salphagome.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Methods in the following modules are needed for a successful run of Salphagome.\n",
    "\n",
    "import glob\n",
    "import argparse\n",
    "import pandas as pd\n",
    "import re\n",
    "from Bio import Entrez\n",
    "import time\n",
    "import xml.etree.ElementTree as ET\n",
    "import seaborn as sns; sns.set()\n",
    "import copy\n",
    "import numpy as np\n",
    "import subprocess\n",
    "from Bio import SeqIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The user determines the files.\n",
    "\n",
    "arg_parser = argparse.ArgumentParser( description = 'Set Salphagome files.' )\n",
    "arg_parser.add_argument( 'files_control' )\n",
    "arg_parser.add_argument( 'files_disease1' )\n",
    "arg_parser.add_argument( 'files_disease2' )\n",
    "arg_parser.add_argument( 'files_disease3' )\n",
    "arg_parser.add_argument( 'location_of_fasta' )\n",
    "\n",
    "control = arguments.files_control\n",
    "disease1 = arguments.files_disease1\n",
    "disease2 = arguments.files_disease2\n",
    "disease3 = arguments.files_disease3\n",
    "fasta_file = arguments.location_of_fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Phages: 153\n"
     ]
    }
   ],
   "source": [
    "#List of sequences in the reference FASTA\n",
    "fasta_ids = []\n",
    "\n",
    "with open(location_of_fasta, \"r\") as handle:\n",
    "    for record in SeqIO.parse(handle, \"fasta\") :\n",
    "        fasta_ids.append(record.id)\n",
    "\n",
    "print(\"Total Phages: \" + str(len(fasta_ids)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Master List\n",
    "master_list = []\n",
    "\n",
    "#Get read hit counts from control folder\n",
    "\n",
    "for file in files_control:\n",
    "    \n",
    "    name = str(file)\n",
    "    \n",
    "    list_of_seqs = copy.deepcopy(fasta_ids)\n",
    "    \n",
    "    with open(file, \"r\") as ifile:\n",
    "        for line in ifile:\n",
    "            \n",
    "            data = line.strip().split(' ')\n",
    "            data = data+[name, 'typhi']\n",
    "            master_list.append(data)\n",
    "            \n",
    "    for seq in list_of_seqs:\n",
    "        \n",
    "        data = [0, seq, name, 'typhi']\n",
    "        \n",
    "        master_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get read hit counts from disease folder\n",
    "\n",
    "for file in files_disease1:\n",
    "    \n",
    "    list_of_seqs = copy.deepcopy(fasta_ids)\n",
    "    \n",
    "    name = str(file)\n",
    "    \n",
    "    with open(file, \"r\") as ifile:\n",
    "        \n",
    "        for line in ifile:\n",
    "            \n",
    "            data = line.strip().split(' ')\n",
    "            data = data+[name, 'ptyphia']\n",
    "            \n",
    "            list_of_seqs.remove(data[1])\n",
    "            \n",
    "            \n",
    "            master_list.append(data)\n",
    "\n",
    "    for seq in list_of_seqs:\n",
    "        \n",
    "        data = [0, seq, name, 'ptyphia']\n",
    "        \n",
    "        master_list.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Name items in our current dataframe.\n",
    "\n",
    "df = pd.DataFrame(columns=['count', 'seq', 'file', 'type'], data= master_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that gets accession Ids from the SRA.\n",
    "\n",
    "def get_srr_accession(df):\n",
    "    \"\"\"\n",
    "    Extract the SRR number from the file_location field of the dataframe.\n",
    "    \"\"\"\n",
    "    pattern = re.compile(r\"[SED]RR[0-9]+\")\n",
    "\n",
    "    file = df['file']\n",
    "\n",
    "    return re.search(pattern, file).group(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Include accession Ids in our dataframe.\n",
    "\n",
    "df['acc'] = df.apply(get_srr_accession, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the counts to integer object.\n",
    "\n",
    "df['count'] = df['count'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that gets the spot counts and use for normalization of the data.\n",
    "\n",
    "def get_spot_count(df, email, max_errors):\n",
    "    \"\"\"\n",
    "    Use the Entrez API to get the spot (read) count for that SRR number.\n",
    "    \"\"\"\n",
    "\n",
    "    srr_acc = df['acc']\n",
    "    \n",
    "    # get SRR id\n",
    "    Entrez.email = email\n",
    "\n",
    "    error_count =0\n",
    "\n",
    "    while error_count < max_errors:\n",
    "\n",
    "        try:\n",
    "\n",
    "            handle = Entrez.esearch(db=\"sra\",term=srr_acc)\n",
    "\n",
    "            record = Entrez.read(handle)\n",
    "\n",
    "            handle.close()\n",
    "\n",
    "            srr_id = record[\"IdList\"][0]\n",
    "\n",
    "            break\n",
    "\n",
    "        except:\n",
    "\n",
    "            print ('error occured collecting ID ', srr_acc, error_count)\n",
    "\n",
    "            time.sleep(10)\n",
    "\n",
    "            error_count = error_count +1\n",
    "\n",
    "\n",
    "    # get SRR summary\n",
    "\n",
    "    error_count =0\n",
    "\n",
    "    while error_count < max_errors:\n",
    "\n",
    "        try:\n",
    "\n",
    "            handle = Entrez.esummary(db='sra', id=srr_id)\n",
    "\n",
    "            record = Entrez.read(handle)\n",
    "\n",
    "            handle.close()\n",
    "\n",
    "            my_xml = record[0]['Runs']\n",
    "\n",
    "            # Parse XML\n",
    "            xml_object = ET.fromstringlist([\"<root>\", my_xml, \"</root>\"])\n",
    "\n",
    "            # Get total_spots (reads)\n",
    "            for child in xml_object:\n",
    "\n",
    "                if child.attrib['acc'] == srr_acc:\n",
    "\n",
    "                    #print (srr_acc, child.attrib['total_spots'])\n",
    "\n",
    "                    return int(child.attrib['total_spots'])\n",
    "\n",
    "        except:\n",
    "\n",
    "            print ('error occured collecting spot count', srr_acc, error_count)\n",
    "\n",
    "            time.sleep(10)\n",
    "\n",
    "            error_count = error_count +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = my_list = list(df['acc'].unique())\n",
    "\n",
    "spot_df = pd.DataFrame(data2, columns=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          acc  spot_count\n",
      "0   DRR071002      668472\n",
      "1   DRR071001      789639\n",
      "2   DRR071000     1137918\n",
      "3   DRR071003      566361\n",
      "4  ERR2011049      702860\n",
      "5  ERR2011050      529341\n",
      "6  ERR2210492      941185\n"
     ]
    }
   ],
   "source": [
    "#Get spot count for each of the sequences\n",
    "\n",
    "spot_df['spot_count'] = spot_df.apply(get_spot_count, axis=1, args=['visiting.bioinformaticians@gmail.com', 3])\n",
    "print(spot_df.drop_duplicates(subset=['acc'], keep='last'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write Spot Dataframe to CSV file\n",
    "\n",
    "spot_df.to_csv('spot_map.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_count_dict ={}\n",
    "\n",
    "for row in spot_df.iterrows():\n",
    "    read_count_dict[(row[1]['acc'])] = row[1]['spot_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that normalizes the data.\n",
    "\n",
    "def normalise(df):\n",
    "    \n",
    "    srr = df['acc']\n",
    "    \n",
    "    read_count = read_count_dict[srr]\n",
    "    \n",
    "    return read_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data.\n",
    "\n",
    "df['read_count'] = df.apply(normalise, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>seq</th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "      <th>acc</th>\n",
       "      <th>read_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11320</td>\n",
       "      <td>NC_010463.1</td>\n",
       "      <td>typhi_results/typhi_phages.DRR071002.sam.speci...</td>\n",
       "      <td>typhi</td>\n",
       "      <td>DRR071002</td>\n",
       "      <td>668472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6915</td>\n",
       "      <td>NC_019488.1</td>\n",
       "      <td>typhi_results/typhi_phages.DRR071002.sam.speci...</td>\n",
       "      <td>typhi</td>\n",
       "      <td>DRR071002</td>\n",
       "      <td>668472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4298</td>\n",
       "      <td>NC_028699.1</td>\n",
       "      <td>typhi_results/typhi_phages.DRR071002.sam.speci...</td>\n",
       "      <td>typhi</td>\n",
       "      <td>DRR071002</td>\n",
       "      <td>668472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3925</td>\n",
       "      <td>NC_010393.1</td>\n",
       "      <td>typhi_results/typhi_phages.DRR071002.sam.speci...</td>\n",
       "      <td>typhi</td>\n",
       "      <td>DRR071002</td>\n",
       "      <td>668472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>931</td>\n",
       "      <td>NC_019545.1</td>\n",
       "      <td>typhi_results/typhi_phages.DRR071002.sam.speci...</td>\n",
       "      <td>typhi</td>\n",
       "      <td>DRR071002</td>\n",
       "      <td>668472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count          seq                                               file  \\\n",
       "0  11320  NC_010463.1  typhi_results/typhi_phages.DRR071002.sam.speci...   \n",
       "1   6915  NC_019488.1  typhi_results/typhi_phages.DRR071002.sam.speci...   \n",
       "2   4298  NC_028699.1  typhi_results/typhi_phages.DRR071002.sam.speci...   \n",
       "3   3925  NC_010393.1  typhi_results/typhi_phages.DRR071002.sam.speci...   \n",
       "4    931  NC_019545.1  typhi_results/typhi_phages.DRR071002.sam.speci...   \n",
       "\n",
       "    type        acc  read_count  \n",
       "0  typhi  DRR071002      668472  \n",
       "1  typhi  DRR071002      668472  \n",
       "2  typhi  DRR071002      668472  \n",
       "3  typhi  DRR071002      668472  \n",
       "4  typhi  DRR071002      668472  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print dataframe's first few lines\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalise_values(df):\n",
    "    \n",
    "    return (df['count'] / df['read_count']) * 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['normalised_count'] = df.apply(normalise_values, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('typhi.ptyphi.comparison.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0ae44c828>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='type', y='normalised_count', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby(['type', 'seq']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "grouped['type2'] = grouped.index.levels[0][ grouped.index.labels[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivoted = grouped.pivot_table(index='seq', values='normalised_count', columns='type2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_diffa(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphia']\n",
    "    \n",
    "    pctdiff = abs(value2-value1)/((value1+value2)/2)*100\n",
    "    \n",
    "    return pctdiff\n",
    "\n",
    "def percent_diffb(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphib']\n",
    "    \n",
    "    pctdiff = abs(value2-value1)/((value1+value2)/2)*100\n",
    "    \n",
    "    return pctdiff\n",
    "\n",
    "def percent_diffc(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphic']\n",
    "    \n",
    "    pctdiff = abs(value2-value1)/((value1+value2)/2)*100\n",
    "    \n",
    "    return pctdiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "pivoted['pct_diffa'] = pivoted.apply(percent_diffa,axis=1)\n",
    "pivoted['pct_diffb'] = pivoted.apply(percent_diffb,axis=1)\n",
    "pivoted['pct_diffc'] = pivoted.apply(percent_diffc,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0ac39cc50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted = pivoted.sort_values('pct_diffa')\n",
    "\n",
    "pivoted[['pct_diffa']].plot(kind='bar', figsize =(75,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0a9c61780>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted = pivoted.sort_values('pct_diffb')\n",
    "\n",
    "pivoted[['pct_diffb']].plot(kind='bar', figsize =(75,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0aa867208>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 5400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted = pivoted.sort_values('pct_diffc')\n",
    "\n",
    "pivoted[['pct_diffc']].plot(kind='bar', figsize =(75,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pct_changea(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphia']\n",
    "    \n",
    "    return ((value1-value2) / value2)*100\n",
    "\n",
    "def pct_changeb(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphib']\n",
    "    \n",
    "    return ((value1-value2) / value2)*100\n",
    "\n",
    "def pct_changec(df):\n",
    "    \n",
    "    value1 = df['typhi']\n",
    "    value2 = df['ptyphic']\n",
    "    \n",
    "    return ((value1-value2) / value2)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0aa7753c8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 14400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted['pct_changea'] = pivoted.apply(pct_changea, axis=1)\n",
    "pivoted = pivoted.sort_values('pct_changea')\n",
    "pivoted['pct_changea'].plot(kind='bar', figsize =(200,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0aa439dd8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 14400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted['pct_changeb'] = pivoted.apply(pct_changeb, axis=1)\n",
    "pivoted = pivoted.sort_values('pct_changeb')\n",
    "pivoted['pct_changeb'].plot(kind='bar', figsize =(200,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0aa24edd8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 14400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted['pct_changec'] = pivoted.apply(pct_changea, axis=1)\n",
    "pivoted = pivoted.sort_values('pct_changec')\n",
    "pivoted['pct_changec'].plot(kind='bar', figsize =(200,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>type2</th>\n",
       "      <th>ptyphia</th>\n",
       "      <th>ptyphib</th>\n",
       "      <th>ptyphic</th>\n",
       "      <th>typhi</th>\n",
       "      <th>pct_diffa</th>\n",
       "      <th>pct_diffb</th>\n",
       "      <th>pct_diffc</th>\n",
       "      <th>pct_changea</th>\n",
       "      <th>pct_changeb</th>\n",
       "      <th>pct_changec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NC_019517.1</th>\n",
       "      <td>5.691034</td>\n",
       "      <td>11.334848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_023556.1</th>\n",
       "      <td>5.691034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_011811.1</th>\n",
       "      <td>2.845517</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_005282.1</th>\n",
       "      <td>36.991720</td>\n",
       "      <td>64.230808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_021777.1</th>\n",
       "      <td>28.455169</td>\n",
       "      <td>3.778283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_025443.1</th>\n",
       "      <td>36.991720</td>\n",
       "      <td>49.117677</td>\n",
       "      <td>23.374788</td>\n",
       "      <td>0.351519</td>\n",
       "      <td>196.234722</td>\n",
       "      <td>197.157672</td>\n",
       "      <td>194.073765</td>\n",
       "      <td>-99.049736</td>\n",
       "      <td>-99.284333</td>\n",
       "      <td>-99.049736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_021779.1</th>\n",
       "      <td>73.983439</td>\n",
       "      <td>52.895959</td>\n",
       "      <td>44.624596</td>\n",
       "      <td>0.920783</td>\n",
       "      <td>195.082875</td>\n",
       "      <td>193.156156</td>\n",
       "      <td>191.913265</td>\n",
       "      <td>-98.755419</td>\n",
       "      <td>-98.259256</td>\n",
       "      <td>-98.755419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_013059.1</th>\n",
       "      <td>1980.479754</td>\n",
       "      <td>1976.041909</td>\n",
       "      <td>227.372939</td>\n",
       "      <td>28.539530</td>\n",
       "      <td>194.317719</td>\n",
       "      <td>194.305139</td>\n",
       "      <td>155.391732</td>\n",
       "      <td>-98.558959</td>\n",
       "      <td>-98.555722</td>\n",
       "      <td>-98.558959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019767.1</th>\n",
       "      <td>264.633071</td>\n",
       "      <td>147.353030</td>\n",
       "      <td>16.999846</td>\n",
       "      <td>7.669425</td>\n",
       "      <td>188.733962</td>\n",
       "      <td>180.210801</td>\n",
       "      <td>75.644071</td>\n",
       "      <td>-97.101864</td>\n",
       "      <td>-94.795203</td>\n",
       "      <td>-97.101864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_018275.1</th>\n",
       "      <td>1435.563270</td>\n",
       "      <td>1356.403528</td>\n",
       "      <td>150.873633</td>\n",
       "      <td>57.605088</td>\n",
       "      <td>184.568361</td>\n",
       "      <td>183.704459</td>\n",
       "      <td>89.475362</td>\n",
       "      <td>-95.987283</td>\n",
       "      <td>-95.753101</td>\n",
       "      <td>-95.987283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_005841.1</th>\n",
       "      <td>1859.545286</td>\n",
       "      <td>1968.485343</td>\n",
       "      <td>123.248883</td>\n",
       "      <td>84.232035</td>\n",
       "      <td>182.666320</td>\n",
       "      <td>183.586238</td>\n",
       "      <td>37.610060</td>\n",
       "      <td>-95.470289</td>\n",
       "      <td>-95.720972</td>\n",
       "      <td>-95.470289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019708.1</th>\n",
       "      <td>200.608941</td>\n",
       "      <td>204.027272</td>\n",
       "      <td>16.999846</td>\n",
       "      <td>9.135008</td>\n",
       "      <td>182.578743</td>\n",
       "      <td>182.858115</td>\n",
       "      <td>60.186584</td>\n",
       "      <td>-95.446360</td>\n",
       "      <td>-95.522653</td>\n",
       "      <td>-95.446360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_030919.1</th>\n",
       "      <td>1313.206044</td>\n",
       "      <td>1484.865144</td>\n",
       "      <td>165.748498</td>\n",
       "      <td>83.773846</td>\n",
       "      <td>176.012870</td>\n",
       "      <td>178.637826</td>\n",
       "      <td>65.705260</td>\n",
       "      <td>-93.620662</td>\n",
       "      <td>-94.358151</td>\n",
       "      <td>-93.620662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031946.1</th>\n",
       "      <td>1169.507441</td>\n",
       "      <td>1193.937367</td>\n",
       "      <td>155.123594</td>\n",
       "      <td>83.431348</td>\n",
       "      <td>173.364589</td>\n",
       "      <td>173.873997</td>\n",
       "      <td>60.105438</td>\n",
       "      <td>-92.866112</td>\n",
       "      <td>-93.012083</td>\n",
       "      <td>-92.866112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_002371.2</th>\n",
       "      <td>1435.563270</td>\n",
       "      <td>1482.976002</td>\n",
       "      <td>301.747265</td>\n",
       "      <td>128.847226</td>\n",
       "      <td>167.055392</td>\n",
       "      <td>168.024477</td>\n",
       "      <td>80.307595</td>\n",
       "      <td>-91.024622</td>\n",
       "      <td>-91.311577</td>\n",
       "      <td>-91.024622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031940.1</th>\n",
       "      <td>845.118516</td>\n",
       "      <td>806.663380</td>\n",
       "      <td>8725.170928</td>\n",
       "      <td>80.865423</td>\n",
       "      <td>165.068326</td>\n",
       "      <td>163.554795</td>\n",
       "      <td>196.326819</td>\n",
       "      <td>-90.431469</td>\n",
       "      <td>-89.975320</td>\n",
       "      <td>-90.431469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010392.1</th>\n",
       "      <td>7532.083203</td>\n",
       "      <td>7554.676475</td>\n",
       "      <td>6418.504332</td>\n",
       "      <td>740.576567</td>\n",
       "      <td>164.191610</td>\n",
       "      <td>164.289139</td>\n",
       "      <td>158.621696</td>\n",
       "      <td>-90.167706</td>\n",
       "      <td>-90.197111</td>\n",
       "      <td>-90.167706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_014900.1</th>\n",
       "      <td>1428.449478</td>\n",
       "      <td>1443.304033</td>\n",
       "      <td>235.872862</td>\n",
       "      <td>159.481582</td>\n",
       "      <td>159.826573</td>\n",
       "      <td>160.198899</td>\n",
       "      <td>38.644453</td>\n",
       "      <td>-88.835336</td>\n",
       "      <td>-88.950243</td>\n",
       "      <td>-88.835336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_028696.2</th>\n",
       "      <td>2276.413511</td>\n",
       "      <td>2461.551250</td>\n",
       "      <td>562.057406</td>\n",
       "      <td>262.034364</td>\n",
       "      <td>158.709514</td>\n",
       "      <td>161.516266</td>\n",
       "      <td>72.813018</td>\n",
       "      <td>-88.489158</td>\n",
       "      <td>-89.354909</td>\n",
       "      <td>-88.489158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_011802.1</th>\n",
       "      <td>1072.759867</td>\n",
       "      <td>1105.147721</td>\n",
       "      <td>172.123440</td>\n",
       "      <td>125.431917</td>\n",
       "      <td>158.126264</td>\n",
       "      <td>159.228346</td>\n",
       "      <td>31.383419</td>\n",
       "      <td>-88.307549</td>\n",
       "      <td>-88.650213</td>\n",
       "      <td>-88.307549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_011976.1</th>\n",
       "      <td>1818.285292</td>\n",
       "      <td>1900.476253</td>\n",
       "      <td>444.120975</td>\n",
       "      <td>216.020741</td>\n",
       "      <td>157.524436</td>\n",
       "      <td>159.173910</td>\n",
       "      <td>69.106444</td>\n",
       "      <td>-88.119535</td>\n",
       "      <td>-88.633336</td>\n",
       "      <td>-88.119535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_004348.1</th>\n",
       "      <td>2004.666648</td>\n",
       "      <td>2011.935595</td>\n",
       "      <td>446.245956</td>\n",
       "      <td>270.550861</td>\n",
       "      <td>152.435165</td>\n",
       "      <td>152.586643</td>\n",
       "      <td>49.022287</td>\n",
       "      <td>-86.503948</td>\n",
       "      <td>-86.552708</td>\n",
       "      <td>-86.503948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_017985.1</th>\n",
       "      <td>2074.381811</td>\n",
       "      <td>2248.078271</td>\n",
       "      <td>533.370166</td>\n",
       "      <td>303.573923</td>\n",
       "      <td>148.935311</td>\n",
       "      <td>152.411395</td>\n",
       "      <td>54.913165</td>\n",
       "      <td>-85.365571</td>\n",
       "      <td>-86.496292</td>\n",
       "      <td>-85.365571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010393.1</th>\n",
       "      <td>18456.022536</td>\n",
       "      <td>19010.429950</td>\n",
       "      <td>8205.613137</td>\n",
       "      <td>3109.240240</td>\n",
       "      <td>142.328730</td>\n",
       "      <td>143.774203</td>\n",
       "      <td>90.082880</td>\n",
       "      <td>-83.153249</td>\n",
       "      <td>-83.644556</td>\n",
       "      <td>-83.153249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019705.1</th>\n",
       "      <td>460.973736</td>\n",
       "      <td>415.611109</td>\n",
       "      <td>155.123594</td>\n",
       "      <td>79.018573</td>\n",
       "      <td>141.466890</td>\n",
       "      <td>136.098802</td>\n",
       "      <td>65.007531</td>\n",
       "      <td>-82.858335</td>\n",
       "      <td>-80.987377</td>\n",
       "      <td>-82.858335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019545.1</th>\n",
       "      <td>4042.056740</td>\n",
       "      <td>4142.887099</td>\n",
       "      <td>4235.086620</td>\n",
       "      <td>715.995687</td>\n",
       "      <td>139.807667</td>\n",
       "      <td>141.056764</td>\n",
       "      <td>142.154410</td>\n",
       "      <td>-82.286352</td>\n",
       "      <td>-82.717470</td>\n",
       "      <td>-82.286352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_022752.1</th>\n",
       "      <td>251.828245</td>\n",
       "      <td>207.805554</td>\n",
       "      <td>90.311682</td>\n",
       "      <td>51.986419</td>\n",
       "      <td>131.555090</td>\n",
       "      <td>119.956852</td>\n",
       "      <td>53.866163</td>\n",
       "      <td>-79.356399</td>\n",
       "      <td>-74.983143</td>\n",
       "      <td>-79.356399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031228.1</th>\n",
       "      <td>110.975159</td>\n",
       "      <td>83.122222</td>\n",
       "      <td>93.499153</td>\n",
       "      <td>25.056014</td>\n",
       "      <td>126.322730</td>\n",
       "      <td>107.352848</td>\n",
       "      <td>115.462095</td>\n",
       "      <td>-77.421962</td>\n",
       "      <td>-69.856420</td>\n",
       "      <td>-77.421962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031082.1</th>\n",
       "      <td>119.511709</td>\n",
       "      <td>151.131312</td>\n",
       "      <td>44.624596</td>\n",
       "      <td>27.487429</td>\n",
       "      <td>125.203836</td>\n",
       "      <td>138.444468</td>\n",
       "      <td>47.529290</td>\n",
       "      <td>-77.000221</td>\n",
       "      <td>-81.812221</td>\n",
       "      <td>-77.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_022768.1</th>\n",
       "      <td>88.211024</td>\n",
       "      <td>68.009091</td>\n",
       "      <td>144.498690</td>\n",
       "      <td>21.313709</td>\n",
       "      <td>122.159283</td>\n",
       "      <td>104.554226</td>\n",
       "      <td>148.583557</td>\n",
       "      <td>-75.837817</td>\n",
       "      <td>-68.660499</td>\n",
       "      <td>-75.837817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_027344.1</th>\n",
       "      <td>219.104800</td>\n",
       "      <td>275.814645</td>\n",
       "      <td>314.497150</td>\n",
       "      <td>250.717718</td>\n",
       "      <td>13.457387</td>\n",
       "      <td>9.532910</td>\n",
       "      <td>22.568208</td>\n",
       "      <td>14.428217</td>\n",
       "      <td>-9.099200</td>\n",
       "      <td>14.428217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_020416.1</th>\n",
       "      <td>56.910338</td>\n",
       "      <td>102.013636</td>\n",
       "      <td>61.624442</td>\n",
       "      <td>74.265976</td>\n",
       "      <td>26.461543</td>\n",
       "      <td>31.481417</td>\n",
       "      <td>18.605483</td>\n",
       "      <td>30.496459</td>\n",
       "      <td>-27.199952</td>\n",
       "      <td>30.496459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_023693.1</th>\n",
       "      <td>88.211024</td>\n",
       "      <td>90.678787</td>\n",
       "      <td>48.874557</td>\n",
       "      <td>117.800642</td>\n",
       "      <td>28.726158</td>\n",
       "      <td>26.018735</td>\n",
       "      <td>82.707068</td>\n",
       "      <td>33.544128</td>\n",
       "      <td>29.909812</td>\n",
       "      <td>33.544128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_015938.1</th>\n",
       "      <td>108.129642</td>\n",
       "      <td>136.018181</td>\n",
       "      <td>240.122824</td>\n",
       "      <td>145.687607</td>\n",
       "      <td>29.594494</td>\n",
       "      <td>6.864911</td>\n",
       "      <td>48.954206</td>\n",
       "      <td>34.734199</td>\n",
       "      <td>7.108921</td>\n",
       "      <td>34.734199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010391.1</th>\n",
       "      <td>193.495148</td>\n",
       "      <td>166.244444</td>\n",
       "      <td>1503.423875</td>\n",
       "      <td>265.403229</td>\n",
       "      <td>31.339435</td>\n",
       "      <td>45.944316</td>\n",
       "      <td>139.982098</td>\n",
       "      <td>37.162730</td>\n",
       "      <td>59.646375</td>\n",
       "      <td>37.162730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019539.1</th>\n",
       "      <td>65.446888</td>\n",
       "      <td>37.782828</td>\n",
       "      <td>53.124519</td>\n",
       "      <td>90.050839</td>\n",
       "      <td>31.645415</td>\n",
       "      <td>81.775031</td>\n",
       "      <td>51.581949</td>\n",
       "      <td>37.593766</td>\n",
       "      <td>138.338005</td>\n",
       "      <td>37.593766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_024204.1</th>\n",
       "      <td>65.446888</td>\n",
       "      <td>37.782828</td>\n",
       "      <td>53.124519</td>\n",
       "      <td>90.050839</td>\n",
       "      <td>31.645415</td>\n",
       "      <td>81.775031</td>\n",
       "      <td>51.581949</td>\n",
       "      <td>37.593766</td>\n",
       "      <td>138.338005</td>\n",
       "      <td>37.593766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_016761.1</th>\n",
       "      <td>93.902057</td>\n",
       "      <td>211.583837</td>\n",
       "      <td>70.124364</td>\n",
       "      <td>157.664771</td>\n",
       "      <td>50.692465</td>\n",
       "      <td>29.204750</td>\n",
       "      <td>76.860915</td>\n",
       "      <td>67.903425</td>\n",
       "      <td>-25.483547</td>\n",
       "      <td>67.903425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_020414.1</th>\n",
       "      <td>14.227584</td>\n",
       "      <td>15.113131</td>\n",
       "      <td>23.374788</td>\n",
       "      <td>26.971473</td>\n",
       "      <td>61.864952</td>\n",
       "      <td>56.354774</td>\n",
       "      <td>14.287793</td>\n",
       "      <td>89.571695</td>\n",
       "      <td>78.463831</td>\n",
       "      <td>89.571695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_021772.1</th>\n",
       "      <td>11.382068</td>\n",
       "      <td>7.556566</td>\n",
       "      <td>59.499461</td>\n",
       "      <td>28.189617</td>\n",
       "      <td>84.947355</td>\n",
       "      <td>115.441986</td>\n",
       "      <td>71.411045</td>\n",
       "      <td>147.666930</td>\n",
       "      <td>273.048006</td>\n",
       "      <td>147.666930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_011041.1</th>\n",
       "      <td>8.536551</td>\n",
       "      <td>15.113131</td>\n",
       "      <td>61.624442</td>\n",
       "      <td>50.450726</td>\n",
       "      <td>142.112596</td>\n",
       "      <td>107.795960</td>\n",
       "      <td>19.939682</td>\n",
       "      <td>490.996616</td>\n",
       "      <td>233.820469</td>\n",
       "      <td>490.996616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_024134.1</th>\n",
       "      <td>8.536551</td>\n",
       "      <td>15.113131</td>\n",
       "      <td>61.624442</td>\n",
       "      <td>50.637719</td>\n",
       "      <td>142.295523</td>\n",
       "      <td>108.058186</td>\n",
       "      <td>19.573331</td>\n",
       "      <td>493.187122</td>\n",
       "      <td>235.057762</td>\n",
       "      <td>493.187122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010495.1</th>\n",
       "      <td>2.845517</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.124981</td>\n",
       "      <td>19.937476</td>\n",
       "      <td>150.041386</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>161.473362</td>\n",
       "      <td>600.662721</td>\n",
       "      <td>inf</td>\n",
       "      <td>600.662721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019402.1</th>\n",
       "      <td>5.691034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.999846</td>\n",
       "      <td>54.510967</td>\n",
       "      <td>162.187079</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>104.910347</td>\n",
       "      <td>857.839451</td>\n",
       "      <td>inf</td>\n",
       "      <td>857.839451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_019488.1</th>\n",
       "      <td>455.282702</td>\n",
       "      <td>453.393937</td>\n",
       "      <td>1107.114967</td>\n",
       "      <td>4571.958170</td>\n",
       "      <td>163.774745</td>\n",
       "      <td>163.911469</td>\n",
       "      <td>122.021433</td>\n",
       "      <td>904.202037</td>\n",
       "      <td>908.385379</td>\n",
       "      <td>904.202037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_029003.2</th>\n",
       "      <td>2.845517</td>\n",
       "      <td>7.556566</td>\n",
       "      <td>8334.174472</td>\n",
       "      <td>33.996534</td>\n",
       "      <td>169.105771</td>\n",
       "      <td>127.258706</td>\n",
       "      <td>198.374960</td>\n",
       "      <td>1094.740206</td>\n",
       "      <td>349.893987</td>\n",
       "      <td>1094.740206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010463.1</th>\n",
       "      <td>489.428905</td>\n",
       "      <td>517.624745</td>\n",
       "      <td>1240.988754</td>\n",
       "      <td>7340.887066</td>\n",
       "      <td>174.998255</td>\n",
       "      <td>173.652785</td>\n",
       "      <td>142.157692</td>\n",
       "      <td>1399.888338</td>\n",
       "      <td>1318.187044</td>\n",
       "      <td>1399.888338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_010807.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.778283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.926749</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>149.122656</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>inf</td>\n",
       "      <td>586.204563</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_027350.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.778283</td>\n",
       "      <td>2.124981</td>\n",
       "      <td>0.506561</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>152.711402</td>\n",
       "      <td>123.001689</td>\n",
       "      <td>inf</td>\n",
       "      <td>-86.592835</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_015271.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>102.109472</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031258.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706263</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_028799.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.060827</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_021563.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.778283</td>\n",
       "      <td>46.749576</td>\n",
       "      <td>32.838024</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>158.725681</td>\n",
       "      <td>34.959094</td>\n",
       "      <td>inf</td>\n",
       "      <td>769.125627</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_028812.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.778283</td>\n",
       "      <td>38.249653</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031250.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.749730</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_004831.2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_006940.2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_015719.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_021317.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC_031271.1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type2             ptyphia       ptyphib      ptyphic        typhi   pct_diffa  \\\n",
       "seq                                                                             \n",
       "NC_019517.1      5.691034     11.334848     0.000000     0.000000  200.000000   \n",
       "NC_023556.1      5.691034      0.000000     0.000000     0.000000  200.000000   \n",
       "NC_011811.1      2.845517      0.000000     0.000000     0.000000  200.000000   \n",
       "NC_005282.1     36.991720     64.230808     0.000000     0.000000  200.000000   \n",
       "NC_021777.1     28.455169      3.778283     0.000000     0.000000  200.000000   \n",
       "NC_025443.1     36.991720     49.117677    23.374788     0.351519  196.234722   \n",
       "NC_021779.1     73.983439     52.895959    44.624596     0.920783  195.082875   \n",
       "NC_013059.1   1980.479754   1976.041909   227.372939    28.539530  194.317719   \n",
       "NC_019767.1    264.633071    147.353030    16.999846     7.669425  188.733962   \n",
       "NC_018275.1   1435.563270   1356.403528   150.873633    57.605088  184.568361   \n",
       "NC_005841.1   1859.545286   1968.485343   123.248883    84.232035  182.666320   \n",
       "NC_019708.1    200.608941    204.027272    16.999846     9.135008  182.578743   \n",
       "NC_030919.1   1313.206044   1484.865144   165.748498    83.773846  176.012870   \n",
       "NC_031946.1   1169.507441   1193.937367   155.123594    83.431348  173.364589   \n",
       "NC_002371.2   1435.563270   1482.976002   301.747265   128.847226  167.055392   \n",
       "NC_031940.1    845.118516    806.663380  8725.170928    80.865423  165.068326   \n",
       "NC_010392.1   7532.083203   7554.676475  6418.504332   740.576567  164.191610   \n",
       "NC_014900.1   1428.449478   1443.304033   235.872862   159.481582  159.826573   \n",
       "NC_028696.2   2276.413511   2461.551250   562.057406   262.034364  158.709514   \n",
       "NC_011802.1   1072.759867   1105.147721   172.123440   125.431917  158.126264   \n",
       "NC_011976.1   1818.285292   1900.476253   444.120975   216.020741  157.524436   \n",
       "NC_004348.1   2004.666648   2011.935595   446.245956   270.550861  152.435165   \n",
       "NC_017985.1   2074.381811   2248.078271   533.370166   303.573923  148.935311   \n",
       "NC_010393.1  18456.022536  19010.429950  8205.613137  3109.240240  142.328730   \n",
       "NC_019705.1    460.973736    415.611109   155.123594    79.018573  141.466890   \n",
       "NC_019545.1   4042.056740   4142.887099  4235.086620   715.995687  139.807667   \n",
       "NC_022752.1    251.828245    207.805554    90.311682    51.986419  131.555090   \n",
       "NC_031228.1    110.975159     83.122222    93.499153    25.056014  126.322730   \n",
       "NC_031082.1    119.511709    151.131312    44.624596    27.487429  125.203836   \n",
       "NC_022768.1     88.211024     68.009091   144.498690    21.313709  122.159283   \n",
       "...                   ...           ...          ...          ...         ...   \n",
       "NC_027344.1    219.104800    275.814645   314.497150   250.717718   13.457387   \n",
       "NC_020416.1     56.910338    102.013636    61.624442    74.265976   26.461543   \n",
       "NC_023693.1     88.211024     90.678787    48.874557   117.800642   28.726158   \n",
       "NC_015938.1    108.129642    136.018181   240.122824   145.687607   29.594494   \n",
       "NC_010391.1    193.495148    166.244444  1503.423875   265.403229   31.339435   \n",
       "NC_019539.1     65.446888     37.782828    53.124519    90.050839   31.645415   \n",
       "NC_024204.1     65.446888     37.782828    53.124519    90.050839   31.645415   \n",
       "NC_016761.1     93.902057    211.583837    70.124364   157.664771   50.692465   \n",
       "NC_020414.1     14.227584     15.113131    23.374788    26.971473   61.864952   \n",
       "NC_021772.1     11.382068      7.556566    59.499461    28.189617   84.947355   \n",
       "NC_011041.1      8.536551     15.113131    61.624442    50.450726  142.112596   \n",
       "NC_024134.1      8.536551     15.113131    61.624442    50.637719  142.295523   \n",
       "NC_010495.1      2.845517      0.000000     2.124981    19.937476  150.041386   \n",
       "NC_019402.1      5.691034      0.000000    16.999846    54.510967  162.187079   \n",
       "NC_019488.1    455.282702    453.393937  1107.114967  4571.958170  163.774745   \n",
       "NC_029003.2      2.845517      7.556566  8334.174472    33.996534  169.105771   \n",
       "NC_010463.1    489.428905    517.624745  1240.988754  7340.887066  174.998255   \n",
       "NC_010807.1      0.000000      3.778283     0.000000    25.926749  200.000000   \n",
       "NC_027350.1      0.000000      3.778283     2.124981     0.506561  200.000000   \n",
       "NC_015271.1      0.000000      0.000000     0.000000   102.109472  200.000000   \n",
       "NC_031258.1      0.000000      0.000000     0.000000     0.706263  200.000000   \n",
       "NC_028799.1      0.000000      0.000000     0.000000    20.060827  200.000000   \n",
       "NC_021563.1      0.000000      3.778283    46.749576    32.838024  200.000000   \n",
       "NC_028812.1      0.000000      3.778283    38.249653     0.000000         NaN   \n",
       "NC_031250.1      0.000000      0.000000    29.749730     0.000000         NaN   \n",
       "NC_004831.2      0.000000      0.000000     0.000000     0.000000         NaN   \n",
       "NC_006940.2      0.000000      0.000000     0.000000     0.000000         NaN   \n",
       "NC_015719.1      0.000000      0.000000     0.000000     0.000000         NaN   \n",
       "NC_021317.1      0.000000      0.000000     0.000000     0.000000         NaN   \n",
       "NC_031271.1      0.000000      0.000000     0.000000     0.000000         NaN   \n",
       "\n",
       "type2         pct_diffb   pct_diffc  pct_changea  pct_changeb  pct_changec  \n",
       "seq                                                                         \n",
       "NC_019517.1  200.000000         NaN  -100.000000  -100.000000  -100.000000  \n",
       "NC_023556.1         NaN         NaN  -100.000000          NaN  -100.000000  \n",
       "NC_011811.1         NaN         NaN  -100.000000          NaN  -100.000000  \n",
       "NC_005282.1  200.000000         NaN  -100.000000  -100.000000  -100.000000  \n",
       "NC_021777.1  200.000000         NaN  -100.000000  -100.000000  -100.000000  \n",
       "NC_025443.1  197.157672  194.073765   -99.049736   -99.284333   -99.049736  \n",
       "NC_021779.1  193.156156  191.913265   -98.755419   -98.259256   -98.755419  \n",
       "NC_013059.1  194.305139  155.391732   -98.558959   -98.555722   -98.558959  \n",
       "NC_019767.1  180.210801   75.644071   -97.101864   -94.795203   -97.101864  \n",
       "NC_018275.1  183.704459   89.475362   -95.987283   -95.753101   -95.987283  \n",
       "NC_005841.1  183.586238   37.610060   -95.470289   -95.720972   -95.470289  \n",
       "NC_019708.1  182.858115   60.186584   -95.446360   -95.522653   -95.446360  \n",
       "NC_030919.1  178.637826   65.705260   -93.620662   -94.358151   -93.620662  \n",
       "NC_031946.1  173.873997   60.105438   -92.866112   -93.012083   -92.866112  \n",
       "NC_002371.2  168.024477   80.307595   -91.024622   -91.311577   -91.024622  \n",
       "NC_031940.1  163.554795  196.326819   -90.431469   -89.975320   -90.431469  \n",
       "NC_010392.1  164.289139  158.621696   -90.167706   -90.197111   -90.167706  \n",
       "NC_014900.1  160.198899   38.644453   -88.835336   -88.950243   -88.835336  \n",
       "NC_028696.2  161.516266   72.813018   -88.489158   -89.354909   -88.489158  \n",
       "NC_011802.1  159.228346   31.383419   -88.307549   -88.650213   -88.307549  \n",
       "NC_011976.1  159.173910   69.106444   -88.119535   -88.633336   -88.119535  \n",
       "NC_004348.1  152.586643   49.022287   -86.503948   -86.552708   -86.503948  \n",
       "NC_017985.1  152.411395   54.913165   -85.365571   -86.496292   -85.365571  \n",
       "NC_010393.1  143.774203   90.082880   -83.153249   -83.644556   -83.153249  \n",
       "NC_019705.1  136.098802   65.007531   -82.858335   -80.987377   -82.858335  \n",
       "NC_019545.1  141.056764  142.154410   -82.286352   -82.717470   -82.286352  \n",
       "NC_022752.1  119.956852   53.866163   -79.356399   -74.983143   -79.356399  \n",
       "NC_031228.1  107.352848  115.462095   -77.421962   -69.856420   -77.421962  \n",
       "NC_031082.1  138.444468   47.529290   -77.000221   -81.812221   -77.000221  \n",
       "NC_022768.1  104.554226  148.583557   -75.837817   -68.660499   -75.837817  \n",
       "...                 ...         ...          ...          ...          ...  \n",
       "NC_027344.1    9.532910   22.568208    14.428217    -9.099200    14.428217  \n",
       "NC_020416.1   31.481417   18.605483    30.496459   -27.199952    30.496459  \n",
       "NC_023693.1   26.018735   82.707068    33.544128    29.909812    33.544128  \n",
       "NC_015938.1    6.864911   48.954206    34.734199     7.108921    34.734199  \n",
       "NC_010391.1   45.944316  139.982098    37.162730    59.646375    37.162730  \n",
       "NC_019539.1   81.775031   51.581949    37.593766   138.338005    37.593766  \n",
       "NC_024204.1   81.775031   51.581949    37.593766   138.338005    37.593766  \n",
       "NC_016761.1   29.204750   76.860915    67.903425   -25.483547    67.903425  \n",
       "NC_020414.1   56.354774   14.287793    89.571695    78.463831    89.571695  \n",
       "NC_021772.1  115.441986   71.411045   147.666930   273.048006   147.666930  \n",
       "NC_011041.1  107.795960   19.939682   490.996616   233.820469   490.996616  \n",
       "NC_024134.1  108.058186   19.573331   493.187122   235.057762   493.187122  \n",
       "NC_010495.1  200.000000  161.473362   600.662721          inf   600.662721  \n",
       "NC_019402.1  200.000000  104.910347   857.839451          inf   857.839451  \n",
       "NC_019488.1  163.911469  122.021433   904.202037   908.385379   904.202037  \n",
       "NC_029003.2  127.258706  198.374960  1094.740206   349.893987  1094.740206  \n",
       "NC_010463.1  173.652785  142.157692  1399.888338  1318.187044  1399.888338  \n",
       "NC_010807.1  149.122656  200.000000          inf   586.204563          inf  \n",
       "NC_027350.1  152.711402  123.001689          inf   -86.592835          inf  \n",
       "NC_015271.1  200.000000  200.000000          inf          inf          inf  \n",
       "NC_031258.1  200.000000  200.000000          inf          inf          inf  \n",
       "NC_028799.1  200.000000  200.000000          inf          inf          inf  \n",
       "NC_021563.1  158.725681   34.959094          inf   769.125627          inf  \n",
       "NC_028812.1  200.000000  200.000000          NaN  -100.000000          NaN  \n",
       "NC_031250.1         NaN  200.000000          NaN          NaN          NaN  \n",
       "NC_004831.2         NaN         NaN          NaN          NaN          NaN  \n",
       "NC_006940.2         NaN         NaN          NaN          NaN          NaN  \n",
       "NC_015719.1         NaN         NaN          NaN          NaN          NaN  \n",
       "NC_021317.1         NaN         NaN          NaN          NaN          NaN  \n",
       "NC_031271.1         NaN         NaN          NaN          NaN          NaN  \n",
       "\n",
       "[153 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_019488.1 :  89.5397\n",
      "NC_019545.1 :  68.1818\n",
      "NC_010392.1 :  58.427\n",
      "NC_018279.1 :  57.7778\n",
      "NC_010463.1 :  90.5455\n",
      "NC_002371.2 :  58.3333\n",
      "NC_004348.1 :  57.1429\n",
      "NC_017985.1 :  57.1429\n",
      "NC_010393.1 :  80.4511\n",
      "NC_028699.1 :  89.3939\n",
      "NC_010391.1 :  51.6129\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_031129.1 :  54.0984\n",
      "NC_030919.1 :  52.6316\n",
      "NC_019488.1 :  88.9362\n",
      "NC_019545.1 :  72.8814\n",
      "NC_005841.1 :  52.9412\n",
      "NC_010392.1 :  70.1493\n",
      "NC_011802.1 :  68.75\n",
      "NC_014900.1 :  63.1579\n",
      "NC_018279.1 :  62.5\n",
      "NC_010463.1 :  91.6981\n",
      "NC_018275.1 :  52.9412\n",
      "NC_004348.1 :  65.625\n",
      "NC_005340.1 :  55.5556\n",
      "NC_017985.1 :  80\n",
      "NC_010393.1 :  76.6917\n",
      "NC_028699.1 :  89.0052\n",
      "NC_031946.1 :  55\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_019488.1 :  90.9465\n",
      "NC_019545.1 :  69.1176\n",
      "NC_005841.1 :  58.8235\n",
      "NC_010392.1 :  58.3333\n",
      "NC_011802.1 :  57.1429\n",
      "NC_014900.1 :  56.5217\n",
      "NC_018279.1 :  63.4146\n",
      "NC_010463.1 :  92.1642\n",
      "NC_002371.2 :  53.5714\n",
      "NC_004348.1 :  63.8889\n",
      "NC_017985.1 :  53.3333\n",
      "NC_010393.1 :  74.4681\n",
      "NC_028699.1 :  89.0547\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_030919.1 :  57.6923\n",
      "NC_019488.1 :  90.6504\n",
      "NC_019545.1 :  68.2353\n",
      "NC_010392.1 :  70.4545\n",
      "NC_018279.1 :  53.7037\n",
      "NC_010463.1 :  91.8819\n",
      "NC_002371.2 :  60.6061\n",
      "NC_004348.1 :  58.6957\n",
      "NC_017985.1 :  60\n",
      "NC_010393.1 :  76.129\n",
      "NC_028699.1 :  90.8257\n",
      "NC_010391.1 :  51.0638\n",
      "NC_004313.1 :  51.2195\n",
      "NC_031946.1 :  55.5556\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_030919.1 :  52.1739\n",
      "NC_019488.1 :  90.2041\n",
      "NC_019545.1 :  77.7778\n",
      "NC_005841.1 :  52.6316\n",
      "NC_010392.1 :  56.5217\n",
      "NC_018843.1 :  95.5451\n",
      "NC_018279.1 :  52\n",
      "NC_010463.1 :  90.9091\n",
      "NC_017985.1 :  60\n",
      "NC_010393.1 :  76.6423\n",
      "NC_028699.1 :  85.6436\n",
      "NC_031946.1 :  54.5455\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_030919.1 :  78.5714\n",
      "NC_019488.1 :  90.7563\n",
      "NC_019545.1 :  77.9661\n",
      "NC_005841.1 :  66.6667\n",
      "NC_010392.1 :  59.2593\n",
      "NC_011802.1 :  55\n",
      "NC_014900.1 :  60\n",
      "NC_018279.1 :  70.2703\n",
      "NC_010463.1 :  92.0152\n",
      "NC_018275.1 :  61.1111\n",
      "NC_002371.2 :  56\n",
      "NC_004348.1 :  74.1935\n",
      "NC_017985.1 :  80\n",
      "NC_010393.1 :  77.5362\n",
      "NC_028699.1 :  89.3401\n",
      "NC_010391.1 :  53.3333\n",
      "NC_031946.1 :  66.6667\n",
      "\n",
      "NOTABLE PHAGE  COVERAGE\n",
      "NC_019488.1 :  90.3361\n",
      "NC_019545.1 :  67.6471\n",
      "NC_010392.1 :  60.2273\n",
      "NC_018279.1 :  55.3191\n",
      "NC_010463.1 :  90.8088\n",
      "NC_004348.1 :  61.1111\n",
      "NC_017985.1 :  61.5385\n",
      "NC_010393.1 :  78.6765\n",
      "NC_028699.1 :  86.6337\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#\"then calculate the coverage\"\n",
    "\n",
    "COMMAND = '''for f in bedgraphs/*.bedgraph; do awk 'NR>1{if ($4 < 3) {mylo_phage[$1]++;} else {myhi_phage[$1]++;}} END {print \"\"; print \"NOTABLE PHAGE\", \" COVERAGE\"; {for (i in myhi_phage) if (mylo_phage[i] < myhi_phage[i]) print i, \": \", 100*myhi_phage[i]/(myhi_phage[i]+mylo_phage[i]);}}' $f; done'''\n",
    "\n",
    "(o,e) = subprocess.Popen(COMMAND, shell=True, stdout=subprocess.PIPE,stderr=subprocess.PIPE,universal_newlines=True).communicate()\n",
    "\n",
    "print(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import rpy2\n",
    "# %load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Cell magic `%%R` not found.\n"
     ]
    }
   ],
   "source": [
   "#Calculate percentage of WGS of Salmonella strain that carry each phage\n",
    "%%R\n",
    "\n",
    "library(plyr)\n",
    "library(reshape2)\n",
    "library(reshape)\n",
    "data=read.csv(\"coverage.csv\",header=T,check.names = F)\n",
    "\n",
    "data$cov[data$cov < 0.5] <- 0\n",
    "data$cov[data$cov >= 0.5] <- 1\n",
    "samples_count <- count(data, c('seq','strain'))\n",
    "\n",
    "cov_count=aggregate(cov~seq+strain, data, sum)\n",
    "\n",
    "merged=merge(samples_count, cov_count, all.x = TRUE)\n",
    "merged$percentage=merged$cov*100/merged$freq\n",
    "casted=cast(merged, seq~strain,value=\"percentage\")\n",
    "write.csv(casted,\"new calculation.csv\")"
   ]
  }
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