{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nglview\n",
    "import rna_pdb_tools.Seq as Seq\n",
    "import rna_pdb_tools.BlastPDB\n",
    "from rna_pdb_tools.BlastPDB import BlastPDB\n",
    "reload(rna_pdb_tools.BlastPDB);\n",
    "reload(Seq);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = Seq.RNASequence(\"CCGGACGAGGGGCGCCGUACCCGGUCAGCGACAAGACGGCGC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CCGGACGAGGGGCGCCGUACCCGGUCAGCGACAAGACGGCGC"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Secondary structure prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='RNAsubopt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "CalledProcessError",
     "evalue": "Command 'ipknot /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpbv_wLi.fa' returned non-zero exit status -4",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-076de9a8db2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_ss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ipknot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/magnus/work/src/rna-pdb-tools/rna_pdb_tools/Seq.pyc\u001b[0m in \u001b[0;36mpredict_ss\u001b[0;34m(self, method, constraints, verbose)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ipknot\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ipknot '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/magnus/work/opt/miniconda2/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command 'ipknot /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpbv_wLi.fa' returned non-zero exit status -4"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='ipknot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "CalledProcessError",
     "evalue": "Command 'centroid_fold /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpTVQqsa.fa' returned non-zero exit status -4",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-a06a125e2463>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_ss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'centroid_fold'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/magnus/work/src/rna-pdb-tools/rna_pdb_tools/Seq.pyc\u001b[0m in \u001b[0;36mpredict_ss\u001b[0;34m(self, method, constraints, verbose)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"centroid_fold\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'centroid_fold '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/magnus/work/opt/miniconda2/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command 'centroid_fold /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpTVQqsa.fa' returned non-zero exit status -4"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='centroid_fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((......)).(((((((.................)))))))\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print seq.predict_ss(method='contextfold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SingleLetterAlphabet() alignment with 39 rows and 89 columns\n",
       "GACGAGCCCG----GUGCG--------CGGGC--UCCCGGACGA...GA- AP006618.1/133448-133513\n",
       "AAAGGGC-------CAAAG-----------GC--GCCCGGACGA...AAG CP000910.1/2096309-2096249\n",
       "ACAGGCGUCG----CGUGUUC------CGGCG--CCCCGGACGA...GAU CP000511.1/851388-851320\n",
       "GAUGGGCGC-----CCAUCC--------GCGC--UCCCGGACGA...AG- AM849034.1/2863085-2863022\n",
       "CGCGCCCCCGCU--UUGCUAC----GGUGGGG-ACACCCGACGA...ACC ABJH01000172.1/10128-10201\n",
       "CGUGAGG-------GUGU------------CU-GGCCCGGACGA...GAC CP000113.1/4374426-4374485\n",
       "GUCGGGCGC-----CCUCCC--------GCGC--UCCCGGACGA...AG- AM711867.1/3063871-3063808\n",
       "GCGGCGCUC-----CUUCAGACA-----GAGC-GUCCCGGACGA...AGC AM746676.1/8188359-8188291\n",
       "AAAUCGUCG-----ACA-----------CGAC--AUCCGGACGA...CC- AP011115.1/5208127-5208067\n",
       "GCCAUGUCA-----CCAGU---------UGAC---ACCGGACGA...UUU CP000580.1/1319367-1319304\n",
       "GAUAUGCUAU----AAA----------AUAAU--CUCCGGACGA...CUU CR628336.1/661276-661213\n",
       "GCAGGCGCCG----UCGAUC-------CGGCG--CCCCGGACGA...GUU CP000656.1/85042-85109\n",
       "AGGCUUUCC-----CUUCC---------GGAG-CCUCCGGACGA...CA- CP000454.1/4240999-4241061\n",
       "CGGU-CGGCC----UUAUA--------GGCUGCGCCCCGGACGA...GGA CP000359.1/2285879-2285947\n",
       "UCAGUUUC------UGCGACU--------GAGGUGCCCGGACGA...UUU BX842652.1/324410-324345\n",
       "GUAUGCCCUUUUGCUACG-----GUGGAGGGGAACCCCCGACGA...ACC ABJF01000294.1/7123-7044\n",
       "CCAA-GGCUC----CGCACCC------GAGCC---UCCGGACGA...A-- CP001341.1/3865849-3865912\n",
       "UCAGAGCCUG----GGAC---------CAGGC--UUCCGGACGA...GC- AAMN01000002.1/564690-564754\n",
       "...\n",
       "UUCGC-CGCG----GCUUG--------CGCGA-GCCCCGGACGA...AG- ACVP01000007.1/106973-107037"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load an alignment\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import rna_pdb_tools.utils.rna_alignment.rna_alignment as ra\n",
    "a = ra.RNAalignment('/Users/magnus/Dropbox/RNA_Puzzles21/Alignment/RF01763.stockholm.txt')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rchie: plot saved to rchie.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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lEBUQ0GhTvIvMRmQ4oq/1ItyRNN3pM57xjJnP+tdUQPCYEb+gRdGuuuqqvrhi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lzF8w2SmdfgvzwIjy1aiprrYSSmN8Zk7TQyRd+IPUKZNcnUjtwb7cKZg1S/ckUp38YTXvGu19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\nMCBqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print a.io.format(\"fasta\")\n",
    "ra.RChie().plot_cov(a.io.format(\"fasta\"), a.ss_cons_std, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# cmscan :: search sequence(s) against a CM database\n",
      "# INFERNAL 1.1.2 (July 2016)\n",
      "# Copyright (C) 2016 Howard Hughes Medical Institute.\n",
      "# Freely distributed under a BSD open source license.\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "# query sequence file:                   /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmpUbTC2W.fa\n",
      "# target CM database:                    /home/magnus/work/db/rfamdb/Rfam.cm\n",
      "# sequence reporting threshold:          E-value <= 1\n",
      "# number of worker threads:              4\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "\n",
      "Query:       target  [L=42]\n",
      "Hit scores:\n",
      " rank     E-value  score  bias  modelname  start    end   mdl trunc   gc  description\n",
      " ----   --------- ------ -----  --------- ------ ------   --- ----- ----  -----------\n",
      "  (1) !   1.6e-11   55.5   0.1  ykkC-III       1     42 +  cm    no 0.76  -\n",
      "\n",
      "\n",
      "Hit alignments:\n",
      ">> ykkC-III  \n",
      " rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc\n",
      " ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----\n",
      "  (1) !   1.6e-11   55.5   0.1  cm       23       64 ..           1          42 + [] 1.00    no 0.76\n",
      "\n",
      "                                                         NC\n",
      "              -----------<<<<<<<_________________>>>>>>> CS\n",
      "  ykkC-III 23 CCGGACGAGggGcGcCGUACCCGGucaGcGACAAGACGgCgC 64\n",
      "              CCGGACGAGGGGCGCCGUACCCGGUCAGCGACAAGACGGCGC\n",
      "    target  1 CCGGACGAGGGGCGCCGUACCCGGUCAGCGACAAGACGGCGC 42\n",
      "              ****************************************** PP\n",
      "\n",
      "\n",
      "\n",
      "Internal HMM-only pipeline statistics summary: (run for model(s) with zero basepairs)\n",
      "--------------------------------------------------------------------------------------\n",
      "Query sequence(s):                                               1  (84 residues searched)\n",
      "Target model(s):                                               347  (40911 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:               2  (0.002642); expected (0.02)\n",
      "Windows   passing  local HMM MSV      bias filter:               2  (0.002642); expected (0.02)\n",
      "Windows   passing  local HMM Viterbi       filter:               0  (0); expected (0.001)\n",
      "Windows   passing  local HMM Forward       filter:               0  (0); expected (1e-05)\n",
      "Total HMM hits reported:                                         0  (0)\n",
      "\n",
      "Internal CM pipeline statistics summary:\n",
      "----------------------------------------\n",
      "Query sequence(s):                                               1  (84 residues searched)\n",
      "Query sequences re-searched for truncated hits:                  1  (250.9 residues re-searched, avg per model)\n",
      "Target model(s):                                              2126  (267652 consensus positions)\n",
      "Windows   passing  local HMM SSV           filter:             483  (0.02761); expected (0.35)\n",
      "Windows   passing  local HMM Viterbi       filter:                  (off)\n",
      "Windows   passing  local HMM Viterbi  bias filter:                  (off)\n",
      "Windows   passing  local HMM Forward       filter:             168  (0.009685); expected (0.02)\n",
      "Windows   passing  local HMM Forward  bias filter:             135  (0.007799); expected (0.02)\n",
      "Windows   passing glocal HMM Forward       filter:              33  (0.001931); expected (0.02)\n",
      "Windows   passing glocal HMM Forward  bias filter:              30  (0.001754); expected (0.02)\n",
      "Envelopes passing glocal HMM envelope defn filter:              29  (0.001632); expected (0.02)\n",
      "Envelopes passing  local CM  CYK           filter:               5  (0.0002641); expected (0.0001)\n",
      "Total CM hits reported:                                          1  (5.9e-05); includes 0 truncated hit(s)\n",
      "\n",
      "Total CM and HMM hits reported:                                  1\n",
      "\n",
      "# CPU time: 0.45u 0.18s 00:00:00.63 Elapsed: 00:00:00.98\n",
      "//\n",
      "[ok]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import rna_pdb_tools.RfamSearch as rf\n",
    "seq = Seq.RNASequence(\"CCGGACGAGGGGCGCCGUACCCGGUCAGCGACAAGACGGCGC\")\n",
    "rs = rf.RfamSearch()\n",
    "print rs.cmscan(seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PDB Blast search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<HTML>\n",
      "<TITLE>BLAST Search Results</TITLE>\n",
      "<BODY BGCOLOR=\"#FFFFFF\" LINK=\"#0000FF\" VLINK=\"#660099\" ALINK=\"#660099\">\n",
      "<PRE>\n",
      "<b>BLASTN 2.2.18 [Mar-02-2008]</b>\n",
      "\n",
      "\n",
      "<b><a href=\"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Retrieve&list_uids\n",
      "=9254694&dopt=Citation\">Reference</a>:</b>\n",
      "Altschul, Stephen F., Thomas L. Madden, Alejandro A. Sch&auml;ffer, \n",
      "Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), \n",
      "\"Gapped BLAST and PSI-BLAST: a new generation of protein database search\n",
      "programs\",  Nucleic Acids Res. 25:3389-3402.\n",
      "\n",
      "<b>Query=</b> UNKNOWN_SEQUENCE\n",
      "         (42 letters)\n",
      "\n",
      "<b>Database:</b> pdb_nucleotide \n",
      "           15,339 sequences; 2,579,352 total letters\n",
      "\n",
      "Searching..................................................done\n",
      "\n",
      "<PRE>\n",
      "\n",
      "\n",
      "                                                                 Score    E\n",
      "Sequences producing significant alignments:                      (bits) Value\n",
      "\n",
      "1M5L:1:A|pdbid|entity|chain(s)|sequence                               <a href = #1717> 26</a>   0.86 \n",
      "5K0Y:2:A|pdbid|entity|chain(s)|sequence                               <a href = #15047> 24</a>   3.4  \n",
      "</PRE>\n",
      "<PRE>\n",
      "><a name = 1717></a>1M5L:1:A|pdbid|entity|chain(s)|sequence\n",
      "          Length = 38\n",
      "\n",
      " Score = 26.3 bits (13), Expect = 0.86\n",
      " Identities = 13/13 (100%)\n",
      " Strand = Plus / Plus\n",
      "\n",
      "                       \n",
      "Query: 3  ggacgaggggcgc 15\n",
      "          |||||||||||||\n",
      "Sbjct: 26 ggacgaggggcgc 38\n",
      "</PRE>\n",
      "\n",
      "\n",
      "<PRE>\n",
      "><a name = 15047></a>5K0Y:2:A|pdbid|entity|chain(s)|sequence\n",
      "          Length = 1776\n",
      "\n",
      " Score = 24.3 bits (12), Expect = 3.4\n",
      " Identities = 12/12 (100%)\n",
      " Strand = Plus / Plus\n",
      "\n",
      "                       \n",
      "Query: 19  acccggtcagcg 30\n",
      "           ||||||||||||\n",
      "Sbjct: 233 acccggtcagcg 244\n",
      "</PRE>\n",
      "\n",
      "\n",
      "<PRE>\n",
      "  Database: pdb_nucleotide\n",
      "    Posted date:  Aug 7, 2016 11:48 AM\n",
      "  Number of letters in database: 2,579,352\n",
      "  Number of sequences in database:  15,339\n",
      "  \n",
      "Lambda     K      H\n",
      "    1.37    0.711     1.31 \n",
      "\n",
      "Gapped\n",
      "Lambda     K      H\n",
      "    1.37    0.711     1.31 \n",
      "\n",
      "\n",
      "Matrix: blastn matrix:1 -3\n",
      "Gap Penalties: Existence: 5, Extension: 2\n",
      "Number of Sequences: 15339\n",
      "Number of Hits to DB: 523\n",
      "Number of extensions: 20\n",
      "Number of successful extensions: 20\n",
      "Number of sequences better than 10.0: 2\n",
      "Number of HSP's gapped: 20\n",
      "Number of HSP's successfully gapped: 2\n",
      "Length of query: 42\n",
      "Length of database: 2,579,352\n",
      "Length adjustment: 13\n",
      "Effective length of query: 29\n",
      "Effective length of database: 2,379,945\n",
      "Effective search space: 69018405\n",
      "Effective search space used: 69018405\n",
      "X1: 9 (17.8 bits)\n",
      "X2: 15 (29.7 bits)\n",
      "X3: 50 (99.1 bits)\n",
      "S1: 9 (18.3 bits)\n",
      "S2: 12 (24.3 bits)\n",
      "</PRE>\n",
      "</BODY>\n",
      "</HTML>\n"
     ]
    }
   ],
   "source": [
    "p = BlastPDB(seq.seq)\n",
    "p.search()\n",
    "print p.result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D structure analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RNARNAStructure report: rna_pdb_tools/data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb \n",
      "A:1-43 B:1-10\n",
      "ATOM      1  P     G A   1     -12.509  18.639  13.726  1.00  0.00\n",
      "ATOM      2  OP1   G A   1     -13.934  18.507  14.168  1.00  0.00\n",
      "ATOM      3  OP2   G A   1     -11.541  17.557  14.097  1.00  0.00\n",
      "ATOM      4  O5'   G A   1     -12.604  18.683  12.146  1.00  0.00\n",
      "ATOM      5  C5'   G A   1     -13.512  19.569  11.525  1.00  0.00\n"
     ]
    }
   ],
   "source": [
    "from rna_pdb_tools.pdb_parser_lib import RNAStructure\n",
    "\n",
    "fn = \"rna_pdb_tools/data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb\"\n",
    "\n",
    "s = RNAStructure(fn)\n",
    "print s.get_report()\n",
    "print s.get_info_chains()\n",
    "print s.get_head()\n",
    "#print s.view() # image paste here :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bash: line 2: ./rna-pdb-tools.py: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd rna_pdb_tools\n",
    "./rna-pdb-tools.py --no_hr --get_seq data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNA 3D structure prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model using SimRNA\n",
    "#res = SimRNA(ss,seq.get_ss())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fake import, should be \n",
    "res = \"rna_pdb_tools/data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb\"\n",
    "# view\n",
    "view = nglview.show_structure_file(res)\n",
    "view.add_representation(repr_type='cartoon')\n",
    "view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# rna_pdb_tools --get_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bash: line 2: ./rna-pdb-tools.py: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd rna_pdb_tools\n",
    "./rna-pdb-tools.py --no_hr --get_seq input/5k7c.pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bash: line 2: ./rna-pdb-tools.py: No such file or directory\n",
      "bash: line 3: ./rna-pdb-tools.py: No such file or directory\n",
      "bash: line 4: ./rna-pdb-tools.py: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd rna_pdb_tools\n",
    "./rna-pdb-tools.py --no_hr --get_seq input/5k7c.pdb\n",
    "./rna-pdb-tools.py --no_hr --get_seq input/tetraloop.pdb\n",
    "./rna-pdb-tools.py --get_seq input/1xjr.pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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