Jupyter_notebooks/Supplementary_Figure_7/Plotting_5'ETS_and_35S_data_with_deltaSHAPE_replicates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['font.sans-serif'] = ['Arial']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def formatAxes(ax,text_size=10,xlim=None,xlabel=None,xticks=None,xticklabels=None,ylim=None,yticks=None,ylabel=None,yticklabels=None):\n",
    "    \"\"\" to tweak the plotting of the axes as well as the fontsize \"\"\"\n",
    "    for loc,spine in list(ax.spines.items()):\n",
    "        if loc == 'left': # settings for the y-axis\n",
    "            if yticklabels and not yticks:\n",
    "                sys.stderr.write(\"Need to provide a list wiht both y-labels and y-ticks!\")\n",
    "            if yticks:\n",
    "                ax.yaxis.set_ticks_position('left')\n",
    "                ax.yaxis.set_tick_params(direction='out')\n",
    "                spine.set_visible(True)\n",
    "                spine.set_position((\"outward\",1))\n",
    "                spine.set_smart_bounds(True)\n",
    "                ax.set_yticks(yticks)\n",
    "                if ylim:\n",
    "                    ax.set_ylim(ylim)\n",
    "                if yticklabels:\n",
    "                    ax.set_yticklabels(yticklabels,fontsize=text_size)\n",
    "            else:\n",
    "                spine.set_visible(False)\n",
    "                ax.set_yticklabels([])\n",
    "                ax.tick_params(axis='y',which='both',length=0)\n",
    "                \n",
    "            if ylabel:\n",
    "                ax.set_ylabel(ylabel,fontsize=text_size,rotation=0,labelpad=160)\n",
    "                ax.get_yaxis().set_label_coords(-0.1,0.5)\n",
    "        elif loc == 'bottom': # settings for x-axis\n",
    "            if xticks:\n",
    "                spine.set_position('zero')\n",
    "                spine.set_visible(False)\n",
    "                spine.set_smart_bounds(False)\n",
    "                ax.set_xticks(xticks)\n",
    "                ax.tick_params(axis='x',which='both',length=0)\n",
    "                if xlim:\n",
    "                    ax.set_xlim(xlim)\n",
    "                if xticklabels:\n",
    "                    ax.set_xticklabels(xticklabels,fontsize=text_size)\n",
    "            else:\n",
    "                spine.set_visible(False)\n",
    "                ax.set_xticklabels([])\n",
    "                ax.tick_params(axis='x',which='both',length=0)\n",
    "                \n",
    "            if xlabel:\n",
    "                ax.tick_params(axis='x',which='both',length=0)\n",
    "                ax.set_xlabel(xlabel,fontsize=text_size+2)\n",
    "                ax.xaxis.labelpad = 10\n",
    "        else:\n",
    "            spine.set_visible(False)\n",
    "    ax.patch.set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the SHAPE-reactivities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltafive = pd.read_csv('../../Data/SHAPE_35S/35S_SHAPE_reactivities.txt',\\\n",
    "                 sep=\"\\t\",header=None,index_col=0,names=[\"position\",\"reactivity\",\"error\"])\n",
    "erb = pd.read_csv('../../Data/SHAPE_35S/35S_Erb1_dep_SHAPE_reactivities.txt',\\\n",
    "                  sep=\"\\t\",header=None,index_col=0,names=[\"position\",\"reactivity\",\"error\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing negative values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltafive[deltafive.reactivity < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactivity</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>position</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010793</td>\n",
       "      <td>0.013891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.022429</td>\n",
       "      <td>0.003348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reactivity     error\n",
       "position                      \n",
       "1           0.010793  0.013891\n",
       "2           0.000000  0.000000\n",
       "3           0.000000  0.000000\n",
       "4           0.000000  0.000000\n",
       "5           0.022429  0.003348"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltafive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "erb[erb.reactivity < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactivity</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>position</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reactivity  error\n",
       "position                   \n",
       "1                0.0    0.0\n",
       "2                0.0    0.0\n",
       "3                0.0    0.0\n",
       "4                0.0    0.0\n",
       "5                0.0    0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the difBUM_HMM output for the entire 35S:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_bumhmm = pd.read_csv('../../Analysis/diffBUM-HMM/35S_diffBUM_HMM_WT_vs_Erb1_diff_BUM_HMM_analysed.txt',\\\n",
    "                                    sep=\"\\t\", header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So UM is more flexible in Erb1 and MU is more flexible in Delta 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UU</th>\n",
       "      <th>UM</th>\n",
       "      <th>MU</th>\n",
       "      <th>MM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.266924e-21</td>\n",
       "      <td>3.750536e-25</td>\n",
       "      <td>1.600325e-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.503509e-28</td>\n",
       "      <td>5.135967e-30</td>\n",
       "      <td>2.312988e-57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.985186e-25</td>\n",
       "      <td>2.027740e-27</td>\n",
       "      <td>1.416414e-51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.859237e-41</td>\n",
       "      <td>2.117164e-50</td>\n",
       "      <td>3.936309e-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.163330e-13</td>\n",
       "      <td>1.314801e-19</td>\n",
       "      <td>5.473952e-32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UU            UM            MU            MM\n",
       "1  1.0  4.266924e-21  3.750536e-25  1.600325e-45\n",
       "2  1.0  4.503509e-28  5.135967e-30  2.312988e-57\n",
       "3  1.0  6.985186e-25  2.027740e-27  1.416414e-51\n",
       "4  1.0  1.859237e-41  2.117164e-50  3.936309e-91\n",
       "5  1.0  4.163330e-13  1.314801e-19  5.473952e-32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_bumhmm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same for deltaSHAPE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltashapeone = pd.read_csv('../../Analysis/deltaSHAPE/35S_deltaerb1_Vs_delta5_reps1.txt',\\\n",
    "                            sep=\"\\t\",header=0,index_col=0)\n",
    "deltashapetwo = pd.read_csv('../../Analysis/deltaSHAPE/35S_deltaerb1_Vs_delta5_reps2.txt',\\\n",
    "                            sep=\"\\t\",header=0,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seq</th>\n",
       "      <th>DeltaSHAPE</th>\n",
       "      <th>Z-factor</th>\n",
       "      <th>Std_Score</th>\n",
       "      <th>Smoothed_Data1</th>\n",
       "      <th>Smoothed_Data2</th>\n",
       "      <th>Unsmoothed_Diff</th>\n",
       "      <th>Data1</th>\n",
       "      <th>Data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nuc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>A</td>\n",
       "      <td>-0.107900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.662575</td>\n",
       "      <td>-0.057420</td>\n",
       "      <td>0.050480</td>\n",
       "      <td>-0.111186</td>\n",
       "      <td>-0.078725</td>\n",
       "      <td>0.032461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6548</th>\n",
       "      <td>T</td>\n",
       "      <td>-0.125399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.932071</td>\n",
       "      <td>-0.073067</td>\n",
       "      <td>0.052332</td>\n",
       "      <td>-0.127085</td>\n",
       "      <td>-0.011144</td>\n",
       "      <td>0.115941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6549</th>\n",
       "      <td>C</td>\n",
       "      <td>-0.160011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.465112</td>\n",
       "      <td>-0.075710</td>\n",
       "      <td>0.084300</td>\n",
       "      <td>-0.137926</td>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.008594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>A</td>\n",
       "      <td>-0.180735</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.784285</td>\n",
       "      <td>-0.072294</td>\n",
       "      <td>0.108442</td>\n",
       "      <td>-0.215021</td>\n",
       "      <td>-0.086655</td>\n",
       "      <td>0.128366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6551</th>\n",
       "      <td>T</td>\n",
       "      <td>-0.202140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.113928</td>\n",
       "      <td>-0.040700</td>\n",
       "      <td>0.158365</td>\n",
       "      <td>-0.189259</td>\n",
       "      <td>-0.000894</td>\n",
       "      <td>0.188365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Seq  DeltaSHAPE  Z-factor  Std_Score  Smoothed_Data1  Smoothed_Data2  \\\n",
       "Nuc                                                                         \n",
       "6547   A   -0.107900       1.0  -1.662575       -0.057420        0.050480   \n",
       "6548   T   -0.125399       1.0  -1.932071       -0.073067        0.052332   \n",
       "6549   C   -0.160011       1.0  -2.465112       -0.075710        0.084300   \n",
       "6550   A   -0.180735       1.0  -2.784285       -0.072294        0.108442   \n",
       "6551   T   -0.202140       1.0  -3.113928       -0.040700        0.158365   \n",
       "\n",
       "      Unsmoothed_Diff     Data1     Data2  \n",
       "Nuc                                        \n",
       "6547        -0.111186 -0.078725  0.032461  \n",
       "6548        -0.127085 -0.011144  0.115941  \n",
       "6549        -0.137926 -0.129332  0.008594  \n",
       "6550        -0.215021 -0.086655  0.128366  \n",
       "6551        -0.189259 -0.000894  0.188365  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltashapeone.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seq</th>\n",
       "      <th>DeltaSHAPE</th>\n",
       "      <th>Z-factor</th>\n",
       "      <th>Std_Score</th>\n",
       "      <th>Smoothed_Data1</th>\n",
       "      <th>Smoothed_Data2</th>\n",
       "      <th>Unsmoothed_Diff</th>\n",
       "      <th>Data1</th>\n",
       "      <th>Data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nuc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>A</td>\n",
       "      <td>-0.151590</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.550403</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.151716</td>\n",
       "      <td>-0.220422</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.220491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6497</th>\n",
       "      <td>G</td>\n",
       "      <td>-0.120014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.227985</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.120123</td>\n",
       "      <td>-0.103913</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.104077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>C</td>\n",
       "      <td>-0.105773</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.082571</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.105832</td>\n",
       "      <td>-0.090477</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.090542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6517</th>\n",
       "      <td>G</td>\n",
       "      <td>-0.107809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.103361</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.107893</td>\n",
       "      <td>-0.152441</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.152507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>A</td>\n",
       "      <td>-0.097712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000270</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.097806</td>\n",
       "      <td>-0.080508</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.080629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Seq  DeltaSHAPE  Z-factor  Std_Score  Smoothed_Data1  Smoothed_Data2  \\\n",
       "Nuc                                                                         \n",
       "6496   A   -0.151590       1.0  -1.550403        0.000125        0.151716   \n",
       "6497   G   -0.120014       1.0  -1.227985        0.000109        0.120123   \n",
       "6516   C   -0.105773       1.0  -1.082571        0.000060        0.105832   \n",
       "6517   G   -0.107809       1.0  -1.103361        0.000084        0.107893   \n",
       "6518   A   -0.097712       1.0  -1.000270        0.000094        0.097806   \n",
       "\n",
       "      Unsmoothed_Diff     Data1     Data2  \n",
       "Nuc                                        \n",
       "6496        -0.220422  0.000069  0.220491  \n",
       "6497        -0.103913  0.000164  0.104077  \n",
       "6516        -0.090477  0.000065  0.090542  \n",
       "6517        -0.152441  0.000066  0.152507  \n",
       "6518        -0.080508  0.000121  0.080629  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltashapetwo.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dStruct analyses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile = pd.read_csv(\"../../Analysis/dStruct/35S/output_dStruct_35S_res_table_5nt_FDR_0.15.txt\",\\\n",
    "                            sep=\"\\t\",header=0)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data for the entire 35S:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = 0\n",
    "numberofplots = 5\n",
    "\n",
    "fig,ax = plt.subplots(numberofplots,figsize=[30,15],sharex=True)\n",
    "\n",
    "### Plotting the raw SHAPE values:\n",
    "\n",
    "plusvalues = erb\n",
    "x = plusvalues.index\n",
    "y = plusvalues.reactivity.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "minusvalues = -deltafive\n",
    "x = minusvalues.index\n",
    "y = minusvalues.reactivity.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "xticks = list(np.arange(1,len(x),200))\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'SHAPE reactivities',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('Erb1', 'Delta5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting both deltaSHAPE replicate results:\n",
    "\n",
    "##1st deltaSHAPE replicate, only shown in main body. \n",
    "selection = deltashapeone\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE\\nrep1',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "count += 1\n",
    "\n",
    "##2nd deltaSHAPE replicate - partially shown in Figure S7\n",
    "selection = deltashapetwo\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE\\nrep2',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "count += 1\n",
    "\n",
    "\n",
    "\n",
    "### Plotting the diffBUM_HMM results:\n",
    "\n",
    "selection = diff_bumhmm\n",
    "x = selection.index\n",
    "y = selection.UM.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "x = selection.index\n",
    "y = -selection.MU.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'diffBUM_HMM',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the dStruct results:\n",
    "\n",
    "### 6 DRRs were called in the 5'ETS region: 68-81, 298-306, 390-397, 463-493, \n",
    "### 702-719 and 767-778. 32 DRRs were called in total.\n",
    "### Plot the SHAPE reactivities for this region.\n",
    "\n",
    "allcoords = list()\n",
    "for i in range(len(datafile)):\n",
    "    DRR = range(datafile.iloc[i, 0], (datafile.iloc[i, 1]+1))\n",
    "    allcoords.extend(DRR)\n",
    "\n",
    "plusvalues = erb.loc[allcoords]\n",
    "x = plusvalues.index\n",
    "y = plusvalues.reactivity.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "minusvalues = -deltafive.loc[allcoords]\n",
    "x = minusvalues.index\n",
    "y = minusvalues.reactivity.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'dStruct results 5nt window',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('Erb1', 'Delta 5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "fig.savefig(\"deltaSHAPE_diffBUM_HMM_dStruct_comparision_35S.pdf\",dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many nucleotides do deltaSHAPE and diffBUM_HMM call differentially modified?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1142.0\n"
     ]
    }
   ],
   "source": [
    "deltashapeaverage = np.mean([len(deltashapeone.index),len(deltashapetwo.index)])\n",
    "print(deltashapeaverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_bumhmmaverage = np.mean([len(diff_bumhmm[diff_bumhmm.UM > 0.90]),len(diff_bumhmm[diff_bumhmm.MU > 0.95])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_bumhmmaverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_allbumhmmaverage = np.mean([len(diff_bumhmm[diff_bumhmm.UM > 0.05]),len(diff_bumhmm[diff_bumhmm.MU > 0.05])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "697.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_allbumhmmaverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6858"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(diff_bumhmm.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data for the 5'ETS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading PE data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pedata = pd.read_csv(\"35S_pre_rRNA_PEA_data_with_5end_18S.txt\",sep=\"\\t\",index_col=0,header=0)\n",
    "pedata[pedata <= 0.25] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deltaErb</th>\n",
       "      <th>delta5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>795</td>\n",
       "      <td>0.849447</td>\n",
       "      <td>0.892464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>796</td>\n",
       "      <td>0.361656</td>\n",
       "      <td>0.331547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>797</td>\n",
       "      <td>0.412652</td>\n",
       "      <td>0.353761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>799</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           deltaErb    delta5\n",
       "positions                    \n",
       "795        0.849447  0.892464\n",
       "796        0.361656  0.331547\n",
       "797        0.412652  0.353761\n",
       "798        0.000000  0.000000\n",
       "799        0.000000  0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedata.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "first = [268,352]\n",
    "second = [405,502]\n",
    "third = [701,797]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = 0\n",
    "numberofplots = 5\n",
    "\n",
    "start = 1\n",
    "end = 800\n",
    "\n",
    "fig,ax = plt.subplots(numberofplots,figsize=[30,15],sharex=True)\n",
    "\n",
    "### Plotting the raw SHAPE values:\n",
    "\n",
    "plusvalues = erb.loc[start:end+1]\n",
    "x = plusvalues.index\n",
    "y = plusvalues.reactivity.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "minusvalues = -deltafive.loc[start:end]\n",
    "x = minusvalues.index\n",
    "y = minusvalues.reactivity.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "xticks = list(np.arange(start,end,50))\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'SHAPE reactivities',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('Erb1', 'Delta5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "ax[count].axvspan(first[0],first[1],color='red',alpha=0.5)\n",
    "ax[count].axvspan(second[0],second[1],color='red',alpha=0.5)\n",
    "ax[count].axvspan(third[0],third[1],color='red',alpha=0.5)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the primer extension data\n",
    "\n",
    "plusvalues = pedata.deltaErb.loc[start:end+1]\n",
    "minusvalues = pedata.delta5.loc[start:end+1]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "\n",
    "x = minusvalues.index\n",
    "y = -minusvalues.values\n",
    "\n",
    "ymin = -2\n",
    "ymax = 2\n",
    "\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'Primer extension results',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].legend((wild_type,mutant), ('Erb1', 'Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the deltaSHAPE results:\n",
    "\n",
    "selection = deltashapeone.loc[start:end+1]\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "#xticks = list(np.arange(start,end,50))\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the diffBUM_HMM results:\n",
    "\n",
    "selection = diff_bumhmm.iloc[start:end+1]\n",
    "x = selection.index\n",
    "y = selection.UM.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "x = selection.index\n",
    "y = -selection.MU.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "xticks = list(np.arange(start,end,50))\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'diffBUM_HMM',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the dStruct results:\n",
    "\n",
    "### 6 DRRs were called in the 5'ETS region: 68-81, 298-306, 390-397, 463-493, \n",
    "### 702-719 and 767-778.\n",
    "### Plot the SHAPE reactivities for this region.\n",
    "\n",
    "ETS_coords = list() \n",
    "ETS_coords = [i for i in allcoords if i <= 778]\n",
    "\n",
    "plusvalues = erb.loc[ETS_coords]\n",
    "x = plusvalues.index\n",
    "y = plusvalues.reactivity.values\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "minusvalues = -deltafive.loc[ETS_coords]\n",
    "x = minusvalues.index\n",
    "y = minusvalues.reactivity.values\n",
    "ymin = -1\n",
    "ymax = 1\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'dStruct results 5nt window',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks)\n",
    "\n",
    "ax[count].set_title('Erb1 vs Delta5',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('Erb1', 'Delta 5'), loc=[1,1], fontsize = 16)\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "fig.savefig(\"deltaSHAPE_diffBUM_HMM_dStruct_comparision_5ETS.pdf\",dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating deltaPE for the primer extension data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deltaErb</th>\n",
       "      <th>delta5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           deltaErb  delta5\n",
       "positions                  \n",
       "1               NaN     NaN\n",
       "2               NaN     NaN\n",
       "3               NaN     NaN\n",
       "4               NaN     NaN\n",
       "5               NaN     NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pedata = pedata.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deltaErb</th>\n",
       "      <th>delta5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           deltaErb  delta5\n",
       "positions                  \n",
       "1               0.0     0.0\n",
       "2               0.0     0.0\n",
       "3               0.0     0.0\n",
       "4               0.0     0.0\n",
       "5               0.0     0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffPE = pedata.deltaErb - pedata.delta5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positions\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "5    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffPE.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deltaSHAPE replicate plots for 3700-3900 and 6500-6857"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "first = [3700,3900]\n",
    "second = [6500,6857]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = open(\"../../Reference_sequences/35S_pre-rRNA_refseq.seq\",\"r\").readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = sequence[0].strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATGCGAAAGC\n"
     ]
    }
   ],
   "source": [
    "print(sequence[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = 0\n",
    "numberofplots = 2\n",
    "fig,ax = plt.subplots(numberofplots,figsize=[30,15],sharex=True)\n",
    "\n",
    "start,end = [3750,3950]\n",
    "\n",
    "### Plotting the deltaSHAPE results:\n",
    "\n",
    "selection = deltashapeone.loc[start:end+1]\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE reps 1',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks,\\\n",
    "           xticklabels = xticklabels)\n",
    "\n",
    "ax[count].set_title('Region 3750-3950',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the deltaSHAPE results:\n",
    "\n",
    "selection = deltashapetwonew.loc[start:end+1]\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "\n",
    "fragment = list(sequence[start-1:end].replace(\"T\",\"U\"))\n",
    "#print(list(fragment))\n",
    "\n",
    "xticks = list(np.arange(start,end+1))\n",
    "xticklabels = fragment\n",
    "\n",
    "\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE reps 2 new ',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks,\\\n",
    "           xticklabels = xticklabels)\n",
    "\n",
    "\n",
    "\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "fig.savefig(\"35S_deltaSHAPE_replicates_S7_%s_%s.pdf\" % (start,end),dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = 0\n",
    "numberofplots = 2\n",
    "fig,ax = plt.subplots(numberofplots,figsize=[30,15],sharex=True)\n",
    "\n",
    "start,end = [6318,6518]\n",
    "\n",
    "\n",
    "### Plotting the deltaSHAPE results:\n",
    "\n",
    "selection = deltashapeone.loc[start:end+1]\n",
    "\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE reps 1',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks,\\\n",
    "           xticklabels = xticklabels)\n",
    "\n",
    "ax[count].set_title('Region 6318-6518',fontsize=18)\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "count += 1\n",
    "\n",
    "### Plotting the deltaSHAPE results:\n",
    "\n",
    "selection = deltashapetwonew.loc[start:end+1]\n",
    "plusvalues  = selection.DeltaSHAPE[selection.DeltaSHAPE > 0]\n",
    "minusvalues = selection.DeltaSHAPE[selection.DeltaSHAPE < 0]\n",
    "\n",
    "x = plusvalues.index\n",
    "y = plusvalues.values\n",
    "\n",
    "wild_type = ax[count].bar(x,y,color='blue')\n",
    "\n",
    "x = minusvalues.index\n",
    "y = minusvalues.values\n",
    "\n",
    "fragment = list(sequence[start-1:end].replace(\"T\",\"U\"))\n",
    "#print(list(fragment))\n",
    "\n",
    "xticks = list(np.arange(start,end+1))\n",
    "xticklabels = fragment\n",
    "\n",
    "\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "mutant = ax[count].bar(x,y,color='red')\n",
    "formatAxes(ax[count],\\\n",
    "           text_size = 18,\\\n",
    "           ylabel = 'deltaSHAPE reps 2 new ',\\\n",
    "           ylim = [ymin,ymax],\\\n",
    "           yticklabels = [ymin,0,ymax],\\\n",
    "           yticks = [ymin,0,ymax],\\\n",
    "           xticks = xticks,\\\n",
    "           xticklabels = xticklabels)\n",
    "\n",
    "ax[count].legend((wild_type,mutant), ('more flexible in Erb1', 'more flexible in Delta 5'), loc=[1,1], fontsize = 16)\n",
    "\n",
    "\n",
    "ax[count].tick_params(axis='x',which='major',pad=150)\n",
    "\n",
    "fig.savefig(\"35S_deltaSHAPE_replicates_S7_%s_%s.pdf\" % (start,end),dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}