(I) Gene Regulator Regulated Schematic Figs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook was created to construct a schematic figure of the regulator/regaulated regions interrogated for this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from dna_features_viewer import GraphicFeature, GraphicRecord\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from dna_features_viewer import GraphicFeature, GraphicRecord, CircularGraphicRecord, BiopythonTranslator\n",
    "from Bio import SeqIO\n",
    "\n",
    "#for exporting to Adobe Illustrator\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import and setup DNA Features Viewer (For plotting H37rv genome annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BiopythonTranslator.label_fields = ['gene', 'label', 'source', 'locus_tag', 'note', 'gene', 'product']\n",
    "BiopythonTranslator.ignored_features_types = [\"source\", \"gene\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "H37rv_Ref_GBK_PATH = \"/n/data1/hms/dbmi/farhat/mm774/References/GCF_000195955.2_ASM19595v2_genomic.gbk\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = list(SeqIO.parse(H37rv_Ref_GBK_PATH, \"genbank\"))\n",
    "len(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mtb_H37rv_SeqIO_Record = records[0]\n",
    "Mtb_H37rv_Graphic_Record = BiopythonTranslator().translate_record(Mtb_H37rv_SeqIO_Record)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use DNA Features Viewer's plotting function to plot loci we're interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 1, sharex=False)\n",
    "\n",
    "#ahpC promoter/ahpC\n",
    "Start_Coord = 2726088\n",
    "End_Coord = 2726780 + 5\n",
    "Mtb_H37rv_Graphic_Record_cropped = Mtb_H37rv_Graphic_Record.crop((Start_Coord, End_Coord + 1))\n",
    "Mtb_H37rv_Graphic_Record_cropped.plot(strand_in_label_threshold=5, ax = axs[0], plot_sequence = False)\n",
    "axs[0].set_xticks([2726088,   2726101,2726146,   2726193, 2726780])\n",
    "axs[0].tick_params(labelcolor = 'k', rotation=45)\n",
    "axs[0].tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "#mmpL5/mmpS5/mmpR\n",
    "Start_Coord = 775586 - 11\n",
    "End_Coord = 779487\n",
    "Mtb_H37rv_Graphic_Record_cropped = Mtb_H37rv_Graphic_Record.crop((Start_Coord, End_Coord + 1))\n",
    "Mtb_H37rv_Graphic_Record_cropped.plot(strand_in_label_threshold=5, ax = axs[1], plot_sequence = False)\n",
    "axs[1].set_xticks([775586, 778480, 778990, 779487])\n",
    "axs[1].tick_params(labelcolor = 'k', rotation=45)\n",
    "axs[1].tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "#eis promoter/eis\n",
    "Start_Coord = 2714124 - 10\n",
    "End_Coord = 2715383\n",
    "Mtb_H37rv_Graphic_Record_cropped = Mtb_H37rv_Graphic_Record.crop((Start_Coord, End_Coord + 1))\n",
    "Mtb_H37rv_Graphic_Record_cropped.plot(strand_in_label_threshold=5, ax = axs[2], plot_sequence = False)\n",
    "axs[2].set_xticks([2714124, 2715332,   2715338,2715374,   2715383])\n",
    "axs[2].tick_params(labelcolor = 'k', rotation=45)\n",
    "axs[2].tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "#whiB7 promoter/whiB7\n",
    "Start_Coord = 3568401 - 8\n",
    "End_Coord = 3569082\n",
    "Mtb_H37rv_Graphic_Record_cropped = Mtb_H37rv_Graphic_Record.crop((Start_Coord, End_Coord + 1))\n",
    "Mtb_H37rv_Graphic_Record_cropped.plot(strand_in_label_threshold=5, ax = axs[3], plot_sequence = False)\n",
    "axs[3].set_xticks([3568401, 3568679, 3569082])\n",
    "axs[3].tick_params(labelcolor = 'k', rotation=45)\n",
    "axs[3].tick_params(axis='both', which='major', labelsize=5)\n",
    "\n",
    "fig.text(0.5, -0.015, 'H37rv Ref Position', ha='center', fontsize=12)\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(6.0, 10.0)\n",
    "fig.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/figures/Genomic_Regions_Interrogated_Schematic.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:DNA_Features_Viewer_virtualenv]",
   "language": "python",
   "name": "conda-env-DNA_Features_Viewer_virtualenv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}