transitions_and_transversions.ipynb

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    "# Transitions and Transversions\n",
    "\n",
    "For two DNA strings ($s_1$ and $s_2$) of the same length, their transition/transversion ratio $R(s_1, s_2)$ where symbol substitutions are inferred from mismatched corresponding symbols as when calculating Hamming distance\n",
    "\n",
    "\n",
    "<img src=\"http://rosalind.info/media/problems/tran/transitions-transversions.png\" width=\"350\">\n",
    "\n",
    "\n",
    "**Given:** Two DNA strings $s_1$ and $s_2$ of equal length (at most 1 kbp)  \n",
    "**Return:** The transition/transversion ratio $R(s_1,s_2)$\n",
    "."
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    "from __future__ import division\n",
    "from Bio import SeqIO\n",
    "\n",
    "def zip_strings(path):\n",
    "    fasta_list = list(SeqIO.parse(open(path), \"fasta\"))\n",
    "    s1 = fasta_list[0].seq\n",
    "    s2 = fasta_list[1].seq\n",
    "    return zip(s1, s2)\n",
    "\n",
    "def is_change(t):\n",
    "    # TODO fix this mess\n",
    "    if t[0] == \"A\" and t[1] == \"G\":\n",
    "        return 1\n",
    "    elif t[0] == \"G\" and t[1] == \"A\":\n",
    "        return 1\n",
    "    elif t[0] == \"C\" and t[1] == \"T\":\n",
    "        return 1\n",
    "    elif t[0] == \"T\" and t[1] == \"C\":\n",
    "        return 1\n",
    "    elif t[0] == t[1]:\n",
    "        pass\n",
    "    else: # is transversion\n",
    "        return 0\n",
    "\n",
    "def is_transx(x):\n",
    "    pre_out = map(is_change, x)\n",
    "    out = [i for i in pre_out if i is not None]\n",
    "    return sum(out) / (len(out) - sum(out))\n"
   ]
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       "2.580246913580247"
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   "source": [
    "path = \"/home/scott/Dropbox/rosalind/rosalind_tran.txt\"\n",
    "is_transx(zip_strings(path))"
   ]
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "--------------------\n",
    "\n",
    "# More details\n",
    "\n",
    "The first 10 elements of zip strings"
   ]
  },
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   "execution_count": 3,
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     "data": {
      "text/plain": [
       "[('G', 'G'),\n",
       " ('G', 'C'),\n",
       " ('A', 'A'),\n",
       " ('G', 'G'),\n",
       " ('G', 'A'),\n",
       " ('G', 'A'),\n",
       " ('A', 'A'),\n",
       " ('C', 'C'),\n",
       " ('C', 'C')]"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "zip_strings(path)[1:10]"
   ]
  },
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   "source": [
    "Then loop through the nucleotides and assign `1` if the change is a transition, or `0` is it's a transversion\n",
    "\n",
    "```python\n",
    "def is_change():\n",
    "    if transition:\n",
    "        return 1\n",
    "    if transversion:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "```"
   ]
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   "execution_count": 4,
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   "outputs": [
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     "data": {
      "text/plain": [
       "[None, 0, None, None, 1, 1, None, None, None]"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "map(is_change, zip_strings(path)[1:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate the ratio of `1`'s to `0`'s in `out`:\n",
    "\n",
    "````python\n",
    "sum(out) / len(out) - sum(out)\n",
    "\n",
    "```"
   ]
  }
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