{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05791736-2c1c-418a-90cd-b7c72ec20c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PATH=/home/preps/golovin/miniconda3/bin:/home/preps/golovin/miniconda3/condabin:/usr/local/bin:/usr/bin:/home/preps/golovin/progs/x3dna-v2.4/bin\n"
     ]
    }
   ],
   "source": [
    "%set_env PATH=/home/preps/golovin/miniconda3/bin:/home/preps/golovin/miniconda3/condabin:/usr/local/bin:/usr/bin:/home/preps/golovin/progs/x3dna-v2.4/bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a058476d-92fa-41f9-9329-c582a71a8898",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find inverted repeats in nucleotide sequences\n"
     ]
    }
   ],
   "source": [
    "! einverted -sequence 1n78.seq -gap 12 -threshold 16 -match 4 -mismatch -9 -maxrepeat 20 -outfile outfile -outseq seqout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24f84e8f-249d-46e1-aff4-00a47ace9772",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((((.......)))))....(((((.......)))))))))))).... (-35.80)\n",
      "(((((((.(((((........)))))(((((.......)))))....(((((.......)))))))))))).... [-36.21]\n",
      "(((((((.(((((........)))))(((((.......)))))....(((((.......)))))))))))).... {-35.80 MEA=72.59}\n",
      " frequency of mfe structure in ensemble 0.511873; ensemble diversity 2.92  \n"
     ]
    }
   ],
   "source": [
    "import RNA \n",
    "seq = \"GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\"\n",
    "\n",
    "# create fold_compound data structure (required for all subsequently applied  algorithms)\n",
    "fc = RNA.fold_compound(seq)\n",
    " \n",
    "# compute MFE and MFE structure\n",
    "(mfe_struct, mfe) = fc.mfe()\n",
    " \n",
    "# rescale Boltzmann factors for partition function computation\n",
    "fc.exp_params_rescale(mfe)\n",
    " \n",
    "# compute partition function\n",
    "(pp, pf) = fc.pf()\n",
    "\n",
    "# compute MEA structure\n",
    "(MEA_struct, MEA) = fc.MEA()\n",
    " \n",
    "# compute free energy of MEA structure\n",
    "MEA_en = fc.eval_structure(MEA_struct)\n",
    " \n",
    "# print everything like RNAfold -p --MEA\n",
    "print(\"%s\\n%s (%6.2f)\" % (seq, mfe_struct, mfe))\n",
    "print(\"%s [%6.2f]\" % (pp, pf))\n",
    "print(\"%s {%6.2f MEA=%.2f}\" % (MEA_struct, MEA_en, MEA))\n",
    "print(\" frequency of mfe structure in ensemble %g; ensemble diversity %-6.2f\" % (fc.pr_structure(mfe_struct), fc.mean_bp_distance()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd13bb47-20f4-405a-9b9b-6405fabb3523",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">subopt 1\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))(((.(((....))))))....(((((.......))))).)))))).... [-32.80]\n",
      ">subopt 2\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))((((.((....))))))....(((((.......))))).)))))).... [-32.80]\n",
      ">subopt 3\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((...((((........)))).(((((.......)))))....(((((.......))))).)))))).... [-33.50]\n",
      ">subopt 4\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))(((((.......)))))....(((((.......))))).)))))).... [-34.90]\n",
      ">subopt 5\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))(((((.......)))))....(((((......)))))..)))))).... [-32.80]\n",
      ">subopt 6\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))(((((.(...).)))))....(((((.......))))).)))))).... [-32.80]\n",
      ">subopt 7\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "((((((..(((((........)))))(((((.(....))))))....(((((.......))))).)))))).... [-32.80]\n",
      ">subopt 8\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........))))).((((.......)))).....(((((.......)))))))))))).... [-33.60]\n",
      ">subopt 9\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((.(((....))))))....(((((.......)))))))))))).... [-33.70]\n",
      ">subopt 10\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))((((.((....))))))....(((((.......)))))))))))).... [-33.70]\n",
      ">subopt 11\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((..((((........)))).(((((.......)))))....(((((.......)))))))))))).... [-34.40]\n",
      ">subopt 12\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.((((((.....))).)))(((((.......)))))....(((((.......)))))))))))).... [-33.10]\n",
      ">subopt 13\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((((...)))).)))(((((.......)))))....(((((.......)))))))))))).... [-33.00]\n",
      ">subopt 14\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.((((..........))))(((((.......)))))....(((((.......)))))))))))).... [-33.20]\n",
      ">subopt 15\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.((((((.....)).))))(((((.......)))))....(((((.......)))))))))))).... [-33.10]\n",
      ">subopt 16\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((((...))).))))(((((.......)))))....(((((.......)))))))))))).... [-33.00]\n",
      ">subopt 17\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((((.......)))))....(((((.......)))))))))))).... [-35.80]\n",
      ">subopt 18\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((((.......)))))....(((((......))))).))))))).... [-33.70]\n",
      ">subopt 19\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((.(....).)))))(((((.......)))))....(((((.......)))))))))))).... [-33.50]\n",
      ">subopt 20\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.((((((.....).)))))(((((.......)))))....(((((.......)))))))))))).... [-32.80]\n",
      ">subopt 21\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((((.(...).)))))....(((((.......)))))))))))).... [-33.70]\n",
      ">subopt 22\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      "(((((((.(((((........)))))(((((.(....))))))....(((((.......)))))))))))).... [-33.70]\n",
      ">subopt 23\n",
      "GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\n",
      ".((((((.(((((........)))))(((((.......)))))....(((((.......)))))))))))..... [-32.80]\n"
     ]
    }
   ],
   "source": [
    "subopt_data = { 'counter' : 1, 'sequence' : seq }\n",
    "def print_subopt_result(structure, energy, data):\n",
    "    if not structure == None:\n",
    "        print(\">subopt %d\" % data['counter'])\n",
    "        print(\"%s\" % data['sequence'])\n",
    "        print(\"%s [%6.2f]\" % (structure, energy))\n",
    "        # increase structure counter\n",
    "        data['counter'] = data['counter'] + 1\n",
    "fc.subopt_cb(300,print_subopt_result,subopt_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6dfdc79c-6a1d-44af-8842-52b6c0cb48d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <text class=\"nucleotide\" x=\"162.290\" y=\"110.769\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"147.342\" y=\"109.530\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"132.393\" y=\"108.292\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"123.180\" y=\"96.455\">U</text>\n",
       "      <text class=\"nucleotide\" x=\"125.649\" y=\"81.659\">G</text>\n",
       "      <text class=\"nucleotide\" x=\"128.118\" y=\"66.864\">G</text>\n",
       "      <text class=\"nucleotide\" x=\"130.586\" y=\"52.068\">G</text>\n",
       "      <text class=\"nucleotide\" x=\"133.055\" y=\"37.273\">G</text>\n",
       "      <text class=\"nucleotide\" x=\"135.524\" y=\"22.477\">U</text>\n",
       "      <text class=\"nucleotide\" x=\"137.993\" y=\"7.682\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"150.162\" y=\"-2.773\">A</text>\n",
       "      <text class=\"nucleotide\" x=\"149.431\" y=\"-18.799\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"136.361\" y=\"-28.103\">C</text>\n",
       "      <text class=\"nucleotide\" x=\"120.978\" y=\"-23.547\">A</text>\n",
       "    </g>\n",
       "  </g>\n",
       "<script type=\"text/ecmascript\">\n",
       "<![CDATA[\n",
       "  let sequence = \"GGCCCCAUCGUCUAGCGGUUAGGACGCGGCCCUCUCAAGGCCGAAACGGGGGUUCGAUUCCCCCUGGGGUCACCA\";\n",
       "  let structure = \"(((((((.(((((........)))))(((((.......)))))....(((((.......))))))))))))....\";\n",
       "  const basepairs = [\n",
       "    { i: 1, j: 71, type: \"cWW\" },\n",
       "    { i: 2, j: 70, type: \"cWW\" },\n",
       "    { i: 3, j: 69, type: \"cWW\" },\n",
       "    { i: 4, j: 68, type: \"cWW\" },\n",
       "    { i: 5, j: 67, type: \"cWW\" },\n",
       "    { i: 6, j: 66, type: \"cWW\" },\n",
       "    { i: 7, j: 65, type: \"cWW\" },\n",
       "    { i: 9, j: 26, type: \"cWW\" },\n",
       "    { i: 10, j: 25, type: \"cWW\" },\n",
       "    { i: 11, j: 24, type: \"cWW\" },\n",
       "    { i: 12, j: 23, type: \"cWW\" },\n",
       "    { i: 13, j: 22, type: \"cWW\" },\n",
       "    { i: 27, j: 43, type: \"cWW\" },\n",
       "    { i: 28, j: 42, type: \"cWW\" },\n",
       "    { i: 29, j: 41, type: \"cWW\" },\n",
       "    { i: 30, j: 40, type: \"cWW\" },\n",
       "    { i: 31, j: 39, type: \"cWW\" },\n",
       "    { i: 48, j: 64, type: \"cWW\" },\n",
       "    { i: 49, j: 63, type: \"cWW\" },\n",
       "    { i: 50, j: 62, type: \"cWW\" },\n",
       "    { i: 51, j: 61, type: \"cWW\" },\n",
       "    { i: 52, j: 60, type: \"cWW\" }];\n",
       "  const coords = [\n",
       "    { x: 123.198, y:  5.213 },\n",
       "    { x: 120.729, y: 20.009 },\n",
       "    { x: 118.260, y: 34.804 },\n",
       "    { x: 115.791, y: 49.599 },\n",
       "    { x: 113.322, y: 64.395 },\n",
       "    { x: 110.853, y: 79.190 },\n",
       "    { x: 108.384, y: 93.986 },\n",
       "    { x: 81.661, y: 98.727 },\n",
       "    { x: 68.742, y: 122.656 },\n",
       "    { x: 53.857, y: 120.801 },\n",
       "    { x: 38.972, y: 118.945 },\n",
       "    { x: 24.087, y: 117.090 },\n",
       "    { x:  9.202, y: 115.235 },\n",
       "    { x:  1.823, y: 101.585 },\n",
       "    { x: -12.185, y: 94.910 },\n",
       "    { x: -27.435, y: 97.777 },\n",
       "    { x: -38.063, y: 109.082 },\n",
       "    { x: -39.983, y: 124.480 },\n",
       "    { x: -32.455, y: 138.049 },\n",
       "    { x: -18.376, y: 144.572 },\n",
       "    { x: -3.158, y: 141.540 },\n",
       "    { x:  7.347, y: 130.119 },\n",
       "    { x: 22.232, y: 131.975 },\n",
       "    { x: 37.117, y: 133.830 },\n",
       "    { x: 52.001, y: 135.686 },\n",
       "    { x: 66.886, y: 137.541 },\n",
       "    { x: 75.348, y: 149.926 },\n",
       "    { x: 71.666, y: 164.467 },\n",
       "    { x: 67.984, y: 179.008 },\n",
       "    { x: 64.301, y: 193.549 },\n",
       "    { x: 60.619, y: 208.090 },\n",
       "    { x: 46.461, y: 214.730 },\n",
       "    { x: 39.931, y: 228.939 },\n",
       "    { x: 44.111, y: 244.007 },\n",
       "    { x: 57.028, y: 252.821 },\n",
       "    { x: 72.584, y: 251.218 },\n",
       "    { x: 83.432, y: 239.955 },\n",
       "    { x: 84.451, y: 224.351 },\n",
       "    { x: 75.160, y: 211.773 },\n",
       "    { x: 78.842, y: 197.232 },\n",
       "    { x: 82.525, y: 182.691 },\n",
       "    { x: 86.207, y: 168.150 },\n",
       "    { x: 89.889, y: 153.609 },\n",
       "    { x: 101.743, y: 155.214 },\n",
       "    { x: 113.323, y: 152.254 },\n",
       "    { x: 122.938, y: 145.169 },\n",
       "    { x: 129.184, y: 135.001 },\n",
       "    { x: 131.154, y: 123.240 },\n",
       "    { x: 146.103, y: 124.479 },\n",
       "    { x: 161.052, y: 125.718 },\n",
       "    { x: 176.000, y: 126.956 },\n",
       "    { x: 190.949, y: 128.195 },\n",
       "    { x: 199.829, y: 141.067 },\n",
       "    { x: 214.919, y: 145.170 },\n",
       "    { x: 229.094, y: 138.566 },\n",
       "    { x: 235.661, y: 124.374 },\n",
       "    { x: 231.519, y: 109.295 },\n",
       "    { x: 218.624, y: 100.449 },\n",
       "    { x: 203.065, y: 102.012 },\n",
       "    { x: 192.188, y: 113.246 },\n",
       "    { x: 177.239, y: 112.008 },\n",
       "    { x: 162.290, y: 110.769 },\n",
       "    { x: 147.342, y: 109.530 },\n",
       "    { x: 132.393, y: 108.292 },\n",
       "    { x: 123.180, y: 96.455 },\n",
       "    { x: 125.649, y: 81.659 },\n",
       "    { x: 128.118, y: 66.864 },\n",
       "    { x: 130.586, y: 52.068 },\n",
       "    { x: 133.055, y: 37.273 },\n",
       "    { x: 135.524, y: 22.477 },\n",
       "    { x: 137.993, y:  7.682 },\n",
       "    { x: 150.162, y: -2.773 },\n",
       "    { x: 149.431, y: -18.799 },\n",
       "    { x: 136.361, y: -28.103 },\n",
       "    { x: 120.978, y: -23.547 }];\n",
       "]]>\n",
       "</script>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RNA.svg_rna_plot(seq, MEA_struct, ssfile='ggg.svg' )\n",
    "from IPython.display import SVG\n",
    "SVG('ggg.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56794588-9d46-454f-b2a4-ba30a0328d52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'MEA' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43mMEA\u001b[49m)\n",
      "\u001b[31mNameError\u001b[39m: name 'MEA' is not defined"
     ]
    }
   ],
   "source": [
    "struct = \"(((((((.(((((........)))))(((((.......)))))....(((((.......))))))))))))....\"\n",
    "out = []\n",
    "for i in range(len(struct)):\n",
    "    if struct[i] == '(':\n",
    "        open "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56fb68b3-e061-41c9-8ab1-a1141aaa1908",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import forgi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31c91628-1abd-4e9f-8d8e-c22aaf23ed00",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import forgi.visual.mplotlib as fvm\n",
    "import forgi\n",
    "cg = forgi.load_rna(\"1n78_rna-Copy1.pdb\", allow_many=False, )\n",
    "fvm.plot_rna(cg, text_kwargs={\"fontweight\":\"black\"}, lighten=0.7,\n",
    "             backbone_kwargs={\"linewidth\":3})\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python NA",
   "language": "python",
   "name": "na2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}