{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cfee1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import basic packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import math\n",
    "from tabulate import tabulate\n",
    "from difflib import SequenceMatcher\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from collections import defaultdict\n",
    "from itertools import chain, combinations\n",
    "from fpgrowth_py import fpgrowth\n",
    "import time\n",
    "from dna import dna\n",
    "from scov import numpy_image_dict\n",
    "from helper import *\n",
    "import zlib\n",
    "import lzma\n",
    "from Bio import pairwise2\n",
    "from Bio.Data import CodonTable\n",
    "from Bio.SeqUtils import ProtParam\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio import SeqIO\n",
    "from Bio import SeqIO\n",
    "import tensorflow as tf\n",
    "from sklearn.feature_selection import SelectKBest,f_classif\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,Dense,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77443182",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dataset\n",
    "input_file1 = r'./input/China_Seq.txt'\n",
    "input_file2=  r'./input/USA_Seq.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c9e519b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sequence = pd.DataFrame(columns=[\"line no\",\"sequence ratios\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ca45b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_lines=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "40affb48",
   "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>line no</th>\n",
       "      <th>sequence ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [line no, sequence ratios]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62ce384c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# start of time when algorithm starts running for matching sequence\n",
    "\n",
    "current_start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03208005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating frame to read all the lines from dataset including line no and sequence\n",
    "# Here SequenceMatcher algorithm is used for the sequence matching identification - module 1\n",
    "\n",
    "with open(input_file1) as file1, open(input_file2) as file2:\n",
    "    for lineno, (sequence1, sequence2) in enumerate(zip(file1, file2), 1):\n",
    "        sequence = SequenceMatcher(a=sequence1 , b=sequence2) #comparing both the strings\n",
    "        #print(lineno,\" - \",sequence.ratio())\n",
    "        df_sequence.loc[len(df_sequence.index)] = [lineno,sequence.ratio()]\n",
    "        df_sequence['line no'] = df_sequence['line no'].astype(int)\n",
    "        count_lines+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "158d0d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# End of time when algorithm finishes running\n",
    "\n",
    "current_end_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff65f4c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time : 1.836871862411499\n"
     ]
    }
   ],
   "source": [
    "# calculate total time for matching sequence\n",
    "\n",
    "print(\"Total Time :\",current_end_time-current_start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cb29e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sequence_missings = df_sequence[df_sequence['sequence ratios']<1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0145ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_changed_genome = len(df_sequence_missings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e1ffa11",
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_missing_values = (length_changed_genome/count_lines)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c444fd4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of changed Genome Sequence  1.31 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of changed Genome Sequence \",round(perc_missing_values,2),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63fdaf18",
   "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>line no</th>\n",
       "      <th>sequence ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>255</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>427</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>590</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>602</td>\n",
       "      <td>0.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>612</td>\n",
       "      <td>0.989474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     line no  sequence ratios\n",
       "121      122         0.985915\n",
       "249      250         0.985915\n",
       "251      252         0.985915\n",
       "254      255         0.985915\n",
       "426      427         0.985915\n",
       "589      590         0.985915\n",
       "601      602         0.985915\n",
       "611      612         0.989474"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sequence_missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c381322a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28eb21f72e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddTnuHVSnXsMMyypUdGZrPXrkcOqyzRvWAdCwccautREREdS/Noad27b6vN9SpUoxZcoUduzYwZw5c4iPjycuLo61a9eydu1a3nnnHWbPnk3jxo0BWLNyOQC/707I0K4C8Nv2XwHY9svPtGjdlh1bfybpz5M0imlKseIlMpRv2bIl48aN8znezCxdutSJb80a1v+6K8P6ndudY7J9688Z/o5ej/1l7rE/fL6dYNmyZQB06NDB53gyu8pdv2YDcP44Vb/iSmpfXZdZX0/h9927aNn2Fho2bsLV9RqSv0CBbPfnT5YIgsxbV0eAfJH5SPZoAD5+zLksLFP2Uq/ly5bLuPzw4UQA4uLiiIuLy7A+ddvHj2dYFhMTQ5UqVdi+fTutWrWifv2M9aqZbs+Ntdyl3rsbpizPrCHur+jYsSPly5fn/fff58yZM/x58gRdMmkkzspllarw2fTveOeNEfy48DvmzZoOOB8Oz/zjaQYOHJha9vPPP2fEiBGMHz8+tYqvUKFCdOvWjddff93nbpfFSmT8kIx0r6aSPUaUTKkiKFPW+zStZS5w+taoqCj69etHv37O1WVCQgKPPvoo06dPp0+fPqxevRqAI4cOAfDtjKlZbu/Pk85V2bGUc9fLOQpQoUKFC4o3vZRquDFjxmRZ7qR7tejJW4JKPfbJ54/94cOHAbj8cq/TqXuNZ/LkyVmWSzlOkZGRvD9xGu+99X/Mnfk1bw2PBaDoJcXo2K07g4YMpUjRS7Ldrz9YIsilLilWHICDB/Z5XX9gf8blKa954okneOONN3K0v0GDBrF9+3bKli3LrFmz+Oyzz3y+hE/Z74H93m9C2r/PWV7MLedP+fPn58EHH+TVV18lISGB8hUvy1B94qvqtWrz2jsfcvbsWX7euJ5lixcw4aMxDBo0iKJFi/LQQw8BULhwYWJjY4mNjWXXrl0sWrSIsWPH8umnn7Jjxw5++OEHf75FihYrBsDBA/u9rj+43/vynKpUqRITJ06kVKlSrFmzhsTERCAi9e/7nw8+o2XbW7LdTsrf+aCXcxTgjz/+8Lo8IiKC06dPe12X8oHsqYSbSNesWQOlq2Qb14VI+eK2e/fubMumxPP111/TqVOnDOu9dbgoXrIkg2OHMzh2OL9t30b8T0v44tOxTBw7hmNHjzD8P+/9pfh9ZW0EuVTRS4pRJao6+/74nV07Mg6pEb90cYZl1zS4loiIiBx/EE2aNInRo0dzww03sHLlSsqVK8cjjzzCL79k7H6XWayVq1Zj3x+/s9OtJvAU96MTz5XX+H6VkRMPP/wwIkJCQgK3332v16q3nMiXLx916jWg96OPM+Jt59vm1KlTvZatXLky99xzD3PmzKFmzZosXrw4Rw3Gvrjq6roArIpblmFdcnIya1Ys99u+ChYsSAG3WiJlPvN6jZyG+JXLl/q0jaiaV1CocBG2bFzv9Sows26upUqVYu/evalVmJ7i4+MzLEu5C93fidfbPmbNmuVz2QuNp0q16nTpfh8fTp5BkaKXsODb7PfpL5YIcrHOd/UkOTmZt16JTVNtlPDbTsZ/mPGbQpmy5bjljjuJj49n2LBhXies+PXXX9m+/Xxi2bZtG3369KFMmTKMHz+eypUrM27cOE6cOMHdd9/NqVOnfIr19rvvQVV58+WhafZ7KPEgo0e+llomEGrUqMHs2bP56quvvDai+2Lj2tVeP7RSvoWn1P/v37+fdevWZSh34sQJjh8/Tr58+VI/SP2lQeMmVK5ajbgff2Dx/Llp1n3x2dgctQ+cOHGCYcOGZTqExFtvvcXx48epU6dOasN3y7a3ULlqNT4f9wE/fP+t19etWbGcP/88CThXabfecScnjh/j3TfStWOtWcVnn33mdRsxMTGcPXuWjz76KM3ysWPHsmRJxu7SDz74ICVLluTFF19k3aoVGdYnJycT5+ULU0507NiRqKgopk2bluE+HEjba6hz587UqFGDUaNGMXPmTK/b8zxOCb/tJGHnjgxljh45zOnTp7x2XAgUqxrKxe7vO4Dv58zku5nT6N7hRpre2JpjR4/w7YypXBvTjAVzM35jeGbY/3Fg906GDh3KJ598QvPmzSlfvjx79uxh06ZNxMXFMWHCBKpVq8aZM2fo3r07R48eZdq0aan1oB06dODJJ5/k9ddf56mnnuK///1vtrE+0O8xFs//jvnfzuTOts1p0fpm/vzzT+Z+8zWJB/bTq/9AGsU09fsxStG2rdMl8UInCJ/x5ed88elYGsY0oVLVKIqXKEnCzh0snDubggULpg6Et3v3bho2bEjdunVTb7w6evQoM2bM4I8//mDgwIEUc6ty/CUiIoIXXhvJo/d1Y1DvnrTp0JHKVavx8+YNLPthAc1b3cTi+d957cWU3pkzZxg6dCgvvvgiMTExNGjQgFKlSpGYmMiSJUtYt24dRYsW5d133019Tf78+XljzMf0v7cbAx64mwbRMdSuU5dChQvzx57dbFizioTfdjBvxWYKF3YS5mP/eJ6fFi/k0w/eYcPaVan3EcyZ/hW33HIL06ZNyxDbY489xkcffUT//v2ZN28elStXZvXq1SxdupTbbrstdUiSFGXKlOGLL77gjjvu4L7ON3Nd8xupccWVCMIfv+9mzYo4jhxOJG6r96ooXxQoUIDJkyfTtm1bevbsyXvvvUeTJk1ISkpi06ZNzJs3L7VTQv78+fnyyy9p164dt956K82aNaNBgwYUKVKEXbt2sWTpT2mO088b1/P3vvdxdf1GVK95BeXKV+BQ4kHmfzuTs2fO8GD/QRccd04FNRHsePXWjGk7hzZu3HhtnTp1fCp7oR8KuUWBggUZPeEr3nljBHOmf8X4D9/jskpV6PPYU7Rpf5vXRHBJseIsXLiQ0aNHM378eKZMmUJSUhLly5enVq1avPnmm9x8s1OHPmTIEOLi4hg4cCAdO3ZMs53hw4ezaNEi3n77bVq3bk2Nxq2yjDV/gQK8N/4rPhkziplTv2DCR2OIzJePK+pczdMvDKfD7d38d2ACoH3nrpw+dYo1K5azce1qTiUlcWmFirTr1IV/DX2Ga665BnAaWF988UUWLFjA/PnzOXDgAKVLl6Z27dq8+uqrdO/ePSDxNW7anA8mz2DUa//ih++dq4K6Da/l/c+n8c1XTuNk8eLZt8EUL16cWbNmMXfuXBYvXszUqVPZv38/hQoVolq1agwaNIjHH388wx2+V1x1DZPm/MAnY0ax6Ls5fD1pPBIRQblLy3PlNfXo/+SQNGM3lSpdhnFfzWbkiGEs+m42G9euJqpGTZ4d/m9aNKrjNRHUqVOH7777jn/+859Mnz6dfPny0aJFC5YuXcqXX36ZIREAtGnThrVr1zIk9l8sXfg9K5cvJX/+ApQrX4GY61twU4eMdfU5FR0dzerVq3n11VeZNWsWP/74I8WKFaNmzZq89NJLacrWq1ePNWvW8MYbbzBjxgw++ugjIiIiqFixYobjdHV9p/pxxU9LWLJwHkePHKZU6bLUqVufnr37XXBb14WQlHpAf1izZs2O+vXrH/DbBr0IZSKoV6mkX7cHgUlWFqd/5fY4H7ijHetWreDIkSM+d/n1VSCOZ+LW1bRq1YoXXnjhgm8mTC8v/N0DFeOmTZu46qqrsi0rIitUNdrbOmsjMCYP+PPPkxw9krEN4+tJ41kdv5ymN7TyexIw4cPaCIzJA/7YncDd7W+kSYuWVI6qzrlzZ9m8fi2r4pZRrEQJnnz+5VCHaPIwSwTG5AFlyl7KLXfcyYplS4hbupjTp09RttyldL7rHvo89qTXu8+N8ZUlAmPygOIlSxL72shQh+EXLVu2xJ9tk+avszYCY4wJc5YIjDEmzPk9EdglnzHGBIe/Pm/9mghE5NDp06fz+3ObxhhjvDtz5sxfHlsL/JwIkpOTZx0+fNi/99cbY4zx6ujRo34Z0sSvieDcuXOj9+7de3jv3r2lT506ld+qiYwxxr9UFT13lrMnj3Lo0CFKly79l7fp1yEmAFasWBEVGRnZNyIiooOqlvLrxoGDBw9W9XXC7YRDf/p135VKFfbr9sD/MYLF6W8Wp3+Fa5z+jPHEmWTid//J811jKOjOE52drIaY8HsiCLTo6Gj1Nja5N1FDvvHrvne8eqtftwf+jxEsTn+zOP0rXOMMdYwhG2tIRNqLyBYR2SoiQ7ysryIi80VklYisFZHspz8yxhjjVwFLBCISCYwCOgB1gB4ikn7Y0OeASaraEOgO/C9Q8RhjjPEukFcEMcBWVd2mqqeBiUDndGUUSBlEvQSwJ4DxGGOM8SKQieByYJfH8wR3madY4F4RSQBmAo9525CI9BWReBGJ3++nibqNMcY4Qj3ERA9grKpWAm4BPhGRDDGp6mhVjVbV6HLlygU9SGOMuZgFMhHsBip7PK/kLvP0EDAJQFWXAoWAsgGMyRhjTDqBTARxQC0RqSYiBXAag9NPVPob0AZARK7CSQRW92OMMUEUsESgqmeBAcAcYBNO76ANIvKSiKTMKP0k0EdE1gATgF6a125sMMaYPC6gE9Oo6kycRmDPZUM9Hm8Erg9kDMYYY7IW6sZiY4wxIWaJwBhjwtwFJQIRudLfgRhjjAmNC70i+NavURhjjAmZTBuLRWRkZquAkgGJxhhjTNBl1WvoQZzunae8rOsRmHCMMcYEW1aJIA5Yr6o/pl8hIrEBi8gYY0xQZZUIugFJ3laoarXAhGOMMSbYMk0EqpoYzECMMcaEht1HYIwxYc4SgTHGhDlLBMYYE+ayTAQiUkpEXk63rIeINAtsWMYYY4Ily0SgqoeAm0WkpsfiocDPAY3KGGNM0PhSNfQB0BtARFoCG1X1QABjMsYYE0S+JIIJQFcREaAXMCagERljjAmqbBOBqh4DfgTuBq7DmXHMGGPMRcLXGcreB6YDb9tUksYYc3Hxqfuoqi4BPsFJCMYYYy4iPs9ZrKqDAhmIMcaY0LAbyowxJsxZIjDGmDBnicAYY8KcT20E7pASUZ7lVfXjAMVkjDEmiLJNBCLyCVADWA2ccxcrYInAGGMuAr5cEUQDdez+AWOMuTj50kawHqgQ6ECMMcaEhi9XBGWBjSKyHDiVslBVOwUsKmOMMUHjSyKIDXQQxhhjQifbRKCqC0WkPNDYXbRcVfcFNixjjDHBkm0bgYjcBSwH7gTuAn4SkW6BDswYY0xw+FI19CzQOOUqQETKAd8BXwQyMGOMMcHhS6+hiHRVQQd9fJ0xxpg8wJcP9NkiMkdEeolIL+AbYKYvGxeR9iKyRUS2isiQTMrcJSIbRWSDiIz3PXRjjDH+4Etj8WAR6Qpc7y4arapfZfc6EYkERgE3AwlAnIhMU9WNHmVqAc8A16vqIRG59ELehDHGmAvn01hDqjoFmJLDbccAW1V1G4CITAQ6Axs9yvQBRqnqIXc/1hvJGGOCLNOqIRFZ7P4+JiJHPX6OichRH7Z9ObDL43mCu8zTFcAVIrJERJaJSPtMYukrIvEiEr9//34fdm2MMcZXmV4RqGpz93exAO+/FtASqAQsEpG6qno4XSyjgdEA0dHRNuaRMcb4kS/3EXziyzIvdgOVPZ5Xcpd5SgCmqeoZVd0O/IyTGIwxxgSJL72GrvZ8IiL5gGt9eF0cUEtEqolIAaA7MC1dmak4VwOISFmcqqJtPmzbGGOMn2TVRvCMiBwD6nm2DwB7ga+z27CqngUGAHOATcAkVd0gIi+JSMqAdXOAgyKyEZgPDFbVg3/xPRljjMmBrNoIXgFeEZFXVPWZC9m4qs4k3T0HqjrU47ECf3d/jDHGhIAv9xE8IyKlcOruC3ksXxTIwIwxxgSHL1NVPgwMwmnsXQ00AZYCrQMamTHGmKDwpbF4EM4Q1DtVtRXQEDgcyKCMMcYEjy+JIElVkwBEpKCqbgZqBzYsY4wxweLLEBMJIlISp6vnXBE5BOwMZFDGGGOCx5fG4jvch7EiMh8oAcwKaFTGGGOCJkfzCqjqQiAJH4ehNsYYk/tldUNZaxH5WUSOi8inIlJXROKBV4B3gheiMcaYQMrqiuDfQF+gDM60lEuBsap6rap+GYzgjDHGBF5WbQSqqgvcx1NFZLeqvh2EmIwxxgRRVomgpIh08Szr+dyuCowx5uKQVSJYCHT0eL7I47kClgiMMeYikNWgcw8GMxBjjDGhkaPuo8YYYy4+lgiMMSbMWSIwxpgw58ucxUVE5HkRGeM+ryUitwU+NGOMMcHgyxXBR8ApoKn7fDfwcsAiMsYYE1S+JIIaqvp/wBkAVT0JSECjMsYYEzS+JILTIlIY594BRKQGzhWCMcaYi4Av8xG8AMwGKovIZ8D1QK9ABmWMMSZ4fJmPYK6IrMSZq1iAQap6IOCRGWOMCQpfeg3dAZxV1W9UdQZwVkRuD3hkxhhjgsKXNoIXVPVIyhNVPYxTXWSMMeYi4Esi8FbGl7YFY4wxeYAviSBeRN4QkRruzxvAikAHZowxJjh8SQSPAaeBz92fU8DfAhmUMcaY4PGl19AJYEgQYjHGGBMC2SYCEbkCeAqI8iyvqq0DF5Yxxphg8aXRdzLwLvA+cC6w4RhjjAk2XxLBWVV9J+CRGGOMCQlfGouni8ijIlJRREqn/AQ8MmOMMUHhyxXBA+7vwR7LFKju/3CMMcYEW7ZXBKpazcuPT0lARNqLyBYR2SoimfY8EpGuIqIiEp2T4I0xxvx1vs5Q9pyIjHaf+zRDmYhEAqOADkAdoIeI1PFSrhgwCPgpp8EbY4z563ydoew00Mx97usMZTHAVlXdpqqngYlAZy/lhgEjgCQftmmMMcbPAjlD2eXALo/nCe6yVCLSCKisqt9ktSER6Ssi8SISv3//fh92bYwxxlchm6FMRCKAN4AnsyurqqNVNVpVo8uVK/dXd22MMcZDIGco2w1U9nheyV2WohhwDbBARAAqANNEpJOqxvuwfWOMMX4QyBnK4oBaIlINJwF0B3p6bPcIUDbluYgsAJ6yJGCMMcHly1hDN7gPj7m/64gIqrooq9ep6lkRGQDMASKBD1V1g4i8BMSr6rS/Ergxxhj/8KVqyPNGskI4vYFWANkOOqeqM4GZ6ZYNzaRsSx9iMcYY42e+VA119HwuIpWBtwIVkDHGmODypddQegnAVf4OxBhjTGj40kbwX9yuoziJowGwMoAxGWOMCSJf2gg8e/GcBSao6pIAxWOMMSbIfGkjGBeMQIwxxoSGL1VD6zhfNZRmFaCqWs/vURljjAkaX6qGZrm/P3F/3+P+tlnLjDHmIuBLIrhZVRt6PB8iIitVNdP5BYwxxuQdvnQfFRG53uNJMx9fZ4wxJg/w5YrgIeBDESnhPj8M9A5YRMYYY4LKl15DK4D6KYnAHSzOGGPMRcKXqSrLi8gHwERVPSIidUTkoSDEZowxJgh8qesfizOC6GXu85+BxwMUjzHGmCDzJRGUVdVJQDI4w0sD5wIalTHGmKDxJRGcEJEynJ+qsglg7QTGGHOR8KXX0N+BaUANEVkClAO6BTQqY4wxQeNLr6GVInIjUBtnWIktqnom4JEZY4wJikyrhkSksYhUgNR2gWuBfwH/FpHSQYrPGGNMgGXVRvAecBpS5y1+FfgYp31gdOBDM8YYEwxZVQ1Fqmqi+/huYLSqTgGmiMjqgEdmjDEmKLK6IogUkZRE0Qb43mOdL43Mxhhj8oCsPtAnAAtF5ADwJ/ADgIjUxLqPGmPMRSPTRKCq/xKReUBF4FtV9Zy3+LFgBGeMMSbwsqziUdVlXpb9HLhwjDHGBJvNK2CMMWHOEoExxoQ5SwTGGBPmLBEYY0yYs0RgjDFhzhKBMcaEOUsExhgT5iwRGGNMmLNEYIwxYS6giUBE2ovIFhHZKiJDvKz/u4hsFJG1IjJPRKoGMh5jjDEZBSwRiEgkMAroANQBeohInXTFVgHRqloP+AL4v0DFY4wxxrtAXhHEAFtVdZuqngYmAp09C6jqfFU96T5dBlQKYDzGGGO8CGQiuBzY5fE8wV2WmYeAWd5WiEhfEYkXkfj9+/f7MURjjDG5orFYRO4FooHXvK1X1dGqGq2q0eXKlQtucMYYc5EL5Exju4HKHs8rucvSEJGbgGeBG1X1VADjMcYY40UgrwjigFoiUk1ECgDdgWmeBUSkIfAe0ElV9wUwFmOMMZkIWCJQ1bPAAGAOsAmYpKobROQlEenkFnsNuASYLCKrRWRaJpszxhgTIAGdhF5VZwIz0y0b6vH4pkDu3xhjTPZyRWOxMcaY0LFEYIwxYc4SgTHGhDlLBMYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwTGGBPmLBEYY0yYs0RgjDFhzhKBMcaEOUsExhgT5iwRGGNMmLNEYIwxYc4SgTHGhDlLBMYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwTGGBPmLBEYY0yYs0RgjDFhzhKBMcaEOUsExhgT5iwRGGNMmLNEYIwxYc4SgTHGhDlLBMYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwTGGBPmLBEYY0yYC2giEJH2IrJFRLaKyBAv6wuKyOfu+p9EJCqQ8RhjjMkoYIlARCKBUUAHoA7QQ0TqpCv2EHBIVWsCbwIjAhWPMcYY7wJ5RRADbFXVbap6GpgIdE5XpjMwzn38BdBGRCSAMRljjElHVDUwGxbpBrRX1Yfd5/cB16nqAI8y690yCe7zX90yB9Jtqy/Q131aG9ji53DLAgeyLRV6Fqd/5YU480KMYHH6WyDirKqq5bytyOfnHQWEqo4GRgdq+yISr6rRgdq+v1ic/pUX4swLMYLF6W/BjjOQVUO7gcoezyu5y7yWEZF8QAngYABjMsYYk04gE0EcUEtEqolIAaA7MC1dmWnAA+7jbsD3Gqi6KmOMMV4FrGpIVc+KyABgDhAJfKiqG0TkJSBeVacBHwCfiMhWIBEnWYRCwKqd/Mzi9K+8EGdeiBEsTn8LapwBayw2xhiTN9idxcYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwS5nIiUE5GGIlJPRC4JdTy+EpErQx1DVkTk0VDHkB0RqSkiXb2M0ZUr5NVzEyCvxBus/yPrNeRBRK5U1c2hjgPA/ecfCUQBVYBVwKXAQmCQqh4JXXTZE5HfVLVKqOMAEJG/p18EPAMMB1DVN4IelBciMh+4U1UPuEOyPA8sAq4DRqvqf0MaoCuvn5uQu87PrAQrzjwxxEQQfYtzYucGHwIPqOoWEYkB/qaq14lIH5z7L7qFNjwQkZGZrQJKBjGU7LwIzAQ24MQGzr0txUIWkXflPMbZGgg0VdWDIlIEWAbkikRAHjg3wesXgNRVQK65IsgN/0dhd0WQzUF/QFWLBzOezIjIGlWt7/F8pao2ch9vUtWrQhddakzHgCeBU15W/1tVywY5JK9EpArwb2Ab8KKqnhSRbapaPcShpSEiq4DbVHW3e3XQQVWT3CHd16rq1SEOEcgb5yaAiCQBrwFnvax+QlVLBjci73LD/1E4XhE8SOYHvUeQY8nKryLyPPA90AVYDSAi+ck9bTtxwHpV/TH9ChGJDX443qnqb8CdItIZmCsib4Y6pkw8AXwrIlNwrl6+F5E5QHPgo5BGllZeODcBVgJTVXVF+hUi8nAI4slMyP+PwvGK4HvguUwO+nZVrRaCsDIQkZLAP3Em9VkDvKqqx0SkBHCVqi4LZXwAIlIaSFLVk6GOxVciUhSIxRnu/IYQh5OB+/ftCVyB80UtAfg6t7RdQd44NwFEpDaQqKr7vawrr6p7QxBWBrnh/ygcE0HID/qFEpFLVXVfqOMwxlxcctNlXFCoamJeSAIiUjrdTxlguYiUcpNZyIlIe4/HJUTkAxFZKyLjRaR8KGPzlC7OkiLyfi6NM1JE+onIMBFplm7dc6GKKz0RiRaR+SLyqYhUFpG5InJEROJEpGGo40vhnpOvishmEUkUkYMissldVjLU8flCRGYFYz9hlwhEpLiIvCIin4hIz3Tr/hequLw4AKzw+IkHLsep94wPYVyehns8/jfwO9ARp87zvZBE5J1nnK8Df5A743wPuBFnTo7/iohnt9YuoQnJq/8B/wd8A/wIvKeqJYAh7rrcYhJwCGipqqVVtQzQyl02KaSReRCRRpn8XAs0CEoMYVg1NAX4Bac7Xm/gDNBTVU959n4INRF5ErgZGKyq69xluaYNAzL0Flmtqg081qV5Hkp5KM61qlrPfZwP50O1LE4nhmWqmiu+bYvIqpRY0vdz91wXaiKyRVVr53RdsInIOZx7MLzN195EVQsHOoZw7DVUQ1W7uo+nisizOL0zOoUyqPRU9d8i8jnwpojsAl4AclvWvtTtqy1AcRERj4mFctPVZl6Js0DKA1U9C/QVkRdweufkmn7vQJKItMWZUVBF5HZVnSoiNwLnQhybp50i8jQwLqVh2K0K7AXsCmVg6WwC+qnqL+lXuP/7AZeb/gmCpaCIpL5vVf0XMAbnDs4yIYvKC1VNUNU7gQXAXKBIaCPKYAzOTVmXAONwvr0iIhVwuxTmEnklznjP9gwAVX0Rp+toVEgi8q4/Thfs3kA7oJWIHMK5ghkUysDSuRvnf3qhiBwSkUSc/6XSwF2hDCydWDL/LH4sGAGEY9XQ/wHfqup36Za3B/6rqrVCE1lG4owzcjnwE843rRqqul5E2qvq7NBG5/CMUVWPeyzPNTFC3okzPRH5WFXvD3UcWRGRFkAMsE5Vvw11PClE5Dpgs6oeEefu7CFAI5x7NIbnlqEw3Dg3qepRESmMM/xJQ2AjQYoz7BJBVkTkQVXNFTfuiMhA4G84l40NcMZw+dpdlyvaMkTkMWAAuThGyFNxpp/TW3AaN78HUNVcUX0pIstVNcZ93Ad4FJgKtAWmq+qrIQwvlYhsAOqrM23uaOAEMAVo4y7PFQ3wXuI8CXxBEOMMxzaCrKRchucGfYBrVfW4iEQBX4hIlKr+B++NSqHQl9wfI+SdOCvjfFt9H6c9SIBonB5ZuUl+j8d9gbaqul9EXsfphJErEgEQ4ba1AER7JPzFIrI6RDF5E/I4wy4RiMjazFYBuaZPOc7JcRxAVXeISEucD7Cq5J4Pr7wQI+SdOK/FqWN/Fqe32GoR+VNVF4Y4rvQiRKQUTr22pNy5q6onRMTbuD6hst7jKn+NiESraryIXIHTWzC3CHmcYZcIcD7s2+H0JfYkOH2ic4u9ItJAVVcDuN9mb8MZ+bFuSCM7Ly/ECHkkTlVNxuklNtn9vZfc+T9aAufeFsHpNVRRVX8XZ4z/3JRYHwb+I87NeAeApW4vnF3uutwi5HGGXRuBiHwAfKSqi72sG6+qPb28LOhEpBJwVlX/8LLuelVdEoKw0seR62OEvBNneiJyK3C9qv4z1LH4wm2QLa+q20MdiycRKQ5Uwx27KbeMMZReKOMMu0RgjDEmrXC8j8AYY4wHSwTGGBPmLBHkkIgcz75UmvItRWSGn/adX0RWelneW0TWiTOi5npxJmAx6fjzb+Hj/mJF5Klg7c/kjIjMlByOQioij4hIrr7B70Lkxh4JJnPNgTQNm25D6LNAI/cOykuAcqEI7mInIpGqGrSxdIK9v9xORPJ59Lf/y1T1lgt4zbv+2n9uYlcEF8j9drlARL4QZ7zzz0RE3HXt3WUr8Rg+WESKisiHIrJcRFalfHMXkf+IyFD3cTsRWSQe4yF5aA+kH5/8UuAYkNJP/nhKrw0RqSEis0VkhYj84A6zgIhUE5Gl7lXEyylXOem/MYvI2yLSy318rYgsdLc1R0QqussXiMgI9z39LM5wAylj67/uXqGsFefu3ky3k+7YjhWRkSLyo4hsE5FuPsS3Q5zhxVeLSLw4w/jOEZFfReQRj80XF5FvRGSLiLybcpxFpK17TFaKyGQ3oaZsd4T7t7zTY9+RIrJdHCVF5JyI3OCuWyQiKUOV1HGP0TZx7hZPef297jFbLSLviTMvMSJyXET+LSJrgKaZlUt3vG5xz7cV7nGb4S7P7HzrJSJfuufGL+IMu5KyrR7uebFeREZ4LD8uIq+JyAYR+U5EYjzeVyePY/KaOPMSrBWRfu7yiu4xWe1ut4WX9zDUfd16ERktkvq/tEBE3hKReGBQDs6fd0RkmRtfS/c4bBKRsR7ldohIWfc4fSMia9z93+2uf1VENrrv5XV3WepVnmR+7hcRkUnua78SkZ9EJDp9nLmKqtpPDn6A4+7vlsARoBJOQl2K8429EE7/31o4faonATPc1wwH7nUflwR+BoriDCa3AWc4gS04Ywp52/dyoEi6ZZHAHOA3nLuiO3qsmwfUch9fB3zvPp4G3O8+/lu69zTD4/Vv44zUmB/nHoty7vK7gQ/dxwtwJtgGuAX4zn3cH+c2+Xzu89JZbSfdexoLTHaPax1ga1bxuY93AP3dx28Ca3EGmisH7PV4fRJQ3T1uc4FuOIPQLQKKuuX+AQz12O7Tmfw9ZgNXA7fhzG3wLFAQ2O6uj3Xfb0F3HwfdY3AVMB3I75b7n8ffQ4G73MeZlvOIIeV8q+Y+n0D251svYBvO/QCFgJ04dzVfhnMelcOpLfgeuN0jrg7u46+Ab933Uh9Y7S7vizMNLO57jsfpDvkk8KzH+VrMy7Es7fH4E9zzGOf8+p/7OCfnz0Sc/7/OwFGc+0UicO5/aODxty0LdAXGeLy+BM5gdVs437OypMff9Klszv2ncOZoALgGOItzx3DIP78y+7Gqob9muaomgDOuPc4IkcdxPgh+cZd/ivMPAs5YLJ3kfL1xIaCKqm4SZ8yWRcATqvpr+h2JyOU486+mmV1NVc+JM2BeY5yxSd4UZ0KL14FmwGT3yxU4/5wA1+Oc/OD8040ga7VxTui57rYicSahSfGl+3sF50fJvAl4V91LeVVNFJFrstmOp6nq3GC1UXyfRSxlrJ51wCWqegw4JiKn5Hxd8HJV3QYgIhNwkncSTsJZ4sZVACexp/g8k/39ANyA82H3Cs6wIAtxkkKKb1T1FHBKRPbh3NDYBucu4jh3f4WBlClIz+GMh0M25VJcCWzT8333J5DN+eY+nqfuYGYishGoivPht0DdO4VF5DP3/U0FTuMkPnCO7ylVPSMi6zj/N28L1BP3Cg7nA7WWezw+FGdy+6nq3tiXTitxhowugvOlYQNOEoTzxz+789DTdFVVN769en5Ojw1uvJ4xrAP+7V4BzVDVH8SZDyIJ+MC9wsqsbcnbud8c+A+AOoNEZjaaQa5hieCvOeXx+BzZH08BuqrqFi/r6uJ8Y7wsk9e2x/nmn4E6Xz2W40xlORfnyuAN4LBmPumKtxtIzpK2urCQR9wbVLVpJttKOQ7ZHYPstuNtmymvyyq+9K9JTvf6ZI+40r/vlDF95qpqj0xiOZHJ8kU4Vz6XAUOBwThXHT94iQnOHx/BGSP/GS/bTNLz7QJZlfOF1/NNnNEuc3runnHPM/A4vqqa7H5opuzvMVXNcJ6KU212KzBWRN5Q1Y891hXCudqJVtVdIhJL2r9tyvG/kPMnq3MB9z38LCKNcL7Vvywi81T1JRGJwUnG3XAGLWydxX58OYa5lrUR+N9mIEpEarjPPT9c5gCPedR/pszyVBXn8rkh0MH9R03PW/sAInKZexKnaADsVNWjwHYRudMtJyJS3y2zBOjuPr7H47U7ceq0C7rfoNu4y7cA5USkqbut/CJyddaHgblAv5QPCXHmWb6Q7XjKLL6ciBGnjSQCp2phMc5AadeLSE03rqLijPOSneU4V13JqpqE8y2zH06CyMo8oJuIXOrur7R7DlxIuS1AdXEG08N9Tym8nm/ZvJ8b3XrzSJxzNyfjHM0B+rvf/BGRK9xjWRXnW/kYnAH10o/2mvKhf0CctpluePdXzx+vROQy4KSqfgq8BjRy4yihqjOBJ3CqwHy1BHe+AxGpQy4axiQzeTaD5VaqmiQifYFvROQkzrfDYu7qYcBbwFr3g2i7iHQEPsCpd9wjIg/hfGtq7H644P5T1lTVzV52mR943T2Zk4D9QErj6D3AO+KMYZIfp950Dc7AZuNF5B/A1x6x7xKRScB6YDuwyl1+2r3cHykiJXDOm7dwLt8z8z5whftez+DUwb59AdtJlVl8ORSH07ZQE5gPfOV+q+0FTBCRlOqz53Dq1LOK55Q4Y8Iscxf9gPPhuS6b1210/ybfuufBGZy2mp05Laeqf4rIo8BsETlB2mqpDOcbTntGZnH9LiJDcI6L4FRrfZ1ZeS/ex6keWekmn/3A7ThXSYPd8+A4kKb7paoeFpExOH/XP9K9B89yF3Ie+qIu8JqIJOMc4/44/7Nfu1crAvw9B9v7HzDOrXLb7MaXK+Y+yIwNMZEHiEhznEa/R7ItfGHbP66quWkqRJMDInKJOgPpCTAK+EVV3wx1XOHK/eKW3/1SWAP4DqitqqdDHFqm7IogD1BngLwMg+QZ4+ojIg/gNHKvAt4LcTzhrggw360iE+DR3JwEwK4IjDEm7FljsTHGhDlLBMYYE+YsERhjTJizRGCMMWHOEoExxoS5/wfqfBFAp44dGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sequence_missings['sequence ratios'].plot(kind='bar')\n",
    "\n",
    "plt.ylabel(\"Sequence Ration < 1\")\n",
    "plt.xlabel(\"Index / Sequence number where genomes are missing\")\n",
    "\n",
    "#add custom legend to bar chart\n",
    "plt.legend(['Index of Missing Sequences'], prop={'size': 20})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b154770",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence1_All=''\n",
    "sequence2_All=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67abf396",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(input_file1) as file1:\n",
    "    for lineno in file1:\n",
    "        lineno=lineno.strip('\\n')\n",
    "        sequence1_All+=lineno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f41d3648",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(input_file2) as file2:\n",
    "    for lineno in file2:\n",
    "        lineno=lineno.strip('\\n')\n",
    "        sequence2_All+=lineno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d85f8eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def basic_Analysis(DNAseq):\n",
    "    total_base = len(DNAseq)\n",
    "    num_Adenine = DNAseq.count('A')\n",
    "    num_Guanine = DNAseq.count('G')\n",
    "    num_Thymine = DNAseq.count('T')\n",
    "    num_Cytosine = DNAseq.count('C')\n",
    "    \n",
    "    if total_base != num_Adenine + num_Guanine + num_Thymine + num_Cytosine:\n",
    "        print('Something is not right')\n",
    "    else : pass\n",
    "    \n",
    "    A_percent = num_Adenine / total_base\n",
    "    G_percent = num_Guanine / total_base\n",
    "    T_percent = num_Thymine / total_base\n",
    "    C_percent = num_Cytosine / total_base\n",
    "    \n",
    "    #visualization\n",
    "    x = np.arange(4)\n",
    "    bases = ['Adenine', 'Guanine', 'Thymine' ,'Cytosine']\n",
    "    values = [num_Adenine, num_Guanine, num_Thymine, num_Cytosine]\n",
    "    plt.bar(x,values)\n",
    "    plt.xticks(x, bases)\n",
    "    plt.show()\n",
    "    table = [['total base',total_base,'Percentage',str('100%')],\n",
    "             ['Adenine:',num_Adenine, 'Percentage:',str(round(A_percent*100,2))+'%'],\n",
    "            ['Guanine:',num_Guanine, 'Percentage:',str(round(G_percent*100,2))+'%'],\n",
    "             ['Thynime:',num_Thymine, 'Percentage:',str(round(T_percent*100,2))+'%'],\n",
    "             ['Cytosine:',num_Cytosine, 'Percentage:',str(round(C_percent*100,2))+'%']]\n",
    "    print(tabulate(table))\n",
    "    print('GC content:', round((((num_Guanine + num_Cytosine) / total_base)*100),2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d725999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------  -----  -----------  ------\n",
      "total base  42483  Percentage   100%\n",
      "Adenine:    12688  Percentage:  29.87%\n",
      "Guanine:     8393  Percentage:  19.76%\n",
      "Thynime:    13709  Percentage:  32.27%\n",
      "Cytosine:    7693  Percentage:  18.11%\n",
      "----------  -----  -----------  ------\n",
      "GC content: 37.86 %\n"
     ]
    }
   ],
   "source": [
    "basic_Analysis(sequence1_All)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "381f8560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------  -----  -----------  ------\n",
      "total base  42483  Percentage   100%\n",
      "Adenine:    12686  Percentage:  29.86%\n",
      "Guanine:     8394  Percentage:  19.76%\n",
      "Thynime:    13713  Percentage:  32.28%\n",
      "Cytosine:    7690  Percentage:  18.1%\n",
      "----------  -----  -----------  ------\n",
      "GC content: 37.86 %\n"
     ]
    }
   ],
   "source": [
    "basic_Analysis(sequence2_All)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d63d504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42483\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'AUGGAGAGCCUUGUCCCUGGUUUCAACGAGAAAACACACGUCCAACUCAGUUUGCCUGUUUUACAGGUUCGCGACGUGCUCGUACGUGGCUUUGGAGACUCCGUGGAGGAGGUCUUAUCAGAGGCACGUCAACAUCUUAAAGAUGGCACUUGUGGCUUAGUAGAAGUUGAAAAAGGCGUUUUGCCUCAACUUGAACAGCCCUAUGUGUUCAUCAAACGUUCGGAUGCUCGAACUGCACCUCAUGGUCAUGUUAUGGUUGAGCUGGUAGCAGAACUCGAAGGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGUCCUUGUCCCUCAUGUGGGCGAAAUACCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAUAGUUACGGCGCCGAUCUAAAGUCAUUUGACUUAGGCGACGAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGGAACACUAAACAUAGCAGUGGUGUUACCCGUGAACUCAUGCGUGAGCUUAACGGAGGGGCAUACACUCGCUAUGUCGAUAACAACUUCUGUGGCCCUGAUGGCUACCCUCUUGAGUGCAUUAAAGACCUUCUAGCACGUGCUGGUAAAGCUUCAUGCACUUUGUCCGAACAACUGGACUUUAUUGACACUAAGAGGGGUGUAUACUGCUGCCGUGAACAUGAGCAUGAAAUUGCUUGGUACACGGAACGUUCUGAAAAGAGCUAUGAAUUGCAGACACCUUUUGAAAUUAAAUUGGCAAAGAAAUUUGACACCUUCAAUGGGGAAUGUCCAAAUUUUGUAUUUCCCUUAAAUUCCAUAAUCAAGACUAUUCAACCAAGGGUUGAAAAGAAAAAGCUUGAUGGCUUUAUGGGUAGAAUUCGAUCUGUCUAUCCAGUUGCGUCACCAAAUGAAUGCAACCAAAUGUGCCUUUCAACUCUCAUGAAGUGUGAUCAUUGUGGUGAAACUUCAUGGCAGACGG'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def transcription(DNAseq):\n",
    "    mRNAseq = DNAseq.replace('T','U')\n",
    "    print(len(mRNAseq))\n",
    "    return mRNAseq\n",
    "COVID1_mRNA = transcription(sequence1_All)\n",
    "COVID1_mRNA[0:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3d1b533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42483\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'AUGGAGAGCCUUGUCCCUGGUUUCAACGAGAAAACACACGUCCAACUCAGUUUGCCUGUUUUACAGGUUCGCGACGUGCUCGUACGUGGCUUUGGAGACUCCGUGGAGGAGGUCUUAUCAGAGGCACGUCAACAUCUUAAAGAUGGCACUUGUGGCUUAGUAGAAGUUGAAAAAGGCGUUUUGCCUCAACUUGAACAGCCCUAUGUGUUCAUCAAACGUUCGGAUGCUCGAACUGCACCUCAUGGUCAUGUUAUGGUUGAGCUGGUAGCAGAACUCGAAGGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGUCCUUGUCCCUCAUGUGGGCGAAAUACCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAUAGUUACGGCGCCGAUCUAAAGUCAUUUGACUUAGGCGACGAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGGAACACUAAACAUAGCAGUGGUGUUACCCGUGAACUCAUGCGUGAGCUUAACGGAGGGGCAUACACUCGCUAUGUCGAUAACAACUUCUGUGGCCCUGAUGGCUACCCUCUUGAGUGCAUUAAAGACCUUCUAGCACGUGCUGGUAAAGCUUCAUGCACUUUGUCCGAACAACUGGACUUUAUUGACACUAAGAGGGGUGUAUACUGCUGCCGUGAACAUGAGCAUGAAAUUGCUUGGUACACGGAACGUUCUGAAAAGAGCUAUGAAUUGCAGACACCUUUUGAAAUUAAAUUGGCAAAGAAAUUUGACACCUUCAAUGGGGAAUGUCCAAAUUUUGUAUUUCCCUUAAAUUCCAUAAUCAAGACUAUUCAACCAAGGGUUGAAAAGAAAAAGCUUGAUGGCUUUAUGGGUAGAAUUCGAUCUGUCUAUCCAGUUGCGUCACCAAAUGAAUGCAACCAAAUGUGCCUUUCAACUCUCAUGAAGUGUGAUCAUUGUGGUGAAACUUCAUGGCAGACGG'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COVID2_mRNA = transcription(sequence2_All)\n",
    "COVID2_mRNA[0:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c385bc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTT'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def translate(seq):\n",
    "    table = {\n",
    "        'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M',\n",
    "        'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T',\n",
    "        'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K',\n",
    "        'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',                \n",
    "        'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L',\n",
    "        'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P',\n",
    "        'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q',\n",
    "        'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R',\n",
    "        'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V',\n",
    "        'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A',\n",
    "        'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E',\n",
    "        'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G',\n",
    "        'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S',\n",
    "        'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L',\n",
    "        'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_',\n",
    "        'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W',}\n",
    "    protein =\"\"\n",
    "    for i in range(0, len(seq), 3):\n",
    "        codon = seq[i:i + 3]\n",
    "        if codon in table:\n",
    "            protein+= table[codon]\n",
    "    return protein\n",
    "\n",
    "protein_seq = translate(sequence1_All)\n",
    "protein_seq[0:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8838827",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualization(protein_seq):\n",
    "    # composition of Amino Acids\n",
    "    plt.figure(figsize=(35,50))\n",
    "    x = np.arange(22)\n",
    "    AA = ['Arginine', 'Histidie','Lysine',\n",
    "          'Aspartic Acid','Glutamic Acid',\n",
    "          'Serine','Threonine','Asparagine','Glutamine',\n",
    "          'Cysteine','Selenocysteine','Glycine','Prolien',\n",
    "          'Alanine','Valine','Isoleucine','Leucine',\n",
    "          'Methionine','Phenylalanine','Tyrosine','Tryptophan',\n",
    "          'Stop Codon']\n",
    "    values = [protein_seq.count('R'),protein_seq.count('H'),protein_seq.count('K'),\n",
    "              protein_seq.count('D'),protein_seq.count('E'),\n",
    "              protein_seq.count('S'),protein_seq.count('T'),protein_seq.count('N'),protein_seq.count('Q'),\n",
    "              protein_seq.count('C'),protein_seq.count('U'),protein_seq.count('G'),protein_seq.count('P'),\n",
    "              protein_seq.count('A'),protein_seq.count('V'),protein_seq.count('I'),protein_seq.count('L'),\n",
    "              protein_seq.count('M'),protein_seq.count('F'),protein_seq.count('Y'),protein_seq.count('W'),\n",
    "              protein_seq.count('_')]\n",
    "    plt.subplot(2,2,1)\n",
    "    plt.rc('font',size = 20)\n",
    "    plt.barh(AA,values,height=0.6)\n",
    "    plt.title('AA in general')\n",
    "    \n",
    "    # visualization by groups as shown in figure\n",
    "    x = np.arange(4)\n",
    "    Electric =  protein_seq.count('R')+protein_seq.count('H')+protein_seq.count('K')+protein_seq.count('D')+protein_seq.count('E')\n",
    "    Uncharged = protein_seq.count('S')+protein_seq.count('T')+protein_seq.count('N')+protein_seq.count('Q')\n",
    "    Special =   protein_seq.count('C')+protein_seq.count('U')+protein_seq.count('G')+protein_seq.count('P')\n",
    "    Hydrophobic = protein_seq.count('A')+protein_seq.count('V')+protein_seq.count('I')+protein_seq.count('L')+protein_seq.count('M')+protein_seq.count('F')+protein_seq.count('Y')+protein_seq.count('W')\n",
    "    \n",
    "    plt.subplot(2,2,2)\n",
    "    types = ['Elecrically charged','Polar uncharged',\n",
    "             'Special case', 'Hydrophobic Side Chain']\n",
    "    values = [Electric,Uncharged,Special,Hydrophobic]\n",
    "    plt.barh(types, values, height = 0.6)\n",
    "    plt.title('AA in groups')\n",
    "    \n",
    "    # Visualization of positive and negative amino acid in Hydrophobic side chain group\n",
    "    plt.subplot(2,2,3)\n",
    "    x = np.arange(2)\n",
    "    positive = protein_seq.count('R')+protein_seq.count('H')+protein_seq.count('K')\n",
    "    negative = protein_seq.count('D')+protein_seq.count('E')\n",
    "    types = ['Positive','Negative']\n",
    "    values = [positive, negative]\n",
    "    plt.barh(types,values, height=0.6)\n",
    "    plt.title('Charge diff in Electrically charged side chain')\n",
    "    #plt.show()\n",
    "    \n",
    "    # Visualization abundance of Amino Acid by relative frequenceies of amino aicd residue in secondary structure\n",
    "    alpha_helix = protein_seq.count('A')+protein_seq.count('C')+protein_seq.count('L')+protein_seq.count('M')+protein_seq.count('E')+protein_seq.count('Q')+protein_seq.count('H')+protein_seq.count('K')\n",
    "    beta_sheet =protein_seq.count('V')+protein_seq.count('I')+protein_seq.count('F')+protein_seq.count('Y')+protein_seq.count('W')+protein_seq.count('T')\n",
    "    turn = protein_seq.count('G')+protein_seq.count('S')+protein_seq.count('D')+protein_seq.count('N')+protein_seq.count('P')\n",
    "    x = np.arange(3)\n",
    "    plt.subplot(2,2,4)\n",
    "    types = ['Alpha helix','beta sheet','Turn']\n",
    "    values = [alpha_helix, beta_sheet, turn]\n",
    "    plt.barh(types,values, height=0.6)\n",
    "    plt.title('AA residues in secondary structure')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f29653e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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C\n",
      "text/plain": [
       "<Figure size 2520x3600 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(protein_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "be92bb3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDDNLIDSYFVVKRHTFSNYQHEETIYNLLKDCPAVAKHDFFKFRIDGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQALLKTVQFCDAMRNAGIVGVLTLDNQDLNGNWYDFGDFIQTTPGSGVPVVDSYYSLLMPILTLTRALTAESHVDTDLTKPYIKWDLLKYDFTEERLKLFDRYFKYWDQTYHPNCVNCLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAALTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVIVNNLDKSAGFPFNKWGKARLYYDSMSYEDQDALFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTMTNRQFHQKLLKSIAATRGATVVIGTSKFYGGWHNMLKTVYSDVENPHLMGWDYPKCDRAMPNMLRIMASLVLARKHTTCCSLSHRFYRLANECAQVLSEMVMCGGSLYVKPGGTSSGDATTAYANSVFNICQAVTANVNALLSTDGNKIADKYVRNLQHRLYECLYRNRDVDTDFVNEFYAYLRKHFSMMILSDDAVVCFNSTYASQGLVASIKNFKSVLYYQNNVFMSEAKCWTETDLTKGPHEFCSQHTMLVKQGDDYVYLPYPDPSRILGAGCFVDDIVKTDGTLMIERFVSLAIDAYPLTKHPNQEYADVFHLYLQYIRKLHDELTGHMLDMYSVMLTNDNTSRYWEPEFYEAMYTPHTVLQAVGACVLCNSQTSLRCGACIRRPFLCCKCCYDHVISTSHKLVLSVNPYVCNAPGCDVTDVTQLYLGGMSYYCKSHKPPISFPLCANGQVFGLYKNTCVGSDNVTDFNAIATCDWTNAGDYILANTCTERLKLFAAETLKATEETFKLSYGIATVREVLSDRELHLSWEVGKPRPPLNRNYVFTGYRVTKNSKVQIGEYTFEKGDYGDAVVYRGTTTYKLNVGDYFVLTSHTVMPLSAPTLVPQEHYVRITGLYPTLNISDEFSSNVANYQKVGMQKYSTLQGPPGTGKSHFAIGLALYYPSARIVYTACSHAAVDALCEKALKYLPIDKCSRIIPARARVECFDKFKVNSTLEQYVFCTVNALPETTADIVVFDEISMATNYDLSVVNARLRAKHYVYIGDPAQLPAPRTLLTKGTLEPEYFNSVCRLMKTIGPDMFLGTCRRCPAEIVDTVSALVYDNKLKAHKDKSAQCFKMFYKGVITHDVSSAINRPQIGVVREFLTRNPAWRKAVFISPYNSQNAVASKILGLPTQTVDSSQGSEYDYVIFTQTTETAHSCNVNRFNVAITRAKVGILCIMSDRDLYDKLQFTSLEIPRRNVATLQAENVTGLFKDCSKVITGLHPTQAPTHLSVDTKFKTEGLCVDIPGIPKDMTYRRLISMMGFKMNYQVNGYPNMFITREEAIRHVRAWIGFDVEGCHATREAVGTNLPLQLGFSTGVNLVAVPTGYVDTPNNTDFSRVSAKPPPGDQFKHLIPLMYKGLPWNVVRIKIVQMLSDTLKNLSDRVVFVLWAHGFELTSMKYFVKIGPERTCCLCDRRATCFSTASDTYACWHHSIGFDYVYNPFMIDVQQWGFTGNLQSNHDLYCQVHGNAHVASCDAIMTRCLAVHECFVKRVDWTIEYPIIGDELKINAACRKVQHMVVKAALLADKFPVLHDIGNPKAIKCVPQADVEWKFYDAQPCSDKAYKIEELFYSYATHSDKFTDGVCLFWNCNVDRYPANSIVCRFDTRVLSNLNLPGCDGGSLYVNKHAFHTPAFDKSAFVNLKQLPFFYYSDSPCESHGKQVVSDIDYVPLKSATCITRCNLGGAVCRHHANEYRLYLDAYNMMISAGFSLWVYKQFDTYNLWNTFTRLQSLENVAFNVVNKGHFDGQQGEVPVSIINNTVYTKVDGVDVELFENKTTLPVNVAFELWAKRNIKPVPEVKILNNLGVDIAANTVIWDYKRDAPAHISTIGVCSMTDIAKKPTETICAPLTVFFDGRVDGQVDLFRNARNGVLITEGSVKGLQPSVGPKQASLNGVTLIGEAVKTQFNYYKKVDGVVQQLPETYFTQSRNLQEFKPRSQMEIDFLELAMDEFIERYKLEGYAFEHIVYGDFSHSQLGGLHLLIGLAKRFKESPFELEDFIPMDSTVKNYFITDAQTGSSKCVCSVIDLLLDDFVEIIKSQDLSVVSKVVKVTIDYTEISFMLWCKDGHVETFYPKLQSSQAWQPGVAMPNLYKMQRMLLEKCDLQNYGDSATLPKGIMMNVAKYTQLCQYLNTLTLAVPYNMRVIHFGAGSDKGVAPGTAVLRQWLPTGTLLVDSDLNDFVSDADSTLIGDCATVHTANKWDLIISDMYDPKTKNVTKENDSKEGFFTYICGFIQQKLALGGSVAIKITEHSWNADLYKLMGHFAWWTAFVTNVNASSSEAFLIGCNYLGKPREQIDGYVMHANYIFWRNTNPIQLSSYSLFDMSKFPLKLRGTAVMSLKEGQINDMILSLLSKGRLIIRENNRVVISSDVLVNN', 'MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNGFAV', 'MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT', 'MDLFMRIFTIGTVTLKQGEIKDATPSDFVRATATIPIQASLPFGWLIVGVALLAVFQSASKIITLKKRWQLALSKGVHFVCNLLLLFVTVYSHLLLVAAGLEAPFLYLYALVYFLQSINFVRIIMRLWLCWKCRSKNPLLYDANYFLCWHTNCYDYCIPYNSVTSSIVITSGDGTTSPISEHDYQIGGYTEKWESGVKDCVVLHSYFTSDYYQLYSTQLSTDTGVEHVTFFIYNKIVDEPEEHVQIHTIDGSSGVVNPVMEPIYDEPTTTTSVPL', 'MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV', 'MADSNGTITVEELKKLLEQWNLVIGFLFLTWICLLQFAYANRNRFLYIIKLIFLWLLWPVTLACFVLAAVYRINWITGGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTILTRPLLESELVIGAVILRGHLRIAGHHLGRCDIKDLPKEITVATSRTLSYYKLGASQRVAGDSGFAAYSRYRIGNYKLNTDHSSSSDNIALLVQ', 'MFHLVDFQVTIAEILLIIMRTFKVSIWNLDYIINLIIKNLSKSLTENKYSQLDEEQPMEID', 'MKIILFLALITLATCELYHYQECVRGTTVLLKEPCSSGTYEGNSPFHPLADNKFALTCFSTQFAFACPDGVKHVYQLRARSVSPKLFIRQEEVQELYSPIFLIVAAIVFITLCFTLKRKTE', 'MIELSLIDFYLCFLAFLLFLVLIMLIIFWFSLELQDHNETCHA', 'MKFLVFLGIITTVAAFHQECSLQSCTQHQPYVVDDPCPIHFYSKWYIRVGARKSAPLIELCVDEAGSKSPIQYIDIGNYTVSCLPFTINCQEPKLGSLVVRCSFYEDFLEYHDVRVVLDFI', 'MSDNGPQNQRNAPRITFGGPSDSTGSNQNGERSGARSKQRRPQGLPNNTASWFTALTQHGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLSPRWYFYYLGTGPEAGLPYGANKDGIIWVATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQASSRSSSRSRNSSRNSTPGSSRGTSPARMAGNGGDAALALLLLDRLNQLESKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATKAYNVTQAFGRRGPEQTQGNFGDQELIRQGTDYKHWPQIAQFAPSASAFFGMSRIGMEVTPSGTWLTYTGAIKLDDKDPNFKDQVILLNKHIDAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDDFSKQLQQSMSSADSTQA', 'MGYINVFAFPFTIYSLLLCRMNSRNYIAQVDVVNFNLT', '']\n",
      "Number of proteins: 13\n"
     ]
    }
   ],
   "source": [
    "def proteinseq_splic(protein_sequence):\n",
    "    protein_group = protein_sequence.split('_')\n",
    "    return protein_group\n",
    "protein_list = proteinseq_splic(protein_seq)\n",
    "print(proteinseq_splic(protein_seq)[0:100])\n",
    "print('Number of proteins:',len(protein_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ebf3ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDDNLIDSYFVVKRHTFSNYQHEETIYNLLKDCPAVAKHDFFKFRIDGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQALLKTVQFCDAMRNAGIVGVLTLDNQDLNGNWYDFGDFIQTTPGSGVPVVDSYYSLLMPILTLTRALTAESHVDTDLTKPYIKWDLLKYDFTEERLKLFDRYFKYWDQTYHPNCVNCLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAALTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVIVNNLDKSAGFPFNKWGKARLYYDSMSYEDQDALFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTMTNRQFHQKLLKSIAATRGATVVIGTSKFYGGWHNMLKTVYSDVENPHLMGWDYPKCDRAMPNMLRIMASLVLARKHTTCCSLSHRFYRLANECAQVLSEMVMCGGSLYVKPGGTSSGDATTAYANSVFNICQAVTANVNALLSTDGNKIADKYVRNLQHRLYECLYRNRDVDTDFVNEFYAYLRKHFSMMILSDDAVVCFNSTYASQGLVASIKNFKSVLYYQNNVFMSEAKCWTETDLTKGPHEFCSQHTMLVKQGDDYVYLPYPDPSRILGAGCFVDDIVKTDGTLMIERFVSLAIDAYPLTKHPNQEYADVFHLYLQYIRKLHDELTGHMLDMYSVMLTNDNTSRYWEPEFYEAMYTPHTVLQAVGACVLCNSQTSLRCGACIRRPFLCCKCCYDHVISTSHKLVLSVNPYVCNAPGCDVTDVTQLYLGGMSYYCKSHKPPISFPLCANGQVFGLYKNTCVGSDNVTDFNAIATCDWTNAGDYILANTCTERLKLFAAETLKATEETFKLSYGIATVREVLSDRELHLSWEVGKPRPPLNRNYVFTGYRVTKNSKVQIGEYTFEKGDYGDAVVYRGTTTYKLNVGDYFVLTSHTVMPLSAPTLVPQEHYVRITGLYPTLNISDEFSSNVANYQKVGMQKYSTLQGPPGTGKSHFAIGLALYYPSARIVYTACSHAAVDALCEKALKYLPIDKCSRIIPARARVECFDKFKVNSTLEQYVFCTVNALPETTADIVVFDEISMATNYDLSVVNARLRAKHYVYIGDPAQLPAPRTLLTKGTLEPEYFNSVCRLMKTIGPDMFLGTCRRCPAEIVDTVSALVYDNKLKAHKDKSAQCFKMFYKGVITHDVSSAINRPQIGVVREFLTRNPAWRKAVFISPYNSQNAVASKILGLPTQTVDSSQGSEYDYVIFTQTTETAHSCNVNRFNVAITRAKVGILCIMSDRDLYDKLQFTSLEIPRRNVATLQAENVTGLFKDCSKVITGLHPTQAPTHLSVDTKFKTEGLCVDIPGIPKDMTYRRLISMMGFKMNYQVNGYPNMFITREEAIRHVRAWIGFDVEGCHATREAVGTNLPLQLGFSTGVNLVAVPTGYVDTPNNTDFSRVSAKPPPGDQFKHLIPLMYKGLPWNVVRIKIVQMLSDTLKNLSDRVVFVLWAHGFELTSMKYFVKIGPERTCCLCDRRATCFSTASDTYACWHHSIGFDYVYNPFMIDVQQWGFTGNLQSNHDLYCQVHGNAHVASCDAIMTRCLAVHECFVKRVDWTIEYPIIGDELKINAACRKVQHMVVKAALLADKFPVLHDIGNPKAIKCVPQADVEWKFYDAQPCSDKAYKIEELFYSYATHSDKFTDGVCLFWNCNVDRYPANSIVCRFDTRVLSNLNLPGCDGGSLYVNKHAFHTPAFDKSAFVNLKQLPFFYYSDSPCESHGKQVVSDIDYVPLKSATCITRCNLGGAVCRHHANEYRLYLDAYNMMISAGFSLWVYKQFDTYNLWNTFTRLQSLENVAFNVVNKGHFDGQQGEVPVSIINNTVYTKVDGVDVELFENKTTLPVNVAFELWAKRNIKPVPEVKILNNLGVDIAANTVIWDYKRDAPAHISTIGVCSMTDIAKKPTETICAPLTVFFDGRVDGQVDLFRNARNGVLITEGSVKGLQPSVGPKQASLNGVTLIGEAVKTQFNYYKKVDGVVQQLPETYFTQSRNLQEFKPRSQMEIDFLELAMDEFIERYKLEGYAFEHIVYGDFSHSQLGGLHLLIGLAKRFKESPFELEDFIPMDSTVKNYFITDAQTGSSKCVCSVIDLLLDDFVEIIKSQDLSVVSKVVKVTIDYTEISFMLWCKDGHVETFYPKLQSSQAWQPGVAMPNLYKMQRMLLEKCDLQNYGDSATLPKGIMMNVAKYTQLCQYLNTLTLAVPYNMRVIHFGAGSDKGVAPGTAVLRQWLPTGTLLVDSDLNDFVSDADSTLIGDCATVHTANKWDLIISDMYDPKTKNVTKENDSKEGFFTYICGFIQQKLALGGSVAIKITEHSWNADLYKLMGHFAWWTAFVTNVNASSSEAFLIGCNYLGKPREQIDGYVMHANYIFWRNTNPIQLSSYSLFDMSKFPLKLRGTAVMSLKEGQINDMILSLLSKGRLIIRENNRVVISSDVLVNN', 'MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNGFAV', 'MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT', 'MDLFMRIFTIGTVTLKQGEIKDATPSDFVRATATIPIQASLPFGWLIVGVALLAVFQSASKIITLKKRWQLALSKGVHFVCNLLLLFVTVYSHLLLVAAGLEAPFLYLYALVYFLQSINFVRIIMRLWLCWKCRSKNPLLYDANYFLCWHTNCYDYCIPYNSVTSSIVITSGDGTTSPISEHDYQIGGYTEKWESGVKDCVVLHSYFTSDYYQLYSTQLSTDTGVEHVTFFIYNKIVDEPEEHVQIHTIDGSSGVVNPVMEPIYDEPTTTTSVPL', 'MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV', 'MADSNGTITVEELKKLLEQWNLVIGFLFLTWICLLQFAYANRNRFLYIIKLIFLWLLWPVTLACFVLAAVYRINWITGGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTILTRPLLESELVIGAVILRGHLRIAGHHLGRCDIKDLPKEITVATSRTLSYYKLGASQRVAGDSGFAAYSRYRIGNYKLNTDHSSSSDNIALLVQ', 'MFHLVDFQVTIAEILLIIMRTFKVSIWNLDYIINLIIKNLSKSLTENKYSQLDEEQPMEID', 'MKIILFLALITLATCELYHYQECVRGTTVLLKEPCSSGTYEGNSPFHPLADNKFALTCFSTQFAFACPDGVKHVYQLRARSVSPKLFIRQEEVQELYSPIFLIVAAIVFITLCFTLKRKTE', 'MIELSLIDFYLCFLAFLLFLVLIMLIIFWFSLELQDHNETCHA', 'MKFLVFLGIITTVAAFHQECSLQSCTQHQPYVVDDPCPIHFYSKWYIRVGARKSAPLIELCVDEAGSKSPIQYIDIGNYTVSCLPFTINCQEPKLGSLVVRCSFYEDFLEYHDVRVVLDFI', 'MSDNGPQNQRNAPRITFGGPSDSTGSNQNGERSGARSKQRRPQGLPNNTASWFTALTQHGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLSPRWYFYYLGTGPEAGLPYGANKDGIIWVATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQASSRSSSRSRNSSRNSTPGSSRGTSPARMAGNGGDAALALLLLDRLNQLESKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATKAYNVTQAFGRRGPEQTQGNFGDQELIRQGTDYKHWPQIAQFAPSASAFFGMSRIGMEVTPSGTWLTYTGAIKLDDKDPNFKDQVILLNKHIDAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDDFSKQLQQSMSSADSTQA', 'MGYINVFAFPFTIYSLLLCRMNSRNYIAQVDVVNFNLT']\n",
      "Number of Functional proteins: 12\n"
     ]
    }
   ],
   "source": [
    "def Functional_protein(protein_list):\n",
    "    funct_group = []\n",
    "    for protein in protein_list:\n",
    "        if len(protein) > 20:\n",
    "            funct_group.append(protein)\n",
    "    return funct_group\n",
    "function_protein = Functional_protein(protein_list)\n",
    "print(function_protein[0:30])\n",
    "print('Number of Functional proteins:',len(function_protein))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "890bd17f",
   "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>line no</th>\n",
       "      <th>sequence ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>608</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>609</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>610</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>611</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>612</td>\n",
       "      <td>0.989474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>612 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     line no  sequence ratios\n",
       "0          1         1.000000\n",
       "1          2         1.000000\n",
       "2          3         1.000000\n",
       "3          4         1.000000\n",
       "4          5         1.000000\n",
       "..       ...              ...\n",
       "607      608         1.000000\n",
       "608      609         1.000000\n",
       "609      610         1.000000\n",
       "610      611         1.000000\n",
       "611      612         0.989474\n",
       "\n",
       "[612 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72b220bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "corona=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ba04cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(input_file1) as file1:\n",
    "    for lineno in file1:\n",
    "        corona+=lineno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "616bba38",
   "metadata": {},
   "outputs": [],
   "source": [
    "for a in \" \\n0123456789\":\n",
    "    corona = corona.replace(a, \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "516ee781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42483"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corona)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "41ed40df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9579"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zlib.compress(corona.encode(\"utf-8\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2077b843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8292"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lzc = lzma.compress(corona.encode(\"utf-8\"))\n",
    "len(lzc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "21ee08bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ala / A', 'GCU, GCC, GCA, GCG']\n",
      "['Ile / I', 'AUU, AUC, AUA']\n",
      "['Arg / R', 'CGU, CGC, CGA, CGG; AGA, AGG, AGR;']\n",
      "['Leu / L', 'CUU, CUC, CUA, CUG; UUA, UUG, UUR;']\n",
      "['Asn / N', 'AAU, AAC']\n",
      "['Lys / K', 'AAA, AAG']\n",
      "['Asp / D', 'GAU, GAC']\n",
      "['Met / M', 'AUG']\n",
      "['Phe / F', 'UUU, UUC']\n",
      "['Cys / C', 'UGU, UGC']\n",
      "['Pro / P', 'CCU, CCC, CCA, CCG']\n",
      "['Gln / Q', 'CAA, CAG']\n",
      "['Ser / S', 'UCU, UCC, UCA, UCG; AGU, AGC;']\n",
      "['Glu / E', 'GAA, GAG']\n",
      "['Thr / T', 'ACU, ACC, ACA, ACG']\n",
      "['Trp / W', 'UGG']\n",
      "['Gly / G', 'GGU, GGC, GGA, GGG']\n",
      "['Tyr / Y', 'UAU, UAC']\n",
      "['His / H', 'CAU, CAC']\n",
      "['Val / V', 'GUU, GUC, GUA, GUG']\n",
      "['STOP', 'UAA, UGA, UAG']\n"
     ]
    }
   ],
   "source": [
    "# Asn or Asp / B\tAAU, AAC; GAU, GAC\n",
    "# Gln or Glu / Z\tCAA, CAG; GAA, GAG\n",
    "# START\tAUG\n",
    "## Seperating them from the table because these duplicates was creating problems\n",
    "codons = \"\"\"\n",
    "Ala / A\tGCU, GCC, GCA, GCG\n",
    "Ile / I\tAUU, AUC, AUA\n",
    "Arg / R\tCGU, CGC, CGA, CGG; AGA, AGG, AGR;\n",
    "Leu / L\tCUU, CUC, CUA, CUG; UUA, UUG, UUR;\n",
    "Asn / N\tAAU, AAC\n",
    "Lys / K\tAAA, AAG\n",
    "Asp / D\tGAU, GAC\n",
    "Met / M\tAUG\n",
    "Phe / F\tUUU, UUC\n",
    "Cys / C\tUGU, UGC\n",
    "Pro / P\tCCU, CCC, CCA, CCG\n",
    "Gln / Q\tCAA, CAG\n",
    "Ser / S\tUCU, UCC, UCA, UCG; AGU, AGC;\n",
    "Glu / E\tGAA, GAG\n",
    "Thr / T\tACU, ACC, ACA, ACG\n",
    "Trp / W\tUGG\n",
    "Gly / G\tGGU, GGC, GGA, GGG\n",
    "Tyr / Y\tUAU, UAC\n",
    "His / H\tCAU, CAC\n",
    "Val / V\tGUU, GUC, GUA, GUG\n",
    "STOP\tUAA, UGA, UAG\"\"\".strip()\n",
    "\n",
    "for t in codons.split('\\n'):\n",
    "    print(t.split('\\t'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc8f10a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gct': 'A',\n",
       " 'gcc': 'A',\n",
       " 'gca': 'A',\n",
       " 'gcg': 'A',\n",
       " 'att': 'I',\n",
       " 'atc': 'I',\n",
       " 'ata': 'I',\n",
       " 'cgt': 'R',\n",
       " 'cgc': 'R',\n",
       " 'cga': 'R',\n",
       " 'cgg': 'R',\n",
       " 'aga': 'R',\n",
       " 'agg': 'R',\n",
       " 'agr': 'R',\n",
       " 'ctt': 'L',\n",
       " 'ctc': 'L',\n",
       " 'cta': 'L',\n",
       " 'ctg': 'L',\n",
       " 'tta': 'L',\n",
       " 'ttg': 'L',\n",
       " 'ttr': 'L',\n",
       " 'aat': 'N',\n",
       " 'aac': 'N',\n",
       " 'aaa': 'K',\n",
       " 'aag': 'K',\n",
       " 'gat': 'D',\n",
       " 'gac': 'D',\n",
       " 'atg': 'M',\n",
       " 'ttt': 'F',\n",
       " 'ttc': 'F',\n",
       " 'tgt': 'C',\n",
       " 'tgc': 'C',\n",
       " 'cct': 'P',\n",
       " 'ccc': 'P',\n",
       " 'cca': 'P',\n",
       " 'ccg': 'P',\n",
       " 'caa': 'Q',\n",
       " 'cag': 'Q',\n",
       " 'tct': 'S',\n",
       " 'tcc': 'S',\n",
       " 'tca': 'S',\n",
       " 'tcg': 'S',\n",
       " 'agt': 'S',\n",
       " 'agc': 'S',\n",
       " 'gaa': 'E',\n",
       " 'gag': 'E',\n",
       " 'act': 'T',\n",
       " 'acc': 'T',\n",
       " 'aca': 'T',\n",
       " 'acg': 'T',\n",
       " 'tgg': 'W',\n",
       " 'ggt': 'G',\n",
       " 'ggc': 'G',\n",
       " 'gga': 'G',\n",
       " 'ggg': 'G',\n",
       " 'tat': 'Y',\n",
       " 'tac': 'Y',\n",
       " 'cat': 'H',\n",
       " 'cac': 'H',\n",
       " 'gtt': 'V',\n",
       " 'gtc': 'V',\n",
       " 'gta': 'V',\n",
       " 'gtg': 'V',\n",
       " 'taa': '*',\n",
       " 'tga': '*',\n",
       " 'tag': '*'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##decoder dictionary\n",
    "dec = {}  \n",
    "\n",
    "for t in codons.split('\\n'):\n",
    "    k, v = t.split('\\t')\n",
    "    if '/' in k:\n",
    "        k = k.split('/')[-1].strip()\n",
    "    k = k.replace(\"STOP\", \"*\")\n",
    "    v = v.replace(\",\", \"\").replace(\";\", \"\").lower().replace(\"u\", \"t\").split(\" \")\n",
    "    for vv in v:\n",
    "        if vv in dec:\n",
    "            print(\"duplicate\", vv)\n",
    "        dec[vv] = k\n",
    "dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "42f57343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(dec.values())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7bb89db",
   "metadata": {},
   "outputs": [],
   "source": [
    "genome_sequence1=''\n",
    "genome_sequence2=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d08540bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_seq_1 = read_dna_seq('./input/China_Seq_2019_Dec.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "285dd2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the sequence with dummy 'N' nucleotide.\n",
    "dict_seq_1 = gene_mod(dict_seq_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4f83d09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the dna sequence file-2 previously downloaded from NCBI.\n",
    "dict_seq_2 = read_dna_seq('./input/USA_Seq_2020_Jan.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e5540550",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the sequence with dummy 'N' nucleotide.\n",
    "dict_seq_2 = gene_mod(dict_seq_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "15540b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2160 with 33 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create matplotlib subplots for each gene. \n",
    "f,ax = plt.subplots(nrows=11,ncols=3,figsize=(25,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "47a4ed1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_name = list(numpy_image_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d92efef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "row = 0\n",
    "col = 0\n",
    "mut_dict={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "809b5738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mutated DNA Base 255 in China and Base 100 in USA at position (74, 6) For the Gene ORF1ab\n",
      "Mutated DNA Base 100 in China and Base 255 in USA at position (12, 10) For the Gene ORF8\n",
      "Mutated DNA Base 255 in China and Base 0 in USA at position (17, 24) For the Gene N\n"
     ]
    }
   ],
   "source": [
    "for i in gene_name:\n",
    "    G = i[5:]\n",
    "    # Loop thru each gene in the Cornona Virus nucleotide sequence.\n",
    "    gene_us = dna(dict_seq_1['gene='+G][1])\n",
    "    # Invoke the transcription method of the class dna \n",
    "    gene_us.transcription()\n",
    "    # Invoke the mothod that converts the gene sequence into a numpy array.\n",
    "    numpfy_usa = gene_us.numpfy()\n",
    "    # Reshape the numpy array with a predeifned shape from the numpy_image_dict dictionary.\n",
    "    numpfy_usa = numpfy_usa.reshape(numpy_image_dict['gene='+G][0])\n",
    "    # sub-plot the numpy array with matplotlib pcolor method.\n",
    "    ax[row][col].pcolor(numpfy_usa)\n",
    "    ax[row][col].set_title(G+' Gene - USA')\n",
    "    col+=1\n",
    "    gene_china = dna(dict_seq_2['gene='+G][1])\n",
    "    # Invoke the transcription method of the class dna \n",
    "    gene_china.transcription()\n",
    "    # Invoke the mothod that converts the gene sequence into a numpy array.\n",
    "    numpfy_china = gene_china.numpfy()\n",
    "    # Reshape the numpy array with a predeifned shape from the numpy_image_dict dictionary.\n",
    "    numpfy_china = numpfy_china.reshape(numpy_image_dict['gene='+G][0])\n",
    "    # sub-plot the numpy array with matplotlib pcolor method.\n",
    "    ax[row][col].pcolor(numpfy_china)\n",
    "    ax[row][col].set_title(G+' Gene - CHINA')\n",
    "    col+=1\n",
    "\n",
    "    # To find the gene mutation subtract the numpy array from base sequence with the newer sequence. Here the \n",
    "    # the Chinese sequence is the base sequence and the USA sequence is a newer sequence.\n",
    "    mut = numpfy_china - numpfy_usa\n",
    "    if mut.any():\n",
    "        # Here we are looking for a non zero value in the mutated numpy array (result of the subtracting the 2 numpy arrays).\n",
    "        # Presence of non-zero value means that there is difference between the 2 numpy arrays and the gene has \n",
    "        # mutataions. If there are mutations in the gene create a python dictionary \"mut_dict\" with details as below.\n",
    "        # {'<Gene_Name-1>': [[<value_of_base_seq>, <value_of_newer_seq>, <value_in_mutated_numpy>, (x_value,y_value)]], '<Gene_Name-2>': [[<value_of_base_seq>, <value_of_newer_seq>, <value_in_mutated_numpy>, (x_value,y_value)]]}\n",
    "        mut_nec = np.nonzero(mut)\n",
    "        x=mut_nec[0]\n",
    "        y=mut_nec[1]\n",
    "        l=0\n",
    "        mut_dict[G]=[]\n",
    "        for i in x:\n",
    "            us_base = numpfy_usa[i][y[l]]\n",
    "            ch_base = numpfy_china[i][y[l]]\n",
    "            mut_base = mut[i][y[l]]\n",
    "            info_list = [ch_base,us_base,mut_base,(i,y[l])]\n",
    "            mut_dict[G].append(info_list)\n",
    "            print(\"Mutated DNA Base {} in China and Base {} in USA at position {} For the Gene {}\".format(ch_base,us_base,(i,y[l]),G))\n",
    "            l+= 1\n",
    "    # Giving a title to the matplotlib subplot\n",
    "    ax[row][col].pcolor(mut)\n",
    "    ax[row][col].set_title(G+' Gene - Mutataion')\n",
    "    row+= 1\n",
    "    col=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eed3339c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MN908947.3\n",
      "ATGGAGAGCCTTGTCCCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTTTACAGGTTCGCGACGTGCTCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGCACGTCAACATCTTAAAGATGGCACTTGTGGCTTAGTAGAAGTTGAAAAAGGCGTTTTGCCTCAACTTGAACAGCCCTATGTGTTCATCAAACGTTCGGATGCTCGAACTGCACCTCATGGTCATGTTATGGTTGAGCTGGTAGCAGAACTCGAAGGCATTCAGTACGGTCGTAGTGGTGAGACACTTGGTGTCCTTGTCCCTCATGTGGGCGAAATACCAGTGGCTTACCGCAAGGTTCTTCTTCGTAAGAACGGTAATAAAGGAGCTGGTGGCCATAGTTACGGCGCCGATCTAAAGTCATTTGACTTAGGCGACGAGCTTGGCACTGATCCTTATGAAGATTTTCAAGAAAACTGGAACACTAAACATAGCAGTGGTGTTACCCGTGAACTCATGCGTGAGCTTAACGGAGGGGCATACACTCGCTATGTCGATAACAACTTCTGTGGCCCTGATGGCTACCCTCTTGAGTGCATTAAAGACCTTCTAGCACGTGCTGGTAAAGCTTCATGCACTTTGTCCGAACAACTGGACTTTATTGACACTAAGAGGGGTGTATACTGCTGCCGTGAACATGAGCATGAAATTGCTTGGTACACGGAACGTTCTGAAAAGAGCTATGAATTGCAGACACCTTTTGAAATTAAATTGGCAAAGAAATTTGACACCTTCAATGGGGAATGTCCAAATTTTGTATTTCCCTTAAATTCCATAATCAAGACTATTCAACCAAGGGTTGAAAAGAAAAAGCTTGATGGCTTTATGGGTAGAATTCGATCTGTCTATCCAGTTGCGTCACCAAATGAATGCAACCAAATGTGCCTTTCAACTCTCATGAAGTGTGATCATTGTGGTGAAACTTCATGGCAGACGGGCGATTTTGTTAAAGCCACTTGCGAATTTTGTGGCACTGAGAATTTGACTAAAGAAGGTGCCACTACTTGTGGTTACTTACCCCAAAATGCTGTTGTTAAAATTTATTGTCCAGCATGTCACAATTCAGAAGTAGGACCTGAGCATAGTCTTGCCGAATACCATAATGAATCTGGCTTGAAAACCATTCTTCGTAAGGGTGGTCGCACTATTGCCTTTGGAGGCTGTGTGTTCTCTTATGTTGGTTGCCATAACAAGTGTGCCTATTGGGTTCCACGTGCTAGCGCTAACATAGGTTGTAACCATACAGGTGTTGTTGGAGAAGGTTCCGAAGGTCTTAATGACAACCTTCTTGAAATACTCCAAAAAGAGAAAGTCAACATCAATATTGTTGGTGACTTTAAACTTAATGAAGAGATCGCCATTATTTTGGCATCTTTTTCTGCTTCCACAAGTGCTTTTGTGGAAACTGTGAAAGGTTTGGATTATAAAGCATTCAAACAAATTGTTGAATCCTGTGGTAATTTTAAAGTTACAAAAGGAAAAGCTAAAAAAGGTGCCTGGAATATTGGTGAACAGAAATCAATACTGAGTCCTCTTTATGCATTTGCATCAGAGGCTGCTCGTGTTGTACGATCAATTTTCTCCCGCACTCTTGAAACTGCTCAAAATTCTGTGCGTGTTTTACAGAAGGCCGCTATAACAATACTAGATGGAATTTCACAGTATTCACTGAGACTCATTGATGCTATGATGTTCACATCTGATTTGGCTACTAACAATCTAGTTGTAATGGCCTACATTACAGGTGGTGTTGTTCAGTTGACTTCGCAGTGGCTAACTAACATCTTTGGCACTGTTTATGAAAAACTCAAACCCGTCCTTGATTGGCTTGAAGAGAAGTTTAAGGAAGGTGTAGAGTTTCTTAGAGACGGTTGGGAAATTGTTAAATTTATCTCAACCTGTGCTTGTGAAATTGTCGGTGGACAAATTGTCACCTGTGCAAAGGAAATTAAGGAGAGTGTTCAGACATTCTTTAAGCTTGTAAATAAATTTTTGGCTTTGTGTGCTGACTCTATCATTATTGGTGGAGCTAAACTTAAAGCCTTGAATTTAGGTGAAACATTTGTCACGCACTCAAAGGGATTGTACAGAAAGTGTGTTAAATCCAGAGAAGAAACTGGCCTACTCATGCCTCTAAAAGCCCCAAAAGAAATTATCTTCTTAGAGGGAGAAACACTTCCCACAGAAGTGTTAACAGAGGAAGTTGTCTTGAAAACTGGTGATTTACAACCATTAGAACAACCTACTAGTGAAGCTGTTGAAGCTCCATTGGTTGGTACACCAGTTTGTATTAACGGGCTTATGTTGCTCGAAATCAAAGACACAGAAAAGTACTGTGCCCTTGCACCTAATATGATGGTAACAAACAATACCTTCACACTCAAAGGCGGTGCACCAACAAAGGTTACTTTTGGTGATGACACTGTGATAGAAGTGCAAGGTTACAAGAGTGTGAATATCACTTTTGAACTTGATGAAAGGATTGATAAAGTACTTAATGAGAAGTGCTCTGCCTATACAGTTGAACTCGGTACAGAAGTAAATGAGTTCGCCTGTGTTGTGGCAGATGCTGTCATAAAAACTTTGCAACCAGTATCTGAATTACTTACACCACTGGGCATTGATTTAGATGAGTGGAGTATGGCTACATACTACTTATTTGATGAGTCTGGTGAGTTTAAATTGGCTTCACATATGTATTGTTCTTTCTACCCTCCAGATGAGGATGAAGAAGAAGGTGATTGTGAAGAAGAAGAGTTTGAGCCATCAACTCAATATGAGTATGGTACTGAAGATGATTACCAAGGTAAACCTTTGGAATTTGGTGCCACTTCTGCTGCTCTTCAACCTGAAGAAGAGCAAGAAGAAGATTGGTTAGATGATGATAGTCAACAAACTGTTGGTCAACAAGACGGCAGTGAGGACAATCAGACAACTACTATTCAAACAATTGTTGAGGTTCAACCTCAATTAGAGATGGAACTTACACCAGTTGTTCAGACTATTGAAGTGAATAGTTTTAGTGGTTATTTAAAACTTACTGACAATGTATACATTAAAAATGCAGACATTGTGGAAGAAGCTAAAAAGGTAAAACCAACAGTGGTTGTTAATGCAGCCAATGTTTACCTTAAACATGGAGGAGGTGTTGCAGGAGCCTTAAATAAGGCTACTAACAATGCCATGCAAGTTGAATCTGATGATTACATAGCTACTAATGGACCACTTAAAGTGGGTGGTAGTTGTGTTTTAAGCGGACACAATCTTGCTAAACACTGTCTTCATGTTGTCGGCCCAAATGTTAACAAAGGTGAAGACATTCAACTTCTTAAGAGTGCTTATGAAAATTTTAATCAGCACGAAGTTCTACTTGCACCATTATTATCAGCTGGTATTTTTGGTGCTGACCCTATACATTCTTTAAGAGTTTGTGTAGATACTGTTCGCACAAATGTCTACTTAGCTGTCTTTGATAAAAATCTCTATGACAAACTTGTTTCAAGCTTTTTGGAAATGAAGAGTGAAAAGCAAGTTGAACAAAAGATCGCTGAGATTCCTAAAGAGGAAGTTAAGCCATTTATAACTGAAAGTAAACCTTCAGTTGAACAGAGAAAACAAGATGATAAGAAAATCAAAGCTTGTGTTGAAGAAGTTACAACAACTCTGGAAGAAACTAAGTTCCTCACAGAAAACTTGTTACTTTATATTGACATTAATGGCAATCTTCATCCAGATTCTGCCACTCTTGTTAGTGACATTGACATCACTTTCTTAAAGAAAGATGCTCCATATATAGTGGGTGATGTTGTTCAAGAGGGTGTTTTAACTGCTGTGGTTATACCTACTAAAAAGGCTGGTGGCACTACTGAAATGCTAGCGAAAGCTTTGAGAAAAGTGCCAACAGACAATTATATAACCACTTACCCGGGTCAGGGTTTAAATGGTTACACTGTAGAGGAGGCAAAGACAGTGCTTAAAAAGTGTAAAAGTGCCTTTTACATTCTACCATCTATTATCTCTAATGAGAAGCAAGAAATTCTTGGAACTGTTTCTTGGAATTTGCGAGAAATGCTTGCACATGCAGAAGAAACACGCAAATTAATGCCTGTCTGTGTGGAAACTAAAGCCATAGTTTCAACTATACAGCGTAAATATAAGGGTATTAAAATACAAGAGGGTGTGGTTGATTATGGTGCTAGATTTTACTTTTACACCAGTAAAACAACTGTAGCGTCACTTATCAACACACTTAACGATCTAAATGAAACTCTTGTTACAATGCCACTTGGCTATGTAACACATGGCTTAAATTTGGAAGAAGCTGCTCGGTATATGAGATCTCTCAAAGTGCCAGCTACAGTTTCTGTTTCTTCACCTGATGCTGTTACAGCGTATAATGGTTATCTTACTTCTTCTTCTAAAACACCTGAAGAACATTTTATTGAAACCATCTCACTTGCTGGTTCCTATAAAGATTGGTCCTATTCTGGACAATCTACACAACTAGGTATAGAATTTCTTAAGAGAGGTGATAAAAGTGTATATTACACTAGTAATCCTACCACATTCCACCTAGATGGTGAAGTTATCACCTTTGACAATCTTAAGACACTTCTTTCTTTGAGAGAAGTGAGGACTATTAAGGTGTTTACAACAGTAGACAACATTAACCTCCACACGCAAGTTGTGGACATGTCAATGACATATGGACAACAGTTTGGTCCAACTTATTTGGATGGAGCTGATGTTACTAAAATAAAACCTCATAATTCACATGAAGGTAAAACATTTTATGTTTTACCTAATGATGACACTCTACGTGTTGAGGCTTTTGAGTACTACCACACAACTGATCCTAGTTTTCTGGGTAGGTACATGTCAGCATTAAATCACACTAAAAAGTGGAAATACCCACAAGTTAATGGTTTAACTTCTATTAAATGGGCAGATAACAACTGTTATCTTGCCACTGCATTGTTAACACTCCAACAAATAGAGTTGAAGTTTAATCCACCTGCTCTACAAGATGCTTATTACAGAGCAAGGGCTGGTGAAGCTGCTAACTTTTGTGCACTTATCTTAGCCTACTGTAATAAGACAGTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTACATGGGCACACTTTCTTATGAACAATTTAAGAAAGGTGTTCAGATACCTTGTACGTGTGGTAAACAAGCTACAAAATATCTAGTACAACAGGAGTCACCTTTTGTTATGATGTCAGCACCACCTGCTCAGTATGAACTTAAGCATGGTACATTTACTTGTGCTAGTGAGTACACTGGTAATTACCAGTGTGGTCACTATAAACATATAACTTCTAAAGAAACTTTGTATTGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGTTACACAACAACCATAAAACCAGTTACTTATAAATTGGATGGTGTTGTTTGTACAGAAATTGACCCTAAGTTGGACAATTATTATAAGAAAGACAATTCTTATTTCACAGAGCAACCAATTGATCTTGTACCAAACCAACCATATCCAAACGCAAGCTTCGATAATTTTAAGTTTGTATGTGATAATATCAAATTTGCTGATGATTTAAACCAGTTAACTGGTTATAAGAAACCTGCTTCAAGAGAGCTTAAAGTTACATTTTTCCCTGACTTAAATGGTGATGTGGTGGCTATTGATTATAAACACTACACACCCTCTTTTAAGAAAGGAGCTAAATTGTTACATAAACCTATTGTTTGGCATGTTAACAATGCAACTAATAAAGCCACGTATAAACCAAATACCTGGTGTATACGTTGTCTTTGGAGCACAAAACCAGTTGAAACATCAAATTCGTTTGATGTACTGAAGTCAGAGGACGCGCAGGGAATGGATAATCTTGCCTGCGAAGATCTAAAACCAGTCTCTGAAGAAGTAGTGGAAAATCCTACCATACAGAAAGACGTTCTTGAGTGTAATGTGAAAACTACCGAAGTTGTAGGAGACATTATACTTAAACCAGCAAATAATAGTTTAAAAATTACAGAAGAGGTTGGCCACACAGATCTAATGGCTGCTTATGTAGACAATTCTAGTCTTACTATTAAGAAACCTAATGAATTATCTAGAGTATTAGGTTTGAAAACCCTTGCTACTCATGGTTTAGCTGCTGTTAATAGTGTCCCTTGGGATACTATAGCTAATTATGCTAAGCCTTTTCTTAACAAAGTTGTTAGTACAACTACTAACATAGTTACACGGTGTTTAAACCGTGTTTGTACTAATTATATGCCTTATTTCTTTACTTTATTGCTACAATTGTGTACTTTTACTAGAAGTACAAATTCTAGAATTAAAGCATCTATGCCGACTACTATAGCAAAGAATACTGTTAAGAGTGTCGGTAAATTTTGTCTAGAGGCTTCATTTAATTATTTGAAGTCACCTAATTTTTCTAAACTGATAAATATTATAATTTGGTTTTTACTATTAAGTGTTTGCCTAGGTTCTTTAATCTACTCAACCGCTGCTTTAGGTGTTTTAATGTCTAATTTAGGCATGCCTTCTTACTGTACTGGTTACAGAGAAGGCTATTTGAACTCTACTAATGTCACTATTGCAACCTACTGTACTGGTTCTATACCTTGTAGTGTTTGTCTTAGTGGTTTAGATTCTTTAGACACCTATCCTTCTTTAGAAACTATACAAATTACCATTTCATCTTTTAAATGGGATTTAACTGCTTTTGGCTTAGTTGCAGAGTGGTTTTTGGCATATATTCTTTTCACTAGGTTTTTCTATGTACTTGGATTGGCTGCAATCATGCAATTGTTTTTCAGCTATTTTGCAGTACATTTTATTAGTAATTCTTGGCTTATGTGGTTAATAATTAATCTTGTACAAATGGCCCCGATTTCAGCTATGGTTAGAATGTACATCTTCTTTGCATCATTTTATTATGTATGGAAAAGTTATGTGCATGTTGTAGACGGTTGTAATTCATCAACTTGTATGATGTGTTACAAACGTAATAGAGCAACAAGAGTCGAATGTACAACTATTGTTAATGGTGTTAGAAGGTCCTTTTATGTCTATGCTAATGGAGGTAAAGGCTTTTGCAAACTACACAATTGGAATTGTGTTAATTGTGATACATTCTGTGCTGGTAGTACATTTATTAGTGATGAAGTTGCGAGAGACTTGTCACTACAGTTTAAAAGACCAATAAATCCTACTGACCAGTCTTCTTACATCGTTGATAGTGTTACAGTGAAGAATGGTTCCATCCATCTTTACTTTGATAAAGCTGGTCAAAAGACTTATGAAAGACATTCTCTCTCTCATTTTGTTAACTTAGACAACCTGAGAGCTAATAACACTAAAGGTTCATTGCCTATTAATGTTATAGTTTTTGATGGTAAATCAAAATGTGAAGAATCATCTGCAAAATCAGCGTCTGTTTACTACAGTCAGCTTATGTGTCAACCTATACTGTTACTAGATCAGGCATTAGTGTCTGATGTTGGTGATAGTGCGGAAGTTGCAGTTAAAATGTTTGATGCTTACGTTAATACGTTTTCATCAACTTTTAACGTACCAATGGAAAAACTCAAAACACTAGTTGCAACTGCAGAAGCTGAACTTGCAAAGAATGTGTCCTTAGACAATGTCTTATCTACTTTTATTTCAGCAGCTCGGCAAGGGTTTGTTGATTCAGATGTAGAAACTAAAGATGTTGTTGAATGTCTTAAATTGTCACATCAATCTGACATAGAAGTTACTGGCGATAGTTGTAATAACTATATGCTCACCTATAACAAAGTTGAAAACATGACACCCCGTGACCTTGGTGCTTGTATTGACTGTAGTGCGCGTCATATTAATGCGCAGGTAGCAAAAAGTCACAACATTGCTTTGATATGGAACGTTAAAGATTTCATGTCATTGTCTGAACAACTACGAAAACAAATACGTAGTGCTGCTAAAAAGAATAACTTACCTTTTAAGTTGACATGTGCAACTACTAGACAAGTTGTTAATGTTGTAACAACAAAGATAGCACTTAAGGGTGGTAAAATTGTTAATAATTGGTTGAAGCAGTTAATTAAAGTTACACTTGTGTTCCTTTTTGTTGCTGCTATTTTCTATTTAATAACACCTGTTCATGTCATGTCTAAACATACTGACTTTTCAAGTGAAATCATAGGATACAAGGCTATTGATGGTGGTGTCACTCGTGACATAGCATCTACAGATACTTGTTTTGCTAACAAACATGCTGATTTTGACACATGGTTTAGCCAGCGTGGTGGTAGTTATACTAATGACAAAGCTTGCCCATTGATTGCTGCAGTCATAACAAGAGAAGTGGGTTTTGTCGTGCCTGGTTTGCCTGGCACGATATTACGCACAACTAATGGTGACTTTTTGCATTTCTTACCTAGAGTTTTTAGTGCAGTTGGTAACATCTGTTACACACCATCAAAACTTATAGAGTACACTGACTTTGCAACATCAGCTTGTGTTTTGGCTGCTGAATGTACAATTTTTAAAGATGCTTCTGGTAAGCCAGTACCATATTGTTATGATACCAATGTACTAGAAGGTTCTGTTGCTTATGAAAGTTTACGCCCTGACACACGTTATGTGCTCATGGATGGCTCTATTATTCAATTTCCTAACACCTACCTTGAAGGTTCTGTTAGAGTGGTAACAACTTTTGATTCTGAGTACTGTAGGCACGGCACTTGTGAAAGATCAGAAGCTGGTGTTTGTGTATCTACTAGTGGTAGATGGGTACTTAACAATGATTATTACAGATCTTTACCAGGAGTTTTCTGTGGTGTAGATGCTGTAAATTTACTTACTAATATGTTTACACCACTAATTCAACCTATTGGTGCTTTGGACATATCAGCATCTATAGTAGCTGGTGGTATTGTAGCTATCGTAGTAACATGCCTTGCCTACTATTTTATGAGGTTTAGAAGAGCTTTTGGTGAATACAGTCATGTAGTTGCCTTTAATACTTTACTATTCCTTATGTCATTCACTGTACTCTGTTTAACACCAGTTTACTCATTCTTACCTGGTGTTTATTCTGTTATTTACTTGTACTTGACATTTTATCTTACTAATGATGTTTCTTTTTTAGCACATATTCAGTGGATGGTTATGTTCACACCTTTAGTACCTTTCTGGATAACAATTGCTTATATCATTTGTATTTCCACAAAGCATTTCTATTGGTTCTTTAGTAATTACCTAAAGAGACGTGTAGTCTTTAATGGTGTTTCCTTTAGTACTTTTGAAGAAGCTGCGCTGTGCACCTTTTTGTTAAATAAAGAAATGTATCTAAAGTTGCGTAGTGATGTGCTATTACCTCTTACGCAATATAATAGATACTTAGCTCTTTATAATAAGTACAAGTATTTTAGTGGAGCAATGGATACAACTAGCTACAGAGAAGCTGCTTGTTGTCATCTCGCAAAGGCTCTCAATGACTTCAGTAACTCAGGTTCTGATGTTCTTTACCAACCACCACAAACCTCTATCACCTCAGCTGTTTTGCAGAGTGGTTTTAGAAAAATGGCATTCCCATCTGGTAAAGTTGAGGGTTGTATGGTACAAGTAACTTGTGGTACAACTACACTTAACGGTCTTTGGCTTGATGACGTAGTTTACTGTCCAAGACATGTGATCTGCACCTCTGAAGACATGCTTAACCCTAATTATGAAGATTTACTCATTCGTAAGTCTAATCATAATTTCTTGGTACAGGCTGGTAATGTTCAACTCAGGGTTATTGGACATTCTATGCAAAATTGTGTACTTAAGCTTAAGGTTGATACAGCCAATCCTAAGACACCTAAGTATAAGTTTGTTCGCATTCAACCAGGACAGACTTTTTCAGTGTTAGCTTGTTACAATGGTTCACCATCTGGTGTTTACCAATGTGCTATGAGGCCCAATTTCACTATTAAGGGTTCATTCCTTAATGGTTCATGTGGTAGTGTTGGTTTTAACATAGATTATGACTGTGTCTCTTTTTGTTACATGCACCATATGGAATTACCAACTGGAGTTCATGCTGGCACAGACTTAGAAGGTAACTTTTATGGACCTTTTGTTGACAGGCAAACAGCACAAGCAGCTGGTACGGACACAACTATTACAGTTAATGTTTTAGCTTGGTTGTACGCTGCTGTTATAAATGGAGACAGGTGGTTTCTCAATCGATTTACCACAACTCTTAATGACTTTAACCTTGTGGCTATGAAGTACAATTATGAACCTCTAACACAAGACCATGTTGACATACTAGGACCTCTTTCTGCTCAAACTGGAATTGCCGTTTTAGATATGTGTGCTTCATTAAAAGAATTACTGCAAAATGGTATGAATGGACGTACCATATTGGGTAGTGCTTTATTAGAAGATGAATTTACACCTTTTGATGTTGTTAGACAATGCTCAGGTGTTACTTTCCAAAGTGCAGTGAAAAGAACAATCAAGGGTACACACCACTGGTTGTTACTCACAATTTTGACTTCACTTTTAGTTTTAGTCCAGAGTACTCAATGGTCTTTGTTCTTTTTTTTGTATGAAAATGCCTTTTTACCTTTTGCTATGGGTATTATTGCTATGTCTGCTTTTGCAATGATGTTTGTCAAACATAAGCATGCATTTCTCTGTTTGTTTTTGTTACCTTCTCTTGCCACTGTAGCTTATTTTAATATGGTCTATATGCCTGCTAGTTGGGTGATGCGTATTATGACATGGTTGGATATGGTTGATACTAGTTTGTCTGGTTTTAAGCTAAAAGACTGTGTTATGTATGCATCAGCTGTAGTGTTACTAATCCTTATGACAGCAAGAACTGTGTATGATGATGGTGCTAGGAGAGTGTGGACACTTATGAATGTCTTGACACTCGTTTATAAAGTTTATTATGGTAATGCTTTAGATCAAGCCATTTCCATGTGGGCTCTTATAATCTCTGTTACTTCTAACTACTCAGGTGTAGTTACAACTGTCATGTTTTTGGCCAGAGGTATTGTTTTTATGTGTGTTGAGTATTGCCCTATTTTCTTCATAACTGGTAATACACTTCAGTGTATAATGCTAGTTTATTGTTTCTTAGGCTATTTTTGTACTTGTTACTTTGGCCTCTTTTGTTTACTCAACCGCTACTTTAGACTGACTCTTGGTGTTTATGATTACTTAGTTTCTACACAGGAGTTTAGATATATGAATTCACAGGGACTACTCCCACCCAAGAATAGCATAGATGCCTTCAAACTCAACATTAAATTGTTGGGTGTTGGTGGCAAACCTTGTATCAAAGTAGCCACTGTACAGTCTAAAATGTCAGATGTAAAGTGCACATCAGTAGTCTTACTCTCAGTTTTGCAACAACTCAGAGTAGAATCATCATCTAAATTGTGGGCTCAATGTGTCCAGTTACACAATGACATTCTCTTAGCTAAAGATACTACTGAAGCCTTTGAAAAAATGGTTTCACTACTTTCTGTTTTGCTTTCCATGCAGGGTGCTGTAGACATAAACAAGCTTTGTGAAGAAATGCTGGACAACAGGGCAACCTTACAAGCTATAGCCTCAGAGTTTAGTTCCCTTCCATCATATGCAGCTTTTGCTACTGCTCAAGAAGCTTATGAGCAGGCTGTTGCTAATGGTGATTCTGAAGTTGTTCTTAAAAAGTTGAAGAAGTCTTTGAATGTGGCTAAATCTGAATTTGACCGTGATGCAGCCATGCAACGTAAGTTGGAAAAGATGGCTGATCAAGCTATGACCCAAATGTATAAACAGGCTAGATCTGAGGACAAGAGGGCAAAAGTTACTAGTGCTATGCAGACAATGCTTTTCACTATGCTTAGAAAGTTGGATAATGATGCACTCAACAACATTATCAACAATGCAAGAGATGGTTGTGTTCCCTTGAACATAATACCTCTTACAACAGCAGCCAAACTAATGGTTGTCATACCAGACTATAACACATATAAAAATACGTGTGATGGTACAACATTTACTTATGCATCAGCATTGTGGGAAATCCAACAGGTTGTAGATGCAGATAGTAAAATTGTTCAACTTAGTGAAATTAGTATGGACAATTCACCTAATTTAGCATGGCCTCTTATTGTAACAGCTTTAAGGGCCAATTCTGCTGTCAAATTACAGAATAATGAGCTTAGTCCTGTTGCACTACGACAGATGTCTTGTGCTGCCGGTACTACACAAACTGCTTGCACTGATGACAATGCGTTAGCTTACTACAACACAACAAAGGGAGGTAGGTTTGTACTTGCACTGTTATCCGATTTACAGGATTTGAAATGGGCTAGATTCCCTAAGAGTGATGGAACTGGTACTATCTATACAGAACTGGAACCACCTTGTAGGTTTGTTACAGACACACCTAAAGGTCCTAAAGTGAAGTATTTATACTTTATTAAAGGATTAAACAACCTAAATAGAGGTATGGTACTTGGTAGTTTAGCTGCCACAGTACGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTGTTAAGATGTTGTGTACACACACTGGTACTGGTCAGGCAATAACAGTTACACCGGAAGCCAATATGGATCAAGAATCCTTTGGTGGTGCATCGTGTTGTCTGTACTGCCGTTGCCACATAGATCATCCAAATCCTAAAGGATTTTGTGACTTAAAAGGTAAGTATGTACAAATACCTACAACTTGTGCTAATGACCCTGTGGGTTTTACACTTAAAAACACAGTCTGTACCGTCTGCGGTATGTGGAAAGGTTATGGCTGTAGTTGTGATCAACTCCGCGAACCCATGCTTCAGTCAGCTGATGCACAATCGTTTTTAAACCGGGTTTGCGGTGTAAGTGCAGCCCGTCTTACACCGTGCGGCACAGGCACTAGTACTGATGTCGTATACAGGGCTTTTGACATCTACAATGATAAAGTAGCTGGTTTTGCTAAATTCCTAAAAACTAATTGTTGTCGCTTCCAAGAAAAGGACGAAGATGACAATTTAATTGATTCTTACTTTGTAGTTAAGAGACACACTTTCTCTAACTACCAACATGAAGAAACAATTTATAATTTACTTAAGGATTGTCCAGCTGTTGCTAAACATGACTTCTTTAAGTTTAGAATAGACGGTGACATGGTACCACATATATCACGTCAACGTCTTACTAAATACACAATGGCAGACCTCGTCTATGCTTTAAGGCATTTTGATGAAGGTAATTGTGACACATTAAAAGAAATACTTGTCACATACAATTGTTGTGATGATGATTATTTCAATAAAAAGGACTGGTATGATTTTGTAGAAAACCCAGATATATTACGCGTATACGCCAACTTAGGTGAACGTGTACGCCAAGCTTTGTTAAAAACAGTACAATTCTGTGATGCCATGCGAAATGCTGGTATTGTTGGTGTACTGACATTAGATAATCAAGATCTCAATGGTAACTGGTATGATTTCGGTGATTTCATACAAACCACGCCAGGTAGTGGAGTTCCTGTTGTAGATTCTTATTATTCATTGTTAATGCCTATATTAACCTTGACCAGGGCTTTAACTGCAGAGTCACATGTTGACACTGACTTAACAAAGCCTTACATTAAGTGGGATTTGTTAAAATATGACTTCACGGAAGAGAGGTTAAAACTCTTTGACCGTTATTTTAAATATTGGGATCAGACATACCACCCAAATTGTGTTAACTGTTTGGATGACAGATGCATTCTGCATTGTGCAAACTTTAATGTTTTATTCTCTACAGTGTTCCCACCTACAAGTTTTGGACCACTAGTGAGAAAAATATTTGTTGATGGTGTTCCATTTGTAGTTTCAACTGGATACCACTTCAGAGAGCTAGGTGTTGTACATAATCAGGATGTAAACTTACATAGCTCTAGACTTAGTTTTAAGGAATTACTTGTGTATGCTGCTGACCCTGCTATGCACGCTGCTTCTGGTAATCTATTACTAGATAAACGCACTACGTGCTTTTCAGTAGCTGCACTTACTAACAATGTTGCTTTTCAAACTGTCAAACCCGGTAATTTTAACAAAGACTTCTATGACTTTGCTGTGTCTAAGGGTTTCTTTAAGGAAGGAAGTTCTGTTGAATTAAAACACTTCTTCTTTGCTCAGGATGGTAATGCTGCTATCAGCGATTATGACTACTATCGTTATAATCTACCAACAATGTGTGATATCAGACAACTACTATTTGTAGTTGAAGTTGTTGATAAGTACTTTGATTGTTACGATGGTGGCTGTATTAATGCTAACCAAGTCATCGTCAACAACCTAGACAAATCAGCTGGTTTTCCATTTAATAAATGGGGTAAGGCTAGACTTTATTATGATTCAATGAGTTATGAGGATCAAGATGCACTTTTCGCATATACAAAACGTAATGTCATCCCTACTATAACTCAAATGAATCTTAAGTATGCCATTAGTGCAAAGAATAGAGCTCGCACCGTAGCTGGTGTCTCTATCTGTAGTACTATGACCAATAGACAGTTTCATCAAAAATTATTGAAATCAATAGCCGCCACTAGAGGAGCTACTGTAGTAATTGGAACAAGCAAATTCTATGGTGGTTGGCACAACATGTTAAAAACTGTTTATAGTGATGTAGAAAACCCTCACCTTATGGGTTGGGATTATCCTAAATGTGATAGAGCCATGCCTAACATGCTTAGAATTATGGCCTCACTTGTTCTTGCTCGCAAACATACAACGTGTTGTAGCTTGTCACACCGTTTCTATAGATTAGCTAATGAGTGTGCTCAAGTATTGAGTGAAATGGTCATGTGTGGCGGTTCACTATATGTTAAACCAGGTGGAACCTCATCAGGAGATGCCACAACTGCTTATGCTAATAGTGTTTTTAACATTTGTCAAGCTGTCACGGCCAATGTTAATGCACTTTTATCTACTGATGGTAACAAAATTGCCGATAAGTATGTCCGCAATTTACAACACAGACTTTATGAGTGTCTCTATAGAAATAGAGATGTTGACACAGACTTTGTGAATGAGTTTTACGCATATTTGCGTAAACATTTCTCAATGATGATACTCTCTGACGATGCTGTTGTGTGTTTCAATAGCACTTATGCATCTCAAGGTCTAGTGGCTAGCATAAAGAACTTTAAGTCAGTTCTTTATTATCAAAACAATGTTTTTATGTCTGAAGCAAAATGTTGGACTGAGACTGACCTTACTAAAGGACCTCATGAATTTTGCTCTCAACATACAATGCTAGTTAAACAGGGTGATGATTATGTGTACCTTCCTTACCCAGATCCATCAAGAATCCTAGGGGCCGGCTGTTTTGTAGATGATATCGTAAAAACAGATGGTACACTTATGATTGAACGGTTCGTGTCTTTAGCTATAGATGCTTACCCACTTACTAAACATCCTAATCAGGAGTATGCTGATGTCTTTCATTTGTACTTACAATACATAAGAAAGCTACATGATGAGTTAACAGGACACATGTTAGACATGTATTCTGTTATGCTTACTAATGATAACACTTCAAGGTATTGGGAACCTGAGTTTTATGAGGCTATGTACACACCGCATACAGTCTTACAGGCTGTTGGGGCTTGTGTTCTTTGCAATTCACAGACTTCATTAAGATGTGGTGCTTGCATACGTAGACCATTCTTATGTTGTAAATGCTGTTACGACCATGTCATATCAACATCACATAAATTAGTCTTGTCTGTTAATCCGTATGTTTGCAATGCTCCAGGTTGTGATGTCACAGATGTGACTCAACTTTACTTAGGAGGTATGAGCTATTATTGTAAATCACATAAACCACCCATTAGTTTTCCATTGTGTGCTAATGGACAAGTTTTTGGTTTATATAAAAATACATGTGTTGGTAGCGATAATGTTACTGACTTTAATGCAATTGCAACATGTGACTGGACAAATGCTGGTGATTACATTTTAGCTAACACCTGTACTGAAAGACTCAAGCTTTTTGCAGCAGAAACGCTCAAAGCTACTGAGGAGACATTTAAACTGTCTTATGGTATTGCTACTGTACGTGAAGTGCTGTCTGACAGAGAATTACATCTTTCATGGGAAGTTGGTAAACCTAGACCACCACTTAACCGAAATTATGTCTTTACTGGTTATCGTGTAACTAAAAACAGTAAAGTACAAATAGGAGAGTACACCTTTGAAAAAGGTGACTATGGTGATGCTGTTGTTTACCGAGGTACAACAACTTACAAATTAAATGTTGGTGATTATTTTGTGCTGACATCACATACAGTAATGCCATTAAGTGCACCTACACTAGTGCCACAAGAGCACTATGTTAGAATTACTGGCTTATACCCAACACTCAATATCTCAGATGAGTTTTCTAGCAATGTTGCAAATTATCAAAAGGTTGGTATGCAAAAGTATTCTACACTCCAGGGACCACCTGGTACTGGTAAGAGTCATTTTGCTATTGGCCTAGCTCTCTACTACCCTTCTGCTCGCATAGTGTATACAGCTTGCTCTCATGCCGCTGTTGATGCACTATGTGAGAAGGCATTAAAATATTTGCCTATAGATAAATGTAGTAGAATTATACCTGCACGTGCTCGTGTAGAGTGTTTTGATAAATTCAAAGTGAATTCAACATTAGAACAGTATGTCTTTTGTACTGTAAATGCATTGCCTGAGACGACAGCAGATATAGTTGTCTTTGATGAAATTTCAATGGCCACAAATTATGATTTGAGTGTTGTCAATGCCAGATTACGTGCTAAGCACTATGTGTACATTGGCGACCCTGCTCAATTACCTGCACCACGCACATTGCTAACTAAGGGCACACTAGAACCAGAATATTTCAATTCAGTGTGTAGACTTATGAAAACTATAGGTCCAGACATGTTCCTCGGAACTTGTCGGCGTTGTCCTGCTGAAATTGTTGACACTGTGAGTGCTTTGGTTTATGATAATAAGCTTAAAGCACATAAAGACAAATCAGCTCAATGCTTTAAAATGTTTTATAAGGGTGTTATCACGCATGATGTTTCATCTGCAATTAACAGGCCACAAATAGGCGTGGTAAGAGAATTCCTTACACGTAACCCTGCTTGGAGAAAAGCTGTCTTTATTTCACCTTATAATTCACAGAATGCTGTAGCCTCAAAGATTTTGGGACTACCAACTCAAACTGTTGATTCATCACAGGGCTCAGAATATGACTATGTCATATTCACTCAAACCACTGAAACAGCTCACTCTTGTAATGTAAACAGATTTAATGTTGCTATTACCAGAGCAAAAGTAGGCATACTTTGCATAATGTCTGATAGAGACCTTTATGACAAGTTGCAATTTACAAGTCTTGAAATTCCACGTAGGAATGTGGCAACTTTACAAGCTGAAAATGTAACAGGACTCTTTAAAGATTGTAGTAAGGTAATCACTGGGTTACATCCTACACAGGCACCTACACACCTCAGTGTTGACACTAAATTCAAAACTGAAGGTTTATGTGTTGACATACCTGGCATACCTAAGGACATGACCTATAGAAGACTCATCTCTATGATGGGTTTTAAAATGAATTATCAAGTTAATGGTTACCCTAACATGTTTATCACCCGCGAAGAAGCTATAAGACATGTACGTGCATGGATTGGCTTCGATGTCGAGGGGTGTCATGCTACTAGAGAAGCTGTTGGTACCAATTTACCTTTACAGCTAGGTTTTTCTACAGGTGTTAACCTAGTTGCTGTACCTACAGGTTATGTTGATACACCTAATAATACAGATTTTTCCAGAGTTAGTGCTAAACCACCGCCTGGAGATCAATTTAAACACCTCATACCACTTATGTACAAAGGACTTCCTTGGAATGTAGTGCGTATAAAGATTGTACAAATGTTAAGTGACACACTTAAAAATCTCTCTGACAGAGTCGTATTTGTCTTATGGGCACATGGCTTTGAGTTGACATCTATGAAGTATTTTGTGAAAATAGGACCTGAGCGCACCTGTTGTCTATGTGATAGACGTGCCACATGCTTTTCCACTGCTTCAGACACTTATGCCTGTTGGCATCATTCTATTGGATTTGATTACGTCTATAATCCGTTTATGATTGATGTTCAACAATGGGGTTTTACAGGTAACCTACAAAGCAACCATGATCTGTATTGTCAAGTCCATGGTAATGCACATGTAGCTAGTTGTGATGCAATCATGACTAGGTGTCTAGCTGTCCACGAGTGCTTTGTTAAGCGTGTTGACTGGACTATTGAATATCCTATAATTGGTGATGAACTGAAGATTAATGCGGCTTGTAGAAAGGTTCAACACATGGTTGTTAAAGCTGCATTATTAGCAGACAAATTCCCAGTTCTTCACGACATTGGTAACCCTAAAGCTATTAAGTGTGTACCTCAAGCTGATGTAGAATGGAAGTTCTATGATGCACAGCCTTGTAGTGACAAAGCTTATAAAATAGAAGAATTATTCTATTCTTATGCCACACATTCTGACAAATTCACAGATGGTGTATGCCTATTTTGGAATTGCAATGTCGATAGATATCCTGCTAATTCCATTGTTTGTAGATTTGACACTAGAGTGCTATCTAACCTTAACTTGCCTGGTTGTGATGGTGGCAGTTTGTATGTAAATAAACATGCATTCCACACACCAGCTTTTGATAAAAGTGCTTTTGTTAATTTAAAACAATTACCATTTTTCTATTACTCTGACAGTCCATGTGAGTCTCATGGAAAACAAGTAGTGTCAGATATAGATTATGTACCACTAAAGTCTGCTACGTGTATAACACGTTGCAATTTAGGTGGTGCTGTCTGTAGACATCATGCTAATGAGTACAGATTGTATCTCGATGCTTATAACATGATGATCTCAGCTGGCTTTAGCTTGTGGGTTTACAAACAATTTGATACTTATAACCTCTGGAACACTTTTACAAGACTTCAGAGTTTAGAAAATGTGGCTTTTAATGTTGTAAATAAGGGACACTTTGATGGACAACAGGGTGAAGTACCAGTTTCTATCATTAATAACACTGTTTACACAAAAGTTGATGGTGTTGATGTAGAATTGTTTGAAAATAAAACAACATTACCTGTTAATGTAGCATTTGAGCTTTGGGCTAAGCGCAACATTAAACCAGTACCAGAGGTGAAAATACTCAATAATTTGGGTGTGGACATTGCTGCTAATACTGTGATCTGGGACTACAAAAGAGATGCTCCAGCACATATATCTACTATTGGTGTTTGTTCTATGACTGACATAGCCAAGAAACCAACTGAAACGATTTGTGCACCACTCACTGTCTTTTTTGATGGTAGAGTTGATGGTCAAGTAGACTTATTTAGAAATGCCCGTAATGGTGTTCTTATTACAGAAGGTAGTGTTAAAGGTTTACAACCATCTGTAGGTCCCAAACAAGCTAGTCTTAATGGAGTCACATTAATTGGAGAAGCCGTAAAAACACAGTTCAATTATTATAAGAAAGTTGATGGTGTTGTCCAACAATTACCTGAAACTTACTTTACTCAGAGTAGAAATTTACAAGAATTTAAACCCAGGAGTCAAATGGAAATTGATTTCTTAGAATTAGCTATGGATGAATTCATTGAACGGTATAAATTAGAAGGCTATGCCTTCGAACATATCGTTTATGGAGATTTTAGTCATAGTCAGTTAGGTGGTTTACATCTACTGATTGGACTAGCTAAACGTTTTAAGGAATCACCTTTTGAATTAGAAGATTTTATTCCTATGGACAGTACAGTTAAAAACTATTTCATAACAGATGCGCAAACAGGTTCATCTAAGTGTGTGTGTTCTGTTATTGATTTATTACTTGATGATTTTGTTGAAATAATAAAATCCCAAGATTTATCTGTAGTTTCTAAGGTTGTCAAAGTGACTATTGACTATACAGAAATTTCATTTATGCTTTGGTGTAAAGATGGCCATGTAGAAACATTTTACCCAAAATTACAATCTAGTCAAGCGTGGCAACCGGGTGTTGCTATGCCTAATCTTTACAAAATGCAAAGAATGCTATTAGAAAAGTGTGACCTTCAAAATTATGGTGATAGTGCAACATTACCTAAAGGCATAATGATGAATGTCGCAAAATATACTCAACTGTGTCAATATTTAAACACATTAACATTAGCTGTACCCTATAATATGAGAGTTATACATTTTGGTGCTGGTTCTGATAAAGGAGTTGCACCAGGTACAGCTGTTTTAAGACAGTGGTTGCCTACGGGTACGCTGCTTGTCGATTCAGATCTTAATGACTTTGTCTCTGATGCAGATTCAACTTTGATTGGTGATTGTGCAACTGTACATACAGCTAATAAATGGGATCTCATTATTAGTGATATGTACGACCCTAAGACTAAAAATGTTACAAAAGAAAATGACTCTAAAGAGGGTTTTTTCACTTACATTTGTGGGTTTATACAACAAAAGCTAGCTCTTGGAGGTTCCGTGGCTATAAAGATAACAGAACATTCTTGGAATGCTGATCTTTATAAGCTCATGGGACACTTCGCATGGTGGACAGCCTTTGTTACTAATGTGAATGCGTCATCATCTGAAGCATTTTTAATTGGATGTAATTATCTTGGCAAACCACGCGAACAAATAGATGGTTATGTCATGCATGCAAATTACATATTTTGGAGGAATACAAATCCAATTCAGTTGTCTTCCTATTCTTTATTTGACATGAGTAAATTTCCCCTTAAATTAAGGGGTACTGCTGTTATGTCTTTAAAAGAAGGTCAAATCAATGATATGATTTTATCTCTTCTTAGTAAAGGTAGACTTATAATTAGAGAAAACAACAGAGTTGTTATTTCTAGTGATGTTCTTGTTAACAACTAAATGGAGAGCCTTGTCCCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTTTACAGGTTCGCGACGTGCTCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGCACGTCAACATCTTAAAGATGGCACTTGTGGCTTAGTAGAAGTTGAAAAAGGCGTTTTGCCTCAACTTGAACAGCCCTATGTGTTCATCAAACGTTCGGATGCTCGAACTGCACCTCATGGTCATGTTATGGTTGAGCTGGTAGCAGAACTCGAAGGCATTCAGTACGGTCGTAGTGGTGAGACACTTGGTGTCCTTGTCCCTCATGTGGGCGAAATACCAGTGGCTTACCGCAAGGTTCTTCTTCGTAAGAACGGTAATAAAGGAGCTGGTGGCCATAGTTACGGCGCCGATCTAAAGTCATTTGACTTAGGCGACGAGCTTGGCACTGATCCTTATGAAGATTTTCAAGAAAACTGGAACACTAAACATAGCAGTGGTGTTACCCGTGAACTCATGCGTGAGCTTAACGGAGGGGCATACACTCGCTATGTCGATAACAACTTCTGTGGCCCTGATGGCTACCCTCTTGAGTGCATTAAAGACCTTCTAGCACGTGCTGGTAAAGCTTCATGCACTTTGTCCGAACAACTGGACTTTATTGACACTAAGAGGGGTGTATACTGCTGCCGTGAACATGAGCATGAAATTGCTTGGTACACGGAACGTTCTGAAAAGAGCTATGAATTGCAGACACCTTTTGAAATTAAATTGGCAAAGAAATTTGACACCTTCAATGGGGAATGTCCAAATTTTGTATTTCCCTTAAATTCCATAATCAAGACTATTCAACCAAGGGTTGAAAAGAAAAAGCTTGATGGCTTTATGGGTAGAATTCGATCTGTCTATCCAGTTGCGTCACCAAATGAATGCAACCAAATGTGCCTTTCAACTCTCATGAAGTGTGATCATTGTGGTGAAACTTCATGGCAGACGGGCGATTTTGTTAAAGCCACTTGCGAATTTTGTGGCACTGAGAATTTGACTAAAGAAGGTGCCACTACTTGTGGTTACTTACCCCAAAATGCTGTTGTTAAAATTTATTGTCCAGCATGTCACAATTCAGAAGTAGGACCTGAGCATAGTCTTGCCGAATACCATAATGAATCTGGCTTGAAAACCATTCTTCGTAAGGGTGGTCGCACTATTGCCTTTGGAGGCTGTGTGTTCTCTTATGTTGGTTGCCATAACAAGTGTGCCTATTGGGTTCCACGTGCTAGCGCTAACATAGGTTGTAACCATACAGGTGTTGTTGGAGAAGGTTCCGAAGGTCTTAATGACAACCTTCTTGAAATACTCCAAAAAGAGAAAGTCAACATCAATATTGTTGGTGACTTTAAACTTAATGAAGAGATCGCCATTATTTTGGCATCTTTTTCTGCTTCCACAAGTGCTTTTGTGGAAACTGTGAAAGGTTTGGATTATAAAGCATTCAAACAAATTGTTGAATCCTGTGGTAATTTTAAAGTTACAAAAGGAAAAGCTAAAAAAGGTGCCTGGAATATTGGTGAACAGAAATCAATACTGAGTCCTCTTTATGCATTTGCATCAGAGGCTGCTCGTGTTGTACGATCAATTTTCTCCCGCACTCTTGAAACTGCTCAAAATTCTGTGCGTGTTTTACAGAAGGCCGCTATAACAATACTAGATGGAATTTCACAGTATTCACTGAGACTCATTGATGCTATGATGTTCACATCTGATTTGGCTACTAACAATCTAGTTGTAATGGCCTACATTACAGGTGGTGTTGTTCAGTTGACTTCGCAGTGGCTAACTAACATCTTTGGCACTGTTTATGAAAAACTCAAACCCGTCCTTGATTGGCTTGAAGAGAAGTTTAAGGAAGGTGTAGAGTTTCTTAGAGACGGTTGGGAAATTGTTAAATTTATCTCAACCTGTGCTTGTGAAATTGTCGGTGGACAAATTGTCACCTGTGCAAAGGAAATTAAGGAGAGTGTTCAGACATTCTTTAAGCTTGTAAATAAATTTTTGGCTTTGTGTGCTGACTCTATCATTATTGGTGGAGCTAAACTTAAAGCCTTGAATTTAGGTGAAACATTTGTCACGCACTCAAAGGGATTGTACAGAAAGTGTGTTAAATCCAGAGAAGAAACTGGCCTACTCATGCCTCTAAAAGCCCCAAAAGAAATTATCTTCTTAGAGGGAGAAACACTTCCCACAGAAGTGTTAACAGAGGAAGTTGTCTTGAAAACTGGTGATTTACAACCATTAGAACAACCTACTAGTGAAGCTGTTGAAGCTCCATTGGTTGGTACACCAGTTTGTATTAACGGGCTTATGTTGCTCGAAATCAAAGACACAGAAAAGTACTGTGCCCTTGCACCTAATATGATGGTAACAAACAATACCTTCACACTCAAAGGCGGTGCACCAACAAAGGTTACTTTTGGTGATGACACTGTGATAGAAGTGCAAGGTTACAAGAGTGTGAATATCACTTTTGAACTTGATGAAAGGATTGATAAAGTACTTAATGAGAAGTGCTCTGCCTATACAGTTGAACTCGGTACAGAAGTAAATGAGTTCGCCTGTGTTGTGGCAGATGCTGTCATAAAAACTTTGCAACCAGTATCTGAATTACTTACACCACTGGGCATTGATTTAGATGAGTGGAGTATGGCTACATACTACTTATTTGATGAGTCTGGTGAGTTTAAATTGGCTTCACATATGTATTGTTCTTTCTACCCTCCAGATGAGGATGAAGAAGAAGGTGATTGTGAAGAAGAAGAGTTTGAGCCATCAACTCAATATGAGTATGGTACTGAAGATGATTACCAAGGTAAACCTTTGGAATTTGGTGCCACTTCTGCTGCTCTTCAACCTGAAGAAGAGCAAGAAGAAGATTGGTTAGATGATGATAGTCAACAAACTGTTGGTCAACAAGACGGCAGTGAGGACAATCAGACAACTACTATTCAAACAATTGTTGAGGTTCAACCTCAATTAGAGATGGAACTTACACCAGTTGTTCAGACTATTGAAGTGAATAGTTTTAGTGGTTATTTAAAACTTACTGACAATGTATACATTAAAAATGCAGACATTGTGGAAGAAGCTAAAAAGGTAAAACCAACAGTGGTTGTTAATGCAGCCAATGTTTACCTTAAACATGGAGGAGGTGTTGCAGGAGCCTTAAATAAGGCTACTAACAATGCCATGCAAGTTGAATCTGATGATTACATAGCTACTAATGGACCACTTAAAGTGGGTGGTAGTTGTGTTTTAAGCGGACACAATCTTGCTAAACACTGTCTTCATGTTGTCGGCCCAAATGTTAACAAAGGTGAAGACATTCAACTTCTTAAGAGTGCTTATGAAAATTTTAATCAGCACGAAGTTCTACTTGCACCATTATTATCAGCTGGTATTTTTGGTGCTGACCCTATACATTCTTTAAGAGTTTGTGTAGATACTGTTCGCACAAATGTCTACTTAGCTGTCTTTGATAAAAATCTCTATGACAAACTTGTTTCAAGCTTTTTGGAAATGAAGAGTGAAAAGCAAGTTGAACAAAAGATCGCTGAGATTCCTAAAGAGGAAGTTAAGCCATTTATAACTGAAAGTAAACCTTCAGTTGAACAGAGAAAACAAGATGATAAGAAAATCAAAGCTTGTGTTGAAGAAGTTACAACAACTCTGGAAGAAACTAAGTTCCTCACAGAAAACTTGTTACTTTATATTGACATTAATGGCAATCTTCATCCAGATTCTGCCACTCTTGTTAGTGACATTGACATCACTTTCTTAAAGAAAGATGCTCCATATATAGTGGGTGATGTTGTTCAAGAGGGTGTTTTAACTGCTGTGGTTATACCTACTAAAAAGGCTGGTGGCACTACTGAAATGCTAGCGAAAGCTTTGAGAAAAGTGCCAACAGACAATTATATAACCACTTACCCGGGTCAGGGTTTAAATGGTTACACTGTAGAGGAGGCAAAGACAGTGCTTAAAAAGTGTAAAAGTGCCTTTTACATTCTACCATCTATTATCTCTAATGAGAAGCAAGAAATTCTTGGAACTGTTTCTTGGAATTTGCGAGAAATGCTTGCACATGCAGAAGAAACACGCAAATTAATGCCTGTCTGTGTGGAAACTAAAGCCATAGTTTCAACTATACAGCGTAAATATAAGGGTATTAAAATACAAGAGGGTGTGGTTGATTATGGTGCTAGATTTTACTTTTACACCAGTAAAACAACTGTAGCGTCACTTATCAACACACTTAACGATCTAAATGAAACTCTTGTTACAATGCCACTTGGCTATGTAACACATGGCTTAAATTTGGAAGAAGCTGCTCGGTATATGAGATCTCTCAAAGTGCCAGCTACAGTTTCTGTTTCTTCACCTGATGCTGTTACAGCGTATAATGGTTATCTTACTTCTTCTTCTAAAACACCTGAAGAACATTTTATTGAAACCATCTCACTTGCTGGTTCCTATAAAGATTGGTCCTATTCTGGACAATCTACACAACTAGGTATAGAATTTCTTAAGAGAGGTGATAAAAGTGTATATTACACTAGTAATCCTACCACATTCCACCTAGATGGTGAAGTTATCACCTTTGACAATCTTAAGACACTTCTTTCTTTGAGAGAAGTGAGGACTATTAAGGTGTTTACAACAGTAGACAACATTAACCTCCACACGCAAGTTGTGGACATGTCAATGACATATGGACAACAGTTTGGTCCAACTTATTTGGATGGAGCTGATGTTACTAAAATAAAACCTCATAATTCACATGAAGGTAAAACATTTTATGTTTTACCTAATGATGACACTCTACGTGTTGAGGCTTTTGAGTACTACCACACAACTGATCCTAGTTTTCTGGGTAGGTACATGTCAGCATTAAATCACACTAAAAAGTGGAAATACCCACAAGTTAATGGTTTAACTTCTATTAAATGGGCAGATAACAACTGTTATCTTGCCACTGCATTGTTAACACTCCAACAAATAGAGTTGAAGTTTAATCCACCTGCTCTACAAGATGCTTATTACAGAGCAAGGGCTGGTGAAGCTGCTAACTTTTGTGCACTTATCTTAGCCTACTGTAATAAGACAGTAGGTGAGTTAGGTGATGTTAGAGAAACAATGAGTTACTTGTTTCAACATGCCAATTTAGATTCTTGCAAAAGAGTCTTGAACGTGGTGTGTAAAACTTGTGGACAACAGCAGACAACCCTTAAGGGTGTAGAAGCTGTTATGTACATGGGCACACTTTCTTATGAACAATTTAAGAAAGGTGTTCAGATACCTTGTACGTGTGGTAAACAAGCTACAAAATATCTAGTACAACAGGAGTCACCTTTTGTTATGATGTCAGCACCACCTGCTCAGTATGAACTTAAGCATGGTACATTTACTTGTGCTAGTGAGTACACTGGTAATTACCAGTGTGGTCACTATAAACATATAACTTCTAAAGAAACTTTGTATTGCATAGACGGTGCTTTACTTACAAAGTCCTCAGAATACAAAGGTCCTATTACGGATGTTTTCTACAAAGAAAACAGTTACACAACAACCATAAAACCAGTTACTTATAAATTGGATGGTGTTGTTTGTACAGAAATTGACCCTAAGTTGGACAATTATTATAAGAAAGACAATTCTTATTTCACAGAGCAACCAATTGATCTTGTACCAAACCAACCATATCCAAACGCAAGCTTCGATAATTTTAAGTTTGTATGTGATAATATCAAATTTGCTGATGATTTAAACCAGTTAACTGGTTATAAGAAACCTGCTTCAAGAGAGCTTAAAGTTACATTTTTCCCTGACTTAAATGGTGATGTGGTGGCTATTGATTATAAACACTACACACCCTCTTTTAAGAAAGGAGCTAAATTGTTACATAAACCTATTGTTTGGCATGTTAACAATGCAACTAATAAAGCCACGTATAAACCAAATACCTGGTGTATACGTTGTCTTTGGAGCACAAAACCAGTTGAAACATCAAATTCGTTTGATGTACTGAAGTCAGAGGACGCGCAGGGAATGGATAATCTTGCCTGCGAAGATCTAAAACCAGTCTCTGAAGAAGTAGTGGAAAATCCTACCATACAGAAAGACGTTCTTGAGTGTAATGTGAAAACTACCGAAGTTGTAGGAGACATTATACTTAAACCAGCAAATAATAGTTTAAAAATTACAGAAGAGGTTGGCCACACAGATCTAATGGCTGCTTATGTAGACAATTCTAGTCTTACTATTAAGAAACCTAATGAATTATCTAGAGTATTAGGTTTGAAAACCCTTGCTACTCATGGTTTAGCTGCTGTTAATAGTGTCCCTTGGGATACTATAGCTAATTATGCTAAGCCTTTTCTTAACAAAGTTGTTAGTACAACTACTAACATAGTTACACGGTGTTTAAACCGTGTTTGTACTAATTATATGCCTTATTTCTTTACTTTATTGCTACAATTGTGTACTTTTACTAGAAGTACAAATTCTAGAATTAAAGCATCTATGCCGACTACTATAGCAAAGAATACTGTTAAGAGTGTCGGTAAATTTTGTCTAGAGGCTTCATTTAATTATTTGAAGTCACCTAATTTTTCTAAACTGATAAATATTATAATTTGGTTTTTACTATTAAGTGTTTGCCTAGGTTCTTTAATCTACTCAACCGCTGCTTTAGGTGTTTTAATGTCTAATTTAGGCATGCCTTCTTACTGTACTGGTTACAGAGAAGGCTATTTGAACTCTACTAATGTCACTATTGCAACCTACTGTACTGGTTCTATACCTTGTAGTGTTTGTCTTAGTGGTTTAGATTCTTTAGACACCTATCCTTCTTTAGAAACTATACAAATTACCATTTCATCTTTTAAATGGGATTTAACTGCTTTTGGCTTAGTTGCAGAGTGGTTTTTGGCATATATTCTTTTCACTAGGTTTTTCTATGTACTTGGATTGGCTGCAATCATGCAATTGTTTTTCAGCTATTTTGCAGTACATTTTATTAGTAATTCTTGGCTTATGTGGTTAATAATTAATCTTGTACAAATGGCCCCGATTTCAGCTATGGTTAGAATGTACATCTTCTTTGCATCATTTTATTATGTATGGAAAAGTTATGTGCATGTTGTAGACGGTTGTAATTCATCAACTTGTATGATGTGTTACAAACGTAATAGAGCAACAAGAGTCGAATGTACAACTATTGTTAATGGTGTTAGAAGGTCCTTTTATGTCTATGCTAATGGAGGTAAAGGCTTTTGCAAACTACACAATTGGAATTGTGTTAATTGTGATACATTCTGTGCTGGTAGTACATTTATTAGTGATGAAGTTGCGAGAGACTTGTCACTACAGTTTAAAAGACCAATAAATCCTACTGACCAGTCTTCTTACATCGTTGATAGTGTTACAGTGAAGAATGGTTCCATCCATCTTTACTTTGATAAAGCTGGTCAAAAGACTTATGAAAGACATTCTCTCTCTCATTTTGTTAACTTAGACAACCTGAGAGCTAATAACACTAAAGGTTCATTGCCTATTAATGTTATAGTTTTTGATGGTAAATCAAAATGTGAAGAATCATCTGCAAAATCAGCGTCTGTTTACTACAGTCAGCTTATGTGTCAACCTATACTGTTACTAGATCAGGCATTAGTGTCTGATGTTGGTGATAGTGCGGAAGTTGCAGTTAAAATGTTTGATGCTTACGTTAATACGTTTTCATCAACTTTTAACGTACCAATGGAAAAACTCAAAACACTAGTTGCAACTGCAGAAGCTGAACTTGCAAAGAATGTGTCCTTAGACAATGTCTTATCTACTTTTATTTCAGCAGCTCGGCAAGGGTTTGTTGATTCAGATGTAGAAACTAAAGATGTTGTTGAATGTCTTAAATTGTCACATCAATCTGACATAGAAGTTACTGGCGATAGTTGTAATAACTATATGCTCACCTATAACAAAGTTGAAAACATGACACCCCGTGACCTTGGTGCTTGTATTGACTGTAGTGCGCGTCATATTAATGCGCAGGTAGCAAAAAGTCACAACATTGCTTTGATATGGAACGTTAAAGATTTCATGTCATTGTCTGAACAACTACGAAAACAAATACGTAGTGCTGCTAAAAAGAATAACTTACCTTTTAAGTTGACATGTGCAACTACTAGACAAGTTGTTAATGTTGTAACAACAAAGATAGCACTTAAGGGTGGTAAAATTGTTAATAATTGGTTGAAGCAGTTAATTAAAGTTACACTTGTGTTCCTTTTTGTTGCTGCTATTTTCTATTTAATAACACCTGTTCATGTCATGTCTAAACATACTGACTTTTCAAGTGAAATCATAGGATACAAGGCTATTGATGGTGGTGTCACTCGTGACATAGCATCTACAGATACTTGTTTTGCTAACAAACATGCTGATTTTGACACATGGTTTAGCCAGCGTGGTGGTAGTTATACTAATGACAAAGCTTGCCCATTGATTGCTGCAGTCATAACAAGAGAAGTGGGTTTTGTCGTGCCTGGTTTGCCTGGCACGATATTACGCACAACTAATGGTGACTTTTTGCATTTCTTACCTAGAGTTTTTAGTGCAGTTGGTAACATCTGTTACACACCATCAAAACTTATAGAGTACACTGACTTTGCAACATCAGCTTGTGTTTTGGCTGCTGAATGTACAATTTTTAAAGATGCTTCTGGTAAGCCAGTACCATATTGTTATGATACCAATGTACTAGAAGGTTCTGTTGCTTATGAAAGTTTACGCCCTGACACACGTTATGTGCTCATGGATGGCTCTATTATTCAATTTCCTAACACCTACCTTGAAGGTTCTGTTAGAGTGGTAACAACTTTTGATTCTGAGTACTGTAGGCACGGCACTTGTGAAAGATCAGAAGCTGGTGTTTGTGTATCTACTAGTGGTAGATGGGTACTTAACAATGATTATTACAGATCTTTACCAGGAGTTTTCTGTGGTGTAGATGCTGTAAATTTACTTACTAATATGTTTACACCACTAATTCAACCTATTGGTGCTTTGGACATATCAGCATCTATAGTAGCTGGTGGTATTGTAGCTATCGTAGTAACATGCCTTGCCTACTATTTTATGAGGTTTAGAAGAGCTTTTGGTGAATACAGTCATGTAGTTGCCTTTAATACTTTACTATTCCTTATGTCATTCACTGTACTCTGTTTAACACCAGTTTACTCATTCTTACCTGGTGTTTATTCTGTTATTTACTTGTACTTGACATTTTATCTTACTAATGATGTTTCTTTTTTAGCACATATTCAGTGGATGGTTATGTTCACACCTTTAGTACCTTTCTGGATAACAATTGCTTATATCATTTGTATTTCCACAAAGCATTTCTATTGGTTCTTTAGTAATTACCTAAAGAGACGTGTAGTCTTTAATGGTGTTTCCTTTAGTACTTTTGAAGAAGCTGCGCTGTGCACCTTTTTGTTAAATAAAGAAATGTATCTAAAGTTGCGTAGTGATGTGCTATTACCTCTTACGCAATATAATAGATACTTAGCTCTTTATAATAAGTACAAGTATTTTAGTGGAGCAATGGATACAACTAGCTACAGAGAAGCTGCTTGTTGTCATCTCGCAAAGGCTCTCAATGACTTCAGTAACTCAGGTTCTGATGTTCTTTACCAACCACCACAAACCTCTATCACCTCAGCTGTTTTGCAGAGTGGTTTTAGAAAAATGGCATTCCCATCTGGTAAAGTTGAGGGTTGTATGGTACAAGTAACTTGTGGTACAACTACACTTAACGGTCTTTGGCTTGATGACGTAGTTTACTGTCCAAGACATGTGATCTGCACCTCTGAAGACATGCTTAACCCTAATTATGAAGATTTACTCATTCGTAAGTCTAATCATAATTTCTTGGTACAGGCTGGTAATGTTCAACTCAGGGTTATTGGACATTCTATGCAAAATTGTGTACTTAAGCTTAAGGTTGATACAGCCAATCCTAAGACACCTAAGTATAAGTTTGTTCGCATTCAACCAGGACAGACTTTTTCAGTGTTAGCTTGTTACAATGGTTCACCATCTGGTGTTTACCAATGTGCTATGAGGCCCAATTTCACTATTAAGGGTTCATTCCTTAATGGTTCATGTGGTAGTGTTGGTTTTAACATAGATTATGACTGTGTCTCTTTTTGTTACATGCACCATATGGAATTACCAACTGGAGTTCATGCTGGCACAGACTTAGAAGGTAACTTTTATGGACCTTTTGTTGACAGGCAAACAGCACAAGCAGCTGGTACGGACACAACTATTACAGTTAATGTTTTAGCTTGGTTGTACGCTGCTGTTATAAATGGAGACAGGTGGTTTCTCAATCGATTTACCACAACTCTTAATGACTTTAACCTTGTGGCTATGAAGTACAATTATGAACCTCTAACACAAGACCATGTTGACATACTAGGACCTCTTTCTGCTCAAACTGGAATTGCCGTTTTAGATATGTGTGCTTCATTAAAAGAATTACTGCAAAATGGTATGAATGGACGTACCATATTGGGTAGTGCTTTATTAGAAGATGAATTTACACCTTTTGATGTTGTTAGACAATGCTCAGGTGTTACTTTCCAAAGTGCAGTGAAAAGAACAATCAAGGGTACACACCACTGGTTGTTACTCACAATTTTGACTTCACTTTTAGTTTTAGTCCAGAGTACTCAATGGTCTTTGTTCTTTTTTTTGTATGAAAATGCCTTTTTACCTTTTGCTATGGGTATTATTGCTATGTCTGCTTTTGCAATGATGTTTGTCAAACATAAGCATGCATTTCTCTGTTTGTTTTTGTTACCTTCTCTTGCCACTGTAGCTTATTTTAATATGGTCTATATGCCTGCTAGTTGGGTGATGCGTATTATGACATGGTTGGATATGGTTGATACTAGTTTGTCTGGTTTTAAGCTAAAAGACTGTGTTATGTATGCATCAGCTGTAGTGTTACTAATCCTTATGACAGCAAGAACTGTGTATGATGATGGTGCTAGGAGAGTGTGGACACTTATGAATGTCTTGACACTCGTTTATAAAGTTTATTATGGTAATGCTTTAGATCAAGCCATTTCCATGTGGGCTCTTATAATCTCTGTTACTTCTAACTACTCAGGTGTAGTTACAACTGTCATGTTTTTGGCCAGAGGTATTGTTTTTATGTGTGTTGAGTATTGCCCTATTTTCTTCATAACTGGTAATACACTTCAGTGTATAATGCTAGTTTATTGTTTCTTAGGCTATTTTTGTACTTGTTACTTTGGCCTCTTTTGTTTACTCAACCGCTACTTTAGACTGACTCTTGGTGTTTATGATTACTTAGTTTCTACACAGGAGTTTAGATATATGAATTCACAGGGACTACTCCCACCCAAGAATAGCATAGATGCCTTCAAACTCAACATTAAATTGTTGGGTGTTGGTGGCAAACCTTGTATCAAAGTAGCCACTGTACAGTCTAAAATGTCAGATGTAAAGTGCACATCAGTAGTCTTACTCTCAGTTTTGCAACAACTCAGAGTAGAATCATCATCTAAATTGTGGGCTCAATGTGTCCAGTTACACAATGACATTCTCTTAGCTAAAGATACTACTGAAGCCTTTGAAAAAATGGTTTCACTACTTTCTGTTTTGCTTTCCATGCAGGGTGCTGTAGACATAAACAAGCTTTGTGAAGAAATGCTGGACAACAGGGCAACCTTACAAGCTATAGCCTCAGAGTTTAGTTCCCTTCCATCATATGCAGCTTTTGCTACTGCTCAAGAAGCTTATGAGCAGGCTGTTGCTAATGGTGATTCTGAAGTTGTTCTTAAAAAGTTGAAGAAGTCTTTGAATGTGGCTAAATCTGAATTTGACCGTGATGCAGCCATGCAACGTAAGTTGGAAAAGATGGCTGATCAAGCTATGACCCAAATGTATAAACAGGCTAGATCTGAGGACAAGAGGGCAAAAGTTACTAGTGCTATGCAGACAATGCTTTTCACTATGCTTAGAAAGTTGGATAATGATGCACTCAACAACATTATCAACAATGCAAGAGATGGTTGTGTTCCCTTGAACATAATACCTCTTACAACAGCAGCCAAACTAATGGTTGTCATACCAGACTATAACACATATAAAAATACGTGTGATGGTACAACATTTACTTATGCATCAGCATTGTGGGAAATCCAACAGGTTGTAGATGCAGATAGTAAAATTGTTCAACTTAGTGAAATTAGTATGGACAATTCACCTAATTTAGCATGGCCTCTTATTGTAACAGCTTTAAGGGCCAATTCTGCTGTCAAATTACAGAATAATGAGCTTAGTCCTGTTGCACTACGACAGATGTCTTGTGCTGCCGGTACTACACAAACTGCTTGCACTGATGACAATGCGTTAGCTTACTACAACACAACAAAGGGAGGTAGGTTTGTACTTGCACTGTTATCCGATTTACAGGATTTGAAATGGGCTAGATTCCCTAAGAGTGATGGAACTGGTACTATCTATACAGAACTGGAACCACCTTGTAGGTTTGTTACAGACACACCTAAAGGTCCTAAAGTGAAGTATTTATACTTTATTAAAGGATTAAACAACCTAAATAGAGGTATGGTACTTGGTAGTTTAGCTGCCACAGTACGTCTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAATTCAACTGTATTATCTTTCTGTGCTTTTGCTGTAGATGCTGCTAAAGCTTACAAAGATTATCTAGCTAGTGGGGGACAACCAATCACTAATTGTGTTAAGATGTTGTGTACACACACTGGTACTGGTCAGGCAATAACAGTTACACCGGAAGCCAATATGGATCAAGAATCCTTTGGTGGTGCATCGTGTTGTCTGTACTGCCGTTGCCACATAGATCATCCAAATCCTAAAGGATTTTGTGACTTAAAAGGTAAGTATGTACAAATACCTACAACTTGTGCTAATGACCCTGTGGGTTTTACACTTAAAAACACAGTCTGTACCGTCTGCGGTATGTGGAAAGGTTATGGCTGTAGTTGTGATCAACTCCGCGAACCCATGCTTCAGTCAGCTGATGCACAATCGTTTTTAAACGGGTTTGCGGTGTAAATGTTTGTTTTTCTTGTTTTATTGCCACTAGTCTCTAGTCAGTGTGTTAATCTTACAACCAGAACTCAATTACCCCCTGCATACACTAATTCTTTCACACGTGGTGTTTATTACCCTGACAAAGTTTTCAGATCCTCAGTTTTACATTCAACTCAGGACTTGTTCTTACCTTTCTTTTCCAATGTTACTTGGTTCCATGCTATACATGTCTCTGGGACCAATGGTACTAAGAGGTTTGATAACCCTGTCCTACCATTTAATGATGGTGTTTATTTTGCTTCCACTGAGAAGTCTAACATAATAAGAGGCTGGATTTTTGGTACTACTTTAGATTCGAAGACCCAGTCCCTACTTATTGTTAATAACGCTACTAATGTTGTTATTAAAGTCTGTGAATTTCAATTTTGTAATGATCCATTTTTGGGTGTTTATTACCACAAAAACAACAAAAGTTGGATGGAAAGTGAGTTCAGAGTTTATTCTAGTGCGAATAATTGCACTTTTGAATATGTCTCTCAGCCTTTTCTTATGGACCTTGAAGGAAAACAGGGTAATTTCAAAAATCTTAGGGAATTTGTGTTTAAGAATATTGATGGTTATTTTAAAATATATTCTAAGCACACGCCTATTAATTTAGTGCGTGATCTCCCTCAGGGTTTTTCGGCTTTAGAACCATTGGTAGATTTGCCAATAGGTATTAACATCACTAGGTTTCAAACTTTACTTGCTTTACATAGAAGTTATTTGACTCCTGGTGATTCTTCTTCAGGTTGGACAGCTGGTGCTGCAGCTTATTATGTGGGTTATCTTCAACCTAGGACTTTTCTATTAAAATATAATGAAAATGGAACCATTACAGATGCTGTAGACTGTGCACTTGACCCTCTCTCAGAAACAAAGTGTACGTTGAAATCCTTCACTGTAGAAAAAGGAATCTATCAAACTTCTAACTTTAGAGTCCAACCAACAGAATCTATTGTTAGATTTCCTAATATTACAAACTTGTGCCCTTTTGGTGAAGTTTTTAACGCCACCAGATTTGCATCTGTTTATGCTTGGAACAGGAAGAGAATCAGCAACTGTGTTGCTGATTATTCTGTCCTATATAATTCCGCATCATTTTCCACTTTTAAGTGTTATGGAGTGTCTCCTACTAAATTAAATGATCTCTGCTTTACTAATGTCTATGCAGATTCATTTGTAATTAGAGGTGATGAAGTCAGACAAATCGCTCCAGGGCAAACTGGAAAGATTGCTGATTATAATTATAAATTACCAGATGATTTTACAGGCTGCGTTATAGCTTGGAATTCTAACAATCTTGATTCTAAGGTTGGTGGTAATTATAATTACCTGTATAGATTGTTTAGGAAGTCTAATCTCAAACCTTTTGAGAGAGATATTTCAACTGAAATCTATCAGGCCGGTAGCACACCTTGTAATGGTGTTGAAGGTTTTAATTGTTACTTTCCTTTACAATCATATGGTTTCCAACCCACTAATGGTGTTGGTTACCAACCATACAGAGTAGTAGTACTTTCTTTTGAACTTCTACATGCACCAGCAACTGTTTGTGGACCTAAAAAGTCTACTAATTTGGTTAAAAACAAATGTGTCAATTTCAACTTCAATGGTTTAACAGGCACAGGTGTTCTTACTGAGTCTAACAAAAAGTTTCTGCCTTTCCAACAATTTGGCAGAGACATTGCTGACACTACTGATGCTGTCCGTGATCCACAGACACTTGAGATTCTTGACATTACACCATGTTCTTTTGGTGGTGTCAGTGTTATAACACCAGGAACAAATACTTCTAACCAGGTTGCTGTTCTTTATCAGGATGTTAACTGCACAGAAGTCCCTGTTGCTATTCATGCAGATCAACTTACTCCTACTTGGCGTGTTTATTCTACAGGTTCTAATGTTTTTCAAACACGTGCAGGCTGTTTAATAGGGGCTGAACATGTCAACAACTCATATGAGTGTGACATACCCATTGGTGCAGGTATATGCGCTAGTTATCAGACTCAGACTAATTCTCCTCGGCGGGCACGTAGTGTAGCTAGTCAATCCATCATTGCCTACACTATGTCACTTGGTGCAGAAAATTCAGTTGCTTACTCTAATAACTCTATTGCCATACCCACAAATTTTACTATTAGTGTTACCACAGAAATTCTACCAGTGTCTATGACCAAGACATCAGTAGATTGTACAATGTACATTTGTGGTGATTCAACTGAATGCAGCAATCTTTTGTTGCAATATGGCAGTTTTTGTACACAATTAAACCGTGCTTTAACTGGAATAGCTGTTGAACAAGACAAAAACACCCAAGAAGTTTTTGCACAAGTCAAACAAATTTACAAAACACCACCAATTAAAGATTTTGGTGGTTTTAATTTTTCACAAATATTACCAGATCCATCAAAACCAAGCAAGAGGTCATTTATTGAAGATCTACTTTTCAACAAAGTGACACTTGCAGATGCTGGCTTCATCAAACAATATGGTGATTGCCTTGGTGATATTGCTGCTAGAGACCTCATTTGTGCACAAAAGTTTAACGGCCTTACTGTTTTGCCACCTTTGCTCACAGATGAAATGATTGCTCAATACACTTCTGCACTGTTAGCGGGTACAATCACTTCTGGTTGGACCTTTGGTGCAGGTGCTGCATTACAAATACCATTTGCTATGCAAATGGCTTATAGGTTTAATGGTATTGGAGTTACACAGAATGTTCTCTATGAGAACCAAAAATTGATTGCCAACCAATTTAATAGTGCTATTGGCAAAATTCAAGACTCACTTTCTTCCACAGCAAGTGCACTTGGAAAACTTCAAGATGTGGTCAACCAAAATGCACAAGCTTTAAACACGCTTGTTAAACAACTTAGCTCCAATTTTGGTGCAATTTCAAGTGTTTTAAATGATATCCTTTCACGTCTTGACAAAGTTGAGGCTGAAGTGCAAATTGATAGGTTGATCACAGGCAGACTTCAAAGTTTGCAGACATATGTGACTCAACAATTAATTAGAGCTGCAGAAATCAGAGCTTCTGCTAATCTTGCTGCTACTAAAATGTCAGAGTGTGTACTTGGACAATCAAAAAGAGTTGATTTTTGTGGAAAGGGCTATCATCTTATGTCCTTCCCTCAGTCAGCACCTCATGGTGTAGTCTTCTTGCATGTGACTTATGTCCCTGCACAAGAAAAGAACTTCACAACTGCTCCTGCCATTTGTCATGATGGAAAAGCACACTTTCCTCGTGAAGGTGTCTTTGTTTCAAATGGCACACACTGGTTTGTAACACAAAGGAATTTTTATGAACCACAAATCATTACTACAGACAACACATTTGTGTCTGGTAACTGTGATGTTGTAATAGGAATTGTCAACAACACAGTTTATGATCCTTTGCAACCTGAATTAGACTCATTCAAGGAGGAGTTAGATAAATATTTTAAGAATCATACATCACCAGATGTTGATTTAGGTGACATCTCTGGCATTAATGCTTCAGTTGTAAACATTCAAAAAGAAATTGACCGCCTCAATGAGGTTGCCAAGAATTTAAATGAATCTCTCATCGATCTCCAAGAACTTGGAAAGTATGAGCAGTATATAAAATGGCCATGGTACATTTGGCTAGGTTTTATAGCTGGCTTGATTGCCATAGTAATGGTGACAATTATGCTTTGCTGTATGACCAGTTGCTGTAGTTGTCTCAAGGGCTGTTGTTCTTGTGGATCCTGCTGCAAATTTGATGAAGACGACTCTGAGCCAGTGCTCAAAGGAGTCAAATTACATTACACATAAATGGATTTGTTTATGAGAATCTTCACAATTGGAACTGTAACTTTGAAGCAAGGTGAAATCAAGGATGCTACTCCTTCAGATTTTGTTCGCGCTACTGCAACGATACCGATACAAGCCTCACTCCCTTTCGGATGGCTTATTGTTGGCGTTGCACTTCTTGCTGTTTTTCAGAGCGCTTCCAAAATCATAACCCTCAAAAAGAGATGGCAACTAGCACTCTCCAAGGGTGTTCACTTTGTTTGCAACTTGCTGTTGTTGTTTGTAACAGTTTACTCACACCTTTTGCTCGTTGCTGCTGGCCTTGAAGCCCCTTTTCTCTATCTTTATGCTTTAGTCTACTTCTTGCAGAGTATAAACTTTGTAAGAATAATAATGAGGCTTTGGCTTTGCTGGAAATGCCGTTCCAAAAACCCATTACTTTATGATGCCAACTATTTTCTTTGCTGGCATACTAATTGTTACGACTATTGTATACCTTACAATAGTGTAACTTCTTCAATTGTCATTACTTCAGGTGATGGCACAACAAGTCCTATTTCTGAACATGACTACCAGATTGGTGGTTATACTGAAAAATGGGAATCTGGAGTAAAAGACTGTGTTGTATTACACAGTTACTTCACTTCAGACTATTACCAGCTGTACTCAACTCAATTGAGTACAGACACTGGTGTTGAACATGTTACCTTCTTCATCTACAATAAAATTGTTGATGAGCCTGAAGAACATGTCCAAATTCACACAATCGACGGTTCATCCGGAGTTGTTAATCCAGTAATGGAACCAATTTATGATGAACCGACGACGACTACTAGCGTGCCTTTGTAAATGTACTCATTCGTTTCGGAAGAGACAGGTACGTTAATAGTTAATAGCGTACTTCTTTTTCTTGCTTTCGTGGTATTCTTGCTAGTTACACTAGCCATCCTTACTGCGCTTCGATTGTGTGCGTACTGCTGCAATATTGTTAACGTGAGTCTTGTAAAACCTTCTTTTTACGTTTACTCTCGTGTTAAAAATCTGAATTCTTCTAGAGTTCCTGATCTTCTGGTCTAAATGGCAGATTCCAACGGTACTATTACCGTTGAAGAGCTTAAAAAGCTCCTTGAACAATGGAACCTAGTAATAGGTTTCCTATTCCTTACATGGATTTGTCTTCTACAATTTGCCTATGCCAACAGGAATAGGTTTTTGTATATAATTAAGTTAATTTTCCTCTGGCTGTTATGGCCAGTAACTTTAGCTTGTTTTGTGCTTGCTGCTGTTTACAGAATAAATTGGATCACCGGTGGAATTGCTATCGCAATGGCTTGTCTTGTAGGCTTGATGTGGCTCAGCTACTTCATTGCTTCTTTCAGACTGTTTGCGCGTACGCGTTCCATGTGGTCATTCAATCCAGAAACTAACATTCTTCTCAACGTGCCACTCCATGGCACTATTCTGACCAGACCGCTTCTAGAAAGTGAACTCGTAATCGGAGCTGTGATCCTTCGTGGACATCTTCGTATTGCTGGACACCATCTAGGACGCTGTGACATCAAGGACCTGCCTAAAGAAATCACTGTTGCTACATCACGAACGCTTTCTTATTACAAATTGGGAGCTTCGCAGCGTGTAGCAGGTGACTCAGGTTTTGCTGCATACAGTCGCTACAGGATTGGCAACTATAAATTAAACACAGACCATTCCAGTAGCAGTGACAATATTGCTTTGCTTGTACAGTAAATGTTTCATCTCGTTGACTTTCAGGTTACTATAGCAGAGATATTACTAATTATTATGAGGACTTTTAAAGTTTCCATTTGGAATCTTGATTACATCATAAACCTCATAATTAAAAATTTATCTAAGTCACTAACTGAGAATAAATATTCTCAATTAGATGAAGAGCAACCAATGGAGATTGATTAAATGAAAATTATTCTTTTCTTGGCACTGATAACACTCGCTACTTGTGAGCTTTATCACTACCAAGAGTGTGTTAGAGGTACAACAGTACTTTTAAAAGAACCTTGCTCTTCTGGAACATACGAGGGCAATTCACCATTTCATCCTCTAGCTGATAACAAATTTGCACTGACTTGCTTTAGCACTCAATTTGCTTTTGCTTGTCCTGACGGCGTAAAACACGTCTATCAGTTACGTGCCAGATCAGTTTCACCTAAACTGTTCATCAGACAAGAGGAAGTTCAAGAACTTTACTCTCCAATTTTTCTTATTGTTGCGGCAATAGTGTTTATAACACTTTGCTTCACACTCAAAAGAAAGACAGAATGAATGATTGAACTTTCATTAATTGACTTCTATTTGTGCTTTTTAGCCTTTCTGCTATTCCTTGTTTTAATTATGCTTATTATCTTTTGGTTCTCACTTGAACTGCAAGATCATAATGAAACTTGTCACGCCTAAATGAAATTTCTTGTTTTCTTAGGAATCATCACAACTGTAGCTGCATTTCACCAAGAATGTAGTTTACAGTCATGTACTCAACATCAACCATATGTAGTTGATGACCCGTGTCCTATTCACTTCTATTCTAAATGGTATATTAGAGTAGGAGCTAGAAAATCAGCACCTTTAATTGAATTGTGCGTGGATGAGGCTGGTTCTAAATCACCCATTCAGTACATCGATATCGGTAATTATACAGTTTCCTGTTTACCTTTTACAATTAATTGCCAGGAACCTAAATTGGGTAGTCTTGTAGTGCGTTGTTCGTTCTATGAAGACTTTTTAGAGTATCATGACGTTCGTGTTGTTTTAGATTTCATCTAAATGTCTGATAATGGACCCCAAAATCAGCGAAATGCACCCCGCATTACGTTTGGTGGACCCTCAGATTCAACTGGCAGTAACCAGAATGGAGAACGCAGTGGGGCGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGCGTCTTGGTTCACCGCTCTCACTCAACATGGCAAGGAAGACCTTAAATTCCCTCGAGGACAAGGCGTTCCAATTAACACCAATAGCAGTCCAGATGACCAAATTGGCTACTACCGAAGAGCTACCAGACGAATTCGTGGTGGTGACGGTAAAATGAAAGATCTCAGTCCAAGATGGTATTTCTACTACCTAGGAACTGGGCCAGAAGCTGGACTTCCCTATGGTGCTAACAAAGACGGCATCATATGGGTTGCAACTGAGGGAGCCTTGAATACACCAAAAGATCACATTGGCACCCGCAATCCTGCTAACAATGCTGCAATCGTGCTACAACTTCCTCAAGGAACAACATTGCCAAAAGGCTTCTACGCAGAAGGGAGCAGAGGCGGCAGTCAAGCCTCTTCTCGTTCCTCATCACGTAGTCGCAACAGTTCAAGAAATTCAACTCCAGGCAGCAGTAGGGGAACTTCTCCTGCTAGAATGGCTGGCAATGGCGGTGATGCTGCTCTTGCTTTGCTGCTGCTTGACAGATTGAACCAGCTTGAGAGCAAAATGTCTGGTAAAGGCCAACAACAACAAGGCCAAACTGTCACTAAGAAATCTGCTGCTGAGGCTTCTAAGAAGCCTCGGCAAAAACGTACTGCCACTAAAGCATACAATGTAACACAAGCTTTCGGCAGACGTGGTCCAGAACAAACCCAAGGAAATTTTGGGGACCAGGAACTAATCAGACAAGGAACTGATTACAAACATTGGCCGCAAATTGCACAATTTGCCCCCAGCGCTTCAGCGTTCTTCGGAATGTCGCGCATTGGCATGGAAGTCACACCTTCGGGAACGTGGTTGACCTACACAGGTGCCATCAAATTGGATGACAAAGATCCAAATTTCAAAGATCAAGTCATTTTGCTGAATAAGCATATTGACGCATACAAAACATTCCCACCAACAGAGCCTAAAAAGGACAAAAAGAAGAAGGCTGATGAAACTCAAGCCTTACCGCAGAGACAGAAGAAACAGCAAACTGTGACTCTTCTTCCTGCTGCAGATTTGGATGATTTCTCCAAACAATTGCAACAATCCATGAGCAGTGCTGACTCAACTCAGGCCTAAATGGGCTATATAAACGTTTTCGCTTTTCCGTTTACGATATATAGTCTACTCTTGTGCAGAATGAATTCTCGTAACTACATAGCACAAGTAGATGTAGTTAACTTTAATCTCACATAG\n",
      "42483 nucliotides\n"
     ]
    }
   ],
   "source": [
    "for sequence in SeqIO.parse('./input/MN908947_test.fna', \"fasta\"):\n",
    "    print(sequence.id)\n",
    "    print(sequence.seq)\n",
    "    print(len(sequence),'nucliotides')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72a35e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "DNAsequence = SeqIO.read('./input/MN908947_test.fna', \"fasta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "56f92f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUGGAGAGCCUUGUCCCUGGUUUCAACGAGAAAACACACGUCCAACUCAGUUUGCCUGUUUUACAGGUUCGCGACGUGCUCGUACGUGGCUUUGGAGACUCCGUGGAGGAGGUCUUAUCAGAGGCACGUCAACAUCUUAAAGAUGGCACUUGUGGCUUAGUAGAAGUUGAAAAAGGCGUUUUGCCUCAACUUGAACAGCCCUAUGUGUUCAUCAAACGUUCGGAUGCUCGAACUGCACCUCAUGGUCAUGUUAUGGUUGAGCUGGUAGCAGAACUCGAAGGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGUCCUUGUCCCUCAUGUGGGCGAAAUACCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAUAGUUACGGCGCCGAUCUAAAGUCAUUUGACUUAGGCGACGAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGGAACACUAAACAUAGCAGUGGUGUUACCCGUGAACUCAUGCGUGAGCUUAACGGAGGGGCAUACACUCGCUAUGUCGAUAACAACUUCUGUGGCCCUGAUGGCUACCCUCUUGAGUGCAUUAAAGACCUUCUAGCACGUGCUGGUAAAGCUUCAUGCACUUUGUCCGAACAACUGGACUUUAUUGACACUAAGAGGGGUGUAUACUGCUGCCGUGAACAUGAGCAUGAAAUUGCUUGGUACACGGAACGUUCUGAAAAGAGCUAUGAAUUGCAGACACCUUUUGAAAUUAAAUUGGCAAAGAAAUUUGACACCUUCAAUGGGGAAUGUCCAAAUUUUGUAUUUCCCUUAAAUUCCAUAAUCAAGACUAUUCAACCAAGGGUUGAAAAGAAAAAGCUUGAUGGCUUUAUGGGUAGAAUUCGAUCUGUCUAUCCAGUUGCGUCACCAAAUGAAUGCAACCAAAUGUGCCUUUCAACUCUCAUGAAGUGUGAUCAUUGUGGUGAAACUUCAUGGCAGACGGGCGAUUUUGUUAAAGCCACUUGCGAAUUUUGUGGCACUGAGAAUUUGACUAAAGAAGGUGCCACUACUUGUGGUUACUUACCCCAAAAUGCUGUUGUUAAAAUUUAUUGUCCAGCAUGUCACAAUUCAGAAGUAGGACCUGAGCAUAGUCUUGCCGAAUACCAUAAUGAAUCUGGCUUGAAAACCAUUCUUCGUAAGGGUGGUCGCACUAUUGCCUUUGGAGGCUGUGUGUUCUCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCUAGCGCUAACAUAGGUUGUAACCAUACAGGUGUUGUUGGAGAAGGUUCCGAAGGUCUUAAUGACAACCUUCUUGAAAUACUCCAAAAAGAGAAAGUCAACAUCAAUAUUGUUGGUGACUUUAAACUUAAUGAAGAGAUCGCCAUUAUUUUGGCAUCUUUUUCUGCUUCCACAAGUGCUUUUGUGGAAACUGUGAAAGGUUUGGAUUAUAAAGCAUUCAAACAAAUUGUUGAAUCCUGUGGUAAUUUUAAAGUUACAAAAGGAAAAGCUAAAAAAGGUGCCUGGAAUAUUGGUGAACAGAAAUCAAUACUGAGUCCUCUUUAUGCAUUUGCAUCAGAGGCUGCUCGUGUUGUACGAUCAAUUUUCUCCCGCACUCUUGAAACUGCUCAAAAUUCUGUGCGUGUUUUACAGAAGGCCGCUAUAACAAUACUAGAUGGAAUUUCACAGUAUUCACUGAGACUCAUUGAUGCUAUGAUGUUCACAUCUGAUUUGGCUACUAACAAUCUAGUUGUAAUGGCCUACAUUACAGGUGGUGUUGUUCAGUUGACUUCGCAGUGGCUAACUAACAUCUUUGGCACUGUUUAUGAAAAACUCAAACCCGUCCUUGAUUGGCUUGAAGAGAAGUUUAAGGAAGGUGUAGAGUUUCUUAGAGACGGUUGGGAAAUUGUUAAAUUUAUCUCAACCUGUGCUUGUGAAAUUGUCGGUGGACAAAUUGUCACCUGUGCAAAGGAAAUUAAGGAGAGUGUUCAGACAUUCUUUAAGCUUGUAAAUAAAUUUUUGGCUUUGUGUGCUGACUCUAUCAUUAUUGGUGGAGCUAAACUUAAAGCCUUGAAUUUAGGUGAAACAUUUGUCACGCACUCAAAGGGAUUGUACAGAAAGUGUGUUAAAUCCAGAGAAGAAACUGGCCUACUCAUGCCUCUAAAAGCCCCAAAAGAAAUUAUCUUCUUAGAGGGAGAAACACUUCCCACAGAAGUGUUAACAGAGGAAGUUGUCUUGAAAACUGGUGAUUUACAACCAUUAGAACAACCUACUAGUGAAGCUGUUGAAGCUCCAUUGGUUGGUACACCAGUUUGUAUUAACGGGCUUAUGUUGCUCGAAAUCAAAGACACAGAAAAGUACUGUGCCCUUGCACCUAAUAUGAUGGUAACAAACAAUACCUUCACACUCAAAGGCGGUGCACCAACAAAGGUUACUUUUGGUGAUGACACUGUGAUAGAAGUGCAAGGUUACAAGAGUGUGAAUAUCACUUUUGAACUUGAUGAAAGGAUUGAUAAAGUACUUAAUGAGAAGUGCUCUGCCUAUACAGUUGAACUCGGUACAGAAGUAAAUGAGUUCGCCUGUGUUGUGGCAGAUGCUGUCAUAAAAACUUUGCAACCAGUAUCUGAAUUACUUACACCACUGGGCAUUGAUUUAGAUGAGUGGAGUAUGGCUACAUACUACUUAUUUGAUGAGUCUGGUGAGUUUAAAUUGGCUUCACAUAUGUAUUGUUCUUUCUACCCUCCAGAUGAGGAUGAAGAAGAAGGUGAUUGUGAAGAAGAAGAGUUUGAGCCAUCAACUCAAUAUGAGUAUGGUACUGAAGAUGAUUACCAAGGUAAACCUUUGGAAUUUGGUGCCACUUCUGCUGCUCUUCAACCUGAAGAAGAGCAAGAAGAAGAUUGGUUAGAUGAUGAUAGUCAACAAACUGUUGGUCAACAAGACGGCAGUGAGGACAAUCAGACAACUACUAUUCAAACAAUUGUUGAGGUUCAACCUCAAUUAGAGAUGGAACUUACACCAGUUGUUCAGACUAUUGAAGUGAAUAGUUUUAGUGGUUAUUUAAAACUUACUGACAAUGUAUACAUUAAAAAUGCAGACAUUGUGGAAGAAGCUAAAAAGGUAAAACCAACAGUGGUUGUUAAUGCAGCCAAUGUUUACCUUAAACAUGGAGGAGGUGUUGCAGGAGCCUUAAAUAAGGCUACUAACAAUGCCAUGCAAGUUGAAUCUGAUGAUUACAUAGCUACUAAUGGACCACUUAAAGUGGGUGGUAGUUGUGUUUUAAGCGGACACAAUCUUGCUAAACACUGUCUUCAUGUUGUCGGCCCAAAUGUUAACAAAGGUGAAGACAUUCAACUUCUUAAGAGUGCUUAUGAAAAUUUUAAUCAGCACGAAGUUCUACUUGCACCAUUAUUAUCAGCUGGUAUUUUUGGUGCUGACCCUAUACAUUCUUUAAGAGUUUGUGUAGAUACUGUUCGCACAAAUGUCUACUUAGCUGUCUUUGAUAAAAAUCUCUAUGACAAACUUGUUUCAAGCUUUUUGGAAAUGAAGAGUGAAAAGCAAGUUGAACAAAAGAUCGCUGAGAUUCCUAAAGAGGAAGUUAAGCCAUUUAUAACUGAAAGUAAACCUUCAGUUGAACAGAGAAAACAAGAUGAUAAGAAAAUCAAAGCUUGUGUUGAAGAAGUUACAACAACUCUGGAAGAAACUAAGUUCCUCACAGAAAACUUGUUACUUUAUAUUGACAUUAAUGGCAAUCUUCAUCCAGAUUCUGCCACUCUUGUUAGUGACAUUGACAUCACUUUCUUAAAGAAAGAUGCUCCAUAUAUAGUGGGUGAUGUUGUUCAAGAGGGUGUUUUAACUGCUGUGGUUAUACCUACUAAAAAGGCUGGUGGCACUACUGAAAUGCUAGCGAAAGCUUUGAGAAAAGUGCCAACAGACAAUUAUAUAACCACUUACCCGGGUCAGGGUUUAAAUGGUUACACUGUAGAGGAGGCAAAGACAGUGCUUAAAAAGUGUAAAAGUGCCUUUUACAUUCUACCAUCUAUUAUCUCUAAUGAGAAGCAAGAAAUUCUUGGAACUGUUUCUUGGAAUUUGCGAGAAAUGCUUGCACAUGCAGAAGAAACACGCAAAUUAAUGCCUGUCUGUGUGGAAACUAAAGCCAUAGUUUCAACUAUACAGCGUAAAUAUAAGGGUAUUAAAAUACAAGAGGGUGUGGUUGAUUAUGGUGCUAGAUUUUACUUUUACACCAGUAAAACAACUGUAGCGUCACUUAUCAACACACUUAACGAUCUAAAUGAAACUCUUGUUACAAUGCCACUUGGCUAUGUAACACAUGGCUUAAAUUUGGAAGAAGCUGCUCGGUAUAUGAGAUCUCUCAAAGUGCCAGCUACAGUUUCUGUUUCUUCACCUGAUGCUGUUACAGCGUAUAAUGGUUAUCUUACUUCUUCUUCUAAAACACCUGAAGAACAUUUUAUUGAAACCAUCUCACUUGCUGGUUCCUAUAAAGAUUGGUCCUAUUCUGGACAAUCUACACAACUAGGUAUAGAAUUUCUUAAGAGAGGUGAUAAAAGUGUAUAUUACACUAGUAAUCCUACCACAUUCCACCUAGAUGGUGAAGUUAUCACCUUUGACAAUCUUAAGACACUUCUUUCUUUGAGAGAAGUGAGGACUAUUAAGGUGUUUACAACAGUAGACAACAUUAACCUCCACACGCAAGUUGUGGACAUGUCAAUGACAUAUGGACAACAGUUUGGUCCAACUUAUUUGGAUGGAGCUGAUGUUACUAAAAUAAAACCUCAUAAUUCACAUGAAGGUAAAACAUUUUAUGUUUUACCUAAUGAUGACACUCUACGUGUUGAGGCUUUUGAGUACUACCACACAACUGAUCCUAGUUUUCUGGGUAGGUACAUGUCAGCAUUAAAUCACACUAAAAAGUGGAAAUACCCACAAGUUAAUGGUUUAACUUCUA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      "Size :  42483\n"
     ]
    }
   ],
   "source": [
    "DNA = DNAsequence.seq#Convert DNA into mRNA Sequence\n",
    "mRNA = DNA.transcribe() #Transcribe a DNA sequence into RNA.\n",
    "print(mRNA)\n",
    "print('Size : ',len(mRNA))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6d1f4ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amino Acid MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDDNLIDSYFVVKRHTFSNYQHEETIYNLLKDCPAVAKHDFFKFRIDGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQALLKTVQFCDAMRNAGIVGVLTLDNQDLNGNWYDFGDFIQTTPGSGVPVVDSYYSLLMPILTLTRALTAESHVDTDLTKPYIKWDLLKYDFTEERLKLFDRYFKYWDQTYHPNCVNCLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAALTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVIVNNLDKSAGFPFNKWGKARLYYDSMSYEDQDALFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTMTNRQFHQKLLKSIAATRGATVVIGTSKFYGGWHNMLKTVYSDVENPHLMGWDYPKCDRAMPNMLRIMASLVLARKHTTCCSLSHRFYRLANECAQVLSEMVMCGGSLYVKPGGTSSGDATTAYANSVFNICQAVTANVNALLSTDGNKIADKYVRNLQHRLYECLYRNRDVDTDFVNEFYAYLRKHFSMMILSDDAVVCFNSTYASQGLVASIKNFKSVLYYQNNVFMSEAKCWTETDLTKGPHEFCSQHTMLVKQGDDYVYLPYPDPSRILGAGCFVDDIVKTDGTLMIERFVSLAIDAYPLTKHPNQEYADVFHLYLQYIRKLHDELTGHMLDMYSVMLTNDNTSRYWEPEFYEAMYTPHTVLQAVGACVLCNSQTSLRCGACIRRPFLCCKCCYDHVISTSHKLVLSVNPYVCNAPGCDVTDVTQLYLGGMSYYCKSHKPPISFPLCANGQVFGLYKNTCVGSDNVTDFNAIATCDWTNAGDYILANTCTERLKLFAAETLKATEETFKLSYGIATVREVLSDRELHLSWEVGKPRPPLNRNYVFTGYRVTKNSKVQIGEYTFEKGDYGDAVVYRGTTTYKLNVGDYFVLTSHTVMPLSAPTLVPQEHYVRITGLYPTLNISDEFSSNVANYQKVGMQKYSTLQGPPGTGKSHFAIGLALYYPSARIVYTACSHAAVDALCEKALKYLPIDKCSRIIPARARVECFDKFKVNSTLEQYVFCTVNALPETTADIVVFDEISMATNYDLSVVNARLRAKHYVYIGDPAQLPAPRTLLTKGTLEPEYFNSVCRLMKTIGPDMFLGTCRRCPAEIVDTVSALVYDNKLKAHKDKSAQCFKMFYKGVITHDVSSAINRPQIGVVREFLTRNPAWRKAVFISPYNSQNAVASKILGLPTQTVDSSQGSEYDYVIFTQTTETAHSCNVNRFNVAITRAKVGILCIMSDRDLYDKLQFTSLEIPRRNVATLQAENVTGLFKDCSKVITGLHPTQAPTHLSVDTKFKTEGLCVDIPGIPKDMTYRRLISMMGFKMNYQVNGYPNMFITREEAIRHVRAWIGFDVEGCHATREAVGTNLPLQLGFSTGVNLVAVPTGYVDTPNNTDFSRVSAKPPPGDQFKHLIPLMYKGLPWNVVRIKIVQMLSDTLKNLSDRVVFVLWAHGFELTSMKYFVKIGPERTCCLCDRRATCFSTASDTYACWHHSIGFDYVYNPFMIDVQQWGFTGNLQSNHDLYCQVHGNAHVASCDAIMTRCLAVHECFVKRVDWTIEYPIIGDELKINAACRKVQHMVVKAALLADKFPVLHDIGNPKAIKCVPQADVEWKFYDAQPCSDKAYKIEELFYSYATHSDKFTDGVCLFWNCNVDRYPANSIVCRFDTRVLSNLNLPGCDGGSLYVNKHAFHTPAFDKSAFVNLKQLPFFYYSDSPCESHGKQVVSDIDYVPLKSATCITRCNLGGAVCRHHANEYRLYLDAYNMMISAGFSLWVYKQFDTYNLWNTFTRLQSLENVAFNVVNKGHFDGQQGEVPVSIINNTVYTKVDGVDVELFENKTTLPVNVAFELWAKRNIKPVPEVKILNNLGVDIAANTVIWDYKRDAPAHISTIGVCSMTDIAKKPTETICAPLTVFFDGRVDGQVDLFRNARNGVLITEGSVKGLQPSVGPKQASLNGVTLIGEAVKTQFNYYKKVDGVVQQLPETYFTQSRNLQEFKPRSQMEIDFLELAMDEFIERYKLEGYAFEHIVYGDFSHSQLGGLHLLIGLAKRFKESPFELEDFIPMDSTVKNYFITDAQTGSSKCVCSVIDLLLDDFVEIIKSQDLSVVSKVVKVTIDYTEISFMLWCKDGHVETFYPKLQSSQAWQPGVAMPNLYKMQRMLLEKCDLQNYGDSATLPKGIMMNVAKYTQLCQYLNTLTLAVPYNMRVIHFGAGSDKGVAPGTAVLRQWLPTGTLLVDSDLNDFVSDADSTLIGDCATVHTANKWDLIISDMYDPKTKNVTKENDSKEGFFTYICGFIQQKLALGGSVAIKITEHSWNADLYKLMGHFAWWTAFVTNVNASSSEAFLIGCNYLGKPREQIDGYVMHANYIFWRNTNPIQLSSYSLFDMSKFPLKLRGTAVMSLKEGQINDMILSLLSKGRLIIRENNRVVISSDVLVNN*MESLVPGFNEKTHVQLSLPVLQVRDVLVRGFGDSVEEVLSEARQHLKDGTCGLVEVEKGVLPQLEQPYVFIKRSDARTAPHGHVMVELVAELEGIQYGRSGETLGVLVPHVGEIPVAYRKVLLRKNGNKGAGGHSYGADLKSFDLGDELGTDPYEDFQENWNTKHSSGVTRELMRELNGGAYTRYVDNNFCGPDGYPLECIKDLLARAGKASCTLSEQLDFIDTKRGVYCCREHEHEIAWYTERSEKSYELQTPFEIKLAKKFDTFNGECPNFVFPLNSIIKTIQPRVEKKKLDGFMGRIRSVYPVASPNECNQMCLSTLMKCDHCGETSWQTGDFVKATCEFCGTENLTKEGATTCGYLPQNAVVKIYCPACHNSEVGPEHSLAEYHNESGLKTILRKGGRTIAFGGCVFSYVGCHNKCAYWVPRASANIGCNHTGVVGEGSEGLNDNLLEILQKEKVNINIVGDFKLNEEIAIILASFSASTSAFVETVKGLDYKAFKQIVESCGNFKVTKGKAKKGAWNIGEQKSILSPLYAFASEAARVVRSIFSRTLETAQNSVRVLQKAAITILDGISQYSLRLIDAMMFTSDLATNNLVVMAYITGGVVQLTSQWLTNIFGTVYEKLKPVLDWLEEKFKEGVEFLRDGWEIVKFISTCACEIVGGQIVTCAKEIKESVQTFFKLVNKFLALCADSIIIGGAKLKALNLGETFVTHSKGLYRKCVKSREETGLLMPLKAPKEIIFLEGETLPTEVLTEEVVLKTGDLQPLEQPTSEAVEAPLVGTPVCINGLMLLEIKDTEKYCALAPNMMVTNNTFTLKGGAPTKVTFGDDTVIEVQGYKSVNITFELDERIDKVLNEKCSAYTVELGTEVNEFACVVADAVIKTLQPVSELLTPLGIDLDEWSMATYYLFDESGEFKLASHMYCSFYPPDEDEEEGDCEEEEFEPSTQYEYGTEDDYQGKPLEFGATSAALQPEEEQEEDWLDDDSQQTVGQQDGSEDNQTTTIQTIVEVQPQLEMELTPVVQTIEVNSFSGYLKLTDNVYIKNADIVEEAKKVKPTVVVNAANVYLKHGGGVAGALNKATNNAMQVESDDYIATNGPLKVGGSCVLSGHNLAKHCLHVVGPNVNKGEDIQLLKSAYENFNQHEVLLAPLLSAGIFGADPIHSLRVCVDTVRTNVYLAVFDKNLYDKLVSSFLEMKSEKQVEQKIAEIPKEEVKPFITESKPSVEQRKQDDKKIKACVEEVTTTLEETKFLTENLLLYIDINGNLHPDSATLVSDIDITFLKKDAPYIVGDVVQEGVLTAVVIPTKKAGGTTEMLAKALRKVPTDNYITTYPGQGLNGYTVEEAKTVLKKCKSAFYILPSIISNEKQEILGTVSWNLREMLAHAEETRKLMPVCVETKAIVSTIQRKYKGIKIQEGVVDYGARFYFYTSKTTVASLINTLNDLNETLVTMPLGYVTHGLNLEEAARYMRSLKVPATVSVSSPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWSYSGQSTQLGIEFLKRGDKSVYYTSNPTTFHLDGEVITFDNLKTLLSLREVRTIKVFTTVDNINLHTQVVDMSMTYGQQFGPTYLDGADVTKIKPHNSHEGKTFYVLPNDDTLRVEAFEYYHTTDPSFLGRYMSALNHTKKWKYPQVNGLTSIKWADNNCYLATALLTLQQIELKFNPPALQDAYYRARAGEAANFCALILAYCNKTVGELGDVRETMSYLFQHANLDSCKRVLNVVCKTCGQQQTTLKGVEAVMYMGTLSYEQFKKGVQIPCTCGKQATKYLVQQESPFVMMSAPPAQYELKHGTFTCASEYTGNYQCGHYKHITSKETLYCIDGALLTKSSEYKGPITDVFYKENSYTTTIKPVTYKLDGVVCTEIDPKLDNYYKKDNSYFTEQPIDLVPNQPYPNASFDNFKFVCDNIKFADDLNQLTGYKKPASRELKVTFFPDLNGDVVAIDYKHYTPSFKKGAKLLHKPIVWHVNNATNKATYKPNTWCIRCLWSTKPVETSNSFDVLKSEDAQGMDNLACEDLKPVSEEVVENPTIQKDVLECNVKTTEVVGDIILKPANNSLKITEEVGHTDLMAAYVDNSSLTIKKPNELSRVLGLKTLATHGLAAVNSVPWDTIANYAKPFLNKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQLCTFTRSTNSRIKASMPTTIAKNTVKSVGKFCLEASFNYLKSPNFSKLINIIIWFLLLSVCLGSLIYSTAALGVLMSNLGMPSYCTGYREGYLNSTNVTIATYCTGSIPCSVCLSGLDSLDTYPSLETIQITISSFKWDLTAFGLVAEWFLAYILFTRFFYVLGLAAIMQLFFSYFAVHFISNSWLMWLIINLVQMAPISAMVRMYIFFASFYYVWKSYVHVVDGCNSSTCMMCYKRNRATRVECTTIVNGVRRSFYVYANGGKGFCKLHNWNCVNCDTFCAGSTFISDEVARDLSLQFKRPINPTDQSSYIVDSVTVKNGSIHLYFDKAGQKTYERHSLSHFVNLDNLRANNTKGSLPINVIVFDGKSKCEESSAKSASVYYSQLMCQPILLLDQALVSDVGDSAEVAVKMFDAYVNTFSSTFNVPMEKLKTLVATAEAELAKNVSLDNVLSTFISAARQGFVDSDVETKDVVECLKLSHQSDIEVTGDSCNNYMLTYNKVENMTPRDLGACIDCSARHINAQVAKSHNIALIWNVKDFMSLSEQLRKQIRSAAKKNNLPFKLTCATTRQVVNVVTTKIALKGGKIVNNWLKQLIKVTLVFLFVAAIFYLITPVHVMSKHTDFSSEIIGYKAIDGGVTRDIASTDTCFANKHADFDTWFSQRGGSYTNDKACPLIAAVITREVGFVVPGLPGTILRTTNGDFLHFLPRVFSAVGNICYTPSKLIEYTDFATSACVLAAECTIFKDASGKPVPYCYDTNVLEGSVAYESLRPDTRYVLMDGSIIQFPNTYLEGSVRVVTTFDSEYCRHGTCERSEAGVCVSTSGRWVLNNDYYRSLPGVFCGVDAVNLLTNMFTPLIQPIGALDISASIVAGGIVAIVVTCLAYYFMRFRRAFGEYSHVVAFNTLLFLMSFTVLCLTPVYSFLPGVYSVIYLYLTFYLTNDVSFLAHIQWMVMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTFEEAALCTFLLNKEMYLKLRSDVLLPLTQYNRYLALYNKYKYFSGAMDTTSYREAACCHLAKALNDFSNSGSDVLYQPPQTSITSAVLQSGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQSAVKRTIKGTHHWLLLTILTSLLVLVQSTQWSLFFFLYENAFLPFAMGIIAMSAFAMMFVKHKHAFLCLFLLPSLATVAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVVLLILMTARTVYDDGARRVWTLMNVLTLVYKVYYGNALDQAISMWALIISVTSNYSGVVTTVMFLARGIVFMCVEYCPIFFITGNTLQCIMLVYCFLGYFCTCYFGLFCLLNRYFRLTLGVYDYLVSTQEFRYMNSQGLLPPKNSIDAFKLNIKLLGVGGKPCIKVATVQSKMSDVKCTSVVLLSVLQQLRVESSSKLWAQCVQLHNDILLAKDTTEAFEKMVSLLSVLLSMQGAVDINKLCEEMLDNRATLQAIASEFSSLPSYAAFATAQEAYEQAVANGDSEVVLKKLKKSLNVAKSEFDRDAAMQRKLEKMADQAMTQMYKQARSEDKRAKVTSAMQTMLFTMLRKLDNDALNNIINNARDGCVPLNIIPLTTAAKLMVVIPDYNTYKNTCDGTTFTYASALWEIQQVVDADSKIVQLSEISMDNSPNLAWPLIVTALRANSAVKLQNNELSPVALRQMSCAAGTTQTACTDDNALAYYNTTKGGRFVLALLSDLQDLKWARFPKSDGTGTIYTELEPPCRFVTDTPKGPKVKYLYFIKGLNNLNRGMVLGSLAATVRLQAGNATEVPANSTVLSFCAFAVDAAKAYKDYLASGGQPITNCVKMLCTHTGTGQAITVTPEANMDQESFGGASCCLYCRCHIDHPNPKGFCDLKGKYVQIPTTCANDPVGFTLKNTVCTVCGMWKGYGCSCDQLREPMLQSADAQSFLNGFAV*MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT*MDLFMRIFTIGTVTLKQGEIKDATPSDFVRATATIPIQASLPFGWLIVGVALLAVFQSASKIITLKKRWQLALSKGVHFVCNLLLLFVTVYSHLLLVAAGLEAPFLYLYALVYFLQSINFVRIIMRLWLCWKCRSKNPLLYDANYFLCWHTNCYDYCIPYNSVTSSIVITSGDGTTSPISEHDYQIGGYTEKWESGVKDCVVLHSYFTSDYYQLYSTQLSTDTGVEHVTFFIYNKIVDEPEEHVQIHTIDGSSGVVNPVMEPIYDEPTTTTSVPL*MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV*MADSNGTITVEELKKLLEQWNLVIGFLFLTWICLLQFAYANRNRFLYIIKLIFLWLLWPVTLACFVLAAVYRINWITGGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTILTRPLLESELVIGAVILRGHLRIAGHHLGRCDIKDLPKEITVATSRTLSYYKLGASQRVAGDSGFAAYSRYRIGNYKLNTDHSSSSDNIALLVQ*MFHLVDFQVTIAEILLIIMRTFKVSIWNLDYIINLIIKNLSKSLTENKYSQLDEEQPMEID*MKIILFLALITLATCELYHYQECVRGTTVLLKEPCSSGTYEGNSPFHPLADNKFALTCFSTQFAFACPDGVKHVYQLRARSVSPKLFIRQEEVQELYSPIFLIVAAIVFITLCFTLKRKTE*MIELSLIDFYLCFLAFLLFLVLIMLIIFWFSLELQDHNETCHA*MKFLVFLGIITTVAAFHQECSLQSCTQHQPYVVDDPCPIHFYSKWYIRVGARKSAPLIELCVDEAGSKSPIQYIDIGNYTVSCLPFTINCQEPKLGSLVVRCSFYEDFLEYHDVRVVLDFI*MSDNGPQNQRNAPRITFGGPSDSTGSNQNGERSGARSKQRRPQGLPNNTASWFTALTQHGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLSPRWYFYYLGTGPEAGLPYGANKDGIIWVATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQASSRSSSRSRNSSRNSTPGSSRGTSPARMAGNGGDAALALLLLDRLNQLESKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATKAYNVTQAFGRRGPEQTQGNFGDQELIRQGTDYKHWPQIAQFAPSASAFFGMSRIGMEVTPSGTWLTYTGAIKLDDKDPNFKDQVILLNKHIDAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDDFSKQLQQSMSSADSTQA*MGYINVFAFPFTIYSLLLCRMNSRNYIAQVDVVNFNLT*\n",
      "Length of Protein: 14161\n",
      "Length of Original mRNA: 42483\n"
     ]
    }
   ],
   "source": [
    "Amino_Acid = mRNA.translate(table=1, cds=False)\n",
    "print('Amino Acid', Amino_Acid)\n",
    "print(\"Length of Protein:\",len(Amino_Acid))\n",
    "print(\"Length of Original mRNA:\",len(mRNA))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7d619af8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 1 Standard, SGC0\n",
      "\n",
      "  |  U      |  C      |  A      |  G      |\n",
      "--+---------+---------+---------+---------+--\n",
      "U | UUU F   | UCU S   | UAU Y   | UGU C   | U\n",
      "U | UUC F   | UCC S   | UAC Y   | UGC C   | C\n",
      "U | UUA L   | UCA S   | UAA Stop| UGA Stop| A\n",
      "U | UUG L(s)| UCG S   | UAG Stop| UGG W   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "C | CUU L   | CCU P   | CAU H   | CGU R   | U\n",
      "C | CUC L   | CCC P   | CAC H   | CGC R   | C\n",
      "C | CUA L   | CCA P   | CAA Q   | CGA R   | A\n",
      "C | CUG L(s)| CCG P   | CAG Q   | CGG R   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "A | AUU I   | ACU T   | AAU N   | AGU S   | U\n",
      "A | AUC I   | ACC T   | AAC N   | AGC S   | C\n",
      "A | AUA I   | ACA T   | AAA K   | AGA R   | A\n",
      "A | AUG M(s)| ACG T   | AAG K   | AGG R   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "G | GUU V   | GCU A   | GAU D   | GGU G   | U\n",
      "G | GUC V   | GCC A   | GAC D   | GGC G   | C\n",
      "G | GUA V   | GCA A   | GAA E   | GGA G   | A\n",
      "G | GUG V   | GCG A   | GAG E   | GGG G   | G\n",
      "--+---------+---------+---------+---------+--\n"
     ]
    }
   ],
   "source": [
    "print(CodonTable.unambiguous_rna_by_name['Standard'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "25ff22ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify all the Proteins (chains of amino acids)\n",
    "Proteins = Amino_Acid.split('*') # * is translated stop codon\n",
    "for i in Proteins[:]:\n",
    "    if len(i) < 20:\n",
    "        Proteins.remove(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "15c4e3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total proteins: 12\n",
      "Total functional proteins: 0\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>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       length\n",
       "count     0.0\n",
       "mean      NaN\n",
       "std       NaN\n",
       "min       NaN\n",
       "25%       NaN\n",
       "50%       NaN\n",
       "75%       NaN\n",
       "max       NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify all the Proteins (chains of amino acids)\n",
    "\n",
    "df = pd.DataFrame(Proteins)\n",
    "df.describe()\n",
    "print('Total proteins:', len(df))\n",
    "def conv(item):\n",
    "    return len(item)\n",
    "def to_str(item):\n",
    "    return str(item)\n",
    "df['sequence_str'] = df[0].apply(to_str)\n",
    "df['length'] = df[0].apply(conv)\n",
    "df.rename(columns={0: \"sequence\"}, inplace=True)\n",
    "df.head()# Take only longer than 20\n",
    "functional_proteins = df.loc[df['length'] >= 20]\n",
    "print('Total functional proteins:', len(functional_proteins))\n",
    "functional_proteins.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88a98b8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Protein of Interest =  {'A': 487, 'C': 226, 'D': 389, 'E': 340, 'F': 349, 'G': 412, 'H': 145, 'I': 343, 'K': 434, 'L': 668, 'M': 168, 'N': 384, 'P': 274, 'Q': 239, 'R': 244, 'S': 456, 'T': 527, 'V': 598, 'W': 78, 'Y': 335}\n",
      "Amino acids percent =  {'A': 0.06863021420518602, 'C': 0.0318489289740699, 'D': 0.054819616685456596, 'E': 0.047914317925591886, 'F': 0.049182638105975195, 'G': 0.0580608793686584, 'H': 0.020434047350620068, 'I': 0.04833709131905299, 'K': 0.06116121758737317, 'L': 0.09413754227733935, 'M': 0.02367531003382187, 'N': 0.05411499436302142, 'P': 0.03861330326944758, 'Q': 0.033680947012401354, 'R': 0.034385569334836524, 'S': 0.06426155580608793, 'T': 0.07426719278466742, 'V': 0.0842728297632469, 'W': 0.010992108229988726, 'Y': 0.04720969560315671}\n",
      "Molecular weight =  794048.9257999966\n",
      "Aromaticity =  0.10738444193912064\n",
      "Flexibility =  [1.0167738095238095, 0.9989523809523808, 0.985202380952381, 1.0379047619047619, 1.033297619047619, 1.0296309523809524, 1.0036785714285714, 1.0014404761904763, 0.9991547619047619, 1.010654761904762, 0.9679285714285716, 1.002142857142857, 0.9607619047619048, 1.0042261904761904, 0.96, 0.9771785714285715, 1.000392857142857, 0.9622261904761906, 0.9965833333333334, 0.9915833333333334, 0.9560833333333333, 0.9789761904761906, 0.9704642857142858, 0.9886190476190476, 0.9854761904761903, 0.9665238095238096, 1.0162499999999999, 1.0155952380952382, 1.0176190476190476, 1.0005238095238096, 1.048547619047619, 1.022952380952381, 0.9901547619047619, 1.0122857142857145, 1.0311785714285715, 1.0270119047619048, 0.9942380952380953, 1.0112261904761906, 1.0157142857142856, 0.9960952380952383, 1.0066666666666666, 1.0423214285714286, 1.009892857142857, 1.0096309523809521, 1.0021309523809525, 0.9808333333333333, 1.015440476190476, 0.9648095238095239, 0.9809285714285714, 1.0225833333333334, 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      "Isoelectric point =  6.323246955871583\n",
      "Secondary structure fraction =  (0.3341319052987599, 0.21505073280721532, 0.2343573844419391)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 309, 'C': 138, 'D': 211, 'E': 239, 'F': 208, 'G': 264, 'H': 75, 'I': 215, 'K': 276, 'L': 435, 'M': 105, 'N': 233, 'P': 164, 'Q': 151, 'R': 131, 'S': 294, 'T': 345, 'V': 371, 'W': 46, 'Y': 195}\n",
      "Amino acids percent =  {'A': 0.07014755959137343, 'C': 0.031328036322360954, 'D': 0.04790011350737798, 'E': 0.05425652667423383, 'F': 0.04721906923950057, 'G': 0.05993189557321226, 'H': 0.0170261066969353, 'I': 0.04880817253121453, 'K': 0.06265607264472191, 'L': 0.09875141884222474, 'M': 0.02383654937570942, 'N': 0.052894438138479, 'P': 0.03723041997729853, 'Q': 0.03427922814982974, 'R': 0.02973893303064699, 'S': 0.06674233825198637, 'T': 0.07832009080590238, 'V': 0.08422247446083996, 'W': 0.010442678774120319, 'Y': 0.04426787741203178}\n",
      "Molecular weight =  489983.4465000064\n",
      "Aromaticity =  0.10192962542565268\n",
      "Flexibility =  [1.0167738095238095, 0.9989523809523808, 0.985202380952381, 1.0379047619047619, 1.033297619047619, 1.0296309523809524, 1.0036785714285714, 1.0014404761904763, 0.9991547619047619, 1.010654761904762, 0.9679285714285716, 1.002142857142857, 0.9607619047619048, 1.0042261904761904, 0.96, 0.9771785714285715, 1.000392857142857, 0.9622261904761906, 0.9965833333333334, 0.9915833333333334, 0.9560833333333333, 0.9789761904761906, 0.9704642857142858, 0.9886190476190476, 0.9854761904761903, 0.9665238095238096, 1.0162499999999999, 1.0155952380952382, 1.0176190476190476, 1.0005238095238096, 1.048547619047619, 1.022952380952381, 0.9901547619047619, 1.0122857142857145, 1.0311785714285715, 1.0270119047619048, 0.9942380952380953, 1.0112261904761906, 1.0157142857142856, 0.9960952380952383, 1.0066666666666666, 1.0423214285714286, 1.009892857142857, 1.0096309523809521, 1.0021309523809525, 0.9808333333333333, 1.015440476190476, 0.9648095238095239, 0.9809285714285714, 1.0225833333333334, 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      "Isoelectric point =  6.035812187194824\n",
      "Secondary structure fraction =  (0.33371169125993183, 0.21679909194097619, 0.24699205448354142)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 79, 'C': 40, 'D': 62, 'E': 48, 'F': 77, 'G': 82, 'H': 17, 'I': 76, 'K': 61, 'L': 108, 'M': 14, 'N': 88, 'P': 58, 'Q': 62, 'R': 42, 'S': 99, 'T': 97, 'V': 97, 'W': 12, 'Y': 54}\n",
      "Amino acids percent =  {'A': 0.062058130400628436, 'C': 0.031421838177533384, 'D': 0.04870384917517675, 'E': 0.037706205813040065, 'F': 0.060487038491751764, 'G': 0.06441476826394343, 'H': 0.01335428122545169, 'I': 0.05970149253731343, 'K': 0.047918303220738416, 'L': 0.08483896307934014, 'M': 0.010997643362136685, 'N': 0.06912804399057344, 'P': 0.04556166535742341, 'Q': 0.04870384917517675, 'R': 0.032992930086410056, 'S': 0.07776904948939513, 'T': 0.07619795758051846, 'V': 0.07619795758051846, 'W': 0.009426551453260016, 'Y': 0.04241948153967007}\n",
      "Molecular weight =  141176.8160000004\n",
      "Aromaticity =  0.11233307148468186\n",
      "Flexibility =  [0.9346190476190477, 0.9408333333333333, 0.9459047619047621, 0.9778809523809523, 0.9487261904761907, 0.9783928571428572, 1.0072261904761906, 0.9917380952380952, 0.9939404761904762, 0.9639880952380951, 0.9880833333333334, 1.0043214285714286, 0.9607380952380952, 0.9922738095238095, 0.9868571428571429, 0.9949047619047617, 0.9963333333333333, 1.0089880952380954, 0.9868095238095238, 1.0239285714285713, 1.0034285714285716, 0.9908452380952381, 0.9910833333333334, 1.0088095238095238, 1.008345238095238, 0.9979761904761905, 0.9728333333333333, 1.0084166666666665, 0.9934999999999999, 0.9887261904761905, 0.9601309523809524, 0.9758095238095239, 0.9853809523809525, 1.0139880952380953, 1.0021071428571429, 1.0102261904761907, 0.9759761904761907, 1.0022142857142857, 1.01825, 1.0028809523809525, 0.9900952380952384, 0.9641666666666666, 0.9845119047619049, 0.9887380952380953, 1.009595238095238, 0.986904761904762, 1.0038690476190477, 1.0033095238095238, 0.9711785714285716, 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0.9903928571428571, 0.9816904761904761, 1.0089285714285714, 0.9898690476190476, 0.9742500000000001, 0.9912142857142857, 1.0106428571428572, 0.9993095238095239, 0.9674047619047619, 0.9881428571428573, 1.012357142857143, 0.9947500000000001, 0.987404761904762, 1.0321666666666667, 1.025702380952381, 0.9961547619047619, 1.0539404761904763, 1.0057142857142858, 1.0419166666666668, 0.9855595238095238, 1.0273214285714285, 0.9841190476190476, 1.0201190476190476, 0.9806309523809524, 0.9717261904761905, 0.9824166666666666, 0.9933095238095236, 0.9928690476190476, 0.9861071428571428, 0.9779642857142857, 1.015797619047619, 1.0044166666666665, 0.9784642857142858, 1.0097023809523809, 0.9800238095238095, 0.9723690476190476, 0.9813809523809522, 0.992, 0.9906666666666666, 0.984595238095238, 0.9650119047619048, 0.9802500000000001, 1.0012857142857143, 0.9827380952380954, 0.9888571428571428, 1.0129642857142858, 0.9694166666666668, 1.0156190476190479, 0.9987023809523811, 1.0145, 0.9908809523809525, 1.0091428571428573, 0.9762261904761906, 1.0057738095238096, 0.9968452380952381, 1.0023214285714286, 1.0302976190476192, 0.9854642857142857, 1.0292857142857144, 1.0161190476190476, 0.9651547619047618, 0.9936666666666667, 0.9847142857142858, 1.011952380952381, 1.0055, 1.0122380952380954, 1.0343690476190477, 1.0071785714285715, 0.9985357142857144, 1.0259761904761904, 0.9678690476190477, 0.9890476190476191, 1.0116190476190476, 1.0083214285714288, 0.9847499999999999, 0.9694285714285714, 0.9882380952380952, 0.974309523809524, 0.9754047619047619, 0.9911547619047619, 0.959047619047619, 1.018, 1.0044761904761903, 1.0146904761904763, 1.0042857142857142, 1.0218214285714287, 0.9858095238095237, 1.0039642857142854, 0.9567380952380953, 0.9604285714285714, 0.9466428571428571, 0.9470238095238097, 0.9452500000000001, 0.937404761904762, 0.96125, 0.9444999999999999, 0.9664999999999999, 1.003095238095238, 0.9889047619047618, 1.0280595238095238, 1.0387738095238097, 1.0383214285714286, 1.0260119047619047, 0.9985833333333335, 1.0287738095238095, 1.0059166666666666, 0.9937738095238096, 0.9988333333333331, 0.9684404761904762, 0.9679642857142857, 0.9706190476190476, 0.9883928571428572, 1.0126666666666666, 0.9933571428571428, 1.018357142857143, 0.9880238095238095, 0.9998452380952382, 0.9971190476190476, 1.0275119047619048, 0.9976666666666667, 1.0188809523809523, 0.9897261904761903, 0.9780357142857143, 0.9875476190476191, 0.9883571428571429, 1.0097261904761905, 0.9925595238095238, 0.990142857142857, 0.9840714285714286, 0.9720000000000001, 0.9602619047619049, 0.9607261904761906, 0.9597023809523809, 0.9774285714285712, 0.9825, 0.9776785714285714, 1.0005833333333334, 0.9744761904761906, 1.0038452380952383, 1.0328809523809526, 0.9950357142857142, 0.9991547619047619, 0.9745238095238097, 0.993440476190476, 1.0082261904761904, 0.9963452380952381, 1.0114166666666666, 0.9954642857142857, 0.9917261904761904, 0.9828809523809525, 0.9972976190476192, 1.0128214285714288, 0.9930595238095238, 0.9994404761904763, 0.9670833333333333, 1.0177023809523809, 0.9592976190476191, 0.9714047619047619, 0.9609880952380954, 0.9808452380952382, 0.9508452380952382, 0.9728214285714285, 1.0017738095238096, 0.9868214285714286, 0.9826785714285714, 0.9796428571428573, 0.9905357142857144, 1.020904761904762, 0.9974285714285713, 0.986702380952381, 1.0298214285714284, 1.023988095238095, 1.0378809523809525, 0.9943452380952381, 1.0470952380952379, 1.0257142857142858, 1.0080595238095238, 1.0654166666666667, 1.036357142857143, 1.0479761904761904, 1.0125714285714287, 1.0558452380952381, 1.038357142857143, 0.9912500000000002, 1.007357142857143, 1.0403452380952383, 1.0026904761904762, 0.9931547619047618, 1.0208690476190476, 1.0265238095238094, 1.0249404761904761, 1.0227142857142855, 0.9907619047619048, 1.041607142857143, 0.9871309523809525, 1.0252142857142856, 1.0156190476190479, 0.9788214285714286, 1.0278095238095237, 1.0015357142857144, 0.9869404761904761, 1.0226428571428572, 0.9836666666666667, 1.0077142857142858, 0.9671190476190477, 0.9833095238095237, 1.0115714285714286, 0.9763571428571429, 1.0249285714285716, 1.0289404761904763, 1.0275595238095239, 0.9968571428571428, 1.047595238095238, 1.013654761904762, 1.0023452380952382, 1.0289761904761905, 1.0208452380952382, 0.9886547619047621, 1.02275, 1.0392142857142859, 1.0167380952380953, 1.005904761904762, 1.043452380952381, 1.0324285714285715, 1.0138452380952383, 0.9885714285714284, 0.998452380952381, 1.0288333333333333, 0.9759761904761904, 1.0186309523809522, 1.0210119047619048, 0.9865476190476191, 1.0381785714285714, 1.0374761904761904, 0.9930357142857144, 1.046, 1.0037380952380954, 0.9867976190476191, 0.9971309523809525, 1.0211666666666668, 0.9491190476190476, 0.999035714285714, 0.9355714285714287, 0.9568809523809524, 0.9472619047619046, 0.9347500000000001, 0.9452738095238098, 0.9597023809523809, 0.9311547619047619, 0.9579642857142856, 0.9698214285714286, 0.9741904761904762, 0.9472857142857143, 0.9562857142857143, 0.9665714285714285, 0.9420833333333333, 0.9494642857142856, 0.9491071428571428, 0.9432738095238096, 0.9606904761904763, 0.9335714285714284, 0.9474166666666667, 0.9365595238095239, 0.9325595238095238, 0.9417738095238095, 0.9526666666666667, 0.9586904761904762, 0.9695, 0.9372261904761905, 0.9603809523809522, 0.9925238095238095, 0.9477380952380953, 0.978095238095238, 1.0052142857142858, 0.9719761904761904, 0.9581190476190476, 0.9737142857142855, 1.0040119047619047, 0.952297619047619, 0.9934404761904762, 0.9803333333333333, 0.9537738095238094, 0.9810119047619046, 1.0242738095238095, 0.9693928571428572, 1.0425238095238096, 1.0350833333333336, 1.044309523809524, 1.0595357142857142, 1.051202380952381, 1.0591309523809525, 1.0334999999999999, 1.0073452380952381, 1.0148452380952382, 1.0431904761904764, 0.9981785714285714, 0.9851547619047619, 1.0281666666666667]\n",
      "Isoelectric point =  6.236453819274902\n",
      "Secondary structure fraction =  (0.3330714846818539, 0.25687352710133543, 0.19560094265514533)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 13, 'C': 7, 'D': 13, 'E': 11, 'F': 14, 'G': 14, 'H': 8, 'I': 21, 'K': 11, 'L': 30, 'M': 4, 'N': 8, 'P': 12, 'Q': 9, 'R': 6, 'S': 22, 'T': 24, 'V': 25, 'W': 6, 'Y': 17}\n",
      "Amino acids percent =  {'A': 0.04727272727272727, 'C': 0.025454545454545455, 'D': 0.04727272727272727, 'E': 0.04, 'F': 0.05090909090909091, 'G': 0.05090909090909091, 'H': 0.02909090909090909, 'I': 0.07636363636363637, 'K': 0.04, 'L': 0.10909090909090909, 'M': 0.014545454545454545, 'N': 0.02909090909090909, 'P': 0.04363636363636364, 'Q': 0.03272727272727273, 'R': 0.02181818181818182, 'S': 0.08, 'T': 0.08727272727272728, 'V': 0.09090909090909091, 'W': 0.02181818181818182, 'Y': 0.06181818181818182}\n",
      "Molecular weight =  31122.55440000004\n",
      "Aromaticity =  0.13454545454545455\n",
      "Flexibility =  [0.9693333333333333, 0.9412261904761904, 0.9510357142857143, 0.9743214285714284, 0.9514880952380954, 0.9881666666666666, 0.9651904761904762, 0.9670714285714285, 0.9984880952380951, 0.9823333333333336, 1.0392261904761906, 1.0045119047619049, 1.0251785714285713, 1.047809523809524, 1.0064523809523809, 1.0680119047619048, 1.027892857142857, 1.0167976190476191, 1.0329285714285714, 1.034654761904762, 1.0217619047619049, 1.0171190476190475, 0.9742857142857143, 0.9955952380952382, 1.000952380952381, 0.9797023809523809, 0.9825119047619046, 0.9790476190476189, 0.9858928571428571, 0.9725238095238096, 1.0071666666666668, 0.9669285714285716, 1.0094880952380951, 0.9851785714285713, 1.0058214285714289, 0.9759642857142856, 1.0096309523809524, 0.959095238095238, 0.993297619047619, 0.939202380952381, 0.9602619047619049, 0.9499642857142857, 0.950404761904762, 0.9724999999999998, 0.936404761904762, 0.9679642857142857, 0.9489880952380952, 0.9544642857142859, 0.9647023809523809, 0.9489166666666667, 0.9617380952380953, 0.9980119047619047, 0.9968690476190477, 0.9940714285714285, 1.0196190476190476, 1.0223333333333333, 0.9735714285714285, 0.9959999999999999, 1.0085, 0.9892857142857142, 1.0330714285714286, 1.009952380952381, 0.9956428571428572, 0.987452380952381, 1.0193928571428572, 0.9718809523809524, 0.99525, 0.9797380952380953, 1.0156190476190476, 1.0151547619047618, 0.9892261904761903, 0.9749404761904763, 0.9800714285714287, 0.9605952380952383, 0.9573809523809524, 0.9413690476190476, 0.9720714285714285, 0.9290833333333334, 0.9467976190476192, 0.9439880952380955, 0.9432738095238097, 0.9379761904761904, 0.9403809523809524, 0.9576190476190476, 0.9398690476190475, 0.9548452380952381, 0.9805833333333335, 0.9437499999999999, 0.9538928571428572, 0.9518452380952382, 0.9516904761904763, 0.9546428571428572, 0.9664642857142858, 0.970297619047619, 0.9908333333333332, 0.9765238095238095, 1.028857142857143, 0.9816666666666668, 1.0066309523809522, 0.9586785714285714, 0.9707261904761905, 0.9568928571428573, 0.9478214285714288, 0.941404761904762, 0.9496309523809524, 0.9316428571428572, 0.9378214285714286, 0.9407380952380953, 0.9450595238095238, 0.9569404761904763, 0.9839999999999999, 0.9777619047619048, 0.9576666666666666, 1.0031428571428573, 0.9537976190476191, 0.9719047619047619, 0.9743690476190475, 0.9422142857142859, 0.9548809523809525, 0.9542500000000002, 0.9613214285714287, 0.9344166666666667, 0.9326785714285715, 0.9466428571428571, 0.9359285714285716, 0.9450238095238096, 0.9935, 0.9411547619047619, 1.012452380952381, 1.0194761904761906, 1.0358928571428574, 1.0189880952380954, 1.0178333333333331, 0.9909285714285714, 0.9979999999999999, 0.9891666666666667, 1.0107619047619048, 0.9684166666666668, 0.9915, 0.9520833333333334, 0.9583214285714287, 0.9526190476190477, 0.9334404761904762, 0.9391309523809523, 0.9491547619047619, 0.957952380952381, 0.9726071428571428, 0.9424880952380952, 0.9715238095238096, 0.9922500000000002, 0.9422261904761905, 0.9560833333333334, 0.9637142857142857, 0.993940476190476, 0.9565833333333335, 1.0038333333333334, 0.9938928571428572, 0.9814285714285714, 1.014047619047619, 1.007404761904762, 0.9954047619047619, 0.9635595238095237, 0.9784880952380952, 0.9776190476190477, 0.9969166666666665, 1.003309523809524, 1.002857142857143, 1.0241666666666667, 1.0188333333333333, 1.0226785714285715, 1.0227142857142857, 1.0281904761904763, 1.0215357142857142, 0.9937142857142858, 1.0388095238095238, 1.0270119047619048, 0.9913809523809524, 1.030904761904762, 0.9790952380952381, 1.0227380952380951, 0.9751071428571427, 1.008892857142857, 0.9969047619047618, 0.9836309523809526, 1.01825, 1.0328809523809523, 1.028952380952381, 0.993107142857143, 1.0605714285714287, 1.023654761904762, 1.0303214285714286, 1.0046785714285715, 1.0457261904761905, 1.0045714285714284, 0.9628333333333333, 0.9785595238095238, 0.9652261904761905, 0.960714285714286, 0.9547619047619048, 0.971702380952381, 0.9427976190476189, 0.9698333333333332, 0.9939523809523808, 0.9945238095238096, 0.996559523809524, 0.9633214285714287, 0.9827976190476191, 1.0021071428571429, 0.9612142857142856, 0.9770714285714287, 1.0005595238095237, 0.9811190476190477, 1.0037380952380954, 0.9820238095238096, 1.0268690476190476, 1.0045714285714287, 1.0247380952380953, 1.0027380952380953, 1.018845238095238, 0.9894880952380953, 1.0305714285714285, 0.9662261904761906, 0.9734642857142857, 0.9774642857142857, 0.9389880952380952, 0.9499642857142857, 0.9535595238095237, 0.9594166666666668, 0.9907023809523811, 0.993202380952381, 0.9655714285714286, 1.0065476190476192, 1.044690476190476, 1.042011904761905, 1.0346428571428572, 1.049607142857143, 1.0436666666666667, 1.0055833333333335, 1.0064166666666667, 1.0133928571428572, 0.960797619047619, 0.9773571428571428, 0.9823928571428572, 0.9710357142857143, 1.022452380952381, 1.002190476190476, 1.019857142857143, 1.0172023809523811, 1.0120238095238097, 0.9927142857142857, 0.9982738095238096, 1.013404761904762, 0.9950595238095239, 0.9755714285714286, 0.9994880952380951, 1.0246904761904763, 0.9962738095238093, 0.9819047619047618, 1.0052380952380953, 1.0386904761904763, 1.0267738095238097, 1.0144880952380955, 1.0114642857142857, 1.021607142857143, 1.015595238095238, 1.0087619047619047]\n",
      "Isoelectric point =  5.552055549621582\n",
      "Secondary structure fraction =  (0.41090909090909095, 0.20363636363636362, 0.2109090909090909)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 4, 'C': 3, 'D': 1, 'E': 2, 'F': 5, 'G': 1, 'H': 0, 'I': 3, 'K': 2, 'L': 14, 'M': 1, 'N': 5, 'P': 2, 'Q': 0, 'R': 3, 'S': 8, 'T': 4, 'V': 13, 'W': 0, 'Y': 4}\n",
      "Amino acids percent =  {'A': 0.05333333333333334, 'C': 0.04, 'D': 0.013333333333333334, 'E': 0.02666666666666667, 'F': 0.06666666666666667, 'G': 0.013333333333333334, 'H': 0.0, 'I': 0.04, 'K': 0.02666666666666667, 'L': 0.18666666666666668, 'M': 0.013333333333333334, 'N': 0.06666666666666667, 'P': 0.02666666666666667, 'Q': 0.0, 'R': 0.04, 'S': 0.10666666666666667, 'T': 0.05333333333333334, 'V': 0.17333333333333334, 'W': 0.0, 'Y': 0.05333333333333334}\n",
      "Molecular weight =  8364.941399999996\n",
      "Aromaticity =  0.12\n",
      "Flexibility =  [1.0188809523809523, 1.0245238095238096, 1.0285476190476193, 1.0095357142857142, 1.0277380952380952, 1.0026071428571428, 0.9821547619047619, 0.9802261904761904, 0.9744642857142858, 0.9953928571428572, 0.9742261904761904, 0.9490119047619048, 0.9632500000000002, 0.9567738095238095, 0.9453809523809524, 0.9439285714285717, 0.9457261904761906, 0.9239523809523811, 0.9373928571428571, 0.9330238095238096, 0.9289880952380952, 0.9371904761904762, 0.9348333333333334, 0.9404285714285714, 0.9582499999999999, 0.9385952380952383, 0.9630357142857142, 0.9467857142857143, 0.9590238095238094, 0.9732619047619047, 0.9611309523809524, 0.9535714285714285, 0.9747023809523809, 0.9463452380952381, 0.9472738095238095, 0.9584285714285715, 0.9340357142857145, 0.9381785714285714, 0.9400714285714284, 0.9756071428571429, 0.9348809523809524, 0.963357142857143, 0.9912023809523809, 0.9531071428571429, 1.0026666666666668, 0.9659880952380953, 0.9942142857142857, 1.0327857142857144, 0.9940595238095239, 1.000904761904762, 0.963952380952381, 0.9817142857142857, 0.9732023809523811, 0.9634047619047619, 0.9919166666666668, 0.9761666666666667, 0.9787619047619047, 1.035309523809524, 1.0057500000000001, 0.9997261904761905, 1.0404642857142856, 1.019607142857143, 1.0219642857142859, 1.0115714285714286, 0.9980833333333333, 1.0293333333333332]\n",
      "Isoelectric point =  8.568766975402834\n",
      "Secondary structure fraction =  (0.52, 0.21333333333333335, 0.28)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 19, 'C': 4, 'D': 6, 'E': 7, 'F': 11, 'G': 14, 'H': 5, 'I': 20, 'K': 7, 'L': 35, 'M': 4, 'N': 11, 'P': 5, 'Q': 4, 'R': 14, 'S': 15, 'T': 13, 'V': 12, 'W': 7, 'Y': 9}\n",
      "Amino acids percent =  {'A': 0.08558558558558559, 'C': 0.018018018018018018, 'D': 0.02702702702702703, 'E': 0.03153153153153153, 'F': 0.04954954954954955, 'G': 0.06306306306306306, 'H': 0.02252252252252252, 'I': 0.09009009009009009, 'K': 0.03153153153153153, 'L': 0.15765765765765766, 'M': 0.018018018018018018, 'N': 0.04954954954954955, 'P': 0.02252252252252252, 'Q': 0.018018018018018018, 'R': 0.06306306306306306, 'S': 0.06756756756756757, 'T': 0.05855855855855856, 'V': 0.05405405405405406, 'W': 0.03153153153153153, 'Y': 0.04054054054054054}\n",
      "Molecular weight =  25146.305500000013\n",
      "Aromaticity =  0.12162162162162163\n",
      "Flexibility =  [1.0157142857142856, 1.004547619047619, 0.9907499999999998, 1.0124761904761905, 0.9853214285714287, 1.0351547619047619, 1.0208095238095238, 0.9977142857142857, 1.0586904761904763, 1.0322142857142858, 0.9997738095238095, 1.01425, 1.0386309523809525, 0.9978095238095238, 0.9701190476190478, 1.0080119047619047, 0.9590238095238094, 0.9695833333333335, 0.9577261904761907, 0.9721309523809523, 0.9300952380952381, 0.9491428571428573, 0.9392619047619049, 0.9446547619047619, 0.9504761904761904, 0.9175952380952381, 0.9367142857142857, 0.9314761904761905, 0.9427857142857144, 0.9427380952380953, 0.9660357142857143, 0.9329642857142857, 0.9750714285714286, 0.9607023809523811, 0.9905357142857143, 1.0036904761904761, 0.9887857142857144, 1.0061904761904763, 0.9860833333333334, 0.9651428571428572, 0.969452380952381, 0.9577500000000001, 0.9542857142857143, 0.9526190476190477, 0.9881309523809524, 0.9296904761904761, 0.9523214285714287, 0.9443452380952382, 0.9436071428571429, 0.9267261904761904, 0.934797619047619, 0.9337738095238096, 0.935845238095238, 0.9764642857142857, 0.937059523809524, 0.9730595238095238, 0.9478333333333333, 0.96375, 0.9347499999999999, 0.9425119047619047, 0.9443333333333334, 0.9422619047619049, 0.954047619047619, 0.9508928571428571, 0.9449047619047619, 0.9595595238095239, 0.9764880952380951, 0.947738095238095, 0.9851904761904762, 0.9398452380952381, 0.9745238095238093, 0.9869166666666667, 0.985345238095238, 0.9823333333333333, 0.9598333333333334, 0.9869285714285713, 0.9629642857142858, 0.9758452380952379, 0.9527857142857142, 0.958607142857143, 0.9393571428571428, 0.9558333333333332, 0.9509523809523809, 0.9695238095238095, 0.9337380952380953, 0.9554642857142858, 0.9443571428571428, 0.957202380952381, 0.972357142857143, 0.9299404761904763, 0.9519166666666666, 0.9571785714285713, 0.9723809523809525, 0.9781904761904763, 0.9405119047619047, 0.9822619047619048, 0.954, 0.9634642857142857, 0.9815595238095239, 0.9809880952380953, 0.9838333333333333, 0.9888214285714285, 0.9994642857142857, 0.9725714285714288, 0.9757380952380953, 1.0106666666666666, 0.9685714285714285, 1.0239761904761906, 1.0121785714285714, 1.0291904761904764, 1.0039047619047619, 1.0197023809523809, 0.9810238095238096, 0.9877738095238097, 0.9785000000000001, 0.994452380952381, 0.9526190476190476, 0.9972857142857142, 0.9605714285714287, 0.9829761904761904, 0.9934761904761903, 0.9700357142857143, 0.962952380952381, 0.9761666666666665, 0.989452380952381, 0.9825595238095237, 0.9938095238095237, 0.9718333333333335, 1.0022500000000003, 1.0389642857142856, 1.0050357142857143, 1.0192142857142859, 0.9745357142857145, 0.9940714285714287, 0.983309523809524, 0.9911785714285715, 0.9566309523809524, 0.9497142857142856, 0.9610476190476192, 0.9652380952380953, 0.9798214285714286, 0.974452380952381, 0.9563214285714287, 0.9685238095238095, 0.9877380952380952, 0.9600476190476191, 0.9824285714285714, 0.9793452380952381, 0.9592499999999999, 0.9751547619047619, 0.9685833333333332, 0.9951904761904761, 0.9750595238095239, 0.9649285714285712, 1.0236547619047618, 0.9754047619047619, 1.041702380952381, 1.0140238095238094, 1.0046547619047619, 1.0532380952380953, 1.0432380952380953, 1.0305952380952381, 0.9873690476190475, 1.016952380952381, 0.9869404761904763, 1.0018690476190475, 0.9923214285714285, 0.9990714285714287, 0.9909166666666666, 0.9938214285714285, 0.9794404761904761, 1.0105476190476192, 0.9648809523809525, 0.9832023809523809, 1.0182261904761905, 0.9634047619047621, 1.018, 0.9962023809523809, 1.0198452380952383, 1.0078690476190477, 0.9993928571428571, 0.9914404761904763, 1.0147380952380953, 1.0196547619047618, 1.0192261904761906, 1.00925, 1.0080833333333334, 0.9810714285714287, 1.0062619047619048, 0.987845238095238, 0.9704285714285715, 1.001309523809524, 0.9732261904761904, 0.968404761904762, 0.9989761904761905, 0.9695238095238095, 1.011, 0.9976428571428573, 0.9778928571428571, 1.03675, 0.9800952380952381, 1.032309523809524, 1.0035357142857142, 1.028357142857143, 0.9989285714285716, 1.038452380952381, 1.0308214285714288, 1.036, 1.036166666666667, 1.0363928571428573, 1.0220714285714287, 0.986404761904762, 0.9995833333333334]\n",
      "Isoelectric point =  9.509102821350098\n",
      "Secondary structure fraction =  (0.42342342342342343, 0.20270270270270271, 0.2927927927927928)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 1, 'C': 0, 'D': 4, 'E': 5, 'F': 3, 'G': 0, 'H': 1, 'I': 10, 'K': 4, 'L': 8, 'M': 3, 'N': 4, 'P': 1, 'Q': 3, 'R': 1, 'S': 4, 'T': 3, 'V': 3, 'W': 1, 'Y': 2}\n",
      "Amino acids percent =  {'A': 0.01639344262295082, 'C': 0.0, 'D': 0.06557377049180328, 'E': 0.08196721311475409, 'F': 0.04918032786885246, 'G': 0.0, 'H': 0.01639344262295082, 'I': 0.16393442622950818, 'K': 0.06557377049180328, 'L': 0.13114754098360656, 'M': 0.04918032786885246, 'N': 0.06557377049180328, 'P': 0.01639344262295082, 'Q': 0.04918032786885246, 'R': 0.01639344262295082, 'S': 0.06557377049180328, 'T': 0.04918032786885246, 'V': 0.04918032786885246, 'W': 0.01639344262295082, 'Y': 0.03278688524590164}\n",
      "Molecular weight =  7272.456599999998\n",
      "Aromaticity =  0.09836065573770492\n",
      "Flexibility =  [0.9877738095238093, 0.9425357142857143, 0.995345238095238, 0.9550714285714286, 0.9914047619047618, 0.9671309523809525, 0.9868452380952382, 0.9996071428571428, 0.9434642857142858, 0.9666785714285714, 0.9555833333333335, 0.9508095238095238, 0.9502023809523811, 0.9521547619047619, 0.9716785714285714, 0.9687619047619048, 0.9652023809523811, 1.0203690476190477, 0.9558690476190477, 1.0072738095238096, 0.958511904761905, 0.97375, 1.004404761904762, 0.9532619047619049, 0.9975714285714284, 0.9471785714285714, 0.9692261904761904, 0.9655952380952383, 0.982095238095238, 0.9449761904761906, 0.9661666666666665, 0.9739404761904763, 1.0191904761904762, 0.9944047619047619, 0.9916428571428572, 1.0387738095238097, 1.035809523809524, 1.020607142857143, 1.005452380952381, 1.0390119047619049, 1.0493928571428572, 1.0254761904761904, 1.0433214285714287, 0.9962261904761905, 1.0472976190476193, 1.0248095238095236, 1.0074166666666668, 1.0544047619047618, 1.043857142857143, 1.0467142857142857, 1.0380714285714288, 1.0438690476190478]\n",
      "Isoelectric point =  4.677076148986816\n",
      "Secondary structure fraction =  (0.4426229508196722, 0.14754098360655737, 0.2786885245901639)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 9, 'C': 6, 'D': 2, 'E': 8, 'F': 10, 'G': 4, 'H': 3, 'I': 8, 'K': 7, 'L': 15, 'M': 1, 'N': 2, 'P': 6, 'Q': 5, 'R': 5, 'S': 7, 'T': 10, 'V': 8, 'W': 0, 'Y': 5}\n",
      "Amino acids percent =  {'A': 0.0743801652892562, 'C': 0.049586776859504134, 'D': 0.01652892561983471, 'E': 0.06611570247933884, 'F': 0.08264462809917356, 'G': 0.03305785123966942, 'H': 0.024793388429752067, 'I': 0.06611570247933884, 'K': 0.05785123966942149, 'L': 0.12396694214876033, 'M': 0.008264462809917356, 'N': 0.01652892561983471, 'P': 0.049586776859504134, 'Q': 0.04132231404958678, 'R': 0.04132231404958678, 'S': 0.05785123966942149, 'T': 0.08264462809917356, 'V': 0.06611570247933884, 'W': 0.0, 'Y': 0.04132231404958678}\n",
      "Molecular weight =  13744.013599999984\n",
      "Aromaticity =  0.12396694214876033\n",
      "Flexibility =  [0.9447142857142858, 0.9467857142857143, 0.9500595238095239, 0.936452380952381, 0.9478690476190478, 0.9683214285714286, 0.9474642857142859, 0.9754285714285715, 0.9729166666666667, 0.9559999999999998, 1.0084880952380952, 0.9422261904761907, 0.9677261904761907, 0.9750000000000001, 0.9678809523809525, 0.9957142857142858, 0.9899404761904764, 0.9512023809523809, 0.9902261904761905, 1.0024047619047618, 0.9945357142857143, 0.9794285714285713, 0.9777619047619048, 0.9684880952380952, 0.9849404761904763, 0.990059523809524, 1.0273452380952381, 1.008952380952381, 1.011095238095238, 0.9965119047619047, 1.0479761904761904, 1.0199166666666668, 1.013392857142857, 1.0069166666666667, 1.0011785714285715, 1.0515833333333335, 1.0160119047619047, 1.029404761904762, 1.0221785714285716, 1.0205357142857143, 0.9855833333333334, 1.0009285714285714, 1.0098809523809522, 0.9720238095238096, 1.0083095238095237, 1.021202380952381, 1.0123571428571427, 1.026202380952381, 0.9742857142857143, 1.0084166666666665, 0.9715476190476192, 0.9810714285714285, 0.9469285714285715, 0.9640595238095238, 0.9934761904761906, 0.9678333333333333, 0.9862976190476191, 0.9543571428571428, 0.9837023809523809, 0.9564404761904761, 0.980952380952381, 0.9601428571428572, 1.0063333333333335, 1.0019761904761904, 0.9996190476190476, 0.9886309523809524, 1.0326309523809525, 0.9713809523809525, 0.981107142857143, 0.9749880952380953, 0.9935238095238096, 0.959047619047619, 0.9928571428571428, 0.9811190476190477, 0.9978809523809524, 1.0086547619047619, 0.9875, 1.0342380952380954, 1.014297619047619, 1.0200357142857144, 0.9712380952380952, 0.9865595238095237, 0.9916666666666668, 1.0094166666666669, 1.0065357142857143, 1.0220238095238097, 1.0283452380952383, 1.0014642857142857, 1.0443690476190477, 1.0310952380952383, 0.985095238095238, 0.9992500000000002, 1.012511904761905, 0.9889166666666666, 0.9535119047619047, 0.9575952380952382, 0.9573452380952381, 0.9489166666666667, 0.9455357142857143, 0.953607142857143, 0.9471547619047618, 0.9361547619047619, 0.9462261904761905, 0.940797619047619, 0.9430833333333333, 0.9511309523809524, 0.9285, 0.9345595238095238, 0.9491904761904761, 0.9740952380952381, 0.9653214285714284, 1.0230119047619048]\n",
      "Isoelectric point =  8.23082370758057\n",
      "Secondary structure fraction =  (0.3801652892561984, 0.15702479338842976, 0.2727272727272727)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 2, 'C': 2, 'D': 2, 'E': 3, 'F': 6, 'G': 0, 'H': 2, 'I': 5, 'K': 0, 'L': 11, 'M': 2, 'N': 1, 'P': 0, 'Q': 1, 'R': 0, 'S': 2, 'T': 1, 'V': 1, 'W': 1, 'Y': 1}\n",
      "Amino acids percent =  {'A': 0.046511627906976744, 'C': 0.046511627906976744, 'D': 0.046511627906976744, 'E': 0.06976744186046512, 'F': 0.13953488372093023, 'G': 0.0, 'H': 0.046511627906976744, 'I': 0.11627906976744186, 'K': 0.0, 'L': 0.2558139534883721, 'M': 0.046511627906976744, 'N': 0.023255813953488372, 'P': 0.0, 'Q': 0.023255813953488372, 'R': 0.0, 'S': 0.046511627906976744, 'T': 0.023255813953488372, 'V': 0.023255813953488372, 'W': 0.023255813953488372, 'Y': 0.023255813953488372}\n",
      "Molecular weight =  5180.213099999999\n",
      "Aromaticity =  0.18604651162790697\n",
      "Flexibility =  [0.9632500000000002, 0.9761666666666665, 0.9988214285714287, 0.9340119047619049, 0.9544761904761905, 0.9419523809523809, 0.9323333333333333, 0.9351785714285715, 0.9332261904761907, 0.9427023809523809, 0.9223452380952382, 0.9378690476190477, 0.9342142857142856, 0.9294642857142857, 0.9348095238095239, 0.9297142857142857, 0.9330833333333334, 0.931595238095238, 0.938392857142857, 0.9308571428571429, 0.9296309523809524, 0.9276785714285716, 0.9278214285714286, 0.9297857142857143, 0.9441666666666665, 0.9783452380952381, 0.9499523809523809, 1.0159880952380953, 0.969011904761905, 1.0255833333333333, 1.023904761904762, 0.9988690476190476, 1.0370238095238098, 1.0222261904761905]\n",
      "Isoelectric point =  4.167344093322753\n",
      "Secondary structure fraction =  (0.5813953488372092, 0.06976744186046512, 0.4186046511627907)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 5, 'C': 7, 'D': 7, 'E': 6, 'F': 8, 'G': 5, 'H': 4, 'I': 10, 'K': 5, 'L': 10, 'M': 1, 'N': 2, 'P': 7, 'Q': 6, 'R': 4, 'S': 9, 'T': 5, 'V': 12, 'W': 1, 'Y': 7}\n",
      "Amino acids percent =  {'A': 0.04132231404958678, 'C': 0.05785123966942149, 'D': 0.05785123966942149, 'E': 0.049586776859504134, 'F': 0.06611570247933884, 'G': 0.04132231404958678, 'H': 0.03305785123966942, 'I': 0.08264462809917356, 'K': 0.04132231404958678, 'L': 0.08264462809917356, 'M': 0.008264462809917356, 'N': 0.01652892561983471, 'P': 0.05785123966942149, 'Q': 0.049586776859504134, 'R': 0.03305785123966942, 'S': 0.0743801652892562, 'T': 0.04132231404958678, 'V': 0.09917355371900827, 'W': 0.008264462809917356, 'Y': 0.05785123966942149}\n",
      "Molecular weight =  13830.853599999988\n",
      "Aromaticity =  0.1322314049586777\n",
      "Flexibility =  [0.9480595238095239, 0.9510476190476191, 0.9649523809523809, 0.9366904761904762, 0.9575952380952379, 0.9750238095238095, 0.9681547619047619, 0.9551309523809525, 0.9727261904761905, 0.9671904761904762, 0.9611785714285714, 0.9802261904761904, 0.9986428571428573, 1.002047619047619, 0.961297619047619, 1.024154761904762, 0.9786666666666667, 1.015797619047619, 0.9982619047619048, 0.9652380952380952, 1.0092619047619047, 1.0031547619047618, 0.9817857142857145, 1.0091309523809522, 0.9913690476190476, 0.9674761904761905, 0.9864523809523809, 0.9878214285714284, 1.017892857142857, 1.0013333333333332, 0.9995119047619048, 0.9745, 1.0180714285714285, 0.9585714285714284, 0.9726071428571428, 0.9599285714285715, 0.9680714285714287, 0.9937857142857143, 0.9856428571428573, 0.9419047619047619, 0.9754761904761906, 0.9692142857142859, 0.9846428571428573, 0.9560238095238095, 0.9963809523809521, 0.9869285714285715, 1.0117500000000001, 1.0375357142857144, 1.0135952380952382, 1.0082976190476192, 1.0267380952380951, 0.9854880952380953, 0.9904642857142859, 1.0102261904761907, 0.9481428571428572, 0.97225, 0.983297619047619, 1.0168809523809526, 1.012511904761905, 0.9982380952380953, 1.0392857142857141, 1.0452857142857144, 1.055261904761905, 1.0313452380952381, 1.0310119047619046, 0.9943809523809523, 1.0252261904761906, 0.9722142857142857, 0.9815952380952382, 1.0093214285714287, 0.9589642857142857, 1.0080357142857141, 0.9919166666666667, 0.9698690476190477, 0.9983809523809525, 0.9645595238095239, 0.9981666666666668, 0.9451785714285714, 0.9714404761904761, 0.988107142857143, 0.945797619047619, 0.9840000000000001, 0.9572261904761906, 1.0033452380952381, 0.9657142857142856, 1.0273452380952381, 1.0286309523809525, 1.0201785714285714, 1.045654761904762, 0.9993690476190477, 1.038690476190476, 1.00975, 0.9718690476190476, 0.974404761904762, 0.9663928571428573, 0.9782500000000002, 0.9404404761904762, 0.9885, 0.9506428571428571, 0.9816071428571428, 1.0152380952380955, 0.9906666666666668, 0.9685714285714285, 0.9922023809523809, 1.0224642857142858, 0.9618809523809524, 0.9884285714285715, 1.0033809523809523, 0.9566428571428572, 0.9888333333333333, 0.955654761904762, 0.9663690476190476]\n",
      "Isoelectric point =  5.416778755187988\n",
      "Secondary structure fraction =  (0.396694214876033, 0.19008264462809918, 0.18181818181818182)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 37, 'C': 0, 'D': 24, 'E': 12, 'F': 13, 'G': 43, 'H': 4, 'I': 14, 'K': 31, 'L': 27, 'M': 7, 'N': 22, 'P': 28, 'Q': 35, 'R': 29, 'S': 37, 'T': 32, 'V': 8, 'W': 5, 'Y': 11}\n",
      "Amino acids percent =  {'A': 0.0883054892601432, 'C': 0.0, 'D': 0.057279236276849645, 'E': 0.028639618138424822, 'F': 0.031026252983293555, 'G': 0.1026252983293556, 'H': 0.00954653937947494, 'I': 0.03341288782816229, 'K': 0.07398568019093078, 'L': 0.06443914081145585, 'M': 0.016706443914081145, 'N': 0.05250596658711217, 'P': 0.06682577565632458, 'Q': 0.08353221957040573, 'R': 0.06921241050119331, 'S': 0.0883054892601432, 'T': 0.07637231503579953, 'V': 0.01909307875894988, 'W': 0.011933174224343675, 'Y': 0.026252983293556086}\n",
      "Molecular weight =  45625.13830000008\n",
      "Aromaticity =  0.06921241050119331\n",
      "Flexibility =  [1.044154761904762, 1.0403214285714284, 1.0424404761904762, 1.033392857142857, 1.0268690476190476, 1.035988095238095, 1.0089047619047617, 1.025392857142857, 0.9954404761904763, 0.9788928571428572, 1.0009285714285714, 0.9722380952380951, 1.0145119047619047, 1.004392857142857, 1.021202380952381, 1.0289285714285714, 1.0399880952380953, 1.0373095238095238, 1.0303333333333333, 1.0433214285714285, 1.0398333333333334, 1.0379642857142857, 1.038297619047619, 1.0438214285714285, 1.0414999999999999, 1.0567380952380954, 1.0259404761904762, 1.0426190476190476, 1.028952380952381, 1.0215, 1.0339166666666668, 1.036916666666667, 1.0447738095238097, 1.0249404761904761, 1.0307261904761904, 1.035238095238095, 1.0371428571428571, 1.0235119047619048, 1.020297619047619, 1.0022857142857144, 1.0401547619047617, 1.0203214285714286, 1.0212619047619047, 1.005642857142857, 1.0020357142857144, 1.0088095238095238, 0.9591785714285714, 0.9731190476190477, 0.980642857142857, 0.9667380952380953, 0.9615119047619047, 0.9861904761904763, 0.9979999999999999, 0.9897261904761907, 1.0331071428571428, 1.0422976190476192, 1.0417738095238096, 1.0372619047619047, 1.011845238095238, 1.06125, 0.9893571428571429, 1.040404761904762, 1.0078214285714284, 1.0172738095238096, 1.0178928571428572, 1.0088809523809523, 0.9906309523809524, 1.0242499999999997, 0.9801190476190474, 1.0261190476190476, 1.0009642857142855, 1.02775, 1.0301785714285716, 1.0382142857142858, 1.0473095238095236, 1.0476309523809524, 1.042452380952381, 1.0256666666666665, 0.9940833333333335, 1.0185357142857143, 0.9700952380952381, 0.9787738095238097, 0.9906190476190475, 0.9806547619047619, 0.9835119047619049, 0.99, 0.9937738095238096, 0.9948928571428571, 0.9797380952380953, 1.013452380952381, 1.0117142857142856, 1.0198333333333334, 1.0334285714285714, 1.0293809523809523, 1.0584166666666668, 1.015095238095238, 1.066702380952381, 1.0324404761904762, 1.007547619047619, 1.0413333333333332, 1.0107380952380953, 0.9912738095238096, 0.9588214285714285, 0.9638809523809525, 0.9455595238095239, 0.9436785714285715, 0.9409642857142857, 0.9508809523809523, 0.9853571428571428, 0.9827380952380952, 1.0113690476190476, 1.0226785714285715, 1.033964285714286, 1.0075833333333333, 1.0264880952380953, 0.9932261904761907, 1.023345238095238, 0.9769047619047619, 1.0193214285714287, 1.0028333333333332, 1.0331904761904762, 1.040047619047619, 1.0223333333333333, 1.013, 0.9807261904761905, 0.9805357142857144, 0.9600833333333334, 0.9643333333333334, 0.9752738095238094, 0.9803095238095239, 1.0095476190476191, 0.9929761904761905, 1.0001666666666669, 1.0003333333333335, 1.0340595238095238, 1.0116309523809524, 1.0321785714285714, 1.0347380952380953, 1.021047619047619, 1.0001428571428572, 1.0052261904761903, 1.0244880952380953, 0.9995238095238095, 1.0069761904761905, 1.0181190476190476, 1.0212857142857141, 1.0130357142857143, 1.0340119047619047, 1.017595238095238, 0.995095238095238, 0.9910833333333334, 0.969404761904762, 0.9703333333333333, 0.9666309523809524, 0.9894166666666667, 0.9628333333333335, 1.0125238095238094, 1.0017142857142856, 1.0068333333333332, 1.0034285714285711, 1.010357142857143, 1.002011904761905, 1.0311190476190477, 1.01925, 0.9933571428571428, 0.9814047619047619, 1.0008214285714285, 1.011809523809524, 1.0266904761904763, 1.0017857142857143, 1.0235714285714286, 1.0225, 1.0378690476190475, 1.0322738095238095, 1.032202380952381, 1.0282380952380952, 1.0179404761904762, 1.0394761904761904, 1.0300952380952382, 1.0247500000000003, 1.0385952380952381, 1.034, 1.0351428571428571, 1.0266428571428574, 1.0398333333333334, 1.0272380952380953, 1.040357142857143, 1.0338809523809527, 1.0356190476190479, 1.027857142857143, 1.0411071428571428, 1.0339166666666668, 1.0239761904761906, 1.0423214285714286, 1.0297857142857143, 1.036845238095238, 1.031845238095238, 1.0231428571428571, 1.0340238095238095, 1.0197857142857143, 1.0325357142857143, 1.0194761904761904, 1.000392857142857, 1.0114761904761904, 0.9934404761904762, 1.00925, 1.015, 1.017797619047619, 1.0185714285714285, 1.024202380952381, 1.0294761904761904, 1.0003571428571427, 1.0013928571428572, 0.9768214285714285, 0.9809880952380953, 0.95125, 0.9553333333333333, 0.9577261904761907, 0.9610000000000002, 0.9973214285714287, 0.9744761904761906, 0.9775357142857143, 1.020857142857143, 1.0085238095238094, 1.0010357142857143, 1.050547619047619, 1.021464285714286, 1.0469880952380952, 1.0110000000000001, 1.0555476190476192, 1.0402142857142858, 1.0558452380952383, 1.0310833333333331, 1.043047619047619, 1.0434523809523808, 1.0409166666666667, 1.0369761904761903, 1.0262619047619044, 1.0215, 1.009404761904762, 1.0034404761904763, 1.0300833333333332, 1.043952380952381, 1.0314285714285716, 1.026654761904762, 1.0240357142857144, 1.0341428571428573, 1.0555952380952383, 1.0176190476190476, 1.052357142857143, 1.0584404761904762, 1.055761904761905, 1.0502142857142855, 1.0470476190476192, 1.055857142857143, 1.0555833333333335, 1.016547619047619, 1.024857142857143, 1.018702380952381, 1.0171428571428571, 1.0323690476190477, 0.9844999999999999, 0.9892619047619049, 1.0185238095238096, 0.9736071428571429, 1.0004880952380952, 0.9936190476190476, 0.9792619047619047, 0.9772976190476191, 1.012190476190476, 0.9965119047619049, 1.0101190476190478, 1.0232142857142859, 1.0317738095238094, 1.0462619047619048, 1.029297619047619, 1.0315833333333333, 1.038892857142857, 1.0249166666666667, 1.025202380952381, 0.9924880952380952, 1.0404166666666665, 1.0295238095238095, 1.0152261904761903, 1.0284285714285715, 0.983654761904762, 1.0064047619047618, 1.017059523809524, 1.0100119047619047, 1.004238095238095, 1.0035, 1.0275357142857144, 0.9855833333333335, 1.0381071428571427, 0.9754880952380951, 0.9851547619047619, 1.0162023809523808, 0.9888214285714285, 0.9687738095238096, 0.9922142857142857, 0.9962738095238095, 0.9692738095238095, 1.0028809523809523, 1.0096904761904761, 1.0067738095238097, 0.9949285714285714, 1.0104166666666667, 0.9850714285714285, 0.9722142857142858, 0.9763928571428571, 0.9972142857142856, 0.9636785714285714, 0.9990595238095239, 0.9795, 0.978904761904762, 1.0137261904761905, 0.9799285714285714, 1.0252857142857144, 0.972654761904762, 1.018047619047619, 1.0228214285714285, 1.0116190476190476, 1.005202380952381, 0.991392857142857, 0.9697023809523809, 0.9805357142857144, 0.981297619047619, 0.9566904761904763, 0.9814999999999999, 0.9862380952380951, 0.9789285714285715, 0.9831428571428572, 1.033964285714286, 0.9854166666666667, 1.050547619047619, 1.038857142857143, 1.0574880952380954, 1.0473214285714285, 1.046857142857143, 1.049761904761905, 1.017642857142857, 1.0668928571428573, 1.018535714285714, 1.0073809523809525, 0.976845238095238, 0.9822023809523809, 0.9829285714285715, 0.976202380952381, 0.9983333333333333, 0.9992500000000002, 0.96975, 0.9911666666666668, 1.0258214285714287, 0.9879166666666667, 0.985857142857143, 1.027261904761905, 0.9845595238095238, 0.9896666666666667, 1.0319761904761906, 1.0183333333333333, 1.0211666666666666, 1.0578333333333334, 1.046202380952381, 1.0784880952380953, 1.0775238095238096, 1.0782857142857143, 1.097202380952381, 1.0885952380952382, 1.0865, 1.081904761904762, 1.0499285714285715, 1.0774642857142858, 1.0574404761904763, 1.0209166666666667, 1.033952380952381, 1.0082142857142857, 1.0032976190476188, 1.0299047619047619, 1.0126428571428572, 1.0207976190476191, 1.0405595238095238, 1.0588333333333335, 1.054654761904762, 1.0362619047619048, 1.0379166666666666, 1.0155357142857144, 0.9907976190476191, 1.0000238095238094, 0.9669523809523811, 0.9741309523809524, 1.0006071428571428, 0.9738095238095238, 0.9946785714285714, 1.0194880952380951, 0.9838809523809525, 1.0362976190476192, 1.017988095238095, 0.9940833333333333, 1.0444523809523811, 1.0334761904761907, 1.0157857142857143, 1.0046547619047619, 1.0363809523809524, 1.0214761904761904, 1.0221190476190478, 0.998452380952381, 1.0347738095238097, 1.0247261904761906, 1.013261904761905, 1.04125]\n",
      "Isoelectric point =  10.069010734558105\n",
      "Secondary structure fraction =  (0.18615751789976134, 0.31026252983293556, 0.19809069212410502)\n",
      "\n",
      "\n",
      "Protein of Interest =  {'A': 2, 'C': 1, 'D': 1, 'E': 0, 'F': 4, 'G': 1, 'H': 0, 'I': 3, 'K': 0, 'L': 4, 'M': 2, 'N': 5, 'P': 1, 'Q': 1, 'R': 2, 'S': 2, 'T': 2, 'V': 4, 'W': 0, 'Y': 3}\n",
      "Amino acids percent =  {'A': 0.05263157894736842, 'C': 0.02631578947368421, 'D': 0.02631578947368421, 'E': 0.0, 'F': 0.10526315789473684, 'G': 0.02631578947368421, 'H': 0.0, 'I': 0.07894736842105263, 'K': 0.0, 'L': 0.10526315789473684, 'M': 0.05263157894736842, 'N': 0.13157894736842105, 'P': 0.02631578947368421, 'Q': 0.02631578947368421, 'R': 0.05263157894736842, 'S': 0.05263157894736842, 'T': 0.05263157894736842, 'V': 0.10526315789473684, 'W': 0.0, 'Y': 0.07894736842105263}\n",
      "Molecular weight =  4449.177900000001\n",
      "Aromaticity =  0.18421052631578946\n",
      "Flexibility =  [0.9412261904761905, 0.958297619047619, 0.9699642857142857, 0.9484166666666665, 0.9870119047619047, 0.9354047619047619, 0.9786309523809523, 0.9519166666666669, 0.961904761904762, 0.9832261904761905, 0.935952380952381, 0.9548095238095239, 0.9507738095238096, 0.9494404761904762, 0.9822142857142857, 0.9625595238095238, 1.0065357142857143, 1.000047619047619, 0.9969880952380953, 1.0104880952380955, 0.9756547619047619, 0.9871904761904763, 0.993952380952381, 0.9915714285714285, 0.9604404761904762, 1.006154761904762, 0.9568333333333334, 0.9817261904761905, 1.0007857142857144]\n",
      "Isoelectric point =  7.933108711242677\n",
      "Secondary structure fraction =  (0.47368421052631576, 0.23684210526315788, 0.21052631578947367)\n"
     ]
    }
   ],
   "source": [
    "# 1. Protein Analysis With The Protparam Module In Biopython\n",
    "#from __future__ import division\n",
    "poi_list = []\n",
    "MW_list = []\n",
    "for record in Proteins[:]: \n",
    "    print(\"\\n\")\n",
    "    X = ProtParam.ProteinAnalysis(str(record))\n",
    "    POI = X.count_amino_acids()\n",
    "    poi_list.append(POI)\n",
    "    MW = X.molecular_weight()\n",
    "    MW_list.append(MW)\n",
    "    print(\"Protein of Interest = \", POI) \n",
    "    print(\"Amino acids percent = \", str(X.get_amino_acids_percent())) \n",
    "    print(\"Molecular weight = \", MW)\n",
    "    print(\"Aromaticity = \", X.aromaticity()) \n",
    "    print(\"Flexibility = \", X.flexibility()) \n",
    "    print(\"Isoelectric point = \", X.isoelectric_point()) \n",
    "    print(\"Secondary structure fraction = \", X.secondary_structure_fraction())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2ea116ab",
   "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>Molecular Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>794048.9258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489983.4465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141176.8160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31122.5544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8364.9414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Molecular Weights\n",
       "0        794048.9258\n",
       "1        489983.4465\n",
       "2        141176.8160\n",
       "3         31122.5544\n",
       "4          8364.9414"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MoW = pd.DataFrame(data = MW_list,columns = [\"Molecular Weights\"] )\n",
    "MoW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6c73119e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(poi_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4ed94a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot POI\n",
    "poi_list = poi_list[len(poi_list)-1]\n",
    "plt.figure(figsize=(10,6));\n",
    "plt.bar(poi_list.keys(), list(poi_list.values()), align='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "348e45e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Length of proteins -- histogram')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot lengths\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(111)\n",
    "plt.hist(functional_proteins['length'])\n",
    "plt.title('Length of proteins -- histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "86a509ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lenght of proteins (where < 60)')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove the extremes\n",
    "plt.figure(figsize=(20,5))\n",
    "wo = functional_proteins.loc[functional_proteins['length'] < 60]\n",
    "plt.subplot(121)\n",
    "plt.hist(wo['length'])\n",
    "plt.title('Lenght of proteins (where < 60)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a1423aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Length of proteins (where > 1000)')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IUyMPcdRjRW/XmZJe2WD5R0kHffeyZueoa0iPE4wjPWi8mlze6/PL75SX5c5is/PLf6x2qMoH2ifklz/O+28jryF9bh8jjVQyqE4D17qSthjkb92I+CtpBIMA3inpsvI4VPlD3Sfp30hHLsMid3cuNu75kt4laXyucxdSj59WN2j/X57uP5Sus0PwedKR8VbAf0vaEdJ4YnmAzGLMsW/ldR0JjwI/lPTm4otX0t6kbbc+aRs1HHCzlYj4OWmYI4ALJB1S9LBT+umDq0mXC+4HvlLJfjvp2asJkt5GA73a51rYFfhjPlqfXNqW6+Z1KAYVbdh1v4XiqHX3QdIV96uKdNWzqesry4f7/hYRcSXpYEzApZL+SdKqjj2Snq/0U0I/oov7QjnQnplfni3pXyUVl5uRtImkmZK+TecHW68jDSM1HriRNAjtiyLifRFxXYsepl3J+8Wq71EGuo6r8v3aKABfThqGbBxpO++ZM64v6UTSM3wAn4mI1Tr2RBqXcFZ+eYKkE5XGL0TSXqTOO+NIBz4/aVD36aTvjG1I3xvb5LwTSQcuu5POtlpdEi/2wRsjop375B0/gNzO3/RSviMZeDgzSEcsS1l9CJ6o1DWp0fxKmuk0eSCVdLb0m1L5xVh1QYro78z/r2iQd4vcviDdFL6X9FDronbqLqU5gg4e2K7kfSurD/n0MKsPG/Q/lIZ86qRdg9Q7N+evDvlU/nmDB4BdGuSdlZdfNEgdEyvvzVOsOeTTXk3y/kcp3bLifQEOqaTreJ/L+YplkzpdNxo8uA68qlxf3g+XsvpwVL+iyfhtLeraJ+e9kxbjYZI+B+W6pjRIM7+0/MPd7sutthHpWZ9LS/WszPt0+X0P4MIu96l1SN3Py2U9wuo/rRHAdR1u57eQntOc3Em+bv9oY7xUWny+GfxnTb45SP3lnzV5mlH6WZOc/39y2iPb3V4jOjp8RFxIGnHhLNKR+rOkYVSWkr4oP5OXD2edy0hdgT9L+gIW6Uvju6SjqFty0mUN8j5Iur/0Q1Jngomkh1pfOpxtbCUifgy8nNRTaRHpDPFJ0pH2UaRRITrpJt6ppaTtdBbp7Gc9Uu+h80jjw3V9OSnSjeS9SPcJ+kk79XqkM6qzSENk/V+T7EeTvkhuJZ35Fe/Lar3yerHPNXEL6dLqOaRgvSy34xHSe/lh0jBHzS6fNBQR80jbaxsGnmNqlG4Z6Z4MpP2n0TOR5bOwYT/jyu14IiIOJg0h9kPSvrQRqev8HaSz9yNJ26Ob8p+NiH8E/pY0ZuGdpP1jA9JD4z8inYl39MB+RPx3RPxLRMzvpl2jLSLuIR0sfZ70GRlPCj7XAW+PiGMGyX8UadzT60jd1sfnck4jfe6bPkYUET/LdV9I6riyIem74xLS2KPfbpY33yKYntva9pUc5Yi31pD0D6QRKK6PiOk9bs6YofTr1tNIRz0X9bY11oqkj5Hu430jIo7tdXvMuiXpw6RbIOdGRMv7lGVr1e9xKf0WzXH55c962RazIfgm6fmhI7T6sE1mtZHvcR9PurT/+U7yPucCl6RtJF0oae+il4ukcZJeR7oR/nLS5ZrzW5VjNlblS8Wnku4fnTBIcrOx6l2koe7OiQ57Z47UkE+9tB7phvIRAJKWka53F092LyfdLLy/QV6zujiPNIyTf/Ha6ipII+5/o9OMz8XAtZjUM24maRiWiaQNdDtpMMwvR/Px2MxqIdIzWZ/rdTvMutWq08Zg1rrOGda9LbbYIiZNmtTrZthz1M033/xgRIzU4NH2HPJcPOOyETJp0iT6+/sHT2jWBUljYRQKq4HnXOcMMzN7bnPgMjOzWnHgMjOzWnHg6pKkrSVdIGmxpBWSFkk6q4tRqJ+f8y3K5SzO5W7dJP0iSdHk774W9UyRdIWkhyQ9Jen3eQDYofyUvJnZqHPnjC5I2p40YvSWpJGZbyWN73ccaWT5qREx6C955lEPbgQmk7rqzyZ14T8SeIukvSL9im7VIwz8nlNZw2d6JB0I/ID0DNv3SIPZvpU0svZU4NDB2mpmNlY4cHXnbFLQ+khEfK2YKekM0kgGp9H6d78KnycFrTMiYtVv4Uj6COmnPc6m8Y/kLYs2f3pe6TfIziMNNjs9Ivrz/JNIwfIQSYdFxOwWxZiZjRm+VNihfLY1kzRye/WJ78+Qhvd/b/VH1RqU8zzgvTn9rMrir5NGuX6j0k+vD8UhpIewZxdBCyAilgOfzi9bjhxtZjaWOHB1bt88vTrSL4CuEhGPATeQfrZhz2rGij1Jw//fkPOVy1nJwA8M7lvNCKwv6T2SPinpOEn7trhXtV+eXtlg2TzST15MKX48zsxsrHPg6lzxW07NfqenGE5q8giW8yLST4ifRrrXdS1wu6RpndSThw36M+mSccMzO0lHSeqX1L9kyZImTTUzGz0OXJ2bkKePNFlezN9shMq5EJhBCl4bk0a7/3fSL0f/VNIrh7O9EXFuRPRFRN/EiR6Nx8x6z50zaiYiTqnM+iNwtKTHSYMLzwIOHu12mZmNFp9xda44Q5nQZHkxf9kolVM4J0/3GeF6zMx6yoGrc7flabN7WDvkabN7V8NdTqG4AVXtzdi0HknjgW2BZ4BGz4uZmY05Dlyduy5PZ0pabftJ2oT0QO+TwE2DlHMT8BQwNecrlzOO1OW+XN9gil6M1QB0bZ42eh5sH1IPyBsjYkWb9ZiZ9ZQDV4ciYgFwNakzxIcqi08hnfFcnH9eHQBJO0naqVLO46SegRuz5nNcx+byryqPnCFp50bPh0maRHr2C6D642xzgAeBwyT1lfJswMAPEX6z4cqamY1B/iHJLjQY8ukWYA/SM1fzgSnlIZ8kBUBEqFJOdcinXwI7AwcCD+RyFpTSzyJ1wJhHekD5MWB74C3ABsAVwMER8XSlnoNIAWw5aViph4ADSF3l5wBvjzZ2hL6+vvDvcdlIkXRzRPQNntLWdu5V2IWIWJDPXk4lXYJ7M3AvaZimUyLi4TbLWSppL9KIGwcBewNLSV3eT46IeypZriMFm1eTLkluTOpU8b+ks7eLGwWgiLgsP+P1KeBtpCB3B/BR4KvtBC0zs7HCZ1zWNp9x2UjyGZe1y/e4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4zMysVhy4uiRpa0kXSFosaYWkRZLOkrR5h+U8P+dblMtZnMvdukHaF0h6v6RLJd0h6SlJj0j6X0n/IGmN91PSJEnR4m/2ULaDmdloG9/rBtSRpO2BG4EtgcuBW4HXAccB+0uaGhFL2yjnBbmcycC1wGxgJ+BI4C2S9oqIhaUshwLfBO4FrgPuAl4I/D1wPvAmSYdGRDSo7nfAZQ3m/3HQFTYzG0McuLpzNilofSQivlbMlHQGcAJwGnB0G+V8nhS0zoiIE0vlfAT4Sq5n/1L6+cABwH9HxMpS+k8CvwTeRgpiP2hQ128jYlY7K2dmNpb5UmGH8tnWTGAR8I3K4s8ATwDvlbTxIOU8D3hvTj+rsvjrwJ3AGyVtV8yMiGsj4sfloJXn3weck19O72B1zMxqx4Grc/vm6dUNAshjwA3ARsCeg5SzJ7AhcEPOVy5nJXBVpb7B/DVPn2myfCtJH5T0yTx9RZvlmpmNKb5U2Lkd83R+k+W3k87IJgPXDLEccjktSRoPvC+/vLJJsjfkv3K+ucDhEXHXYHWYmY0VPuPq3IQ8faTJ8mL+ZqNUDsDpwG7AFRFxVWXZk8BngdcCm+e/aaTOHdOBa1pd1pR0lKR+Sf1LlixpoylmZiPLgavmckeOE0k9G99bXR4RD0TEyRHx64hYlv/mkc4KfwG8DHh/s/Ij4tyI6IuIvokTJ47QWpiZtc+Bq3PFmdCEJsuL+ctGuhxJx5J6H/4J2DciHhqkzlUi4hlSF3qAfdrNZ2bWaw5cnbstT5vde9ohT5vduxqWciQdD3yN9BzWvrlnYaeKa38te0CamY0lDlyduy5PZ1ZHqpC0CTCVdF/ppkHKuQl4Cpia85XLGUe6lFeur7z8n4Ezgd+SgtYDHa5Doej5uLBlKjOzMcSBq0MRsQC4GpgEfKiy+BTS2cvFEfFEMVPSTpJ2qpTzOHBxTj+rUs6xufyrKiNnIOkkUmeMm4EZEfFgq/ZKek2ToaBmkB6WBvh2qzLMzMYSNR4dyFppMOTTLcAepGeu5gNTykM+SQqAiFClnOqQT78EdgYOBB7I5SwopT8cuAh4lnSZsFGPxEURcVEpz1zSZccbgXvy7FcA++X/T4qIz7Wz3n19fdHf399OUrOOSbo5Ivp63Q4b+/wcVxciYoGkPuBU0pBMbyaNH/gV4JSIeLjNcpZK2os04sZBwN7AUuBC4OSIuKeSZds8XQc4vkmx15OCW+Fi4GBgd+BNwLrA/cAlwNcj4ufttNXMbKzwGZe1zWdcNpJ8xmXt8j0uMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQcuMzOrFQeuLknaWtIFkhZLWiFpkaSzJG3eYTnPz/kW5XIW53K3Hs66Je0i6RJJD0haLuk2SadI2rCT9pqZ9dr4XjegjiRtD9wIbAlcDtwKvA44Dthf0tSIWNpGOS/I5UwGrgVmAzsBRwJvkbRXRCwcat2S9sjlrwvMAe4G9gNOBmZImhERK7rZFmZmo81nXN05mxQ4PhIRB0XEJyJiP+BMYEfgtDbL+TwpaJ0RETNyOQeRgtCWuZ4h1S1pHeBCYCPgkIh4V0T8M7AH8ANgKnBCB+tuZtZTiohet6FW8hnPHcAiYPuIWFlatglwLyBgy4h4okU5zwMeAFYCL46Ix0rLxgELgZfmOhZ2W7ek/YBrgHkRMa3Shu2ABcCdwLYxyM7Q19cX/f39rZKYdU3SzRHR1+t22NjnM67O7ZunV5cDB0AOPjeQzm72HKScPYENgRvKQSuXsxK4qlJft3Xvl6dXVhuQA+J8UoDcbpD2mpmNCQ5cndsxT+c3WX57nk4egXJGK4+Z2ZjlwNW5CXn6SJPlxfzNRqCc0cqziqSjJPVL6l+yZEmTIszMRo8Dl7UUEedGRF9E9E2cOLHXzTEzc+DqQnGGMqHJ8mL+shEoZ7TymJmNWQ5cnbstT5vdE9ohT5vdUxpKOaOVx8xszHLg6tx1eTozd1tfJXdJnwo8Cdw0SDk3AU8BU3O+cjnjgJmV+rqt+9o83b/agNwdfjKpO/zC6nIzs7HIgatDEbEAuBqYBHyosvgUYGPg4vIzXJJ2krRTpZzHgYtz+lmVco7N5V9VHjmjm7qB64FbgH0kHVBq0zjgC/nlOYM9w2VmNlb4AeQuNBh26RbSSBT7ki65TSkPuyQpACJClXKqQz79EtgZOJD0cPKUHKy6rjvnqQ75dBcwA+gjPfvV1pBPfgDZRpIfQLZ2+YyrCzmY9AEXkYLGicD2wFeAPdsZpzCXsxTYC/gq8LJczh6kIZpeWw1a3dYdEb8AdicFupmkIZ4mAKcCb/A4hWZWJz7jsrb5jMtGks+4rF0+4zIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4DIzs1px4OqCpCmSrpD0kKSnJP1e0vGS1umirF0kXSLpAUnLJd0m6RRJGzZIu4Okf5Z0raS7JT0t6X5Jl0vat0n5R0iKFn9Hd7MNzMx6ZXyvG1A3kg4EfgAsB74HPAS8FTgTmAoc2kFZewDXAusCc4C7gf2Ak4EZkmZExIpSls8C7wD+BFyR694ROAA4QNJxEfHVJtVdDvy2wfz+dttrZjYWOHB1QNKmwHnAs8D0iOjP808iBaBDJB0WEbPbKGsd4EJgI+DAiPhRnj8OuAR4G3ACcHop25XAFyLiN5WypgE/A74o6fsRcW+DKi+LiIs6WV8zs7HIlwo7cwgwEZhdBC2AiFgOfDq/PKbNsqYBOwPziqCVy1oJfDy/PFqSSssuqgatPP96YC6wHjCl7bUxM6shn3F1Zr88vbLBsnnAk8AUSetXLvF1VFZELJQ0H5gMbAcsaKNtf83TZ5osf5Wk44ENgL8A10XEPW2Ua2Y2pjhwdWbHPJ1fXRARz0j6M7ArKdjc0m1Z2e2kwDWZQQKXpJcCM0iBc16TZMdVXj8r6Xzg+HzG2Kzso4CjALbZZptWzTAzGxW+VNiZCXn6SJPlxfzNRqssSesD3wHWB2ZFxMOVJH8GPkwKlBsDWwFvBxYBHwQuaFV+RJwbEX0R0Tdx4sRWSc3MRsVaF7gkLRqke3j179u9bnMzuYPHxaTejN8DvlRNExHXR8TXI2J+RDwZEfdGxPeBfYGHgXdKeuWoNtzMbAjWxkuFC0hd2du1uPR/cRY0oVHC0vxlbZQ7pLJy0Po2qfv9JcB7IiLaqBeAiLhb0hXAu4F9gN+1m9fMrJfWusAVETOGkP02oI903+nm8gJJ44FtSZ0jFrZZFrmsRnbI0zXugUlal3R58FDgv4D3RcSzbdRZtSRPN+4ir5lZT6x1lwqH6No83b/Bsn1Iz2Td2EaPwpZlSdqOFNDupBIEJa0HfJ8UtP4TeG+XQQtgjzxtJ9CamY0JDlydmQM8CBwmqa+YKWkD4HP55TfLGSRtJGknSdUuedeTeh7uI+mAUvpxwBfyy3PKl/9yR4xLgQOBbwFH5ue+miq3s1yHpH8B9srr06h7v5nZmLTWXSocioh4VNIHSAFsrqTZpGGXDiD12ptD6iRR9jrgOlKgml4q61lJR5LOvOZImgPcRerW3gfcQBpGquwc4M2kYPMX4OTS88mFuRExt/T6V5L+SLqH9RfSvbOpwG6k7vPvjohHO9oQZmY95MDVoYi4LA+x9CnSsEwbAHcAHwW+2mEHiV9I2h04BZgJbEK6PHgqcHqDS47b5ukWpPEMm5lb+v9LpOC5H/B8YCUpQH4DOCMifJnQzGpFHXzP2lqur68v+vs9Jq+NDEk3R8Qal7bNqnyPy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBy8zMasWBqwuSpki6QtJDkp6S9HtJx0tap4uydpF0iaQHJC2XdJukUyRt2CDtJEnR4m92i3oOl/RLSY9LekTSXEl/12l7zcx6bXyvG1A3kg4EfgAsB74HPAS8FTgTmAoc2kFZewDXAusCc4C7gf2Ak4EZkmZExIoGWX8HXNZg/h+b1PMl4ETgHuA8YD3gMODHkj4cEV9vt81mZr3mwNUBSZuSvvifBaZHRH+efxIpAB0i6bCIaHrmUyprHeBCYCPgwIj4UZ4/DrgEeBtwAnB6g+y/jYhZbbZ5CiloLQB2j4iH8/wvAjcDX5L0k4hY1E55Zma95kuFnTkEmAjMLoIWQEQsBz6dXx7TZlnTgJ2BeUXQymWtBD6eXx4tSUNs89F5eloRtHI9i4BvAOsDRw6xDjOzUePA1Zn98vTKBsvmAU8CUyStP5SyImIhMB94KbBdg7xbSfqgpE/m6Su6bPNPK2nMzMY8XyrszI55Or+6ICKekfRnYFdSsLml27Ky24HJ+W9BZdkb8t8qkuYCh0fEXaV5GwMvAR6PiHub1EGuw8ysFnzG1ZkJefpIk+XF/M1GqKwngc8CrwU2z3/TgOuA6cA1OVgNW3slHSWpX1L/kiVLmiUzMxs1a13gkrRokC7l1b9v97rNhYh4ICJOjohfR8Sy/DcPmAn8AngZ8P5hrvPciOiLiL6JEycOZ9FmZl1ZGy8VLiB1ZW/X4tL/xRnKhEYJS/OXtVHusJWVL1OeD+wB7AN8ZbjrMDMbK9a6wBURM4aQ/Tagj3RP6ObyAknjgW2BZ4CFbZYFze8v7ZCnze6BVRXX8VZdKoyIJyT9BXiJpBc3uM/VaR1mZj231l0qHKJr83T/Bsv2IT2TdWOTh4bbLkvSdqSAdiftBUGAPfO0mr5Vm99USWNmNuY5cHVmDvAgcJikvmKmpA2Az+WX3yxnkLSRpJ0kbVMp63pSz8N9JB1QSj8O+EJ+eU5ERGnZa/Ly1UiaQXpYGaB6T+6cPP2UpM1LeSYBHwJWkB6ENjOrhbXuUuFQRMSjkj5ACmBz89iADwEHkLq3zyENA1X2OlKvv+tJPf+Ksp6VdCTpbGeOpDnAXcAM0uXIG0jDSJWdAewg6UbS8E0Ar2DgOayTIuLGSptvlHQG8FHg97me9YB3AM8HPuxRM8ysThy4OhQRl0maBnyKNCzTBsAdpMDw1fIZUhtl/ULS7sAppJ6Bm5AuD54KnN7gkuPFwMHA7qTLfOsC95OGiPp6RPy8ST0nSvoD6QzrKGAl8GvgixHxk3bba2Y2FqiD71lby/X19UV/f//gCc26IOnmiOgbPKWt7XyPy8zMasVnXNY2SUtIlzIb2YLUccWslVb7yUsjwk+526AcuGxYSOr3ZR4bjPcTGw6+VGhmZrXiwGVmZrXiwGXD5dxeN8BqwfuJDZnvcZmZWa34jMvMzGrFgcvMzGrFgcvMzGrFgcu6JmlrSRdIWixpRf516bPKo9Db2k3SIZK+Junnkh4da78qbvXkQXatK5K2B24EtgQuB24ljYR/HLC/pKkRsbSHTbSx4dPAK4HHSb9osFNvm2PPBT7jsm6dTQpaH4mIgyLiExGxH+mnWHYETutp62ysOIH0o6ibAsf0uC32HOHu8NaxfLZ1B7AI2D4iVpaWbQLcCwjYMiKe6EkjbcyRNJ3023TfiYj39LY1Vmc+47Ju7JunV5eDFkBEPEb6EcyNgD1Hu2Fm9tznwGXd2DFP5zdZfnueTh6FtpjZWsaBy7oxIU8fabK8mL/ZyDfFzNY2DlxmZlYrDlzWjeKMakKT5cX8ZSPfFDNb2zhwWTduy9Nm97B2yNNm98DMzLrmwGXduC5PZ0pabR/K3eGnAk8CN412w8zsuc+ByzoWEQuAq4FJwIcqi08BNgYu9jNcZjYS/ACydaXBkE+3AHuQnvGaD0zxkE8m6SDgoPzyRcAbgYXAz/O8ByPiY6PfMqszBy7rmqS/AU4F9gdeQBox41LglIh4uJdts7FB0izgMy2S3BkRk0anNfZc4cBlZma14ntcZmZWKw5cZmZWKw5cZmZWKw5cZmZWKw5cZmZWKw5cZmZWKw5cZmZWKw5cZmZWKw5cZmZWK/8fc+wRqnn+rdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wo = functional_proteins.loc[functional_proteins['length'] > 1000]\n",
    "plt.subplot(122)\n",
    "plt.hist(wo['length'])\n",
    "plt.title('Length of proteins (where > 1000)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b425f4a6",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [62]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# See what's about that huge protein\u001b[39;00m\n\u001b[0;32m      2\u001b[0m large_prot \u001b[38;5;241m=\u001b[39m functional_proteins\u001b[38;5;241m.\u001b[39mloc[functional_proteins[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlength\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2700\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m l \u001b[38;5;241m=\u001b[39m \u001b[43mlarge_prot\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msequence\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSequence sample:\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m'\u001b[39m,l[\u001b[38;5;241m1000\u001b[39m:\u001b[38;5;241m1150\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# See what's about that huge protein\n",
    "large_prot = functional_proteins.loc[functional_proteins['length'] > 2700]\n",
    "l = large_prot['sequence'].tolist()[0]\n",
    "print('Sequence sample:', '...',l[1000:1150],'...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "72c24b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define sequences to be aligned\n",
    "SARS = SeqIO.read(\"./input/sars.fasta\", \"fasta\")\n",
    "MERS = SeqIO.read(\"./input/mers.fasta\", \"fasta\")\n",
    "COV2 = SeqIO.read(\"./input/cov2.fasta\", \"fasta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dd069723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence Lengths:\n",
      "SARS: 29751\n",
      "COV2: 29903\n",
      "MERS: 30119\n"
     ]
    }
   ],
   "source": [
    "print('Sequence Lengths:')\n",
    "print('SARS:', len(SARS.seq))\n",
    "print('COV2:', len(COV2.seq))\n",
    "print('MERS:', len(MERS.seq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0b201d6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARS/COV Similarity (%): 83.33837518066619\n",
      "MERS/COV Similarity (%): 69.39141405757164\n",
      "MERS/SARS Similarity (%): 69.93714496991697\n"
     ]
    }
   ],
   "source": [
    "#Visualize DNA sequence using Squiggle #run in terminal\n",
    "#Squiggle cov2.fasta sars.fasta mers.fasta --method=gates --separate\n",
    "\n",
    "# Alignments using pairwise2 alghoritm\n",
    "SARS_COV = pairwise2.align.globalxx(SARS.seq, COV2.seq, one_alignment_only=True, score_only=True)\n",
    "print('SARS/COV Similarity (%):', SARS_COV / len(SARS.seq) * 100)\n",
    "MERS_COV = pairwise2.align.globalxx(MERS.seq, COV2.seq, one_alignment_only=True, score_only=True)\n",
    "print('MERS/COV Similarity (%):', MERS_COV / len(MERS.seq) * 100)\n",
    "MERS_SARS = pairwise2.align.globalxx(MERS.seq, SARS.seq, one_alignment_only=True, score_only=True)\n",
    "print('MERS/SARS Similarity (%):', MERS_SARS / len(SARS.seq) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a6ded23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 66,
     "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": [
    "# Plot the data\n",
    "X = ['SARS/COV2', 'MERS/COV2', 'MERS/SARS']\n",
    "Y = [SARS_COV/ len(SARS.seq) * 100, MERS_COV/ len(MERS.seq)*100, MERS_SARS/len(SARS.seq)*100]\n",
    "plt.title('Sequence identity (%)')\n",
    "plt.bar(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "49e30fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"input/Sequence.txt\"\n",
    "file=open(path,'r')\n",
    "data=[[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7139ebf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for line in file:\n",
    "    data.append([line])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bc4e481b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&gt;MN908947.3 Severe acute respiratory syndrome ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>GTGCAGAATGAATTCTCGTAACTACATAGCACAAGTAGATGTAGTT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>TTAATCAGTGTGTAACATTAGGGAGGACTTGAAAGAGCCACCACAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>GATCGAGTGTACAGTGAACAATGCTAGGGAGAGCTGCCTATATGGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>TTTAGTAGTGCTATCCCCATGTGATTTTAATAGCTTCTTAGGAGAA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>AAAAAAAAAAAAA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              sequence\n",
       "0                                                 None\n",
       "1    >MN908947.3 Severe acute respiratory syndrome ...\n",
       "2    ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...\n",
       "3    CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTG...\n",
       "4    TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGC...\n",
       "..                                                 ...\n",
       "425  GTGCAGAATGAATTCTCGTAACTACATAGCACAAGTAGATGTAGTT...\n",
       "426  TTAATCAGTGTGTAACATTAGGGAGGACTTGAAAGAGCCACCACAT...\n",
       "427  GATCGAGTGTACAGTGAACAATGCTAGGGAGAGCTGCCTATATGGA...\n",
       "428  TTTAGTAGTGCTATCCCCATGTGATTTTAATAGCTTCTTAGGAGAA...\n",
       "429                                      AAAAAAAAAAAAA\n",
       "\n",
       "[430 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(data,columns=['sequence'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "507627f5",
   "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>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TTGCAGCCGATCATCAGCACATCTAGGTTTCGTCCGGGTGTGACCG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>CAGACCACACAAGGCAGATGGGCTATATAAACGTTTTCGCTTTTCC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>GTGCAGAATGAATTCTCGTAACTACATAGCACAAGTAGATGTAGTT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>TTAATCAGTGTGTAACATTAGGGAGGACTTGAAAGAGCCACCACAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>GATCGAGTGTACAGTGAACAATGCTAGGGAGAGCTGCCTATATGGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>TTTAGTAGTGCTATCCCCATGTGATTTTAATAGCTTCTTAGGAGAA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              sequence\n",
       "2    ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...\n",
       "3    CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTG...\n",
       "4    TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGC...\n",
       "5    TTGCAGCCGATCATCAGCACATCTAGGTTTCGTCCGGGTGTGACCG...\n",
       "6    CCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTT...\n",
       "..                                                 ...\n",
       "424  CAGACCACACAAGGCAGATGGGCTATATAAACGTTTTCGCTTTTCC...\n",
       "425  GTGCAGAATGAATTCTCGTAACTACATAGCACAAGTAGATGTAGTT...\n",
       "426  TTAATCAGTGTGTAACATTAGGGAGGACTTGAAAGAGCCACCACAT...\n",
       "427  GATCGAGTGTACAGTGAACAATGCTAGGGAGAGCTGCCTATATGGA...\n",
       "428  TTTAGTAGTGCTATCCCCATGTGATTTTAATAGCTTCTTAGGAGAA...\n",
       "\n",
       "[427 rows x 1 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df1.drop(df1.index[0],axis=0)\n",
    "df1=df1.drop(df1.index[0],axis=0)\n",
    "df1=df1.drop(df1.index[427],axis=0)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0a6030b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "abstracts=df1['sequence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dbe8c35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer=tf.keras.preprocessing.text.Tokenizer(num_words=None,filters='\\n',lower=False,char_level=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "00f6244d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.fit_on_texts(abstracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a4d5e0a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences=tokenizer.texts_to_sequences(abstracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d9ac4957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAAAA'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_word=tokenizer.index_word\n",
    "''.join(idx_word[2] for w in sequences[10][:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5601a226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(idx_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b6f6fb50",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=[]\n",
    "labels=[]\n",
    "training_length=70\n",
    "for seq in sequences:\n",
    "    for i in range(training_length,len(seq)):\n",
    "        extract=seq[i-training_length:i+1]\n",
    "        features.append(extract[:-1])\n",
    "        labels.append(extract[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6e738104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_words=len(idx_word)+1\n",
    "num_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "28d72078",
   "metadata": {},
   "outputs": [],
   "source": [
    "selector=SelectKBest(f_classif,k=10)\n",
    "selected_features=selector.fit_transform(features,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "865251b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_array=np.zeros((len(selected_features),num_words),dtype=np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e973d423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(427, 6)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for example_index,word_index in enumerate(labels):\n",
    "    label_array[example_index,word_index]=1\n",
    "label_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "31fcc381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_word[np.argmax(label_array)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "98f3d4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences=np.array(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "70adf085",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences=sequences.reshape(7,61,71)\n",
    "label_array=label_array.reshape(7,61,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0bacb1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test=train_test_split(sequences,test_size=1/7,shuffle=False)\n",
    "y_train,y_test=train_test_split(label_array,test_size=1/7,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8272a24a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "409184a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#recurrent layer\n",
    "model.add(LSTM(16,input_shape=(x_train.shape[1:]),activation='tanh',return_sequences=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "58ed3ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0a5036b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fully connected layer\n",
    "model.add(Dense(16,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6a9997cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3747c23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#output layer\n",
    "model.add(Dense(num_words,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9946a804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 61, 16)            5632      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 61, 16)            0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 61, 16)            272       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 61, 16)            0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 61, 6)             102       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,006\n",
      "Trainable params: 6,006\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7550b948",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt=tf.keras.optimizers.Adam(learning_rate=0.001,weight_decay=1e-5)\n",
    "cce=tf.keras.losses.CategoricalCrossentropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "65c55a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=opt,loss=cce,metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "96381346",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1/1 [==============================] - 4s 4s/step - loss: 1.7344 - accuracy: 0.0574 - val_loss: 1.6515 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/15\n",
      "1/1 [==============================] - 0s 94ms/step - loss: 1.6735 - accuracy: 0.0820 - val_loss: 1.6050 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/15\n",
      "1/1 [==============================] - 0s 91ms/step - loss: 1.6278 - accuracy: 0.1339 - val_loss: 1.5618 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/15\n",
      "1/1 [==============================] - 0s 91ms/step - loss: 1.5796 - accuracy: 0.1530 - val_loss: 1.5224 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/15\n",
      "1/1 [==============================] - 0s 84ms/step - loss: 1.5553 - accuracy: 0.1803 - val_loss: 1.4864 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/15\n",
      "1/1 [==============================] - 0s 89ms/step - loss: 1.5124 - accuracy: 0.2022 - val_loss: 1.4532 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/15\n",
      "1/1 [==============================] - 0s 86ms/step - loss: 1.4897 - accuracy: 0.2432 - val_loss: 1.4224 - val_accuracy: 0.0492\n",
      "Epoch 8/15\n",
      "1/1 [==============================] - 0s 91ms/step - loss: 1.4469 - accuracy: 0.3087 - val_loss: 1.3935 - val_accuracy: 0.1311\n",
      "Epoch 9/15\n",
      "1/1 [==============================] - 0s 86ms/step - loss: 1.4052 - accuracy: 0.3770 - val_loss: 1.3663 - val_accuracy: 0.5410\n",
      "Epoch 10/15\n",
      "1/1 [==============================] - 0s 86ms/step - loss: 1.3834 - accuracy: 0.4317 - val_loss: 1.3406 - val_accuracy: 0.8525\n",
      "Epoch 11/15\n",
      "1/1 [==============================] - 0s 85ms/step - loss: 1.3710 - accuracy: 0.5027 - val_loss: 1.3162 - val_accuracy: 1.0000\n",
      "Epoch 12/15\n",
      "1/1 [==============================] - 0s 84ms/step - loss: 1.3412 - accuracy: 0.5219 - val_loss: 1.2929 - val_accuracy: 1.0000\n",
      "Epoch 13/15\n",
      "1/1 [==============================] - 0s 91ms/step - loss: 1.3171 - accuracy: 0.5601 - val_loss: 1.2706 - val_accuracy: 1.0000\n",
      "Epoch 14/15\n",
      "1/1 [==============================] - 0s 91ms/step - loss: 1.2952 - accuracy: 0.5902 - val_loss: 1.2489 - val_accuracy: 1.0000\n",
      "Epoch 15/15\n",
      "1/1 [==============================] - 0s 83ms/step - loss: 1.2929 - accuracy: 0.6066 - val_loss: 1.2279 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(x_train,y_train,epochs=15,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fadcfa7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "158fe5d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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