{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a short example of how the DNA agarose gel electrophoresis in pydna can be used.\n",
    "The %matplotlib inline in the line above is there to facilitate viewing matplotlib plots in    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pydna Gel Electrophoresis Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pydna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pydna.gel import weight_standard_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "st = weight_standard_sample('1kb+_GeneRuler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gb = pydna.Genbank(\"bjornjobb@gmail.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gene = gb.nucleotide(\"X06997\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "primer_f, primer_r = pydna.parse(''' >760_KlLAC12_rv (20-mer)\n",
    "                                     ttaaacagattctgcctctg\n",
    "\n",
    "                                     >759_KlLAC12_fw (19-mer)\n",
    "                                     aaatggcagatcattcgag\n",
    "                                     ''', ds=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PCR\n",
    "pcr_prod = pydna.pcr(primer_f, primer_r, gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(-1766)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcr_prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lxrljEWavJuU43Wp8DFupVNLGxoYajYakIa6n0AR/Dhbn6VJE8PnYPCCKaY/dgQ9jVCwp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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8862852450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pydna.Gel([ st,[pcr_prod]] ).run()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
