Lab6/Coverage_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 6 - Sequence alignment mapping analysis\n",
    "Here we analyse the alignment of the filtered reads obtained with samtools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import mean\n",
    "from collections import Counter\n",
    "from collections import OrderedDict\n",
    "from operator import itemgetter\n",
    "import numpy as np\n",
    "import matplotlib.ticker as mtick\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coverage plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coverage_plots(file, name):\n",
    "    \"\"\" Generates two coverage plots for a .tab file. The first plot displays \n",
    "    the depth of coverage per position whilst the second displays the distribution\n",
    "    of coverage. The function saves both plots to a specified file using name.\"\"\"\n",
    "    \n",
    "    \n",
    "    print(\"# Depth of coverage per position being calculated\")\n",
    "    # depth[i] contains the depth of coverage of position (i+1)\n",
    "    depth = []\n",
    "    reader = csv.reader(file, delimiter=\"\\t\")\n",
    "    for row in reader:\n",
    "        depth.append(int(row[2]))\n",
    "    \n",
    "    # We plot depth as a function of position\n",
    "    pos = [i+1 for i in range(len(depth))]\n",
    "    plt.plot(pos, depth, \"r\")\n",
    "    plt.xlabel(\"Position in Genome\")\n",
    "    plt.ylabel(\"Depth of Coverage\")\n",
    "    plt.savefig(\"IMG/%s_coverage_depth.png\" %name)\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"# Coverage statistics being calculated\")\n",
    "    print(\"Maximum coverage = \", max(depth))\n",
    "    print(\"Minimum coverage = \", min(depth))\n",
    "    print(\"Average coverage = %.3f\" % mean(depth))\n",
    "    \n",
    "    # We plot the distribution of coverage depth\n",
    "    print(\"\\n# Distribution of coverage depth being calculated\")\n",
    "    plt.hist(depth,100)\n",
    "    plt.xlabel(\"Depth of coverage\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.savefig(\"IMG/%s_coverage_depth_distribution.png\" %name)\n",
    "    plt.show()\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shewanella coverage plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Depth of coverage per position being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Coverage statistics being calculated\n",
      "Maximum coverage =  281\n",
      "Minimum coverage =  0\n",
      "Average coverage = 37.04\n",
      "\n",
      "# Distribution of coverage depth being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"Coverage_Plot/shewanella_pileup.tab\") as f:\n",
    "    name = \"shewanella\"\n",
    "    coverage_plots(f, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Human coverage plot - Extra credits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Coverage plot for whole human genome without zeros\n",
    "Here we run the coverage_plot function on the pileup file obtained using samtools. The file does not indicate all positions which have no coverage.\n",
    "\n",
    "**Note:** the average coverage value returned here is not representative since positions with non-zero coverage are not all taken into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Depth of coverage per position being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Coverage statistics being calculated\n",
      "Maximum coverage =  2\n",
      "Minimum coverage =  0\n",
      "Average coverage = 1.00\n",
      "\n",
      "# Distribution of coverage depth being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"Coverage_Plot_human/human_pileup_nozero.tab\") as f:\n",
    "    name = \"human_nozero\"\n",
    "    coverage_plots(f, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Coverage plot for 3 chromosomes\n",
    "Here we calculate the average coverage depth of chromosome 22, X and Y. Due to the size of the files, this can take several minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### WARNING: THIS WORKS ONE THE BIOE131.COM SERVER. ON OTHER SERVERS WITH INSUFFICIENT MEMORY THIS COULD OVERLOAD CRASH THE SERVER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GENERATING PLOTS FOR chr22\n",
      "# Depth of coverage per position being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Coverage statistics being calculated\n",
      "Maximum coverage =  1\n",
      "Minimum coverage =  0\n",
      "Average coverage = 0.007\n",
      "\n",
      "# Distribution of coverage depth being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------\n",
      "\n",
      "GENERATING PLOTS FOR chrX\n",
      "# Depth of coverage per position being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Coverage statistics being calculated\n",
      "Maximum coverage =  1\n",
      "Minimum coverage =  0\n",
      "Average coverage = 0.010\n",
      "\n",
      "# Distribution of coverage depth being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------\n",
      "\n",
      "GENERATING PLOTS FOR chrY\n",
      "# Depth of coverage per position being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAHuVJREFUeJzt3Xu8XWV95/HP1yQqIio20aEJIVgZRQoCHqEUarECxkuJrXYMFaVWm9YBb52ZFtABxXasZV51dEQxxRQ6RbCCaGhTMVUBFdEk3MJFJAYtSdEEw1UomOQ7f6znyM7hXNY5Z6+z9j7n+3691uvs9azL/u0F2b/9XNZ6ZJuIiIixPKntACIioj8kYURERC1JGBERUUsSRkRE1JKEERERtSRhRERELUkYERFRSxJGRETUkoQRERG1zG47gG6aO3euFy1a1HYYERF9Y926dffYnldn32mVMBYtWsTatWvbDiMiom9I+lHdfdMkFRERtSRhRERELUkYERFRSxJGRETUkoQRERG1NJYwJO0t6euSbpV0i6R3D7OPJH1c0gZJN0k6tGPbSZLuKMtJTcUZERH1NDmsdjvw32xfJ2kPYJ2k1bZv7djnVcB+ZTkc+BRwuKRnA2cCA4DLsStt39tgvBERMYrGahi277Z9XXn9IHAbMH/IbkuAv3flWuBZkvYCXgmstr2tJInVwOKmYo2W2HD++fDYY21HUt/NN8O3vtV2FBGtmJIb9yQtAg4BvjNk03zgro71TaVspPLhzr0MWAawcOHCrsQbU+SSS+Ctb4U77oC//Mu2o6nnwAOrv3a7cUS0oPFOb0lPBy4F3mP7gW6f3/Zy2wO2B+bNq3V3e/SK++6r/m7Z0m4cEVFLowlD0hyqZHGh7S8Ms8tmYO+O9QWlbKTyiIhoSZOjpAR8BrjN9t+MsNtK4C1ltNSvAffbvhu4AjhO0p6S9gSOK2UREdGSJvswjgTeDKyXdEMpOx1YCGD7XGAV8GpgA/Aw8NaybZukDwFrynFn2d7WYKwRETGGxhKG7W8CGmMfAyePsG0FsKKB0CIiYgJyp3dERNSShBHtyxDViL6QhBHt0agtlhHRY5IwIiKiliSMiIioJQkjIiJqScKIiIhakjAiIqKWJIxoX4bVRvSFJIxoT4bVRvSVJIyIiKglCSMiImpJwoiIiFqSMCIiopYkjIiIqKWx+TAkrQBeC2yx/avDbP8fwJs64tgfmFcmT/oh8CCwA9hue6CpOKMHZFhtRF9osoZxPrB4pI22z7Z9sO2DgdOAq4bMqvfysj3JYrrKsNqIvtJYwrB9NVB3WtUTgIuaiiUiIiav9T4MSU+jqolc2lFs4CuS1kla1k5kERHRqbE+jHH4beBbQ5qjjrK9WdJzgNWSvldqLE9QEsoygIULFzYfbUTEDNV6DQNYypDmKNuby98twGXAYSMdbHu57QHbA/PmzWs00IiImazVhCHpmcBvAl/qKNtd0h6Dr4HjgJvbiTAiIgY1Oaz2IuBoYK6kTcCZwBwA2+eW3X4H+Irtn3Uc+lzgMlUjaGYDn7X95abijB6QYbURfaGxhGH7hBr7nE81/LazbCPw4maiip6SYbURfaUX+jAiIqIPJGFEREQtSRgREVFLEkZERNSShBEREbUkYUT7Mqw2oi8kYUR7Mqw2oq8kYURERC1JGBERUUsSRkRE1JKEERERtSRhRERELUkY0b4Mq43oC0kY0Z4Mq43oK0kYERFRSxJGRETU0ljCkLRC0hZJw06vKuloSfdLuqEsZ3RsWyzpdkkbJJ3aVIwREVFfkzWM84HFY+zzDdsHl+UsAEmzgHOAVwEvAk6Q9KIG44yIiBoaSxi2rwa2TeDQw4ANtjfafgy4GFjS1eAiImLc2u7DOELSjZL+RdIBpWw+cFfHPptKWUxXGVYb0Rdmt/je1wH72H5I0quBLwL7jfckkpYBywAWLlzY3QijWRlWG9FXWqth2H7A9kPl9SpgjqS5wGZg745dF5Sykc6z3PaA7YF58+Y1GnNExEzWWsKQ9J+k6iempMNKLD8F1gD7SdpX0pOBpcDKtuKMiIhKrSYpSfsA+9n+V0m7AbNtPzjGMRcBRwNzJW0CzgTmANg+F3gD8A5J24FHgKW2DWyXdApwBTALWGH7lgl9uoiI6JoxE4akP6LqI3g28CtUTUTnAq8Y7TjbJ4yx/RPAJ0bYtgpYNVZsERExdeo0SZ0MHAk8AGD7DuA5TQYVERG9p07CeLTcDwGApNlAxkFG92RYbURfqJMwrpJ0OrCbpGOBzwOXNxtWzAgZVhvRV+okjFOBrcB64I+p+hbe32RQERHRe8bs9La9E/jbskRExAxVZ5TUep7YZ3E/sBb4C9s/bSKwiIjoLXXuw/gXYAfw2bK+FHga8GOqJ9L+diORRURET6mTMI6xfWjH+npJ19k+VNKJTQUWERG9pU6n96zy6A4AJL2U6g5sgO2NRBUzS4bVRvSFOjWMtwMrJD0dENUNfG+XtDvw4SaDi2kuw2oj+kqdUVJrgAMlPbOs39+x+R+bCiwiInpL3YcPvgY4AHhqecAsg1OqRkTEzDBmH4akc4E3Au+kapL6PWCfhuOKiIgeU6fT+9dtvwW41/YHgSOA/9xsWBER0WvqJIz/KH8flvTLwM+BvZoLKSIielGdPozLJT0LOJtqHm6Tx4REN2VYbURfGDVhSHoS8FXb9wGXSvon4KlDRkqNdOwK4LXAFtu/Osz2NwF/TtUv8iDwDts3lm0/LGU7gO22B8b1qaI/ZFhtRF8ZtUmqPHjwnI71R+ski+J8YPEo2+8EftP2gcCHgOVDtr/c9sFJFhERvaFOH8ZXJb1eGt/PQdtXA9tG2X6N7XvL6rVUU79GRESPqpMw/phq0qTHJD0g6UFJD3Q5jrdRPeRwkIGvSFonadloB0paJmmtpLVbt27tclgRETGozp3eezQZgKSXUyWMozqKj7K9WdJzgNWSvldqLMPFt5zSnDUwMJDe04iIhtS5cU+STpT0P8v63p0PI5wMSQcB5wFLOufVsL25/N0CXAZ05f0iImLi6jRJfZLqZr3fL+sP0dERPlGSFgJfAN5s+/sd5btL2mPwNXAccPNk3y96WIbVRvSFOvdhHF7mvrgewPa9kp481kGSLgKOBuZK2gScCcwp5zgXOAP4JeCTpT99cPjsc4HLStls4LO2vzzeDxZ9IMNqI/pKnYTxc0mzKNO0SpoH7BzrINsnjLH97VSPTh9avhF4cY24IiJiCtVpkvo4VT/CcyT9JfBN4H81GlVERPScOqOkLpS0DngF1V3Zr7N9W+ORRURETxkzYUj6OHCx7Ul3dEdERP+q0yS1Dni/pB9I+t+S8qiOiIgZaMyEYfsC268GXgrcDnxE0h2NRxYzR4bVRvSFOjWMQc8HXkg12973mgknZpQMq43oK3Xu9P7rUqM4i+oGugHbv914ZBER0VPq3IfxA+AI2/c0HUxERPSuOsNqPy3peEkvK0VX2b684bgiIqLH1GmS+jDwbuDWsrxLUm7ci4iYYeo0Sb0GOLjMvoekC4DrgdObDCwiInpL3VFSz+p4/cwmAomIiN5Wp4bxYeB6SV+nejTIy4BTG40qZpbchxHRF+p0el8k6UqqG/cA/tz2jxuNKmaG3IcR0VdGTBiSXgnsYfsS23cDK0v5GyTdb3v1VAUZERHtG60P4wzgqmHKr6S6iW9MklZI2iJp2BnzyvSvH5e0QdJNkg7t2HaSpDvKclKd94uIiOaMljCeYnvr0MJyA9/uNc9/PrB4lO2vAvYryzLgUwCSnk01Q9/hVPN5nylpz5rvGRERDRgtYTxD0hOarCTNAXarc3LbVwPbRtllCfD3rlwLPEvSXsArgdW2t9m+F1jN6IknIiIaNlrC+ALwt5J+UZuQ9HTg3LKtG+YDd3WsbyplI5U34/774fjjq07YzmX33Z9YNrgcccTI23phue++iV2LnTunLsYTyiy+n/tc+9er7jJorP127Jj8/5cRI7n77l3/f/vYx6bkbUdLGO8HfgL8SNK6MuvencDWsq0nSFomaa2ktVu3PqEFrZ73vQ8uH+ZpJw8/PPIx1147sfeaKgcdNLHjrrmmu3HMVKtWtR1BTGcveMGu6+95z5S87YijpGxvB06V9EGqR5sDbLD9SBfffzOwd8f6glK2GTh6SPmVI8S5HFgOMDAwMLEB/Y8+OqHDetojE/zPlF/G3ZHrGE362c9aeds6Eyg9Ynt9WbqZLKAaqvuWMlrq14D7yxDeK4DjJO1ZOruPK2UREdGSOnd6T5iki6hqCnMlbaIa+TQHwPa5wCrg1cAG4GHgrWXbNkkfAtaUU51le7TO8xgqd0+3K9c/mtTZnzaFRrtx70jb35L0FNsTarOxfcIY2w2cPMK2FcCKibzvuLV08WMaS8KIJrX0nTVak9THy99vT0UgERHR20Zrkvq5pOXAfEkfH7rR9ruaCysmLb9wI6LLRksYrwWOobqJbt3UhBNdk4TRrlz/aFKv9WGUR4BcLOk22zdOYUxTL30Y0W1JGNGkHuzDGPRTSZeVhwhukXSppAWNRxaTkyQYEV1WJ2H8HdX9Er9clstLWfSy/MJtV65/NKmHaxjPsf13treX5XxgXsNxTa38Go9uS8KIJvVwwrhH0omSZpXlROCnTQcWk5QvrIjosjoJ4w+B/wL8GLgbeAPljuyYhlLbiogR1JnT+0fA8VMQS3RTahjtyvWPJvVwk9T0l1/Vj8sXXXfkOkaTkjAiIqKXJWFENCE1jGhSr93pPUjSU4DXA4s697d9VnNhTbHp2CSVL6x25fpHk3o1YQBfAu6nep7UNJyaLnYxHZNnRHRFnYSxwPbixiOJiIieVqcP4xpJB07k5JIWS7pd0gZJpw6z/aOSbijL9yXd17FtR8e2lRN5/xktTSLtyvWPJvVak5Sk9YDLPm+VtJGqSUpUk+UdNNqJJc0CzgGOBTYBaySttH3r4D6239ux/zuBQzpO8Yjtg8f/kSYgzTCPyxddd+Q6RpN6LWFQzYcxGYcBG2xvBJB0MbAEuHWE/U+gmvM7IiJ60IhNUrZ/VO7y/ovB151lNc49H7irY31TKXsCSfsA+wJf6yh+qqS1kq6V9Loa7xed8gu3Xbn+0aQerGEMOqBzpTQ1vaTLcSwFLrG9o6NsH9ubJT0P+Jqk9bZ/MPRAScuAZQALFy6c2LtPxyapiX5hTcdr0YYkjGhSr93pLek0SQ8CB0l6QNKDZX0L1VDbsWwG9u5YX1DKhrMUuKizwPbm8ncjcCW79m907rfc9oDtgXnzptdT1yMiesloTVIftr0HcLbtZ9jeoyy/ZPu0GudeA+wnaV9JT6ZKCk8Y7STphcCewLc7yvYsNwwiaS5wJCP3fURExBSo0yR1uqTfBY6iGjX1DdtfHOsg29slnQJcAcwCVti+RdJZwFrbg8ljKXCxvUsdfn/g05J2UiW1v+ocXRU1pEmkXbn+0aQe7sM4B3g+jzcZ/YmkY22fPNaBtlcBq4aUnTFk/QPDHHcNMKF7PyZkOrbb5wurXbn+0aQeThi/Bew/WAOQdAFwS6NRxeTlCysiuqzOnd4bgM7hR3uXsuhlSRjtyvWPJvVwDWMP4DZJ36XqwzgMWDv4uA7b/T8bX5qkotty/aNJPZwwzhh7l4iImO7qzOl9VbkTez/b/yppN2C27QebDy8mbDrWmiKiVWP2YUj6I+AS4NOlaAEw5rDaaFmaRNqV6x9N6rU7vTucTHXj3AMAtu8AntNkUFNuOv4azxdWu3L9o0k9nDAetf3Y4Iqk2VSd39HL8oUVEV1WJ2FcJel0YDdJxwKfBy5vNqyYtCSMduX6R5N6uIZxKrAVWA/8MdWd2+9vMqgplyap6LZc/2hSrw6rtb1T0heBL9reOgUxRUREDxrt8eaS9AFJ9wC3A7dL2iop92VERMxAozVJvZdqdNRLbT/b9rOBw4EjJb13lOMiIk1S0aQe7MN4M3CC7TsHC8pkRicCb2k6sCk1Hfswdu5sO4KZLQkjmtSDCWOO7XuGFpZ+jDnNhRStmo7JMyK6YrSE8dgEt0U/yy/j7sh1jCb1YA3jxWUu76HLg9Sc3EjSYkm3S9og6dRhtv9B6Ui/oSxv79h2kqQ7ynLS+D/aOORXdXRbEkY0qdeG1dqeNZkTS5pFNVvfscAmYI2klcNMtfo526cMOfbZwJnAANVd5evKsfdOJqaIiJi4OjfuTdRhwAbbG8ujRS4GltQ89pXAatvbSpJYDSxuKM6IiKihyYQxH7irY31TKRvq9ZJuknSJpL3HeSySlklaK2nt1q25rzB6RJqkokk92IcxFS4HFtk+iKoWccF4T2B7ue0B2wPz5s2bWBTpw3hcrkV3JGFEk6ZhwthMNf/3oAWl7Bds/9T2o2X1POAldY+NiIip1WTCWAPsJ2lfSU8GlgIrO3eQtFfH6vHAbeX1FcBxkvaUtCdwXCmLpuWXcXfkOsY0VGdO7wmxvV3SKVRf9LOAFbZvkXQWsNb2SuBdko4HtgPbgD8ox26T9CGqpANwlu1tTcUa0XVJGNGkXhtW2w22V1E9Dr2z7IyO16cBp41w7ApgRZPx/ULa7SOin0zDPoyIiJhGkjBiV6ltdUeapKJJqWG0KF+S0W1JGNGkJIyIiOhlSRgRTUgNI6ahJIzYVb7ouiPXMZqUJqkWpQ8jIvpJEkb0hCTPiBhBEkZEE9IkFU1KDaNF+VUd3ZaEEU1KwoiIiF6WhBHRhNQwYhpKwohd5YuuO3Ido0lpkmpR+jAiop8kYURPSPKMiBEkYUQ0IU1S0aTpWMOQtFjS7ZI2SDp1mO1/KulWSTdJ+qqkfTq27ZB0Q1lWDj22y4E2evqYgZIwoknTbcY9SbOAc4BjgU3AGkkrbd/asdv1wIDthyW9A/hr4I1l2yO2D24qvoiIGJ8maxiHARtsb7T9GHAxsKRzB9tft/1wWb0WWNBgPBFTJzWMmIaaTBjzgbs61jeVspG8DfiXjvWnSlor6VpJrxvpIEnLyn5rt27dOrmII7olCSOaNN2apMZD0onAAPCbHcX72N4s6XnA1yStt/2DocfaXg4sBxgYGJjYv9L0YUREP5mGnd6bgb071heUsl1IOgZ4H3C87UcHy21vLn83AlcChzQYa0REjKHJhLEG2E/SvpKeDCwFdhntJOkQ4NNUyWJLR/mekp5SXs8FjgQ6O8sjeluapKJJ061JyvZ2SacAVwCzgBW2b5F0FrDW9krgbODpwOdVXYB/s308sD/waUk7qZLaXw0ZXdVdaZKKbkvCiCZNt4QBYHsVsGpI2Rkdr48Z4bhrgAObjC0iIsYnd3pHNCE1jJiGkjBiV2me644kjGjSNBwl1T/yJRkR/SQJI3pCfhlHxAiSMCKakMQbTUoNo0VpkopuS8KIJiVhREREL0vCiF2lttUdqWHENJSEEdGEJIxoUpqkWpRf1RHRT5Iwoifkl3FEjCAJI6IJSbzRpNQwWpQmqei2JIxoUhJG9IQkz4gYQRJGRBNSw4hpqNGEIWmxpNslbZB06jDbnyLpc2X7dyQt6th2Wim/XdIrm4wzouuSMKJJ061JStIs4BzgVcCLgBMkvWjIbm8D7rX9fOCjwEfKsS+imtL1AGAx8MlyvqaCbezUERFdN90SBnAYsMH2RtuPARcDS4bsswS4oLy+BHiFqrlalwAX237U9p3AhnK+iIhoSZNTtM4H7upY3wQcPtI+ZQ7w+4FfKuXXDjl2fmORfvCDjZ26VQccMP5jfvKT7scxE334w/ClL7UdRUxXt97ayts2Oqf3VJC0DFgGsHDhwomd5PWvh0sv7WJUPeDoo2Hu3PEft//+0+9atGHJEpgzp+0oYrqaPx9Wr57yt20yYWwG9u5YX1DKhttnk6TZwDOBn9Y8FgDby4HlAAMDAxPrabzkkgkdFhExkzTZh7EG2E/SvpKeTNWJvXLIPiuBk8rrNwBfs+1SvrSMotoX2A/4boOxRkTEGBqrYZQ+iVOAK4BZwArbt0g6C1hreyXwGeD/SdoAbKNKKpT9/hG4FdgOnGx7R1OxRkTE2ORpNF58YGDAa9eubTuMiIi+IWmd7YE6++ZO74iIqCUJIyIiaknCiIiIWpIwIiKiliSMiIioZVqNkpK0FfjRBA+fC9zTxXDalM/Su6bT58ln6U3j/Sz72J5XZ8dplTAmQ9LaukPLel0+S++aTp8nn6U3NflZ0iQVERG1JGFEREQtSRiPW952AF2Uz9K7ptPnyWfpTY19lvRhRERELalhRERELTM+YUhaLOl2SRskndp2PJMhaYWkLZJubjuWyZK0t6SvS7pV0i2S3t12TBMl6amSvivpxvJZ+n6KR0mzJF0v6Z/ajmWyJP1Q0npJN0jq66eXSnqWpEskfU/SbZKO6Or5Z3KTlKRZwPeBY6mmgV0DnGC7nfkPJ0nSy4CHgL+3/attxzMZkvYC9rJ9naQ9gHXA6/rxv02Zp3532w9JmgN8E3i37WvHOLRnSfpTYAB4hu3Xth3PZEj6ITBgu+/vw5B0AfAN2+eVeYieZvu+bp1/ptcwDgM22N5o+zHgYmBJyzFNmO2rqeYV6Xu277Z9XXn9IHAbTc7r3iBXHiqrc8rSt7/UJC0AXgOc13Ys8ThJzwReRjXPELYf62aygCSM+cBdHeub6NMvpelM0iLgEOA77UYycaUJ5wZgC7Dadt9+FuD/AH8G7Gw7kC4x8BVJ6yQtazuYSdgX2Ar8XWkuPE/S7t18g5meMKLHSXo6cCnwHtsPtB3PRNneYftgqvnpD5PUl02Gkl4LbLG9ru1Yuugo24cCrwJOLk27/Wg2cCjwKduHAD8DutovO9MTxmZg7471BaUsekBp778UuND2F9qOpxtKE8HXgcVtxzJBRwLHl3b/i4HfkvQP7YY0ObY3l79bgMuomqr70SZgU0ft9RKqBNI1Mz1hrAH2k7Rv6SBaCqxsOabgFx3FnwFus/03bcczGZLmSXpWeb0b1SCL77Ub1cTYPs32AtuLqP69fM32iS2HNWGSdi+DKijNN8cBfTnK0PaPgbskvaAUvQLo6iCR2d08Wb+xvV3SKcAVwCxghe1bWg5rwiRdBBwNzJW0CTjT9mfajWrCjgTeDKwvbf8Ap9te1WJME7UXcEEZlfck4B9t9/1w1GniucBl1e8TZgOftf3ldkOalHcCF5YfwBuBt3bz5DN6WG1ERNQ305ukIiKipiSMiIioJQkjIiJqScKIiIhakjAiIvrUeB44Kumj5QGLN0j6vqRxPzYkCSP6hqQd5X/2myV9XtLTJnCO8yS9qLw+fci2a7oU51mSjhnnMYdJulLSHZKuk/TPkg7sRjwxrZ1PzZtAbb/X9sHliQP/Fxj3zbAZVht9Q9JDtp9eXl8IrJvMTX2d52uTpOdSPSfr921fU8qOAuba/mKrwUXPK89a+6fBJ1RL+hXgHGAe8DDwR7a/N+SYa6ju01o9nvdKDSP61TeA50P1qO1S67hZ0ntK2e7lV/qNpfyNpfxKSQOS/grYrdRYLizbHip/Jensctz6jmOPLscPzjdwYbkjfReSzpf0hvL6h5I+WGoN6yW9cJjPcgpwwWCyALD9zcFkUe4Uv1TSmrIcWco/UJokrpS0UdK7OmIY7posKnGfX5okLpR0jKRvlZrNYR3XboWqOTyul9S3T3CeoZYD77T9EuC/A5/s3ChpH6oHFX5tvCee0Xd6R3+SNJvqQXFflvQSqrtZDwcEfEfSVcDzgH+3/ZpyzDM7z2H7VEmnlOr5UL8LHAy8GJgLrJF0ddl2CHAA8O/At6juSP/mGCHfY/tQSf+V6h/w24dsPwC4YJTjPwZ81PY3JS2kejLB/mXbC4GXA3sAt0v6FHAQw1+Te6mS7O8Bf0j1aJzfB44CjgdOB14HvI/qkR9/WB5p8l1J/2r7Z2N8zmiZqod1/jrw+Y7fMk8ZsttS4BLbO8Z7/tQwop/sVh4Tshb4N6pnTR0FXGb7Z2XOiS8AvwGsB46V9BFJv2H7/nG8z1HAReUJsz8BrgJeWrZ91/Ym2zuBG4BFNc432Fa8rs7+kr6jara0j5WiY4BPlM++EnhG+WIA+Gfbj5bJf7ZQPepipGsCcKft9SX+W4CvumqXXt8R23HAqeX9rgSeCiys8TmjfU8C7hvsqyjL/kP2WQpcNJGTp4YR/eSRoTWCYVqEALD9fUmHAq8G/kLSV22f1YUYHu14vYN6/4YGjxlp/1uonir6JQDbh5cmrcGZ7J4E/Jrt/+g8qHz28cbTuf/OjvWdHccKeL3t28c4V/QY2w9IulPS79n+fGkyPcj2jQClSXRP4NsTOX9qGNHvvgG8TtLTVD1t9HeAb0j6ZeBh2/8AnM3wj3n+uapHqA93zjeqmvRoHtUsZt9tKH6oOij/QNKvd5R1jgD7CtVD5QCQNFwzWqdhr8k44rkCeOdg/4ykQ8ZxbEwhVQ8c/TbwAkmbJL0NeBPwNkk3Uv0Y6eyDWgpc7AmOdkoNI/pamfP7fB7/Qj/P9vWSXgmcLWkn8HPgHcMcvhy4SdJ1tt/UUX4ZcARwI9VsbH9m+8cjdFh34zP8uHSsf0TSfKqmpXuAwRrRu4BzJN1E9W/2auBPRjnfSNdkUc2QPkQ1q95Nkp4E3MnjtZ3oIbZPGGHTsENtbX9gMu+XYbUREVFLmqQiIqKWJIyIiKglCSMiImpJwoiIiFqSMCIiopYkjIiIqCUJIyIiaknCiIiIWv4/iq0UF5G6P6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Coverage statistics being calculated\n",
      "Maximum coverage =  2\n",
      "Minimum coverage =  0\n",
      "Average coverage = 0.004\n",
      "\n",
      "# Distribution of coverage depth being calculated\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAERCAYAAAB4jRxOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFGZJREFUeJzt3XmUZnV95/H3h2YVEDDUMJwoNCQugUwEUmhEhhFMDK5MEhc8mIiS6ZgoRydqjoaZyWTmnIxZTmJiFtPDmGjCNGpHM6KRJYJBoyzVyNYsLoAJHpMuMYg4Iwj5zh/3FjxWanmqu+5T1fzer3Oe0/e52+9bt25/6j53+T2pKiRJj317rHUBkqTJMPAlqREGviQ1wsCXpEYY+JLUCANfkhqx7gI/yXuS7Ehy8xjz/k6S6/vX55PcO4kaJWl3lPV2H36SU4D7gfdV1Q+uYLlzgeOr6rWDFSdJu7F1d4RfVVcCXx8dl+T7klycZFuSTyV52gKLvhLYMpEiJWk3tOdaFzCmzcDrquoLSZ4J/CFw2tzEJEcCRwGXr1F9krTurfvAT3IAcBLwwSRzo/eZN9uZwNaqeniStUnS7mTdBz7daad7q+q4JeY5E3j9hOqRpN3SujuHP19V3QfcmeRlAOk8fW56fz7/EOCza1SiJO0W1l3gJ9lCF95PTXJ3knOAs4BzktwAbAfOGFnkTODCWm+3G0nSOrPubsuUJA1j3R3hS5KGsa4u2h566KG1cePGtS5DknYb27Zt+1pVTY0z77oK/I0bNzIzM7PWZUjSbiPJl8ed11M6ktQIA1+SGmHgS1IjBg38JAcn2ZrktiS3JnnWkO1JkhY39EXb3wUurqqXJtkbeNzA7UmSFjFY4Cc5CDgFOBugqh4EHhyqPUnS0oY8pXMUMAv8SZLPJTk/yf7zZ0qyKclMkpnZ2dkBy5Gktg0Z+HsCJwB/VFXHA98C3jZ/pqraXFXTVTU9NTXWswOSpJ0wZODfDdxdVVf377fS/QGQJK2Bwc7hV9U/JPn7JE+tqtuB5wK3DNXexrd97JHhu97xwqGakaTd1tB36ZwLXNDfoXMH8JqB25MkLWLQwK+q64HpIduQJI3HJ20lqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1Ys8hV57kLuCbwMPAQ1U1PWR7kqTFDRr4vVOr6msTaEeStARP6UhSI4YO/AIuTbItyaaFZkiyKclMkpnZ2dmBy5Gkdg0d+CdX1QnA84HXJzll/gxVtbmqpqtqempqauByJKldgwZ+VX2l/3cH8GHgGUO2J0la3GCBn2T/JAfODQPPA24eqj1J0tKGvEvnMODDSeba+d9VdfGA7UmSljBY4FfVHcDTh1q/JGllvC1Tkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwYP/CQbknwuyUeHbkuStLhJHOG/Ebh1Au1IkpYwaOAneSLwQuD8IduRJC1v6CP8dwK/BPzzYjMk2ZRkJsnM7OzswOVIUrsGC/wkLwJ2VNW2pearqs1VNV1V01NTU0OVI0nNG/II/9nAS5LcBVwInJbkzwdsT5K0hMECv6reXlVPrKqNwJnA5VX1qqHakyQtzfvwJakRe06ikar6JPDJSbQlSVqYR/iS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNGCvwkzx7nHGSpPVr3CP8d405TpK0Ti3ZH36SZwEnAVNJfnFk0uOBDUMWJklaXct9AcrewAH9fAeOjL8PeOlQRUmSVt+SgV9VfwP8TZI/raovT6gmSdIAxv2Kw32SbAY2ji5TVacNUZQkafWNG/gfBN4NnA88PFw5kqShjBv4D1XVHw1aiSRpUOPelnlRkl9IcniSJ8y9Bq1MkrSqxj3Cf3X/71tHxhVw9OqWI0kayliBX1VHDV2IJGlYYwV+kp9ZaHxVvW91y5EkDWXcUzonjgzvCzwXuA4w8CVpNzHuKZ1zR98nORi4cKllkuwLXAns07eztap+ZSfrlCTtonGP8Of7FrDcef0HgNOq6v4kewGfTvLxqrpqJ9uUJO2Ccc/hX0R3Vw50nab9APCBpZapqgLu79/u1b9q8SUkSUMa9wj/t0aGHwK+XFV3L7dQkg3ANuD7gT+oqqsXmGcTsAngiCOOGLMcSdJKjfXgVd+J2m10PWYeAjw45nIPV9VxwBOBZyT5wQXm2VxV01U1PTU1NX7lkqQVGfcbr14OXAO8DHg5cHWSsbtHrqp7gSuA03emSEnSrhv3lM55wIlVtQMgyRTw18DWxRbo5/lOVd2bZD/gx4Bf38V6JUk7adzA32Mu7Hv3sPyng8OB9/bn8fcAPlBVH92JGiVJq2DcwL84ySXAlv79K4C/WmqBqroROH4XapMkraLlvtP2+4HDquqtSX4SOLmf9FnggqGLkyStnuWO8N8JvB2gqj4EfAggyb/pp7140OokSatmufPwh1XVTfNH9uM2DlKRJGkQywX+wUtM2281C5EkDWu5wJ9J8h/mj0zys3RP0EqSdhPLncN/E/DhJGfxaMBPA3sDPzFkYZKk1bVk4FfVPwInJTkVmOsW4WNVdfnglUmSVtW4/eFfQdc1giRpNzVWXzqSpN2fgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNGCzwkzwpyRVJbkmyPckbh2pLkrS8sb7Tdic9BLy5qq5LciCwLcllVXXLgG1KkhYx2BF+VX21qq7rh78J3Ap871DtSZKWNpFz+Ek2AscDVy8wbVOSmSQzs7OzkyhHkpo0eOAnOQD4C+BNVXXf/OlVtbmqpqtqempqauhyJKlZgwZ+kr3owv6CqvrQkG1JkpY25F06Af4XcGtV/fZQ7UiSxjPkEf6zgZ8GTktyff96wYDtSZKWMNhtmVX1aSBDrV+StDI+aStJjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRgwV+kvck2ZHk5qHakCSNb8gj/D8FTh9w/ZKkFRgs8KvqSuDrQ61fkrQya34OP8mmJDNJZmZnZ9e6HEl6zFrzwK+qzVU1XVXTU1NTa12OJD1mrXngS5Imw8CXpEYMeVvmFuCzwFOT3J3knKHakiQtb8+hVlxVrxxq3ZKklfOUjiQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYMGvhJTk9ye5IvJnnbkG1JkpY2WOAn2QD8AfB84BjglUmOGao9SdLS9hxw3c8AvlhVdwAkuRA4A7hlwDalQWx828ceGb7rHS9cw0qknTdk4H8v8Pcj7+8Gnjl/piSbgE392/uT3L6T7R0KfA0gv76TaxjGI3WtM9a1Mu5fK2NdK7MrdR057oxDBv5YqmozsHlX15NkpqqmV6GkVWVdK2NdK2NdK9N6XUNetP0K8KSR90/sx0mS1sCQgX8t8OQkRyXZGzgT+MiA7UmSljDYKZ2qeijJG4BLgA3Ae6pq+1DtsQqnhQZiXStjXStjXSvTdF2pqkm0I0laYz5pK0mNMPAlqRHrPvCX654hyT5J3t9PvzrJxpFpb+/H357kxydc1y8muSXJjUk+keTIkWkPJ7m+f63qhewx6jo7yexI+z87Mu3VSb7Qv1494bp+Z6Smzye5d2TakNvrPUl2JLl5kelJ8nt93TcmOWFk2pDba7m6zurruSnJZ5I8fWTaXf3465PMTLiu5yT5xsjv67+MTBusq5Ux6nrrSE039/vUE/ppQ26vJyW5os+C7UneuMA8k9vHqmrdvugu9n4JOBrYG7gBOGbePL8AvLsfPhN4fz98TD//PsBR/Xo2TLCuU4HH9cM/P1dX//7+NdxeZwO/v8CyTwDu6P89pB8+ZFJ1zZv/XLqL/INur37dpwAnADcvMv0FwMeBAD8CXD309hqzrpPm2qPrvuTqkWl3AYeu0fZ6DvDRXd0HVruuefO+GLh8QtvrcOCEfvhA4PML/J+c2D623o/wH+meoaoeBOa6Zxh1BvDefngr8Nwk6cdfWFUPVNWdwBf79U2krqq6oqr+b//2KrrnEIY2zvZazI8Dl1XV16vqn4DLgNPXqK5XAltWqe0lVdWVwNeXmOUM4H3VuQo4OMnhDLu9lq2rqj7TtwuT27/G2V6L2ZV9c7XrmuT+9dWquq4f/iZwK10vBKMmto+t98BfqHuG+RvrkXmq6iHgG8D3jLnskHWNOofuL/icfZPMJLkqyb9fpZpWUtdP9R8dtyaZezhuXWyv/tTXUcDlI6OH2l7jWKz2IbfXSs3fvwq4NMm2dF2XTNqzktyQ5ONJju3HrYvtleRxdKH5FyOjJ7K90p1uPh64et6kie1ja961wmNdklcB08C/Gxl9ZFV9JcnRwOVJbqqqL02opIuALVX1QJKfo/t0dNqE2h7HmcDWqnp4ZNxabq91LcmpdIF/8sjok/vt9a+Ay5Lc1h8BT8J1dL+v+5O8APhL4MkTanscLwb+tqpGPw0Mvr2SHED3R+ZNVXXfaq57Jdb7Ef443TM8Mk+SPYGDgHvGXHbIukjyo8B5wEuq6oG58VX1lf7fO4BP0v3Vn0hdVXXPSC3nAz887rJD1jXiTOZ93B5we41jsdrXvOuQJD9E9zs8o6rumRs/sr12AB9m9U5lLquq7quq+/vhvwL2SnIo62B79ZbavwbZXkn2ogv7C6rqQwvMMrl9bIgLFav1ovsEcgfdR/y5Cz3Hzpvn9Xz3RdsP9MPH8t0Xbe9g9S7ajlPX8XQXqZ48b/whwD798KHAF1ili1dj1nX4yPBPAFfVoxeI7uzrO6QffsKk6urnexrdBbRMYnuNtLGRxS9CvpDvvqB2zdDba8y6jqC7LnXSvPH7AweODH8GOH2Cdf3rud8fXXD+Xb/txtoHhqqrn34Q3Xn+/Se1vfqf/X3AO5eYZ2L72Kpt7KFedFewP08Xnuf14/4b3VEzwL7AB/ud/xrg6JFlz+uXux14/oTr+mvgH4Hr+9dH+vEnATf1O/xNwDkTrut/ANv79q8Anjay7Gv77fhF4DWTrKt//1+Bd8xbbujttQX4KvAdunOk5wCvA17XTw/dF/l8qW9/ekLba7m6zgf+aWT/munHH91vqxv63/N5E67rDSP711WM/EFaaB+YVF39PGfT3cgxutzQ2+tkumsEN478rl6wVvuYXStIUiPW+zl8SdIqMfAlqREGviQ1wsCXpEYY+JLUCANfEzfS++X2/hH8NyfZ6X0xyS+PDG9crMfEMdc1la7X1c8l+bc7ux5pPTLwtRb+X1UdV1XHAj9G19vjr+zC+n55+VnG9lzgpqo6vqo+tYrrHVv/xLi06gx8ranqHmffBLyh7xd8Q5LfTHJt38Hbz8Ej/axfmeRjfZ/q706yR5J3APv1nxgu6Fe7Icn/7D9BXJpkv/nt9p8ELs+j31dwRJLjgN8AzujXt9+8ZU5M1/f8DUmuSXJgkn2T/Enfn/rn+r5t6Dt6O3Zk2U8mmU6yf7q+26/p5z+jn352ko8kuRz4RJID+rqu69d9xsi6/nO/DT6dZEuSt/Tjvy/JxX0nYJ9K8rRV/FXpsWA1nyrz5WucFwv0bw/cCxxGF/7/qR+3DzBD9zj+c4Bv0z0ZuYGuq9iXzl8f3eP1DwHH9e8/ALxqgfYuAl7dD78W+Mt++GwW/r6Avem6Bjixf/94uu4C3kzfdz9d1xB/R/f0938EfrUffzhwez/8a3P1AAfTPXm6f9/u3fSPzvfrfnw/fCjdk5YBTqR7WnNfuv7VvwC8pZ/vE/RdeQDPZKTPd1++qsreMrXuPA/4oSQv7d8fRNfb4oN0fYzcAZBkC91j61sXWMedVXV9P7yN7o/AfM8CfrIf/jO6I/ulPBX4alVdC10nYX0dJwPv6sfdluTLwFPo/tBcSneq6uUjdT4PeMncUTldcB/RD19Wj/biGODXkpwC/DNdt7iHAc8G/k9VfRv4dpKL+joOoOuG4oNJ5mreZ5mfSY0x8LXm+m6PHwZ20AXduVV1ybx5nkPXJ8moxfoFeWBk+GHgX5zSGVp13e3e0/do+Qq6vlOg+/l+qqpuH50/yTOBb42MOguYAn64qr6T5C66Pw6L2QO4t6qOW62fQY89nsPXmkoyBbyb7jRKAZcAP993KUuSpyTZv5/9GUmO6u/oeQXw6X78d+bmX4HP0PWuCl24LneB9nbg8CQn9nUd2F9c/VS/PEmeQne0Phfm7wd+CTioqm7sx10CnJv+MDzJYl09HwTs6MP+VODIfvzfAi/urx0cALwIHvnEcWeSl/XrTUa+51YCA19rY+4i63a6XkUvBX61n3Y+cAtwXX975R/z6CfRa4Hfp/uauDvp+i4H2AzcOHLRdhznAq9JciPw08C/+HLpUdV9Ld8rgHcluYHuGsK+wB8CeyS5iS7gz65Hv29gK32X3SOr+u/AXn292/v3C7kAmO7X+zPAbX0d1wIfoet98eN0vSt+o1/mLOCcvr7trOJXCOqxwd4ytVvoT+m8papetNa1rLUkB1T3jVKPA64ENlX/vanSUjyHL+1+Nic5hu4TxnsNe43LI3xJaoTn8CWpEQa+JDXCwJekRhj4ktQIA1+SGvH/ATfsmVtgm+iMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "chromosomes = ['chr22', 'chrX', 'chrY']\n",
    "for c in chromosomes:\n",
    "    print(\"GENERATING PLOTS FOR %s\" %c)\n",
    "    with open(\"Coverage_Plot_human/human_pileup_%s.tab\" %c) as f:\n",
    "        coverage_plots(f, c)\n",
    "    print(\"\\n---------------------------------------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average coverage for 3 chromosomes\n",
    "Here we calculate the average coverage depth of chromosome 22, X and Y. Due to the size of the files, this can take several minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_depth(file):\n",
    "    \"\"\" Calculates the average depth of coverage for a given file. \"\"\"\n",
    "    \n",
    "    # total_p is the total number of positions in the file, total_d is the cumulated depth\n",
    "    total_p = 0\n",
    "    total_d = 0\n",
    "    reader = csv.reader(file, delimiter=\"\\t\")\n",
    "    for row in reader:\n",
    "        total_p += 1\n",
    "        total_d += int(row[2])\n",
    "    return total_d/total_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CALCULATING AVERAGE FOR chr22\n",
      "Average depth of coverage in chr22 : 0.6868%\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "\n",
      "CALCULATING AVERAGE FOR chrX\n",
      "Average depth of coverage in chrX : 0.9715%\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "\n",
      "CALCULATING AVERAGE FOR chrY\n",
      "Average depth of coverage in chrY : 0.4118%\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chromosomes contains the chromosomes of interest\n",
    "chromosomes = ['chr22', 'chrX', 'chrY']\n",
    "\n",
    "# chr_avg will contain the average for each chromosome\n",
    "chr_avg = []\n",
    "\n",
    "for c in chromosomes:\n",
    "    print(\"CALCULATING AVERAGE FOR %s\" %c)\n",
    "    with open(\"Coverage_Plot_human/human_pileup_%s.tab\" %c) as f:\n",
    "        avg = avg_depth(f)\n",
    "        chr_avg.append(avg)\n",
    "        print(\"Average depth of coverage in %s : %s\" %(c, \"{:.4%}\".format(avg)))\n",
    "    print(\"\\n---------------------------------------------------------------------\\n\")\n",
    "    \n",
    "# Generate bar plot\n",
    "lab = np.arange(len(chromosomes))\n",
    "plt.bar(lab, chr_avg)\n",
    "plt.xticks(lab, chromosomes)\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.ylabel(\"Average depth of coverage\")\n",
    "plt.savefig(\"IMG/average_depth_chromosome.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Zooming in on high coverage regions\n",
    "Here we identify the regions that have higher coverage than average. Given previous results, we will look at sequences of 20 or more nucleotides which have a coverage of 2. We will therefore focus on chromosome Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def high_coverage_region(file):\n",
    "    \"\"\" Returns a list of indices pairs (i,j) such that j>=i+20 and the coverage of all positions\n",
    "    from i to j is equal to 2. \"\"\"\n",
    "    \n",
    "    min_length = 20\n",
    "    \n",
    "    indices = []\n",
    "    reader = csv.reader(file, delimiter=\"\\t\")\n",
    "    \n",
    "    # i is the position being investigated\n",
    "    # if postion i has a coverage of 2 imem the smallest position such that positions from imem to i all have a coverage of 2\n",
    "    i = 1\n",
    "    imem = 1\n",
    "\n",
    "    for row in reader:\n",
    "        i+=1\n",
    "        # if the position does not have a coverage of 2\n",
    "        if not(int(row[2])==2):\n",
    "            # check if continuous sequence of coverage 2 is larger that min_length\n",
    "            if (i-imem)>min_length:\n",
    "                indices.append((imem,i))\n",
    "            imem = i\n",
    "\n",
    "    return indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High coverage regions in chrY:  [(18485047, 18485210), (27170225, 27170466)]\n"
     ]
    }
   ],
   "source": [
    "with open(\"Coverage_Plot_human/human_pileup_chrY.tab\") as f:\n",
    "    region = high_coverage_region(f)\n",
    "    print(\"High coverage regions in chrY: \", region)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}