Nucleotide Composition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a379e6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "beginF = \"Data/Sequencing Data/Raw Sequencing Data/\" # where sequencing data files are located\n",
    "\n",
    "f = open(beginF + \"F3_sequences.fasta\")\n",
    "\n",
    "\n",
    "raw_sequences = []\n",
    "for line in f:\n",
    "    if not line[0] == '>':\n",
    "        raw_sequences.append(line[0:-1])\n",
    "        \n",
    "        \n",
    "beginPrimersF = \"Data/Sequencing Data/primers.fasta\"\n",
    "\n",
    "f2 = open(beginPrimersF)\n",
    "forward_primers = []\n",
    "reverse_primers = []\n",
    "\n",
    "for line in f2:\n",
    "    if not line[0] == '>':\n",
    "        if len(forward_primers) < 9:\n",
    "            forward_primers.append(line[0:-1])\n",
    "        else:\n",
    "            reverse_primers.append(line[0:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ba5d4d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ACAGGAGCAGAATTGAGTCGACAAAACACAGTAACCCAGGCTTATCTTCGTGCTGCCAGACCCTAACGATCCAACGCACCCAACAGATCGGAAGAGCACACGTTGAACTCCAGTCACCGCTCATTATCTCGTATGCCGTCTTCTGCTTGAAAAAAGGGGGGGGGGGGGGGGGGGGGG', 'ACAGGAGCAGAATTGAGTCGTCCCCCCCGCCAGCCCACACCTTATCTTCGTGCTGCCAGAGCAATAATTTTGTGTAATAGTTGGG', 'ACAGGAGCAGAATTGAGTCGGGTTAACCAGGCCACATAACCTTATCTTCGTGCTGCCAGAATTGACCAATAGAATCCAAAAGGGG', 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGCTCATTATCTCGTATGCCGTCTTCTGCTTGAAAAAAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG', 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGCTCATTATCTCGTATGCCGTCTTCTGCTTGAAAAAAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG']\n",
      "['CTATCTGGCCTCAAAGCACT', 'TCTGGCAGCACGAAGATAAG', 'AGGAGACAAGTAACGTGTGC', 'TAAGAAAGGCATCGTCACCG', 'GTCACGTCACCAGGTAACAA', 'TTTCCGATAGTTGAGGCAGG', 'TGGTGAAACTACCGACTTCC', 'AGTTGGTGACTATCCGTCCT', 'CACATAGGCAAAGCGGAGTA']\n",
      "['TTGACTCCTCCTCTTGCCTA', 'CGACTCAATTCTGCTCCTGT', 'GTCGAGCACTGATTGTGGAA', 'GTACTGCTCGGCCACTTATT', 'GCGTTGTCTCTAGCGAAAGA', 'TCATTTCTCCGACAGGCTTG', 'CCTCTCTTCGCGTTGATCTT', 'GCAAGACAATAGGCTTCGGT', 'GCAAGACAATAGGCTTCGGT', '']\n"
     ]
    }
   ],
   "source": [
    "print(raw_sequences[0:5])\n",
    "\n",
    "print(forward_primers)\n",
    "print(reverse_primers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11f8c74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_payloads = []\n",
    "pairs_indexes = []\n",
    "for i in range(0, len(raw_sequences)):\n",
    "    sequence = raw_sequences[i]\n",
    "    \n",
    "    fp_index = -100\n",
    "    rp_index = -100\n",
    "    for fp in forward_primers:\n",
    "        if fp in sequence:\n",
    "            fp_index = sequence.find(fp)\n",
    "    for rp in reverse_primers:       \n",
    "        if rp in sequence:\n",
    "            rp_index = sequence.find(rp)\n",
    "            break\n",
    "    \n",
    "    if rp_index == fp_index + 40:\n",
    "        payload = sequence[fp_index + 20: rp_index]\n",
    "        actual_payloads.append(payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "320b2441",
   "metadata": {},
   "outputs": [],
   "source": [
    "position_A = []\n",
    "position_C = []\n",
    "position_G = []\n",
    "position_T = []\n",
    "\n",
    "for i in range(0, 20):\n",
    "    countA = 0.0\n",
    "    countC = 0.0\n",
    "    countG = 0.0\n",
    "    countT = 0.0\n",
    "    \n",
    "    for seq in actual_payloads:\n",
    "        if seq[i] == 'A':\n",
    "            countA = countA + 1\n",
    "        elif seq[i] == 'C':\n",
    "            countC = countC + 1\n",
    "        elif seq[i] == 'G':\n",
    "            countG = countG + 1\n",
    "        elif seq[i] == 'T':\n",
    "            countT = countT + 1\n",
    "            \n",
    "    position_A.append((countA / len(actual_payloads)) * 100)\n",
    "    position_C.append((countC / len(actual_payloads))* 100)\n",
    "    position_G.append((countG / len(actual_payloads))* 100)\n",
    "    position_T.append((countT / len(actual_payloads))* 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b58ebccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1775 22.5242\n",
      "0.8474 19.1847\n",
      "2.0409 30.2203\n",
      "0.7231 28.0708\n"
     ]
    }
   ],
   "source": [
    "std_a = np.std(np.array(position_A))\n",
    "std_c = np.std(np.array(position_C))\n",
    "std_g = np.std(np.array(position_G))\n",
    "std_t = np.std(np.array(position_T))\n",
    "\n",
    "avg_a = np.mean(np.array(position_A))\n",
    "avg_c = np.mean(np.array(position_C))\n",
    "avg_g = np.mean(np.array(position_G))\n",
    "avg_t = np.mean(np.array(position_T))\n",
    "\n",
    "print(str(round(std_a, 4)) + \" \" + str(round(avg_a, 4)))\n",
    "print(str(round(std_c, 4)) + \" \" + str(round(avg_c, 4)))\n",
    "print(str(round(std_g, 4)) + \" \" + str(round(avg_g, 4)))\n",
    "print(str(round(std_t, 4)) + \" \" + str(round(avg_t, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5f104805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Distribution of Nucleotides in Sequence of N's\")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OnDmp9Lt895bvCja4fPeVB560dvLVinQ0m9n1ZtZgZscAq4CXgGWSBgPE3+WVsM1xHGd7plKjj3aNv8MJ/Qm/Be4EJsRLJhCamLotb7/9NmeeeSajRo2ioaGBE088kZdeeqnSZjmO43RIpb5T+L2kWmAT8DUza5Z0NTBD0rnAEuCMzjBk6quNTGq8h9ffa2Z4/xomN5zA+FENqeI0M0499VQmTJjQOkvq/PnzWbZsGXvttVcpzHYcxykLFXEKZvaJHGErgbGdacfUVxuZ+JeZrGvZBMCS95qZ+JeZAKkcw5w5c+jVqxfnnXdea9iBBx6YzljHcZxOYLue5mJS4z2tDiHDupZNTGq8J1W8CxcupKEhXW3DcRynEmzXTuH195oLCnccx9nW2a6dwvD+NQWFJ2W//fbzhXUcx+mWbNdOYXLDCfSr6rVVWL+qXkxuOCFVvGPGjGHDhg1MmTKlNWzBggU+fbbjOF2e7dopjB/VwJSjTmNE/5owdXb/GqYcdVrq0UeSuO2225g9ezajRo1iv/324/LLL2e33XYrjeGO4zhlwqfOHtWQ2gnkYsiQIcyYMaPk8TqO45ST7bqm4DiO42yNOwXHcRynFXcKjuM4TivuFBzHcZxW3Ck4juM4rbhTcBzHcVpxp1Amli1bxhe+8AU+8pGP0NDQwBFHHMFtt91WabMcx3E6pFLrKXxd0rOSFkqaJmkHSSMlPSbpFUm/k9S7M2xpfmQqL11cz3MTevDSxfU0PzI1dZxmximnnMIxxxzDokWLaGxsZPr06TQ1NZXAYsdxnPLR6U5B0u7ABcChZrY/UAWcCVwD/MjM9iCsxnZuuW1pfmQqb90wkc0rlwDG5pVLeOuGiakdwwMPPEDv3r23mjp7xIgRnH/++SktdhzHKS+Vaj7qCfSV1BPoB7wFjAFmxvM3AaeU24jlMydhG9dtFWYb17F85qRU8T777LMccsghqeJwHMepBAprOHeyUulCYDLwPnAfcCHwaKwlIGkYcE+sSbSVnQhMBKirq2vIrGyWYcCAAeyxxx55bWhpaWHphTsDue5fDPvZ6rzyVVVVOc9dd911LFmyhKuvvhqAiy++mEcffZRevXrx4IMPbiX/yiuvsHp1x7pysXbtWqqrqwuWc/ltQ74r2ODy3Vd+9OjRjWZ2aM6TZtapGzAQeAAYBPQCbge+CLySdc0wYGG+uBoaGqwtzz333IfCcrFmzRp78esj7Nkv8aHtxa+PSCTfHrNnz7Zjjjlmq7AVK1bYiBEfxJuRT2pvW+bMmVOUnMtvG/JdwQaX777ywJPWTr5aieajccBrZrbCzDYBtwJHATWxOQlgKPBmuQ3Z9bTJqHe/rcLUux+7njY5Vbxjxoxh/fr1XHfdda1h69at60DCcRyna1AJp/A68HFJ/SSJsC7zc8Ac4LR4zQTgjnIbUnPkeAafM4WetSMA0bN2BIPPmULNkeNTxSuJ22+/nQcffJCRI0dy+OGHM2HCBK655prSGO44jlMm8k6dLekoYJ6ZvSfpi8AhwE/MbEkxCs3sMUkzgaeAzcDTwBTgD8B0SVfFsOuLib9Qao4cn9oJ5GLw4MG07e9wHMfp6iRZT+E64EBJBwKXAL8Efg18slilZvYt4FttghcBhxcbp+M4jpOeJM1Hm2PHxMnAz8zs58CO5TXLcRzHqQRJagrvSrocOAv4hKQehFFDjuM4zjZGkprC54ANwJfN7G3CyKDvl9Uqx3EcpyLkdQrREfwe6BOD3gF8ZjfHcZxtkLxOQdJXCdNP/G8M2p3wwZnjOI6zjZGkT+FrhFFBjwGY2cuSdi2rVd2YlStXMnbsWADefvttqqqqGDRoEACPP/44vXt3yuSvjuM4RZHEKWwws43hOzOIXx13/oRJZWLl04tZOmsBG5vX0bumH0OOO4Dag+uLjq+2tpZ58+YBcOWVV1JdXc2ll15aGmMdx3HKTBKn8KCkbxBmNT0O+GfgrvKa1TmsfHoxS25/AtvUAsDG5nUsuf0JgFSOwXEcp7uSZPTRZcAK4BngH4E/AleU06jOYumsBa0OIYNtamHprAUVsshxHKeyJKkp9AV+ZWb/ByCpKoZ1+xneNjbnvoX2wh3HcbZ1ktQU7ic4gQx9gdnlMadz6V3Tr6Bwx3GcbZ0kTmEHM1ubOYj720SuOeS4A1CvrRfKUa8qhhx3QIUschzHqSxJnMJ7klrXlpTUQFgxrSgk7S1pXta2RtJFknaWNEvSy/F3YLE6klJ7cD0jTjmstWbQu6YfI045zDuZHcfZbknSp3ARcIukpYCA3QhTXxSFmb0IHASt/RNvEr6Qvgy438yulnRZPP73YvUkpfbg+rI5gSuvvLIs8TqO45SLvE7BzJ6QtA+wdwx6Ma6YVgrGAq+a2RJJJwPHxvCbgLl0glNwHMdxPiBJTQHgMKA+Xn+IJMzs1yXQfyYwLe7Xmdlbcf9toK4E8TuO4zgFoLBUQgcXSDcDo4B5QGZQv5nZBakUS72BpcB+ZrZMUrOZ1WSdX2VmH+pXkDQRmAhQV1fX0HZ1swEDBjBq1CgyX2C3R0tLC1VVVR1eU275Hj168Oqrr7J69eqC5deuXUt1dXXR+l2+e8t3BRtcvvvKjx49utHMDs150sw63IDnic6jlBth0Z77so5fBAbH/cGEZqoO42hoaLC2LFq0yFasWGFbtmz50Lls1qxZ0+H5fKSVX716ta1YscIWLVpUlPycOXNS6Xf57i3fFWxw+e4rDzxp7eSrSZqPFhI6l9/Kd2GBfJ4Pmo4A7gQmAFfH3zuKiXTo0KE0NTWxYsWKDq9bv349O+ywQzEqSiZfU1PD0KFDi47DcRyn1CRxCrsAz0l6nLDYDgBm9ulilUrqDxxHmDYjw9XADEnnAkuAM4qJu1evXowcOTLvdXPnzuXggw8uRkWXkHccxykHSZzClaVWambvAbVtwlYSRiM5juM4FSLJkNQHJY0A9jSz2ZL6AcX3sDqO4zhdFl95zXEcx2klyTQXXwOOAtZAWHkN8JXXHMdxtkGSOIUNZrYxc7CtrbzmOI7jfEASp9B25bVb2EZWXnMcx3G2Zrteec1xHMfZmiSjj7YA/xc3x3EcZxsmr1OQ9Bo5+hDM7CNlschxHMepGEk+XsueNGkH4HRg5/KY4ziO41SSvH0KZrYya3vTzH4M/EP5TXMcx3E6myTNR4dkHfYg1BySrsPgOI7jdCOSZO4/yNrfDCymyMnqHMdxnK5NktFHozvDEMdxHKfyJGk+urij82b2w0KVSqoBfgnsTxjZ9GXCIju/Iyz7uRg4w8xWFRq34ziOUzxJPl47FPgnwkR4uwPnAYcAO8atGH4C3Gtm+wAHElZ3uwy438z2BO6Px47jOE4nkqRPYShwiJm9CyDpSuAPZvbFYhRKGgAcA5wNEOdV2ijpZODYeNlNwFzg34vR4TiO4xSHwnKdHVwgvQgcYGYb4nEfYIGZ7V2UQukgYArwHKGW0AhcCLxpZjXxGgGrMsdt5CcCEwHq6uoapk+fXowZ3XrRbZd3+a5gg8t3X/nRo0c3mtmhOU+2t3hzZgMmAfMJK7BdCcwDvpFProP4DiWMYvq7ePwT4DtAc5vrVuWLq6GhoeiFq7vzotsu7/JdwQaX777ywJPWTr6a5OO1ycA5wKq4nWNm/1WUewo0AU1m9lg8nknoo1gmaTBA/F2eQofjOI5TBEk6mgH6AWvM7CdAk6SRxSo0s7eBNyRlmp/GEpqS7gQmxLAJwB3F6nAcx3GKI8mQ1G8Rmnz2Bm4AegG/IazGViznA1Ml9QYWEWoiPYAZks4FluAfyDmO43Q6SUYfnQocDDwFYGZLJRU7FJUYxzy2nmgvw9g08TqO4zjpSNJ8tDF2TBiApP7lNclxHMepFEmcwgxJ/wvUSPoqMBtfcMdxHGebJMncR9fGtZnXEPoVvmlms8pumeM4jtPpJOlo7g88YGaz4oihvSX1MrNN5TfPcRzH6UySNB89BPSRtDtwL3AWcGM5jXIcx3EqQxKnIDNbB3wGuM7MTgf2K69ZjuM4TiVI5BQkHQGMB/4Qw6rKZ5LjOI5TKZI4hQuBy4HbzOxZSR8B5pTXLMdxHKcSJBl99BChXyFzvAi4oJxGOY7jOJUh6dxHjuM4znaAOwXHcRynlXadgqRr4u/pnWeO4ziOU0k6qimcGFdAu7yzjHEcx3EqS0cdzfcSFtWplrQGEGFSPAFmZjsVq1TSYuBdoAXYbGaHStoZ+B1QDywGzjCzVcXqcBzHcQqn3ZqCmf2rhTWS/2BmO5nZjtm/JdA92swOsg/WCb0MuN/M9gTuj8eO4zhOJ5JkOc6TJdVJOilug8pky8nATXH/JuCUMulxHMdx2kFhqYQOLggdzdcCcwlNR58A/tXMZhatVHqN0DRlwP+a2RRJzbFmQuzLWJU5biM7EZgIUFdX1zB9+vSibFi7di3V1dXF3YDLu3yF5buCDS7ffeVHjx7dmNVKszVm1uEGzAd2zToeBMzPJ5cnzt3j764x/mOA5jbXrMoXT0NDgxXLnDlzipZ1eZevtHxXsMHlu6888KS1k68m+U6hh5ktzzpeScrvG8zszfi7HLgNOBxYJmkwQPxd3n4MjuM4TjlIkrnfK+lPks6WdDZhUrw/FqtQUv/MGs9xrYa/BxYCdwIT4mUTgDuK1eE4juMUR5K5j/5V0meAo2PQFDO7LYXOOuC20G1AT+C3ZnavpCcIS3+eCywBzkihw3EcxymCvE4BwMxuBW4thUILE+odmCN8JTC2FDocx3Gc4vC5jxzHcZxW3Ck4juM4rSRyCpL6Stq73MY4juM4lSWvU5D0KWAeYS4kJB0k6c4y2+U4juNUgCQ1hSsJ3xE0A5jZPGBk2SxyHMdxKkYSp7DJzFa3Cet4bgzHcRynW5JkSOqzkr4AVEnak7A+8yPlNctxHMepBElqCucD+wEbgGnAGuCiMtrkOI7jVIgkXzSvAybFzXEcx9mGyesUJN3Fh/sQVgNPEqa9Xl8OwxzHcZzOJ0nz0SJgLfB/cVtDWEpzr3jsOI7jbCMk6Wg+0swOyzq+S9ITZnaYpGfLZZjjOI7T+SSpKVRLGp45iPuZ5X42lsUqx3EcpyIkqSlcAjws6VXCcpwjgX+OayHc1KFkB0iqIvRLvGlmJ0kaCUwHaoFG4Cwzc6fjOI7TiSQZffTH+H3CPjHoxazO5R+n0H0h8DywUzy+BviRmU2X9AvgXOC6FPE7juM4BZJ0ltQ9gb0J6yCcIelLaZRKGgr8A/DLeCxgDDAzXnITcEoaHY7jOE7hKKzh3MEF0reAY4F9CctwngA8bGanFa1Umgl8F9gRuBQ4G3jUzPaI54cB95jZ/jlkJwITAerq6hqmT59elA1r166luro6/4Uu7/JdUL4r2ODy3Vd+9OjRjWZ2aM6TZtbhBjxDqFHMj8d1wKx8ch3EdxLwP3H/WOBuYBfglaxrhgEL88XV0NBgxTJnzpyiZV3e5Sst3xVscPnuKw88ae3kq0k6mt83sy2SNkvaCVgeM+1iOQr4tKQTgR0IfQo/AWok9TSzzcBQ4M0UOhzHcZwiSNKn8KSkGsKHao3AU8Bfi1VoZpeb2VAzqwfOBB4ws/HAHCDTJDUBuKNYHY7jOE5xJBl99M9x9xeS7gV2MrMFZbDl34Hpkq4CngauL4MOx3EcpwOSzH10v5mNBTCzxW3D0mBmc4G5cX8RYTEfx3Ecp0K06xQk7QD0A3aRNJDw4RqEPoDdO8E2x3Ecp5PpqKbwj4R1E4YQ+hIyTmEN8LPymuU4juNUgnadgpn9BPiJpPPN7KedaJPjOI5TIZJ0NP9U0pFAffb1ZvbrMtrlOI7TJWl+ZCrLZ05i0MrXeenO4ex62mRqjhy/zehP0tF8MzAKmAe0xGAD3Ck4jrNd0fzIVN66YSK2cR0CNq9cwls3TAToFMfQGfqTfLx2KLBv/ArOcRxnu2X5zEnYxnVbhdnGdSyfOalTnEJn6E/y8dpCYLeSaHMcx+nGbF75ekHh3VF/EqewC/CcpD9JujOzlcwCx3GcTqT5kam8dHE9g24Yw0sX19P8yNTEsj1rhxcUXmo6Q38Sp3AlYRrr/wJ+kLU5juMUTJpMuRS637phIptXLkFYa5t8Uht2PW0y6t1vqzD17seup00uyIZi778U+vOR1ymY2YPAYqBX3H+CMP+R4zhOQaTNlNPSUZt8EmqOHM/gc6bQs3YEhuhZO4LB50xJ3J6f9v7T6k9CXqcg6auExW/+NwbtDtxeMgscx9luSJspQ7qSdina5GuOHM9eP1zMinMeYK8fLi4oQy7F/afRn4QkzUdfI0x3vQbAzF4Gdi2pFU6nUcmqu1MaKv0OK5kppy1pV7pPoNId1UlI4hQ2mNnGzIGknoTvFIpC0g6SHpc0X9Kzkr4dw0dKekzSK5J+J6l3sTqc3FS66r4tkDZDLoV8Jd9hpTPltCXtzmiT74hKO6UkJHEKD0r6BtBX0nHALcBdKXRuAMaY2YHAQcDxkj4OXAP8yMKSnKuAc1PoKBsrn17MM9+7k+r73uaZ793JyqcXV9qkxJSi6ro9kzZDLEWGXop3mCYNVzpTTlvS7ow2+Y6otFNKQhKncBmwgrAs5z8S1mm+oliFcTW4tfGwV9wMGEPouwC4iTDiqUux8unFLLn9CTY2h68JNzavY8ntT3Qbx9Adqq7lJk1JPW2GWIoMPe07TJuGK50pl6KkXe42+Xy6K+mUkpDEKfQFfmVmp5vZacCvYljRSKqSNI+wtOcs4FWgOS7FCdBEF5yee+msBdimlq3CbFMLS2clX3Ooks0P3aHqWk7SltTTZoilcMpp32HaNFzpTLk7lLTzUUmnlATlm71C0qPAuEzpXlI1cJ+ZHZlaeVjm8zbgP4AbY9MRkoYB95jZ/jlkJgITAerq6hqmT59elO61a9dSXV1dkEz1fW+3zh+ejQFr/z7/R999Xp1Nv8aHaan+LFZVi1pWUrX296xrOJoNo8Z1ivxOf7kWtWz4wPaqPqw56tJE8tkU8/wqLV8740yq3lv2ofCW/nWsPCN/OqqdcSa2ZRSbdzqz9fn3XDMd9Xg1sXwa/ZA+DVQ6DWdTbBro8+psqht/SY/3lrOl/66sbfhKwbrT6E8r33PpOvq8shat34Lt0IMNe1SzeUi//IIl0g8wevToRjM7NNe5JE5hnpkdlC+sWCR9E3ifsBznbma2WdIRwJVm9v86kj300EPtySefLEhfZobBTStfp1dtYTMMPvO9O9nYvO5D4b1r+vGxf/t0XvmFl57Chp6nQo8+HwRu2UCfzbex/7W3l10e0t0/wBszfsuKp1ZjGoBsNYMOGcCwM76QWL6S+p+b0IPNfY/4UKbe8/2/su9NWxLpXv70JlDW87cN7Hpwr0Q2ND8yldd/ez2b+536gf51tzH8C+cmfgYrn17Mklv/irV8kLWryhjxmSOoPbg+r/z870xj8/sfdgs9+xoH/sfny64/m7lz53LssccWJJOxYemsBWxoXkefmn4MOe6AgnWn0Z9GPtN8l11bU68qRpxyWOJ7KMX9S2rXKSSZEO89SYeY2VMxsgZCJl4UkgYBm8ysWVJf4DhCJ/Mc4DRgOjABuKNYHe3xwZ/yEmxILRtaVrL+t2Ep6CR/ypr6tTkzhZr6/BkKwMYeY7fO0AF69AnhnSAP0NL3KDbs9t9s2GEdW2r60dL3gMSyrZlij4EAmAay/OkNwG8LzBSLe/5p9W8Z9Gk2ZTlV6zmITTUT6VE9KK8sQPPialCbQoH60Ly4H8MSyLf0PYrNA3u0ZqjWcxCbB36Vlr5HJNIPsfmnZetM3VrE0lkLEmUMVat/x+YcBYuq1bcB+Z1CWv3wQaZW3byOZx6/s6BMLTtTze4TAYpyDJ1NR813SezvjPtP0qdwIXCLpD9Lehj4HfAvKXQOBuZIWkD4OnqWmd1NqClcLOkVoBa4PoWOnDTdegubqidgPQeBeoRMoXoCTbfekkj+/Ue/Qa9VU9DmFWBb0OYV9Fo1hfcf/UYieavapaDwUsun7WRc8dTqrR0igPqE8ASkff5p9bcM+FxOp9oy4HOJ5HPVEjsKb0tHGWpS0trQY8Wd9Gpuk4abp9BjRbLpzNLqT5sGS9Gvl3YEYRr5kqShlPefjw5rCpKqgE8A+wB7x+AXzWxTsQrNbAFwcI7wRcDhxcabhLQl7c0rX6cnS+j5/iNbh+eojueiZz/YnKOO1TNhc2Ja+bSlFNOAgsLbkvb5p9Xf3ntK+v561/Rrt/kwCWkzhFLY0LN2OKx85ENpuGftiE7RnzYNlsopFVvSTivfFdJQPjqsKZhZC/B5M9tkZgvjVrRDqDRpS9ppR14MPekIVLV1H46qjKEnJWs+SCufNkHJcpfI2wtvS9rnn1Z/e3+8pH/IIccdgHpVba27VxVDjkvWBJdWfylsSDt6J63+tGkw7TNMW9JOK98V0lA+kjQf/UXSzyR9QtIhma1kFnQi7ZWok5a00/6hag+uZ8RnjqB3TT+M8CIL6aBLK582QQ06ZADYhq0DbUMIT0Da559Wf9o/ZO3B9Yw45bCtn38BHYRp9ZfChrTj5NPqr7RjTuuU0sp3hTSUjySjj+bkCDYzG1MyK4qk0NFHpRg5kXb0TIbuOvIhzeifUjz/tKOfKj1ypVT609hQSflKj75JO4IwrXw2lUxDqUYfmdnogrR1YTIPLs0DrTlyPDVHjk/9h6oEpbj/YWd8gWFnZBJ08sy4VPofGnUMk156gddXvc/wgbsyedQ+FOKSaw+up/bgeubOncuhFXh/ldZfaUqRBtI8wyHHHZDTKSUtaaeVLwXlTkN5nYKkOsICO0PM7ARJ+wJHmFnJRwd1Bv6nTHf/UxubmHRPzJQfns3kE/ZhfMPQxPL3bunJpI39eH2DGL6xL5O39EycqU9tbGLiLQtYF/+QS1a9z8RbQltuITY4laWS/8G0TqkUTq2rk+Q7hRuBG4DMBC0vEYaldkun4BRP2kw5rfyke15olc2wblMLk+55wZ2Ck5i0TmlbL1gmWqPZzGYAWwDi/EQtHYt0XaY2NlF/1WzG3PUu9VfNZmpjU6fKp6WS+jvKlDtD/vVVub+ZbC/ccZzCSeIU3pNUS1xDIU5znWwMYBcjU1Jdsup9jA9Kqkkz1rTymTiKzdQrrT9tppxWfvjA3PMwthfuOE7hJHEKFwN3AqMk/QX4NXB+Wa0qE5Uu6abN1CutP22mnFZ+8gn70K/NcLx+vaqYfMI+ieRh+67pdSUbnK5LXqcQ5zz6JHAkYT2F/eJXyd2OSpd0K918klZ/2kw5rfz4hqFMOf0ARgzsi4ARA/sy5fQDEvcnVLqmVQr9aan0M+gK8k7H5HUKknYALgC+A3wb+FoM63YMH9gXBiyDvR6F/R4MvwOWdVpJt9LNJ2n1p82U08pn4lh8xTge+NSOLL5iXEGyla5ppdWfbUexmWKln0Gl5TNxuFNpnyTNR78G9gN+Cvws7t9cTqPKxYkfB3Z/CXpvABF+d38phCcgbUm30s0npWiTT5Mpl0I+DZWuaZWiozxtpljpZ1Bp+a5SW+vKTimJU9jfzM41szlx+yrBMXQ7/vi3R6FHm2mue2wJ4QlIW9KtdPNJKdrkuzOVrmmVwimnzRQr/QwqLV+q2lqxdAWnlI8kTuGpOOIIAEl/BxS2sk0WkoZJmiPpOUnPSrowhu8saZakl+PvwGJ1tMfr7zUXFJ6LNCXdSjeflEL/1FcbqZ9xFWNeu5v6GVcx9dXGxLKlkE9DpWtapXDKaTPFSj+DSsuXqrZWqea7tPqTkMQpNACPSFosaTHwV+AwSc/ENREKZTNwiZntC3yc0EexL3AZcL+Z7QncH49LyvD+NQWFl4Wa2Kexf+zTqPnw8ozlJI1TmfpqIxP/MpMl7zWHUs57zUz8y8zEGXta+bSMbxjKhHG9qdrnMdjvQar2eYwJ43p3m5oepO8Xq3Rts9LyaZ1KpZvvOqOmkcQpHA+MJIxA+mTcPx44CfhUoQrN7K3MKm5m9i7wPLA7cDJwU7zsJuCUQuPOx+SGE+hX1WursH5VvZjccELiONKUdEuRKVaypD2p8R7WtWw9c/q6lk1MarynU+Qh/fO/aen9tPRcD4KWnuu5aen9ieNI61SA1IWCtP1iaW2o9GCDSju1SjffdUbzV95ZUsuJpHrgIWB/4HUzq4nhAlZljtvITAQmAtTV1TVMn55swfMMs99t4perXmR5y/vsWtWXrwzcm3E7JktQs99t4tqVz7DBPngpfVTFpbUfSxTHma/fz7KWD5cI6qr6Mn14/oVm0urPxFHs/Y957W5ypRYBD4w8qezy3f35l+L9VfoeMnEUm4a6gvyPlyzmrg0vs6XnBnps7sOn+uzJRSPqE8mOuetdbMAyqHsNem2ATX1g2Ui0uo4HPrVjftubNnLtgg1syMrX+1TBpQf0YdzQ3sn05wgXJNKfYfTo0e3OkloxpyCpGngQmGxmt0pqznYCklaZWYf9CoVOnZ1NMbOc1s+4iiU5+h9G9K9h8RlX5JXvccOl7b7QLedcW3b9mZpKdmm9X1Uvphx1GuNHNZRdf6XlK/3808pD5e8hbRrq7vK7/ODXrKxZuPWAlS09qG3en3cu+VJeeWgzqeTAvgVNKll/1WyW5GhqGjGwL4uvGJcoDuh46uwkzUclR1Iv4PfAVDO7NQYvkzQ4nh8MLK+EbR2RtqM6bZ9GWv1pm2/SNr+lle/uz78UAx0qfQ+VbkKstDx1r+UcwUjda8nkIVXzXWeMIOx0pxCbhq4HnjezH2aduhOYEPcnAHd0tm35SPuHTJspVjpDGD+qgSlHncaI/jWhPbd/TeISVinku/vzL8VAh0rfQ6UdY6Xl/7bp3YLC25K2X7Ek/Vp5qERN4SjgLGCMpHlxOxG4GjhO0svAuHjcpUj7h0ybKVY6Q4BwD4vPuIIHRp7E4jOuSGx7KeS7+/MvxUCHSt9DpR1jd5dPW1NJO1giCZ3uFMzsYTOTmR1gZgfF7Y9mttLMxprZnmY2zsz+1tm25SPtHzITR7GZYqUzhErT3Z9/Keyv9D1U2jF2d/lKN98lIckiO04W40c1MH5UQ8WW40yjP/PHn9R4D6+/18zw/jVMbjih4EypknTn518K+VJQyTS0vcsP71+Ts6O/s5rvkuBOYTujK2RKTvem0o6xO8tPbjgh5+inQprv0jiVJFRk9JHjOM72SKWb75LgNQXHcZxOpKs3AbtTcBzH6UaUuwnYm48cx3GcVtwpOI7jOK24U3Acx3FacafgOI7jtOJOwXEcx2nFnYLjOI7TijsFx3EcpxV3Co7jOE4rlVpk51eSlktamBW2s6RZkl6Ovx2uuuY4juOUnkrVFG4Ejm8Tdhlwv5ntCdwfjx3HcZxOpCJOwcweAtqul3AycFPcvwk4pTNtchzHcUBmuZYB7wTFUj1wt5ntH4+bzawm7gtYlTluIzcRmAhQV1fXMH369KL0r127lurq6qJkXd7lKy3fFWxw+e4rP3r06EYzOzTnSTOryAbUAwuzjpvbnF+VL46GhgYrljlz5hQt6/IuX2n5rmCDy3dfeeBJaydf7Uqjj5ZJGgwQf5dX2B7HcZztjq7kFO4EJsT9CcAdFbTFcRxnu6RSQ1KnAX8F9pbUJOlc4GrgOEkvA+PiseM4jtOJVGSRHTP7fDunxnaqIY7jOM5WdKXmI8dxHKfCuFNwHMdxWnGn4DiO47TiTsFxHMdpxZ2C4ziO04o7BcdxHKcVdwqO4zhOK+4UHMdxnFbcKTiO4zituFNwHMdxWnGn4DiO47TiTsFxHMdpxZ2C4ziO00qXcgqSjpf0oqRXJF1WaXscx3G2N7qMU5BUBfwcOAHYF/i8pH0ra5XjOM72RZdxCsDhwCtmtsjMNgLTgZMrbJPjOM52RUUW2WmH3YE3so6bgL9re5GkicDEeLhW0otF6tsFeKdIWZd3+UrLdwUbXL77yo9o70RXcgqJMLMpwJS08Uh60swOdXmX747yXcEGl+/e8u3RlZqP3gSGZR0PjWGO4zhOJ9GVnMITwJ6SRkrqDZwJ3FlhmxzHcbYrukzzkZltlvQvwJ+AKuBXZvZsGVWmbYJyeZevpHxXsMHlu7d8TmRm5YjXcRzH6YZ0peYjx3Ecp8K4U3Acx3Fa2e6cgqRfSVouaWGR8sMkzZH0nKRnJV1YoPwOkh6XND/Kf7tIO6okPS3p7iJkF0t6RtI8SU8WIV8jaaakFyQ9L+mIAmT3jnoz2xpJFxWo/+vx2S2UNE3SDgXKXxhln02iO1eakbSzpFmSXo6/AwuUPz3q3yKpw2GF7ch/Pz7/BZJuk1RToPx3ouw8SfdJGlKIfNa5SySZpF0K1H+lpDez0sGJheqXdH58Bs9K+l6B+n+XpXuxpHkFyh8k6dHMf0jS4QXKHyjpr/F/eJeknTqQz5nnFJIGC8LMtqsNOAY4BFhYpPxg4JC4vyPwErBvAfICquN+L+Ax4ONF2HEx8Fvg7iJkFwO7pHiGNwFfifu9gZoi46kC3gZGFCCzO/Aa0DcezwDOLkB+f2Ah0I8w0GI2sEehaQb4HnBZ3L8MuKZA+Y8CewNzgUOL0P/3QM+4f00R+nfK2r8A+EUh8jF8GGFgyJKO0lM7+q8ELk34znLJj47vrk883rVQ+7PO/wD4ZoH67wNOiPsnAnMLlH8C+GTc/zLwnQ7kc+Y5haTBQrbtrqZgZg8Bf0sh/5aZPRX33wWeJ2RUSeXNzNbGw15xK6i3X9JQ4B+AXxYiVwokDSAk8usBzGyjmTUXGd1Y4FUzW1KgXE+gr6SehMx9aQGyHwUeM7N1ZrYZeBD4TEcC7aSZkwnOkfh7SiHyZva8mSX6Gr8d+fui/QCPEr7rKUR+TdZhfzpIgx38Z34E/FtHsnnkE9GO/D8BV5vZhnjN8mL0SxJwBjCtQHkDMqX7AXSQBtuR3wt4KO7PAj7bgXx7eU7iNFgI251TKCWS6oGDCaX9QuSqYnV1OTDLzAqSB35M+DNuKVAugwH3SWpUmDakEEYCK4AbYvPVLyX1L9KOM+ngz5gLM3sTuBZ4HXgLWG1m9xUQxULgE5JqJfUjlPKG5ZHJRZ2ZvRX33wbqioijVHwZuKdQIUmTJb0BjAe+WaDsycCbZja/UL1Z/EtswvpVEU0fexHe42OSHpR0WJE2fAJYZmYvFyh3EfD9+PyuBS4vUP5ZPpjb7XQSpsE2eU5Z0qA7hSKRVA38HrioTakrL2bWYmYHEUp3h0vavwC9JwHLzayxEJ1tONrMDiHMSPs1SccUINuTUBW+zswOBt4jVF0LQuEDxU8DtxQoN5DwZxoJDAH6S/piUnkze57Q3HIfcC8wD2gpxIYccRoF1vZKhaRJwGZgaqGyZjbJzIZF2X8pQGc/4BsU6EjacB0wCjiI4Nx/UKB8T2Bn4OPAvwIzYqm/UD5PgQWTyD8BX4/P7+vEmnMBfBn4Z0mNhCahjfkEOspzSpkG3SkUgaRehJcz1cxuLTae2OwyBzi+ALGjgE9LWkyYSXaMpN8UqPfN+LscuI0wQ21SmoCmrNrNTIKTKJQTgKfMbFmBcuOA18xshZltAm4FjiwkAjO73swazOwYYBWhjbZQlkkaDBB/222+KBeSzgZOAsbHTKFYptJB80UORhGc8vyYDocCT0naLWkEZrYsFo62AP9HYWkQQjq8NTbHPk6oNbfb2Z2L2Pz4GeB3BeoGmEBIexAKNgXZb2YvmNnfm1kDwSm9msfWXHlOWdKgO4UCiaWR64HnzeyHRcgPyowUkdQXOA54Iam8mV1uZkPNrJ7Q/PKAmSUuKUvqL2nHzD6hwzLxSCwzext4Q9LeMWgs8FxS+SyKLaG9DnxcUr/4LsYS2lgTI2nX+DuckCn8tgg77iRkDMTfO4qIo2gkHU9oQvy0ma0rQn7PrMOTKSwNPmNmu5pZfUyHTYSO0LcL0D846/BUCkiDkdsJnc1I2osw4KHQGUPHAS+YWVOBchD6ED4Z98cABTU/ZaXBHsAVwC86uLa9PKc8abAUvdXdaSNkRG8BmwiJ+dwC5Y8mVNMWEJoe5gEnFiB/APB0lF9IB6MeEsR1LAWOPgI+AsyP27PApCL0HgQ8Ge/hdmBggfL9gZXAgCLv+9uETGwhcDNxBEoB8n8mOLL5wNhi0gxQC9xPyAxmAzsXKH9q3N8ALAP+VKD8K4Sp5jNpsKPRQ7nkfx+f3wLgLmD3Yv8z5BnN1o7+m4Fnov47gcEFyvcGfhPv4SlgTKH2AzcC5xX5/o8GGmMaegxoKFD+QkIN9SXgauLsEu3I58xzCkmDhWw+zYXjOI7TijcfOY7jOK24U3Acx3FacafgOI7jtOJOwXEcx2nFnYLjOI7TijsFZ5tD0nmSvhT3z1bWDKBxWo59S6Dj05IK/pLbcbo6PiTV2aaRNJcwG2fBU4Q7zvaI1xScLoOk+jg//lSFdRpmxnl2kDQ2TsD3TJxArU8MvzrOM79A0rUx7EpJl0o6DTgUmBrnve8raa7i+gWSPh/jWyjpmiw71sbJ4uYrzJn/oYnGYg3kZ3H/Rkn/LekRSYui3rbX95f0hxjnQkmfi+ENcUK3Rkl/ypq2oCFeO19h7YSFbfXG47slHRv3/15hjv6nJN0S58rJrJ/x7Rj+jKR9Yni1pBti2AJJn+0oHmf7wJ2C09XYG/gfM/sosIYwadgOhK9PP2dmHyNMhvZPkmoJXwbvZ2YHAFdlR2RmMwlfXo83s4PM7P3MudikdA1hioKDgMMknRJP9wceNbMDCdMbfzWB3YMJX56eRPhCtS3HA0vN7EAz2x+4N85n81PgNAtz4PwKmByvvwE4P9qQF4VFbq4AxlmY7PBJwpobGd6J4dcBl8aw/yDMMvux+PweSBCPs43jTsHparxhZn+J+78hZLR7EybBy0xcdxNhTYfVwHrgekmfAQqZA+gwwsIoKyysSzA1xglhxsrMinaNQH2C+G43sy1m9hy5pzB+BjhO0jWSPmFmq+N97Q/MUphK/QpgaJwbq8bCPPwQpoTIx8cJC6/8JcY1ARiRdT4ziVr2/YwDfp65wMxWJYjH2cbpWWkDHKcNbTu5Olr8ZbPCMohjgdMI0z+PKYENm+yDzrYWkv1PNmTtf2gKZzN7SdIhhDlrrpJ0P2GG2mfNbKvlTNXB0pqEabKzC3OZpUhFWJvj83nsy3c/+eJxtnG8puB0NYbrgzWfvwA8DLwI1EvaI4afBTwY27oHmNkfCXPa52pqeZcwX31bHgc+KWkXSVWEWVsfLOF9bEVsrlpnZr8Bvk+YbvxFYFDmfiX1krSfhSnVmyUdHcXHZ0W1GDhIUg9Jw/hgyuZHgaMyzyj2YeyVx6xZwNeybBxYZDzONoQ7Baer8SJh4Z/ngYGExXzWA+cAt0h6hjB3/i8Imf3dkhYQnEeutu8bgV9kOpozgRZWrLqMsJ7FfKDRzMo5/fXHgMdjk8y3gKvMbCOhhnONpPmE2S8za0OcA/w8Xp9d8/gLYY3q54D/JswQipmtAM4GpsXn8Vdgnzw2XQUMjB3f84HRRcbjbEP4kFSny6Cw1ODdsSPWifhzcToTryk4juM4rXhNwXEcx2nFawqO4zhOK+4UHMdxnFbcKTiO4zituFNwHMdxWnGn4DiO47Ty/wFy8psYkpbYIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "style.use('seaborn-colorblind')\n",
    "plt.plot(position_A, 'o', label = 'A')\n",
    "plt.plot(position_C, 'o', label = 'C')\n",
    "plt.plot(position_G, 'o', label = 'G')\n",
    "plt.plot(position_T, 'o', label = 'T')\n",
    "plt.ylim((0, 100))\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('percentage of sequences')\n",
    "plt.grid()\n",
    "plt.xlabel('position in sequence')\n",
    "plt.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])\n",
    "plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])\n",
    "plt.title(\"Distribution of Nucleotides in Sequence of N's\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c7ae3e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Distribution of Nucleotides in Sequence of N's\")"
      ]
     },
     "execution_count": 20,
     "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": [
    "import numpy as np\n",
    "style.use('seaborn-colorblind')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']\n",
    "\n",
    "ax.bar(labels, position_A, label='A')\n",
    "ax.bar(labels, position_C, bottom=np.array(position_A), label='C')\n",
    "ax.bar(labels, position_G,  bottom=np.array(position_A) + np.array(position_C), label='G')\n",
    "ax.bar(labels, position_T, bottom=np.array(position_A) + np.array(position_C) + np.array(position_G),\n",
    "       label='T')\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax.set_ylabel('percentage of sequences')\n",
    "ax.set_xlabel('position in sequence')\n",
    "ax.set_title(\"Distribution of Nucleotides in Sequence of N's\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bff37e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}