dna-sample-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DNA FASTA Protein Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing `Biopython` library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting `sample.fa` into a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSSHKGSVVAQGNGAPASNREADTVELAELGPLLEEKGKRVIANPPKAEEEQTCPVPQEEEEEVRVLTLPLQAHHAMEKMEEFVYKVWEGRWRVIPYDVLPDWLKDNDYLLHGHRPPMPSFRACFKSIFRIHTETGNIWTHLLATAHLPLHRLCPGHFCHHCGAVGPVCHS', 'XWISLVTALHLVLGLSAVLGLLLLRWQFPAHYSSQDIVQAPYDDKRGSAKSAPASKHFSLFYPVHQPFQTGFLHKRLRHALWPSLPDLHRVLGQYLRDTAALSPPKATVSDTCEEVEPSLLEILPKSSERTPLPLCSSQAQMDYRRLQPSCLGTMPLSVCPPMAESGSCCTTHIANH', 'MVETPRLRRVTRGSSPSRKSTKKATNENYEDNEKSDSSGSRSSCFGFVAYSVNSGERSRVKIKTLTHQPACSDTPRAGRCLSPSPSPSPRKLRCLDQLESEKEERRERHSKKRRVKEGNPNDTLDFLWLGTPSTMTTSASSHLNKGIKQVYMSLPQGEKVQAMYIWIDGTGEGLRCKTRTLDSEPKCVEELPEWNFDGSSTLQSEGSNSDMYLVPAAMFRDPFRKDPNKLVLCEVFKYNRRPAETNLRHTCKRIMDMVSNQHPWFGMEQEYTLMGTDGHPFGWPSNGFPGPQGPYYCGVGADRAYGRDIVEAHYRACLYAGVKIAGTNAEVMPAQWEFQIGPCEGISMGDHLWVARFILHRVCEDFGVIATFDPKPIPGNWNGAGCHTNFSTKAMREENGLKYIEEAIEKLSKRHQYHIRAYDPKGGLDNARRLTGFHETSNINDFSAGVANRSASIRIPRTVGQEKKGYFEDRRPSANCDPFSVTEALIRTCLLNETGDEPFQYKN', 'MVQAHGGRSRAQPLTLSLGAAMTQPPPEKTPAKKHVRLQERRGSNVALMLDVRSLGAVEPICSVNTPREVTLHFLRTAGHPLTRWALQRQPPSPKQLEEEFLKIPSNFVSPEDLDIPGHASKDRYKTILPNPQSRVCLGRAQSQEDGDYINANY', 'MLTKFETKSARVKGLSFHPKRPWILTSLHNGVIQLWDYRMCTLIDKFDEHDGPVRGIDFHKQQPLFVSGGDDYKIKVWNYKLRRCLFTLLGHLDYIRTTFFHHCVNRAQPLCDVCSVPPHRRLGSISQPGPDCARLGYFWSEEKKPVPWCGGIGCERNNWG']\n"
     ]
    }
   ],
   "source": [
    "fasta_sequences = SeqIO.parse(open('data/sample.fa'),'fasta')\n",
    "seq = [str(fasta.seq) for fasta in fasta_sequences]\n",
    "\n",
    "# printing last 5 sequences\n",
    "print(seq[-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating single string of sequence data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_str = ''\n",
    "\n",
    "for s in seq:\n",
    "    seq_str += s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MAQKRPACTLKPECVQQLLVCSQEAKKSAYCPYSHFPVGAALLTQEGRIFKGCNIENACYPLGICAERTAIQKAVSEGYKDFRAIAIASDMQDDFISPCGACRQVMREFGTNWPVYMTKPDGTYIVMTVQELLPSSFGPEDLQKTQMGKDYYCILGIEKGASDEDIKKAYRKQALKFHPDKNKSPQAEEKFKEVAEAYEVLSDPKKREIYDQFGEEGLKGGAGGTDGQGGTFRYTFHGDPHATFAAFFGGSNPFEIFFGRRMGGGRDSEEMEIDGDPFSAFGFSMNGYPRDRNSVGPSRLKQDPPVIHELRVSLEEIYSGCTKRMKISRKRLNADGRSYRSEDKILTIEIKKGWKEGTKITFPREGDETPNSIPADIVFIIKDKDHPKFKRDGSNIIYTAKISLREALCGCSINVPTLDGRNIPMSVNDIVKPGMRRRIIGYGLPFPKNPDQRGDLLIEFEVSFPDTISSSSKEVLRKHLPASMADSGLLLKRGSCRSTWLRVRKARPQLILSRRPRRRLGSLRWCGRRRLRWRLLQAQASGVDWREGARQVSRAAAARRPNTATPSPIPSPTPASEPESEPELESASSCHRPLLIPPVRPVGPGRALLLLPVEQGFTFSGICRVTCLYGQVQVFGFTISQGQPAQDIFSVYTHSCLSIHALHYSQPEKSKKELKREARNLLKSHLNLDDRRWSMQNFSPQCSIVLLEHLKTATVNFITSYPGSSYIFVQESPTPQIKPEYLALRSVGIRREKKRKGLQLTESTLSALEELVNVSCEEVDGCPVILVCGSQDVGKSTFNRYLINHLLNSLPCVDYLECDLGQTEFTPPGCISLLNITEPVLGPPFTHLRTPQKMVYYGKPSCKNNYENYIDIVKYVFSAYKRESPLIVNTMGWVSDQGLLLLIDLIRLLSPSHVVQFRSDHSKYMPDLTPQYVDDMDGLYTKSKTKMRNRRFRLAAFADALEFADEEKESPVEFTGHKLIGVYTDFAFRITPRNRESHNK'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing first 1000 bases of sequence\n",
    "seq_str[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2294810"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing length of string sequence\n",
    "len(seq_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Length of created string is **2294810 characters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get size of original string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2241.0732421875"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "## size in bytes\n",
    "str_size_b = sys.getsizeof(seq_str)\n",
    "\n",
    "## size in megabytes\n",
    "str_size_mb = str_size_b / 1024\n",
    "\n",
    "str_size_mb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the string in memory is **2241.07 MB**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unique characters in the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'X', 'M', 'R', 'N', 'I', 'W', 'Y', 'A', 'C', 'P', 'K', 'Q', 'T', 'L', 'S', 'V', 'D', 'F', 'G', 'E', 'U', 'H'}\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "print(set(seq_str))\n",
    "\n",
    "# number of unique characters\n",
    "print(len(set(seq_str)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **22** unique characters in the string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting occurences of all the bases in the sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'L': 229594, 'S': 193221, 'E': 169262, 'A': 157284, 'G': 151726, 'P': 142954, 'V': 136178, 'R': 132212, 'K': 131155, 'T': 119582, 'Q': 113936, 'D': 109923, 'I': 96882, 'F': 81872, 'N': 79893, 'Y': 61189, 'H': 57202, 'C': 52756, 'M': 48951, 'W': 28402, 'X': 632, 'U': 4})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter \n",
    "\n",
    "# using collections.Counter() to get count\n",
    "char_count = Counter(seq_str) \n",
    "  \n",
    "print(char_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('L', 229594),\n",
       " ('S', 193221),\n",
       " ('E', 169262),\n",
       " ('A', 157284),\n",
       " ('G', 151726),\n",
       " ('P', 142954),\n",
       " ('V', 136178),\n",
       " ('R', 132212),\n",
       " ('K', 131155),\n",
       " ('T', 119582),\n",
       " ('Q', 113936),\n",
       " ('D', 109923),\n",
       " ('I', 96882),\n",
       " ('F', 81872),\n",
       " ('N', 79893),\n",
       " ('Y', 61189),\n",
       " ('H', 57202),\n",
       " ('C', 52756),\n",
       " ('M', 48951),\n",
       " ('W', 28402),\n",
       " ('X', 632),\n",
       " ('U', 4)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count.most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram representation for base frequency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels, values = zip(*char_count.items())\n",
    "indexes = np.arange(len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the matplotlib figure\n",
    "f1, ax1 = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "sns.barplot(x=list(labels), y=values, label=\"Frequency\", palette=\"rocket\")\n",
    "\n",
    "# Add a legend and informative axis label\n",
    "ax1.legend(ncol=1, loc=\"upper right\", frameon=True)\n",
    "ax1.set(xlabel=\"Nucleobases\",\n",
    "       title=\"Frequencies of nucleobases in the sequence string\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_h, values_h = zip(*char_count.most_common())\n",
    "\n",
    "# reversing the lists for graphing\n",
    "labels_h = list(labels_h)[::-1]\n",
    "values_h = list(values_h)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2, ax2 = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "plt.barh(indexes, values_h)\n",
    "plt.ylabel('Bases')\n",
    "plt.xlabel('Frequency')\n",
    "plt.title('Frequency of bases')\n",
    "plt.yticks(indexes, labels_h)\n",
    "plt.show()"
   ]
  },
  {
   "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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}