Figure_1A_GORG_ANI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "import os.path as op\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set working directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wd = '/mnt/scgc/simon/simonsproject/ani/GORG50_group_190114/'\n",
    "os.chdir(wd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created file called 'contiglist.txt' containing paths to all GORG genomes > 50% complete.\n",
    "\n",
    "fastANI command:\n",
    "\n",
    "```\n",
    "fastANI --rl contiglist.txt --ql contiglist.txt -t 50 -o GORG50_group_190114_fastani_out.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now will remove redundancy in fastANI output comparisons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infile = \"GORG50_group_190114_fastani_out.txt\"\n",
    "\n",
    "names=['c1','c2','ani','frag_mappings','total_frags']\n",
    "\n",
    "df = pd.read_csv(infile, sep=\"\\t\", names=names)\n",
    "\n",
    "# columns with SAG names instead of file paths\n",
    "df['genome1'] = [op.basename(i).split(\"_\")[0] for i in df['c1']]\n",
    "df['genome2'] = [op.basename(i).split(\"_\")[0] for i in df['c2']]\n",
    "\n",
    "pairs = []\n",
    "\n",
    "for i, l in df.iterrows():\n",
    "    genome_pair_string = \"_\".join(sorted([l['genome1'], l['genome2']]))\n",
    "    pairs.append(genome_pair_string)\n",
    "    \n",
    "df['pair'] = pairs\n",
    "df = df[df['genome1'] != df['genome2']]\n",
    "dedupdf = df.sort_values(by='ani', ascending=False).drop_duplicates(subset='pair', keep='first')\n",
    "dedupdf.to_csv(\"GORG50_group_190114_fastani_out_dedup.txt\", index=False, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot ANI distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ebra2tlAoFCaIiNoCJhwi0lFeXoRdu6xgd98s1aWlhViwAOjSpYvhFYnqwYRDRHpZWFhr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab0d8f27f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "ax = sns.distplot(dedupdf['ani'], kde=False, bins=100, color = \"blue\")\n",
    "ax.set_title(\"ANI Distribution\")\n",
    "ax.set_xlabel(\"Pairwise Average Nucleotide Identity\")\n",
    "ax.set_ylabel(\"# Pairs\")\n",
    "\n",
    "plt.xlim(85, 100)\n",
    "plt.ylim(0,10000)\n",
    "plt.savefig('GORG50_ani_85_to_100.svg')\n",
    "plt.savefig('GORG50_ani_85_to_100.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RqKiogI+Pj9QDb8qUKXjzzTfh5+cHJycnbNiwAQDg6OiIBQsWYPLkyVAoFFi4cGGTHSnu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\niIiIZPf/AIljqiwi0YyxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab0d8df208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dedupdf['ani'], kde=False, bins=100, color=\"blue\")\n",
    "ax.set_title(\"ANI Distribution\")\n",
    "ax.set_xlabel(\"Pairwise Average Nucleotide Identity\")\n",
    "ax.set_ylabel(\"# Pairs\")\n",
    "\n",
    "plt.savefig('GORG50_ani.svg')\n",
    "plt.savefig('GORG50_ani.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}