n",
       "b2675                Nucleotide transport and metabolism   \n",
       "b0805             Inorganic ion transport and metabolism   \n",
       "b0586  Secondary metabolites biosynthesis, transport,...   \n",
       "b1681  Post-translational modification, protein turno...   \n",
       "b0803                     Signal transduction mechanisms   \n",
       "b0595  Secondary metabolites biosynthesis, transport,...   \n",
       "b2155             Inorganic ion transport and metabolism   \n",
       "b1102             Inorganic ion transport and metabolism   \n",
       "b1680                  Coenzyme transport and metabolism   \n",
       "b1679                                   Function unknown   \n",
       "b0594  Secondary metabolites biosynthesis, transport,...   \n",
       "b0584             Inorganic ion transport and metabolism   \n",
       "b2676                Nucleotide transport and metabolism   \n",
       "b4567                                   Function unknown   \n",
       "\n",
       "                               regulator uniprot    start      end strand  \\\n",
       "b0583                       RpoD,Crp,Fur  P19925   609459   610079      -   \n",
       "b1656        RpoD,ryhB,fnrS,IHF,Crp,NsrR  P0AGD3  1735378  1735959      +   \n",
       "b1682  RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp  P77499  1761766  1762512      -   \n",
       "b2675           RpoD,Fur,IscR,ppGpp,NrdR  P39452  2801348  2803492      +   \n",
       "b0805                  RpoD,Fur,Crp,rybB  P75780   839249   841531      -   \n",
       "b0586             RpoD,YbaQ,H-NS,Fnr,Fur  P11454   614157   618038      +   \n",
       "b1681  RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp  P77689  1760520  1761791      -   \n",
       "b0803                           RpoS,Lrp  P41039   838190   838456      -   \n",
       "b0595                  RpoD,ryhB,Crp,Fur  P0ADI4   627694   628551      +   \n",
       "b2155    RpoD,omrB,omrA,Crp,Fur,Hfq,ryhB  P17315  2244778  2246769      -   \n",
       "b1102                           Fur,GlaR  P16869  1159362  1161551      -   \n",
       "b1680  RpoD,NsrR,IHF,IscR,OxyR,Fur,ppGpp  P77444  1759303  1760523      -   \n",
       "b1679  RpoD,NsrR,IscR,IHF,OxyR,Fur,ppGpp  P76194  1758874  1759290      -   \n",
       "b0594                  RpoD,ryhB,Crp,Fur  P10378   626070   627680      +   \n",
       "b0584        RpoD,omrA,YfeD,Crp,Fur,omrB  P05825   610254   612494      -   \n",
       "b2676           RpoD,Fur,IscR,ppGpp,NrdR  P37146  2803502  2804461      +   \n",
       "b4567                RpoD,ppGpp,OxyR,Fur  P55914  4605804  4606040      +   \n",
       "\n",
       "      essential  ...  y_ome  k_eff schmidt_prot_med heckmann_prot_med  \\\n",
       "b0583      True  ...  False  False              NaN               NaN   \n",
       "b1656     False  ...  False   True           4451.0            3567.0   \n",
       "b1682     False  ...  False   True            545.0             259.0   \n",
       "b2675     False  ...  False  False             10.5               NaN   \n",
       "b0805     False  ...  False   True            129.5             514.0   \n",
       "b0586     False  ...  False   True             32.5             162.0   \n",
       "b1681     False  ...  False   True            218.5             179.0   \n",
       "b0803     False  ...   True  False              3.0               NaN   \n",
       "b0595     False  ...  False   True            161.5             360.0   \n",
       "b2155     False  ...  False   True            427.5             586.0   \n",
       "b1102     False  ...  False  False             12.0               NaN   \n",
       "b1680     False  ...  False   True            366.5             262.0   \n",
       "b1679     False  ...  False  False             10.5               NaN   \n",
       "b0594     False  ...  False   True             80.5             135.0   \n",
       "b0584     False  ...  False   True            217.0             425.0   \n",
       "b2676     False  ...  False  False             29.5               NaN   \n",
       "b4567     False  ...   True  False              NaN               NaN   \n",
       "\n",
       "      proteomics   p1k p1k_ctrl_log_tpm p1k_median_log_tpm  p1k_mad_log_tpm  \\\n",
       "b0583      False  True         3.528960           3.216326         1.226081   \n",
       "b1656       True  True         9.475278           9.512928         1.160733   \n",
       "b1682       True  True         6.565601           6.213937         1.234267   \n",
       "b2675       True  True         5.605689           4.881500         1.877694   \n",
       "b0805       True  True         6.383400           4.592012         2.194376   \n",
       "b0586       True  True         5.468597           4.647339         1.572830   \n",
       "b1681       True  True         7.049283           6.244747         1.300103   \n",
       "b0803       True  True         5.223423           4.695483         1.306778   \n",
       "b0595       True  True         5.947376           5.332772         1.641641   \n",
       "b2155       True  True         7.420057           6.077157         2.096917   \n",
       "b1102       True  True         4.121129           3.924689         1.540126   \n",
       "b1680       True  True         6.452534           6.089898         1.272178   \n",
       "b1679       True  True         6.542428           5.686782         1.145125   \n",
       "b0594       True  True         6.271929           5.024975         1.894910   \n",
       "b0584       True  True         5.745851           5.043457         1.751003   \n",
       "b2676       True  True         5.079753           4.575434         1.764918   \n",
       "b4567      False  True         3.369153           3.432278         2.032587   \n",
       "\n",
       "       in_iM  \n",
       "b0583   True  \n",
       "b1656   True  \n",
       "b1682   True  \n",
       "b2675   True  \n",
       "b0805   True  \n",
       "b0586   True  \n",
       "b1681   True  \n",
       "b0803   True  \n",
       "b0595   True  \n",
       "b2155   True  \n",
       "b1102   True  \n",
       "b1680   True  \n",
       "b1679   True  \n",
       "b0594   True  \n",
       "b0584   True  \n",
       "b2676   True  \n",
       "b4567   True  \n",
       "\n",
       "[17 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overlap\n"
     ]
    },
    {
     "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>gene_name</th>\n",
       "      <th>synonyms</th>\n",
       "      <th>gene_product</th>\n",
       "      <th>COG</th>\n",
       "      <th>regulator</th>\n",
       "      <th>uniprot</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>essential</th>\n",
       "      <th>...</th>\n",
       "      <th>y_ome</th>\n",
       "      <th>k_eff</th>\n",
       "      <th>schmidt_prot_med</th>\n",
       "      <th>heckmann_prot_med</th>\n",
       "      <th>proteomics</th>\n",
       "      <th>p1k</th>\n",
       "      <th>p1k_ctrl_log_tpm</th>\n",
       "      <th>p1k_median_log_tpm</th>\n",
       "      <th>p1k_mad_log_tpm</th>\n",
       "      <th>in_iM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b0591</th>\n",
       "      <td>entS</td>\n",
       "      <td>entS;ybdA</td>\n",
       "      <td>enterobactin exporter EntS</td>\n",
       "      <td>Amino acid transport and metabolism</td>\n",
       "      <td>RpoD,YdhB,Fur</td>\n",
       "      <td>P24077</td>\n",
       "      <td>622300</td>\n",
       "      <td>623550</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.005500</td>\n",
       "      <td>4.448661</td>\n",
       "      <td>1.244378</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2106</th>\n",
       "      <td>rcnA</td>\n",
       "      <td>rcnA;yohM</td>\n",
       "      <td>Ni(2(+))/Co(2(+)) exporter</td>\n",
       "      <td>Intracellular trafficking, secretion, and vesi...</td>\n",
       "      <td>RpoD,Fur,RcnR</td>\n",
       "      <td>P76425</td>\n",
       "      <td>2185917</td>\n",
       "      <td>2186741</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.658085</td>\n",
       "      <td>6.211121</td>\n",
       "      <td>1.888998</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2674</th>\n",
       "      <td>nrdI</td>\n",
       "      <td>nrdI;ygaO</td>\n",
       "      <td>dimanganese-tyrosyl radical cofactor maintenan...</td>\n",
       "      <td>Nucleotide transport and metabolism</td>\n",
       "      <td>RpoD,YidZ,Fur,IscR,ppGpp,NrdR</td>\n",
       "      <td>P0A772</td>\n",
       "      <td>2800965</td>\n",
       "      <td>2801375</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.251345</td>\n",
       "      <td>5.513240</td>\n",
       "      <td>2.072208</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0597</th>\n",
       "      <td>entH</td>\n",
       "      <td>entH;ybdB</td>\n",
       "      <td>proofreading thioesterase in enterobactin bios...</td>\n",
       "      <td>Secondary metabolites biosynthesis, transport,...</td>\n",
       "      <td>RpoD,ryhB,Crp,Fur</td>\n",
       "      <td>P0A8Y8</td>\n",
       "      <td>629300</td>\n",
       "      <td>629713</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.807065</td>\n",
       "      <td>5.240372</td>\n",
       "      <td>1.452032</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1705</th>\n",
       "      <td>ydiE</td>\n",
       "      <td>ydiE</td>\n",
       "      <td>PF10636 family protein YdiE</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P0ACX9</td>\n",
       "      <td>1789613</td>\n",
       "      <td>1789804</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.888857</td>\n",
       "      <td>4.852042</td>\n",
       "      <td>1.732803</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0592</th>\n",
       "      <td>fepB</td>\n",
       "      <td>fepB</td>\n",
       "      <td>ferric enterobactin ABC transporter periplasmi...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,RutR,Fur</td>\n",
       "      <td>P0AEL6</td>\n",
       "      <td>623554</td>\n",
       "      <td>624510</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>68.5</td>\n",
       "      <td>218.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.615192</td>\n",
       "      <td>4.973863</td>\n",
       "      <td>1.191589</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0588</th>\n",
       "      <td>fepC</td>\n",
       "      <td>fepC</td>\n",
       "      <td>ferric enterobactin ABC transporter ATP bindin...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P23878</td>\n",
       "      <td>619384</td>\n",
       "      <td>620199</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.229638</td>\n",
       "      <td>4.811237</td>\n",
       "      <td>1.222483</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0589</th>\n",
       "      <td>fepG</td>\n",
       "      <td>fepG</td>\n",
       "      <td>ferric enterobactin ABC transporter membrane s...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P23877</td>\n",
       "      <td>620196</td>\n",
       "      <td>621188</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.266241</td>\n",
       "      <td>3.864338</td>\n",
       "      <td>1.294343</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0590</th>\n",
       "      <td>fepD</td>\n",
       "      <td>fepD</td>\n",
       "      <td>ferric enterobactin ABC transporter membrane s...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P23876</td>\n",
       "      <td>621185</td>\n",
       "      <td>622189</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.964611</td>\n",
       "      <td>4.520092</td>\n",
       "      <td>1.292807</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0153</th>\n",
       "      <td>fhuB</td>\n",
       "      <td>fhuB</td>\n",
       "      <td>iron(III) hydroxamate ABC transporter membrane...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P06972</td>\n",
       "      <td>171462</td>\n",
       "      <td>173444</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.520881</td>\n",
       "      <td>3.926250</td>\n",
       "      <td>1.100811</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0596</th>\n",
       "      <td>entA</td>\n",
       "      <td>entA</td>\n",
       "      <td>2%2C3-dihydro-2%2C3-dihydroxybenzoate dehydrog...</td>\n",
       "      <td>Lipid transport and metabolism</td>\n",
       "      <td>RpoD,ryhB,Crp,Fur</td>\n",
       "      <td>P15047</td>\n",
       "      <td>628551</td>\n",
       "      <td>629297</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>107.5</td>\n",
       "      <td>136.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.898930</td>\n",
       "      <td>5.130681</td>\n",
       "      <td>1.535859</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4511</th>\n",
       "      <td>ybdZ</td>\n",
       "      <td>ybdZ</td>\n",
       "      <td>enterobactin biosynthesis protein YbdZ</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>RpoD,Fnr,H-NS,Fur</td>\n",
       "      <td>P18393</td>\n",
       "      <td>613942</td>\n",
       "      <td>614160</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.631005</td>\n",
       "      <td>4.524159</td>\n",
       "      <td>1.785322</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0585</th>\n",
       "      <td>fes</td>\n",
       "      <td>fes</td>\n",
       "      <td>enterochelin esterase</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,H-NS,Fnr,Fur</td>\n",
       "      <td>P13039</td>\n",
       "      <td>612737</td>\n",
       "      <td>613939</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.335392</td>\n",
       "      <td>4.307640</td>\n",
       "      <td>1.945863</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0593</th>\n",
       "      <td>entC</td>\n",
       "      <td>entC</td>\n",
       "      <td>isochorismate synthase EntC</td>\n",
       "      <td>Coenzyme transport and metabolism</td>\n",
       "      <td>RpoD,ryhB,Crp,Fur</td>\n",
       "      <td>P0AEJ2</td>\n",
       "      <td>624885</td>\n",
       "      <td>626060</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>68.5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.635138</td>\n",
       "      <td>5.311383</td>\n",
       "      <td>1.989667</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0804</th>\n",
       "      <td>ybiX</td>\n",
       "      <td>ybiX</td>\n",
       "      <td>PKHD-type hydroxylase YbiX</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P75779</td>\n",
       "      <td>838530</td>\n",
       "      <td>839207</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>11.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.817515</td>\n",
       "      <td>3.677785</td>\n",
       "      <td>1.357584</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gene_name   synonyms                                       gene_product  \\\n",
       "b0591      entS  entS;ybdA                         enterobactin exporter EntS   \n",
       "b2106      rcnA  rcnA;yohM                         Ni(2(+))/Co(2(+)) exporter   \n",
       "b2674      nrdI  nrdI;ygaO  dimanganese-tyrosyl radical cofactor maintenan...   \n",
       "b0597      entH  entH;ybdB  proofreading thioesterase in enterobactin bios...   \n",
       "b1705      ydiE       ydiE                        PF10636 family protein YdiE   \n",
       "b0592      fepB       fepB  ferric enterobactin ABC transporter periplasmi...   \n",
       "b0588      fepC       fepC  ferric enterobactin ABC transporter ATP bindin...   \n",
       "b0589      fepG       fepG  ferric enterobactin ABC transporter membrane s...   \n",
       "b0590      fepD       fepD  ferric enterobactin ABC transporter membrane s...   \n",
       "b0153      fhuB       fhuB  iron(III) hydroxamate ABC transporter membrane...   \n",
       "b0596      entA       entA  2%2C3-dihydro-2%2C3-dihydroxybenzoate dehydrog...   \n",
       "b4511      ybdZ       ybdZ             enterobactin biosynthesis protein YbdZ   \n",
       "b0585       fes        fes                              enterochelin esterase   \n",
       "b0593      entC       entC                        isochorismate synthase EntC   \n",
       "b0804      ybiX       ybiX                         PKHD-type hydroxylase YbiX   \n",
       "\n",
       "                                                     COG  \\\n",
       "b0591                Amino acid transport and metabolism   \n",
       "b2106  Intracellular trafficking, secretion, and vesi...   \n",
       "b2674                Nucleotide transport and metabolism   \n",
       "b0597  Secondary metabolites biosynthesis, transport,...   \n",
       "b1705             Inorganic ion transport and metabolism   \n",
       "b0592             Inorganic ion transport and metabolism   \n",
       "b0588             Inorganic ion transport and metabolism   \n",
       "b0589             Inorganic ion transport and metabolism   \n",
       "b0590             Inorganic ion transport and metabolism   \n",
       "b0153             Inorganic ion transport and metabolism   \n",
       "b0596                     Lipid transport and metabolism   \n",
       "b4511                                   Function unknown   \n",
       "b0585             Inorganic ion transport and metabolism   \n",
       "b0593                  Coenzyme transport and metabolism   \n",
       "b0804                                   Function unknown   \n",
       "\n",
       "                           regulator uniprot    start      end strand  \\\n",
       "b0591                  RpoD,YdhB,Fur  P24077   622300   623550      +   \n",
       "b2106                  RpoD,Fur,RcnR  P76425  2185917  2186741      +   \n",
       "b2674  RpoD,YidZ,Fur,IscR,ppGpp,NrdR  P0A772  2800965  2801375      +   \n",
       "b0597              RpoD,ryhB,Crp,Fur  P0A8Y8   629300   629713      +   \n",
       "b1705                            NaN  P0ACX9  1789613  1789804      +   \n",
       "b0592                  RpoD,RutR,Fur  P0AEL6   623554   624510      -   \n",
       "b0588                       RpoD,Fur  P23878   619384   620199      -   \n",
       "b0589                       RpoD,Fur  P23877   620196   621188      -   \n",
       "b0590                       RpoD,Fur  P23876   621185   622189      -   \n",
       "b0153                       RpoD,Fur  P06972   171462   173444      +   \n",
       "b0596              RpoD,ryhB,Crp,Fur  P15047   628551   629297      +   \n",
       "b4511              RpoD,Fnr,H-NS,Fur  P18393   613942   614160      +   \n",
       "b0585              RpoD,H-NS,Fnr,Fur  P13039   612737   613939      +   \n",
       "b0593              RpoD,ryhB,Crp,Fur  P0AEJ2   624885   626060      +   \n",
       "b0804                            NaN  P75779   838530   839207      -   \n",
       "\n",
       "      essential  ...  y_ome  k_eff schmidt_prot_med heckmann_prot_med  \\\n",
       "b0591     False  ...  False  False              NaN               NaN   \n",
       "b2106     False  ...  False  False              NaN               NaN   \n",
       "b2674     False  ...  False  False              NaN               NaN   \n",
       "b0597     False  ...  False  False             18.0               NaN   \n",
       "b1705     False  ...   True  False              1.0               NaN   \n",
       "b0592     False  ...  False   True             68.5             218.0   \n",
       "b0588     False  ...  False  False             17.0               NaN   \n",
       "b0589     False  ...  False  False              NaN               NaN   \n",
       "b0590     False  ...  False  False              NaN               NaN   \n",
       "b0153     False  ...  False  False              NaN               NaN   \n",
       "b0596     False  ...  False   True            107.5             136.0   \n",
       "b4511     False  ...  False  False             44.0               NaN   \n",
       "b0585     False  ...  False  False              2.0               NaN   \n",
       "b0593     False  ...  False   True             68.5             195.0   \n",
       "b0804     False  ...   True  False             11.5               NaN   \n",
       "\n",
       "      proteomics   p1k p1k_ctrl_log_tpm p1k_median_log_tpm  p1k_mad_log_tpm  \\\n",
       "b0591      False  True         5.005500           4.448661         1.244378   \n",
       "b2106      False  True         6.658085           6.211121         1.888998   \n",
       "b2674      False  True         6.251345           5.513240         2.072208   \n",
       "b0597       True  True         5.807065           5.240372         1.452032   \n",
       "b1705       True  True         4.888857           4.852042         1.732803   \n",
       "b0592       True  True         5.615192           4.973863         1.191589   \n",
       "b0588       True  True         5.229638           4.811237         1.222483   \n",
       "b0589      False  True         4.266241           3.864338         1.294343   \n",
       "b0590      False  True         4.964611           4.520092         1.292807   \n",
       "b0153      False  True         4.520881           3.926250         1.100811   \n",
       "b0596       True  True         5.898930           5.130681         1.535859   \n",
       "b4511       True  True         4.631005           4.524159         1.785322   \n",
       "b0585       True  True         5.335392           4.307640         1.945863   \n",
       "b0593       True  True         6.635138           5.311383         1.989667   \n",
       "b0804       True  True         4.817515           3.677785         1.357584   \n",
       "\n",
       "       in_iM  \n",
       "b0591   True  \n",
       "b2106   True  \n",
       "b2674   True  \n",
       "b0597   True  \n",
       "b1705   True  \n",
       "b0592   True  \n",
       "b0588   True  \n",
       "b0589   True  \n",
       "b0590   True  \n",
       "b0153   True  \n",
       "b0596   True  \n",
       "b4511   True  \n",
       "b0585   True  \n",
       "b0593   True  \n",
       "b0804   True  \n",
       "\n",
       "[15 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fur-2 Only\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_name</th>\n",
       "      <th>synonyms</th>\n",
       "      <th>gene_product</th>\n",
       "      <th>COG</th>\n",
       "      <th>regulator</th>\n",
       "      <th>uniprot</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>essential</th>\n",
       "      <th>...</th>\n",
       "      <th>y_ome</th>\n",
       "      <th>k_eff</th>\n",
       "      <th>schmidt_prot_med</th>\n",
       "      <th>heckmann_prot_med</th>\n",
       "      <th>proteomics</th>\n",
       "      <th>p1k</th>\n",
       "      <th>p1k_ctrl_log_tpm</th>\n",
       "      <th>p1k_median_log_tpm</th>\n",
       "      <th>p1k_mad_log_tpm</th>\n",
       "      <th>in_iM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b2211</th>\n",
       "      <td>yojI</td>\n",
       "      <td>yojI;yojJ</td>\n",
       "      <td>ABC transporter family protein/microcin J25 ef...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,YdcI,Lrp,ppGpp</td>\n",
       "      <td>P33941</td>\n",
       "      <td>2306972</td>\n",
       "      <td>2308615</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.5</td>\n",
       "      <td>139.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.014600</td>\n",
       "      <td>5.413558</td>\n",
       "      <td>0.925280</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2146</th>\n",
       "      <td>preT</td>\n",
       "      <td>preT;yeiT</td>\n",
       "      <td>NAD-dependent dihydropyrimidine dehydrogenase ...</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>RpoD,Crp</td>\n",
       "      <td>P76440</td>\n",
       "      <td>2234033</td>\n",
       "      <td>2235271</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.990620</td>\n",
       "      <td>2.302233</td>\n",
       "      <td>1.084819</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0150</th>\n",
       "      <td>fhuA</td>\n",
       "      <td>fhuA;tonA</td>\n",
       "      <td>ferrichrome outer membrane transporter/phage r...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P06971</td>\n",
       "      <td>167484</td>\n",
       "      <td>169727</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>867.0</td>\n",
       "      <td>1722.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>8.262623</td>\n",
       "      <td>7.689584</td>\n",
       "      <td>1.281706</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0151</th>\n",
       "      <td>fhuC</td>\n",
       "      <td>fhuC</td>\n",
       "      <td>iron(III) hydroxamate ABC transporter ATP bind...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P07821</td>\n",
       "      <td>169778</td>\n",
       "      <td>170575</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.237114</td>\n",
       "      <td>5.484294</td>\n",
       "      <td>1.192050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3006</th>\n",
       "      <td>exbB</td>\n",
       "      <td>exbB</td>\n",
       "      <td>Ton complex subunit ExbB</td>\n",
       "      <td>Intracellular trafficking, secretion, and vesi...</td>\n",
       "      <td>RpoD,Fur,Lrp</td>\n",
       "      <td>P0ABU7</td>\n",
       "      <td>3151250</td>\n",
       "      <td>3151984</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>697.5</td>\n",
       "      <td>727.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>8.606542</td>\n",
       "      <td>8.647990</td>\n",
       "      <td>0.877255</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1452</th>\n",
       "      <td>yncE</td>\n",
       "      <td>yncE</td>\n",
       "      <td>PQQ-like domain-containing protein YncE</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>MarA,ArcA</td>\n",
       "      <td>P76116</td>\n",
       "      <td>1523307</td>\n",
       "      <td>1524368</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9.026050</td>\n",
       "      <td>7.967868</td>\n",
       "      <td>1.404127</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0468</th>\n",
       "      <td>ybaN</td>\n",
       "      <td>ybaN</td>\n",
       "      <td>conserved inner membrane protein YbaN</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>GlaR</td>\n",
       "      <td>P0AAR5</td>\n",
       "      <td>490882</td>\n",
       "      <td>491259</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.404067</td>\n",
       "      <td>6.125759</td>\n",
       "      <td>0.729900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2673</th>\n",
       "      <td>nrdH</td>\n",
       "      <td>nrdH;ygaN</td>\n",
       "      <td>glutaredoxin-like protein</td>\n",
       "      <td>Post-translational modification, protein turno...</td>\n",
       "      <td>RpoD,YedW,Fur,IscR,ppGpp,NrdR</td>\n",
       "      <td>P0AC65</td>\n",
       "      <td>2800723</td>\n",
       "      <td>2800968</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.384154</td>\n",
       "      <td>5.929362</td>\n",
       "      <td>2.380030</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3408</th>\n",
       "      <td>feoA</td>\n",
       "      <td>feoA</td>\n",
       "      <td>ferrous iron transport protein A</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur,Fnr,NagC,ArcA</td>\n",
       "      <td>P0AEL3</td>\n",
       "      <td>3540163</td>\n",
       "      <td>3540390</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.056112</td>\n",
       "      <td>7.301935</td>\n",
       "      <td>1.354488</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3410</th>\n",
       "      <td>feoC</td>\n",
       "      <td>feoC;yhgG</td>\n",
       "      <td>ferrous iron transport protein FeoC</td>\n",
       "      <td>Transcription</td>\n",
       "      <td>RpoD,Fur,Fnr,NagC</td>\n",
       "      <td>P64638</td>\n",
       "      <td>3542728</td>\n",
       "      <td>3542964</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.223544</td>\n",
       "      <td>6.002032</td>\n",
       "      <td>1.072671</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1673</th>\n",
       "      <td>ydhV</td>\n",
       "      <td>ydhV</td>\n",
       "      <td>putative oxidoreductase</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>YidZ,RpoS,NarL,NarP,Fnr</td>\n",
       "      <td>P76192</td>\n",
       "      <td>1751728</td>\n",
       "      <td>1753830</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.444134</td>\n",
       "      <td>1.564376</td>\n",
       "      <td>0.455864</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2147</th>\n",
       "      <td>preA</td>\n",
       "      <td>preA;yeiA</td>\n",
       "      <td>NAD-dependent dihydropyrimidine dehydrogenase ...</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>RpoD,Crp</td>\n",
       "      <td>P25889</td>\n",
       "      <td>2235265</td>\n",
       "      <td>2236500</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.506362</td>\n",
       "      <td>2.699653</td>\n",
       "      <td>0.765406</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4523</th>\n",
       "      <td>yciX</td>\n",
       "      <td>yciX</td>\n",
       "      <td>uncharacterized protein YciX</td>\n",
       "      <td>No COG annotation</td>\n",
       "      <td>RpoD,YjjQ</td>\n",
       "      <td>P58094</td>\n",
       "      <td>1335291</td>\n",
       "      <td>1335458</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>7.995644</td>\n",
       "      <td>7.228489</td>\n",
       "      <td>0.847564</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1821</th>\n",
       "      <td>mntP</td>\n",
       "      <td>mntP;yebN</td>\n",
       "      <td>Mn(2(+)) exporter</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur,MntR,ppGpp,Mn2;</td>\n",
       "      <td>P76264</td>\n",
       "      <td>1905688</td>\n",
       "      <td>1906254</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.511942</td>\n",
       "      <td>2.245239</td>\n",
       "      <td>0.832172</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4490</th>\n",
       "      <td>efeU</td>\n",
       "      <td>efeU;ycdN</td>\n",
       "      <td>inactive ferrous iron permease EfeU%2C N-termi...</td>\n",
       "      <td>No COG annotation</td>\n",
       "      <td>RpoD,CpxR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1081356</td>\n",
       "      <td>1082185</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.975253</td>\n",
       "      <td>6.482379</td>\n",
       "      <td>1.316927</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4292</th>\n",
       "      <td>fecR</td>\n",
       "      <td>fecR</td>\n",
       "      <td>regulator for fec operon%2C periplasmic</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,SlyA,Fur</td>\n",
       "      <td>P23485</td>\n",
       "      <td>4516764</td>\n",
       "      <td>4517717</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.717732</td>\n",
       "      <td>4.883469</td>\n",
       "      <td>1.423920</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4367</th>\n",
       "      <td>fhuF</td>\n",
       "      <td>fhuF;yjjS</td>\n",
       "      <td>hydroxamate siderophore iron reductase</td>\n",
       "      <td>Function unknown</td>\n",
       "      <td>RpoD,YciT,OxyR,Fur,Nac</td>\n",
       "      <td>P39405</td>\n",
       "      <td>4604875</td>\n",
       "      <td>4605663</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9.394053</td>\n",
       "      <td>8.886763</td>\n",
       "      <td>1.417681</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3908</th>\n",
       "      <td>sodA</td>\n",
       "      <td>sodA</td>\n",
       "      <td>superoxide dismutase (Mn)</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Rob,SoxR,fnrS,Fnr,Crp,ArcA,MarA,IHF,SoxS,...</td>\n",
       "      <td>P00448</td>\n",
       "      <td>4100810</td>\n",
       "      <td>4101430</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>25006.0</td>\n",
       "      <td>2782.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>11.529138</td>\n",
       "      <td>11.165507</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3005</th>\n",
       "      <td>exbD</td>\n",
       "      <td>exbD</td>\n",
       "      <td>Ton complex subunit ExbD</td>\n",
       "      <td>Intracellular trafficking, secretion, and vesi...</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P0ABV2</td>\n",
       "      <td>3150818</td>\n",
       "      <td>3151243</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>230.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>8.356529</td>\n",
       "      <td>8.459230</td>\n",
       "      <td>0.912949</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3070</th>\n",
       "      <td>nfeF</td>\n",
       "      <td>yqjH</td>\n",
       "      <td>NADPH-dependent ferric chelate reductase</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur,NfeR</td>\n",
       "      <td>Q46871</td>\n",
       "      <td>3215727</td>\n",
       "      <td>3216491</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>438.5</td>\n",
       "      <td>245.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.337280</td>\n",
       "      <td>5.629838</td>\n",
       "      <td>0.971638</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1018</th>\n",
       "      <td>efeO</td>\n",
       "      <td>efeO;ycdO</td>\n",
       "      <td>ferrous iron transport system protein EfeO</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P0AB24</td>\n",
       "      <td>1082243</td>\n",
       "      <td>1083370</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>544.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.805960</td>\n",
       "      <td>6.527027</td>\n",
       "      <td>1.361117</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b4293</th>\n",
       "      <td>fecI</td>\n",
       "      <td>fecI</td>\n",
       "      <td>RNA polymerase sigma factor FecI</td>\n",
       "      <td>Transcription</td>\n",
       "      <td>RpoD,SlyA,Fur</td>\n",
       "      <td>P23484</td>\n",
       "      <td>4517714</td>\n",
       "      <td>4518235</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6.967692</td>\n",
       "      <td>5.993213</td>\n",
       "      <td>1.421831</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1252</th>\n",
       "      <td>tonB</td>\n",
       "      <td>tonB;exbA</td>\n",
       "      <td>Ton complex subunit TonB</td>\n",
       "      <td>Intracellular trafficking, secretion, and vesi...</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P02929</td>\n",
       "      <td>1311089</td>\n",
       "      <td>1311808</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>87.5</td>\n",
       "      <td>155.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.527353</td>\n",
       "      <td>7.400840</td>\n",
       "      <td>1.096604</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1496</th>\n",
       "      <td>yddA</td>\n",
       "      <td>yddA</td>\n",
       "      <td>ABC transporter family protein YddA</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P31826</td>\n",
       "      <td>1577657</td>\n",
       "      <td>1579342</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.428001</td>\n",
       "      <td>2.514479</td>\n",
       "      <td>1.839885</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1674</th>\n",
       "      <td>ydhY</td>\n",
       "      <td>ydhY</td>\n",
       "      <td>putative 4Fe-4S ferredoxin-type protein</td>\n",
       "      <td>Energy production and conversion</td>\n",
       "      <td>RpoS,NarL,NarP,Fnr</td>\n",
       "      <td>P0AAL6</td>\n",
       "      <td>1753851</td>\n",
       "      <td>1754477</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>1.215495</td>\n",
       "      <td>0.877441</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1905</th>\n",
       "      <td>ftnA</td>\n",
       "      <td>ftnA;ftn;gen-165;rsgA</td>\n",
       "      <td>ferritin iron storage protein</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,H-NS,Fur</td>\n",
       "      <td>P0A998</td>\n",
       "      <td>1988716</td>\n",
       "      <td>1989213</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1097.5</td>\n",
       "      <td>331.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>7.721649</td>\n",
       "      <td>7.136338</td>\n",
       "      <td>1.377318</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3409</th>\n",
       "      <td>feoB</td>\n",
       "      <td>feoB</td>\n",
       "      <td>Fe(2(+)) transporter FeoB</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,YcjW,Fur,Fnr,NagC</td>\n",
       "      <td>P33650</td>\n",
       "      <td>3540407</td>\n",
       "      <td>3542728</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>15.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.875185</td>\n",
       "      <td>6.420762</td>\n",
       "      <td>1.132575</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0152</th>\n",
       "      <td>fhuD</td>\n",
       "      <td>fhuD</td>\n",
       "      <td>iron(III) hydroxamate ABC transporter periplas...</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>RpoD,Fur</td>\n",
       "      <td>P07822</td>\n",
       "      <td>170575</td>\n",
       "      <td>171465</td>\n",
       "      <td>+</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.641231</td>\n",
       "      <td>4.863255</td>\n",
       "      <td>1.081834</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3337</th>\n",
       "      <td>bfd</td>\n",
       "      <td>bfd;yheA</td>\n",
       "      <td>bacterioferritin-associated ferredoxin</td>\n",
       "      <td>Inorganic ion transport and metabolism</td>\n",
       "      <td>Nac</td>\n",
       "      <td>P0AE56</td>\n",
       "      <td>3466797</td>\n",
       "      <td>3466991</td>\n",
       "      <td>-</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>7.211044</td>\n",
       "      <td>7.030968</td>\n",
       "      <td>1.551281</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gene_name               synonyms  \\\n",
       "b2211      yojI              yojI;yojJ   \n",
       "b2146      preT              preT;yeiT   \n",
       "b0150      fhuA              fhuA;tonA   \n",
       "b0151      fhuC                   fhuC   \n",
       "b3006      exbB                   exbB   \n",
       "b1452      yncE                   yncE   \n",
       "b0468      ybaN                   ybaN   \n",
       "b2673      nrdH              nrdH;ygaN   \n",
       "b3408      feoA                   feoA   \n",
       "b3410      feoC              feoC;yhgG   \n",
       "b1673      ydhV                   ydhV   \n",
       "b2147      preA              preA;yeiA   \n",
       "b4523      yciX                   yciX   \n",
       "b1821      mntP              mntP;yebN   \n",
       "b4490      efeU              efeU;ycdN   \n",
       "b4292      fecR                   fecR   \n",
       "b4367      fhuF              fhuF;yjjS   \n",
       "b3908      sodA                   sodA   \n",
       "b3005      exbD                   exbD   \n",
       "b3070      nfeF                   yqjH   \n",
       "b1018      efeO              efeO;ycdO   \n",
       "b4293      fecI                   fecI   \n",
       "b1252      tonB              tonB;exbA   \n",
       "b1496      yddA                   yddA   \n",
       "b1674      ydhY                   ydhY   \n",
       "b1905      ftnA  ftnA;ftn;gen-165;rsgA   \n",
       "b3409      feoB                   feoB   \n",
       "b0152      fhuD                   fhuD   \n",
       "b3337       bfd               bfd;yheA   \n",
       "\n",
       "                                            gene_product  \\\n",
       "b2211  ABC transporter family protein/microcin J25 ef...   \n",
       "b2146  NAD-dependent dihydropyrimidine dehydrogenase ...   \n",
       "b0150  ferrichrome outer membrane transporter/phage r...   \n",
       "b0151  iron(III) hydroxamate ABC transporter ATP bind...   \n",
       "b3006                           Ton complex subunit ExbB   \n",
       "b1452            PQQ-like domain-containing protein YncE   \n",
       "b0468              conserved inner membrane protein YbaN   \n",
       "b2673                          glutaredoxin-like protein   \n",
       "b3408                   ferrous iron transport protein A   \n",
       "b3410                ferrous iron transport protein FeoC   \n",
       "b1673                            putative oxidoreductase   \n",
       "b2147  NAD-dependent dihydropyrimidine dehydrogenase ...   \n",
       "b4523                       uncharacterized protein YciX   \n",
       "b1821                                  Mn(2(+)) exporter   \n",
       "b4490  inactive ferrous iron permease EfeU%2C N-termi...   \n",
       "b4292            regulator for fec operon%2C periplasmic   \n",
       "b4367             hydroxamate siderophore iron reductase   \n",
       "b3908                          superoxide dismutase (Mn)   \n",
       "b3005                           Ton complex subunit ExbD   \n",
       "b3070           NADPH-dependent ferric chelate reductase   \n",
       "b1018         ferrous iron transport system protein EfeO   \n",
       "b4293                   RNA polymerase sigma factor FecI   \n",
       "b1252                           Ton complex subunit TonB   \n",
       "b1496                ABC transporter family protein YddA   \n",
       "b1674            putative 4Fe-4S ferredoxin-type protein   \n",
       "b1905                      ferritin iron storage protein   \n",
       "b3409                          Fe(2(+)) transporter FeoB   \n",
       "b0152  iron(III) hydroxamate ABC transporter periplas...   \n",
       "b3337             bacterioferritin-associated ferredoxin   \n",
       "\n",
       "                                                     COG  \\\n",
       "b2211             Inorganic ion transport and metabolism   \n",
       "b2146                   Energy production and conversion   \n",
       "b0150             Inorganic ion transport and metabolism   \n",
       "b0151             Inorganic ion transport and metabolism   \n",
       "b3006  Intracellular trafficking, secretion, and vesi...   \n",
       "b1452                                   Function unknown   \n",
       "b0468                                   Function unknown   \n",
       "b2673  Post-translational modification, protein turno...   \n",
       "b3408             Inorganic ion transport and metabolism   \n",
       "b3410                                      Transcription   \n",
       "b1673                   Energy production and conversion   \n",
       "b2147                   Energy production and conversion   \n",
       "b4523                                  No COG annotation   \n",
       "b1821             Inorganic ion transport and metabolism   \n",
       "b4490                                  No COG annotation   \n",
       "b4292             Inorganic ion transport and metabolism   \n",
       "b4367                                   Function unknown   \n",
       "b3908             Inorganic ion transport and metabolism   \n",
       "b3005  Intracellular trafficking, secretion, and vesi...   \n",
       "b3070             Inorganic ion transport and metabolism   \n",
       "b1018             Inorganic ion transport and metabolism   \n",
       "b4293                                      Transcription   \n",
       "b1252  Intracellular trafficking, secretion, and vesi...   \n",
       "b1496             Inorganic ion transport and metabolism   \n",
       "b1674                   Energy production and conversion   \n",
       "b1905             Inorganic ion transport and metabolism   \n",
       "b3409             Inorganic ion transport and metabolism   \n",
       "b0152             Inorganic ion transport and metabolism   \n",
       "b3337             Inorganic ion transport and metabolism   \n",
       "\n",
       "                                               regulator uniprot    start  \\\n",
       "b2211                                RpoD,YdcI,Lrp,ppGpp  P33941  2306972   \n",
       "b2146                                           RpoD,Crp  P76440  2234033   \n",
       "b0150                                           RpoD,Fur  P06971   167484   \n",
       "b0151                                           RpoD,Fur  P07821   169778   \n",
       "b3006                                       RpoD,Fur,Lrp  P0ABU7  3151250   \n",
       "b1452                                          MarA,ArcA  P76116  1523307   \n",
       "b0468                                               GlaR  P0AAR5   490882   \n",
       "b2673                      RpoD,YedW,Fur,IscR,ppGpp,NrdR  P0AC65  2800723   \n",
       "b3408                             RpoD,Fur,Fnr,NagC,ArcA  P0AEL3  3540163   \n",
       "b3410                                  RpoD,Fur,Fnr,NagC  P64638  3542728   \n",
       "b1673                            YidZ,RpoS,NarL,NarP,Fnr  P76192  1751728   \n",
       "b2147                                           RpoD,Crp  P25889  2235265   \n",
       "b4523                                          RpoD,YjjQ  P58094  1335291   \n",
       "b1821                           RpoD,Fur,MntR,ppGpp,Mn2;  P76264  1905688   \n",
       "b4490                                          RpoD,CpxR     NaN  1081356   \n",
       "b4292                                      RpoD,SlyA,Fur  P23485  4516764   \n",
       "b4367                             RpoD,YciT,OxyR,Fur,Nac  P39405  4604875   \n",
       "b3908  RpoD,Rob,SoxR,fnrS,Fnr,Crp,ArcA,MarA,IHF,SoxS,...  P00448  4100810   \n",
       "b3005                                           RpoD,Fur  P0ABV2  3150818   \n",
       "b3070                                      RpoD,Fur,NfeR  Q46871  3215727   \n",
       "b1018                                                NaN  P0AB24  1082243   \n",
       "b4293                                      RpoD,SlyA,Fur  P23484  4517714   \n",
       "b1252                                           RpoD,Fur  P02929  1311089   \n",
       "b1496                                                NaN  P31826  1577657   \n",
       "b1674                                 RpoS,NarL,NarP,Fnr  P0AAL6  1753851   \n",
       "b1905                                      RpoD,H-NS,Fur  P0A998  1988716   \n",
       "b3409                             RpoD,YcjW,Fur,Fnr,NagC  P33650  3540407   \n",
       "b0152                                           RpoD,Fur  P07822   170575   \n",
       "b3337                                                Nac  P0AE56  3466797   \n",
       "\n",
       "           end strand essential  ...  y_ome  k_eff schmidt_prot_med  \\\n",
       "b2211  2308615      -     False  ...  False   True              3.5   \n",
       "b2146  2235271      +     False  ...  False  False              NaN   \n",
       "b0150   169727      +     False  ...  False   True            867.0   \n",
       "b0151   170575      +     False  ...  False   True              NaN   \n",
       "b3006  3151984      -     False  ...  False   True            697.5   \n",
       "b1452  1524368      +     False  ...   True   True           3102.0   \n",
       "b0468   491259      +     False  ...   True  False              NaN   \n",
       "b2673  2800968      +     False  ...  False  False              NaN   \n",
       "b3408  3540390      +     False  ...  False  False              0.0   \n",
       "b3410  3542964      +     False  ...   True  False              NaN   \n",
       "b1673  1753830      -     False  ...   True  False              NaN   \n",
       "b2147  2236500      +     False  ...  False  False              NaN   \n",
       "b4523  1335458      +     False  ...   True  False              NaN   \n",
       "b1821  1906254      +     False  ...  False  False              NaN   \n",
       "b4490  1082185      +     False  ...  False  False              NaN   \n",
       "b4292  4517717      -     False  ...  False  False              NaN   \n",
       "b4367  4605663      -     False  ...  False  False              3.0   \n",
       "b3908  4101430      +     False  ...  False   True          25006.0   \n",
       "b3005  3151243      -     False  ...  False   True            230.0   \n",
       "b3070  3216491      -     False  ...  False   True            438.5   \n",
       "b1018  1083370      +     False  ...  False   True            544.0   \n",
       "b4293  4518235      -     False  ...  False  False              NaN   \n",
       "b1252  1311808      +     False  ...  False   True             87.5   \n",
       "b1496  1579342      -     False  ...   True  False              NaN   \n",
       "b1674  1754477      -     False  ...   True  False              NaN   \n",
       "b1905  1989213      +     False  ...  False   True           1097.5   \n",
       "b3409  3542728      +     False  ...  False   True             15.0   \n",
       "b0152   171465      +     False  ...  False  False              NaN   \n",
       "b3337  3466991      -     False  ...   True  False              NaN   \n",
       "\n",
       "      heckmann_prot_med proteomics   p1k p1k_ctrl_log_tpm p1k_median_log_tpm  \\\n",
       "b2211             139.0       True  True         6.014600           5.413558   \n",
       "b2146               NaN      False  True         1.990620           2.302233   \n",
       "b0150            1722.0       True  True         8.262623           7.689584   \n",
       "b0151             133.0       True  True         6.237114           5.484294   \n",
       "b3006             727.0       True  True         8.606542           8.647990   \n",
       "b1452            2135.0       True  True         9.026050           7.967868   \n",
       "b0468               NaN      False  True         6.404067           6.125759   \n",
       "b2673               NaN      False  True         6.384154           5.929362   \n",
       "b3408               NaN       True  True         6.056112           7.301935   \n",
       "b3410               NaN      False  True         5.223544           6.002032   \n",
       "b1673               NaN      False  True         1.444134           1.564376   \n",
       "b2147               NaN      False  True         2.506362           2.699653   \n",
       "b4523               NaN      False  True         7.995644           7.228489   \n",
       "b1821               NaN      False  True         2.511942           2.245239   \n",
       "b4490               NaN      False  True         6.975253           6.482379   \n",
       "b4292               NaN      False  True         5.717732           4.883469   \n",
       "b4367               NaN       True  True         9.394053           8.886763   \n",
       "b3908            2782.0       True  True        11.529138          11.165507   \n",
       "b3005             244.0       True  True         8.356529           8.459230   \n",
       "b3070             245.0       True  True         6.337280           5.629838   \n",
       "b1018             693.0       True  True         7.805960           6.527027   \n",
       "b4293               NaN      False  True         6.967692           5.993213   \n",
       "b1252             155.0       True  True         7.527353           7.400840   \n",
       "b1496               NaN      False  True         2.428001           2.514479   \n",
       "b1674               NaN      False  True         0.412648           1.215495   \n",
       "b1905             331.0       True  True         7.721649           7.136338   \n",
       "b3409             279.0       True  True         5.875185           6.420762   \n",
       "b0152               NaN      False  True         5.641231           4.863255   \n",
       "b3337               NaN      False  True         7.211044           7.030968   \n",
       "\n",
       "       p1k_mad_log_tpm  in_iM  \n",
       "b2211         0.925280   True  \n",
       "b2146         1.084819   True  \n",
       "b0150         1.281706   True  \n",
       "b0151         1.192050   True  \n",
       "b3006         0.877255   True  \n",
       "b1452         1.404127   True  \n",
       "b0468         0.729900   True  \n",
       "b2673         2.380030   True  \n",
       "b3408         1.354488   True  \n",
       "b3410         1.072671   True  \n",
       "b1673         0.455864   True  \n",
       "b2147         0.765406   True  \n",
       "b4523         0.847564   True  \n",
       "b1821         0.832172   True  \n",
       "b4490         1.316927   True  \n",
       "b4292         1.423920   True  \n",
       "b4367         1.417681   True  \n",
       "b3908         0.984474   True  \n",
       "b3005         0.912949   True  \n",
       "b3070         0.971638   True  \n",
       "b1018         1.361117   True  \n",
       "b4293         1.421831   True  \n",
       "b1252         1.096604   True  \n",
       "b1496         1.839885   True  \n",
       "b1674         0.877441   True  \n",
       "b1905         1.377318   True  \n",
       "b3409         1.132575   True  \n",
       "b0152         1.081834   True  \n",
       "b3337         1.551281   True  \n",
       "\n",
       "[29 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to label fig 4E, use the following output:\n",
    "for k, v in fur_gene_dict.items():\n",
    "    print(k)\n",
    "    display(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics\n",
    "\n",
    "### Correlation with PQ\n",
    "\n",
    "We report the OxyR and SoxS correlations with PQ. Those values are generated below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Superoxide</th>\n",
       "      <td>0.718449</td>\n",
       "      <td>2.72273e-17</td>\n",
       "      <td>True</td>\n",
       "      <td>5.47268e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OxyR</th>\n",
       "      <td>0.472355</td>\n",
       "      <td>6.12923e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>6.15988e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crp KO-1</th>\n",
       "      <td>0.406312</td>\n",
       "      <td>2.48764e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00125004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tryptophan</th>\n",
       "      <td>0.379858</td>\n",
       "      <td>8.93178e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00299215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDH-O</th>\n",
       "      <td>0.367361</td>\n",
       "      <td>0.000157459</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00347875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerol</th>\n",
       "      <td>0.358208</td>\n",
       "      <td>0.000235102</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00429596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propionate</th>\n",
       "      <td>0.354853</td>\n",
       "      <td>0.000271496</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00454755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salicylic Acid</th>\n",
       "      <td>0.327626</td>\n",
       "      <td>0.000824534</td>\n",
       "      <td>True</td>\n",
       "      <td>0.011838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thrA KO</th>\n",
       "      <td>-0.28138</td>\n",
       "      <td>0.00436556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0350991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galactose</th>\n",
       "      <td>-0.284188</td>\n",
       "      <td>0.00397498</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0332905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flagella</th>\n",
       "      <td>-0.285584</td>\n",
       "      <td>0.00379273</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0331452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymgI</th>\n",
       "      <td>-0.292063</td>\n",
       "      <td>0.00304097</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0277834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Translation</th>\n",
       "      <td>-0.292474</td>\n",
       "      <td>0.00299813</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0277834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cysteine-1</th>\n",
       "      <td>-0.297475</td>\n",
       "      <td>0.00251875</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0253134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acetate</th>\n",
       "      <td>-0.30598</td>\n",
       "      <td>0.00185968</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0196735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS5</th>\n",
       "      <td>-0.306009</td>\n",
       "      <td>0.00185771</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0196735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phosphate-1</th>\n",
       "      <td>-0.309163</td>\n",
       "      <td>0.00165619</td>\n",
       "      <td>True</td>\n",
       "      <td>0.019582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlhDC-1</th>\n",
       "      <td>-0.320255</td>\n",
       "      <td>0.00109504</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0137565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlhDC-2</th>\n",
       "      <td>-0.323499</td>\n",
       "      <td>0.000967318</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0129621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purine</th>\n",
       "      <td>-0.332231</td>\n",
       "      <td>0.000688125</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0106395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr/Ser-1</th>\n",
       "      <td>-0.365226</td>\n",
       "      <td>0.000173072</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00347875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UC-3</th>\n",
       "      <td>-0.365575</td>\n",
       "      <td>0.000170427</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00347875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sialic Acid</th>\n",
       "      <td>-0.36848</td>\n",
       "      <td>0.000149805</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00347875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malE OE</th>\n",
       "      <td>-0.381508</td>\n",
       "      <td>8.27316e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00299215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyrimidine</th>\n",
       "      <td>-0.416674</td>\n",
       "      <td>1.46282e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00098009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       r            p   sig            q\n",
       "Superoxide      0.718449  2.72273e-17  True  5.47268e-15\n",
       "OxyR            0.472355  6.12923e-07  True  6.15988e-05\n",
       "crp KO-1        0.406312  2.48764e-05  True   0.00125004\n",
       "Tryptophan      0.379858  8.93178e-05  True   0.00299215\n",
       "FDH-O           0.367361  0.000157459  True   0.00347875\n",
       "Glycerol        0.358208  0.000235102  True   0.00429596\n",
       "Propionate      0.354853  0.000271496  True   0.00454755\n",
       "Salicylic Acid  0.327626  0.000824534  True     0.011838\n",
       "thrA KO         -0.28138   0.00436556  True    0.0350991\n",
       "Galactose      -0.284188   0.00397498  True    0.0332905\n",
       "Flagella       -0.285584   0.00379273  True    0.0331452\n",
       "ymgI           -0.292063   0.00304097  True    0.0277834\n",
       "Translation    -0.292474   0.00299813  True    0.0277834\n",
       "Cysteine-1     -0.297475   0.00251875  True    0.0253134\n",
       "Acetate         -0.30598   0.00185968  True    0.0196735\n",
       "IS5            -0.306009   0.00185771  True    0.0196735\n",
       "Phosphate-1    -0.309163   0.00165619  True     0.019582\n",
       "FlhDC-1        -0.320255   0.00109504  True    0.0137565\n",
       "FlhDC-2        -0.323499  0.000967318  True    0.0129621\n",
       "Purine         -0.332231  0.000688125  True    0.0106395\n",
       "Thr/Ser-1      -0.365226  0.000173072  True   0.00347875\n",
       "UC-3           -0.365575  0.000170427  True   0.00347875\n",
       "Sialic Acid     -0.36848  0.000149805  True   0.00347875\n",
       "malE OE        -0.381508  8.27316e-05  True   0.00299215\n",
       "Pyrimidine     -0.416674  1.46282e-05  True   0.00098009"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import packages\n",
    "from scipy.stats import pearsonr\n",
    "from statsmodels.stats.multitest import fdrcorrection\n",
    "\n",
    "# calculate correlations\n",
    "pq_corrs = pd.DataFrame(index = ica.imodulon_names, columns = ['r', 'p'])\n",
    "for k in ica.imodulon_names:\n",
    "    pq_corrs.loc[k] = pearsonr(ros_meta.pq, ica.A.loc[k, ros_meta.index])\n",
    "\n",
    "# run false discovery rate correction\n",
    "pq_corrs['sig'], pq_corrs['q'] = fdrcorrection(pq_corrs.p)\n",
    "\n",
    "# sort & remove insignificant values\n",
    "pq_corrs = pq_corrs.sort_values('r', ascending = False)\n",
    "sig_pq_corrs = pq_corrs[pq_corrs.sig]\n",
    "\n",
    "# display results\n",
    "sig_pq_corrs"
   ]
  }
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