dnaa-box-salmonella-enterica.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding DnaA box in *Salmonella Enterica*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_file = open('data\\Salmonella_enterica.txt')\n",
    "txt = txt_file.read()\n",
    "txt_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">gi|16758993|ref|NC_003198.1| Salmonella enterica subsp. enterica serovar Typhi str. CT18 chromosome, complete genome\n",
      "AGAGATTACGTCTGGTTGCAAGAGATCATAACAGGGGAAATTGATTGAAAATAAATATATCGCCAGCAGC\n",
      "ACATGAACAAGTTTCGGAATGTGATCAATTTAAAAATTTATTGACTTAGGCGGGCAGATACTTTAACCAA\n",
      "TATAGGAATACAAGACAGACAAATAAAAATGACAGAGTAC\n"
     ]
    }
   ],
   "source": [
    "print(txt[:300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_splited = txt.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['>gi|16758993|ref|NC_003198.1| Salmonella enterica subsp. enterica serovar Typhi str. CT18 chromosome, complete genome',\n",
       " 'AGAGATTACGTCTGGTTGCAAGAGATCATAACAGGGGAAATTGATTGAAAATAAATATATCGCCAGCAGC',\n",
       " 'ACATGAACAAGTTTCGGAATGTGATCAATTTAAAAATTTATTGACTTAGGCGGGCAGATACTTTAACCAA',\n",
       " 'TATAGGAATACAAGACAGACAAATAAAAATGACAGAGTACACAACATCCATGAACCGCATCAGCACCACC',\n",
       " 'ACCATTACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAG']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt_splited[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As we can see, we don't need the first line, and other lines should be attached to each other..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_splited = txt_splited[1:]\n",
    "seq = ''.join(txt_splited)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACATGAACAAGTTTCGGAATGTGATCAATTTAAAAATTTATTGACTTAGGCGGGCAGATACTTTAACCAATATAGGAATACAAGACAGACAAATAAAAAT\n"
     ]
    }
   ],
   "source": [
    "print(seq[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data is ready."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skew Diagram\n",
    "Let's find the location of the Ori based on the Skew Diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import skew_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skew_list, min_index = skew_diagram(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our hypothesis for ori index is: [3764786, 3764788]\n"
     ]
    }
   ],
   "source": [
    "print(f'Our hypothesis for ori index is: {min_index}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the DnaA boxes\n",
    "We choose a 1000 nucleotide region and search for common patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import freq_word_with_mistmatch_reverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_slide = seq[min_index[0] - 500 : min_index[0] + 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_patterns = freq_word_with_mistmatch_reverse(seq_slide, 9, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TTATCCACA TGTGGATAA\n"
     ]
    }
   ],
   "source": [
    "print(*freq_patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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