1.06_NaiveExactMatching-MatchingArtificialReads.ipynb

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     "text": [
      "--2015-07-14 11:54:42--  http://d28rh4a8wq0iu5.cloudfront.net/ads1/data/phix.fa\n",
      "Resolving d28rh4a8wq0iu5.cloudfront.net... 54.192.55.189, 54.192.54.164, 54.192.54.222, ...\n",
      "Connecting to d28rh4a8wq0iu5.cloudfront.net|54.192.55.189|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5528 (5.4K) [application/octet-stream]\n",
      "Saving to: 'phix.fa'\n",
      "\n",
      "phix.fa             100%[=====================>]   5.40K  --.-KB/s   in 0.002s \n",
      "\n",
      "2015-07-14 11:54:42 (3.30 MB/s) - 'phix.fa' saved [5528/5528]\n",
      "\n"
     ]
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   ],
   "source": [
    "!wget http://d28rh4a8wq0iu5.cloudfront.net/ads1/data/phix.fa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "def readGenome(filename):\n",
    "    genome = ''\n",
    "    with open(filename, 'r') as f:\n",
    "        for line in f:\n",
    "            # ignore header line with genome information\n",
    "            if not line[0] == '>':\n",
    "                genome += line.rstrip()\n",
    "    return genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "genome = readGenome('phix.fa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "source": [
    "def naive(p, t):\n",
    "    occurrences = []\n",
    "    for i in range(len(t) - len(p) + 1):\n",
    "        match = True\n",
    "        for j in range(len(p)):\n",
    "            if t[i+j] != p[j]:\n",
    "                match = False\n",
    "                break\n",
    "        if match:\n",
    "          occurrences.append(i)\n",
    "    return occurrences"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "t = 'AGCTTAGATAGC'\n",
    "p = 'AG'\n",
    "naive(p, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "def generateReads(genome, numReads, readLen):\n",
    "    ''' Generate reads from random positions in the given genome. '''\n",
    "    reads = []\n",
    "    for _ in range(numReads):\n",
    "        start = random.randint(0, len(genome)-readLen) - 1\n",
    "        reads.append(genome[start : start+readLen])\n",
    "    return reads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 / 100 reads matched the genome exactly!\n"
     ]
    }
   ],
   "source": [
    "# Generate 100 reads of length 100\n",
    "reads = generateReads(genome, 100, 100)\n",
    "\n",
    "# Count how many reads match the genome exactly\n",
    "numMatched = 0\n",
    "for r in reads:\n",
    "    matches = naive(r, genome)\n",
    "    if len(matches) > 0:\n",
    "        numMatched += 1\n",
    "print('%d / %d reads matched the genome exactly!' % (numMatched, len(reads)))"
   ]
  }
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