HINGE_pipeline_NCTC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set variables and Import libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please provide the absolute path to your HINGE installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_to_hinge = '~/HINGE-analyses/HINGE/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The required packages are listed in requirements.txt.\n",
    "The following imports verify that these requirements are properly installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cython\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import random \n",
    "import forceatlas2 \n",
    "import ujson\n",
    "import Bio\n",
    "import colormap\n",
    "import easydev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('mkdir -p data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choose NCTC datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NCTC datasets to be downloaded and assembled are in NCTC_names.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NCTC9657\r\n",
      "NCTC9964\r\n",
      "NCTC11022\r\n"
     ]
    }
   ],
   "source": [
    "!head NCTC_names.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Download the relevant files\n",
    "\n",
    "We use [ascp](http://downloads.asperasoft.com/en/downloads/50) and [Aspera Connect](http://downloads.asperasoft.com/en/downloads/8?list) to download. Once installed on a Linux machine, the default path to the aspera license is `~/.aspera/connect/etc/asperaweb_id_dsa.openssh`.\n",
    "Other ways to download the relevant files from the accession numbers in the paper can be found [here](https://www.ebi.ac.uk/ena/browse/read-download). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_to_aspera_licence = '~/.aspera/connect/etc/asperaweb_id_dsa.openssh'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_cmd = 'python download_wrapper.py '+ path_to_aspera_licence\n",
    "os.system(download_cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtain the overlaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use DAligner in order to find the read overlaps in the NCTC datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_cmd = 'python overlap_wrapper.py'\n",
    "os.system(overlap_cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download NCTC assemblies and extract fastas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We download the current NCTC assemblies (based on HGAP) and convert them into fasta files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assembly_download_cmd = 'python download_assemblies.py'\n",
    "assembly_extract_cmd = 'python get_hgap_fastas.py'\n",
    "os.system(assembly_download_cmd)\n",
    "os.system(assembly_extract_cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_cmd = 'python map_wrapper.py '+path_to_hinge\n",
    "os.system(map_cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Layout and overlay NCTC assembly colours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we use HINGE to assemble the NCTC datasets. In addition, we map the reads in the resulting assembly graph to the NCTC contigs for the coloring of the nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout_cmd = 'python pipeline_layout_wrapper.py '+path_to_hinge\n",
    "os.system(layout_cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate sequence consensus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we create a draft assembly of each of the contigs in the HINGE assembly graph, and then bring back all the original reads, align them to the draft contigs, and use them to produce a consensus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consensus_cmd = 'python pipeline_consensus_wrapper.py '+path_to_hinge\n",
    "os.system(consensus_cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw NCTC9657"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we use ForceAtlas2 to generate a visually interpretable graph. This step may take ~10 minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "H9657 = nx.read_graphml('data/NCTC9657/NCTC9657A.G2c.graphml')\n",
    "G9657 = H9657.to_undirected()\n",
    "pos = { i : (random.random(), random.random()) for i in G9657.nodes()} \n",
    "l9657 = forceatlas2.forceatlas2_networkx_layout(G9657, pos, niter=10000) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use the information from the alignment to the hgap (NCTC pipeline) contigs to color the nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "max_chr = 1\n",
    "for x in H9657.nodes():\n",
    "    try:\n",
    "        max_chr = max(max_chr,H9657.node[x.lstrip('B')]['chr'])\n",
    "    except:\n",
    "        pass\n",
    "chr_lengths = [0]*max_chr\n",
    "for x in H9657.nodes():\n",
    "        try:\n",
    "            chr_num = H9657.node[x.lstrip('B')]['chr']\n",
    "            chr_lengths[chr_num-1] = max(chr_lengths[chr_num-1],H9657.node[x.lstrip('B')]['aln_end'])\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "color_map9657 = {}\n",
    "for x in H9657.nodes():\n",
    "    try:\n",
    "        chr_num = H9657.node[x.lstrip('B')]['chr']\n",
    "        color_map9657[x] = sum(chr_lengths[0:chr_num-1]) + (chr_num-1)*600000 + H9657.node[x.lstrip('B')]['aln_end']\n",
    "        \n",
    "    except:\n",
    "        color_map9657[x] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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IDIZ/sJaYGyZy9tPXYrH03UQJsy5cR0FhLJgOLl3o5o1np/VZ30oNdgNhmEsNYJn/cRqZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0514986190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_max = max(color_map9657.values())\n",
    "values9657 = [color_max - color_map9657.get(node, 1) for node in G9657.nodes()]\n",
    "nx.draw_networkx(G9657, l9657, with_labels=False, node_color=values9657,cmap = 'hsv',vmin = 0,vmax = color_max, node_size=8,linewidths=0.1) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw NCTC9964"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "H9964 = nx.read_graphml('data/NCTC9964/NCTC9964A.G2c.graphml')\n",
    "G9964 = H9964.to_undirected()\n",
    "pos = { i : (random.random(), random.random()) for i in G9964.nodes()} \n",
    "l9964 = forceatlas2.forceatlas2_networkx_layout(G9964, pos, niter=10000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_chr = 1\n",
    "for x in H9964.nodes():\n",
    "    try:\n",
    "        max_chr = max(max_chr,H9964.node[x.lstrip('B')]['chr'])\n",
    "    except:\n",
    "        pass\n",
    "chr_lengths = [0]*max_chr\n",
    "for x in H9964.nodes():\n",
    "        try:\n",
    "            chr_num = H9964.node[x.lstrip('B')]['chr']\n",
    "            chr_lengths[chr_num-1] = max(chr_lengths[chr_num-1],H9964.node[x.lstrip('B')]['aln_end'])\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "color_map9964 = {}\n",
    "for x in H9964.nodes():\n",
    "    try:\n",
    "        chr_num = H9964.node[x.lstrip('B')]['chr']\n",
    "        color_map9964[x] = sum(chr_lengths[0:chr_num-1]) + (chr_num-1)*600000 + H9964.node[x.lstrip('B')]['aln_end']\n",
    "        \n",
    "    except:\n",
    "        color_map9964[x] = 5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1weE6M3gbOON7bQ8BG6SUCcBG4GEAIUQSsBiYDJwFvCr+u6zQa8DNUsp4IF4I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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f050d32dc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_max = max(color_map9964.values())\n",
    "values9964 = [color_max - color_map9964.get(node, 1) for node in G9964.nodes()]\n",
    "nx.draw_networkx(G9964, l9964, with_labels=False, node_color=values9964,cmap = 'hsv',vmin = 0,vmax = color_max, node_size=8,linewidths=0.1) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw NCTC11022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "H11022 = nx.read_graphml('data/NCTC11022/NCTC11022A.G2c.graphml')\n",
    "G11022 = H11022.to_undirected()\n",
    "pos = { i : (random.random(), random.random()) for i in G11022.nodes()} \n",
    "l11022 = forceatlas2.forceatlas2_networkx_layout(G11022, pos, niter=10000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_chr = 1\n",
    "for x in H11022.nodes():\n",
    "    try:\n",
    "        max_chr = max(max_chr,H11022.node[x.lstrip('B')]['chr'])\n",
    "    except:\n",
    "        pass\n",
    "chr_lengths = [0]*max_chr\n",
    "for x in H11022.nodes():\n",
    "        try:\n",
    "            chr_num = H11022.node[x.lstrip('B')]['chr']\n",
    "            chr_lengths[chr_num-1] = max(chr_lengths[chr_num-1],H11022.node[x.lstrip('B')]['aln_end'])\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "color_map11022 = {}\n",
    "for x in H11022.nodes():\n",
    "    try:\n",
    "        chr_num = H11022.node[x.lstrip('B')]['chr']\n",
    "        color_map11022[x] = sum(chr_lengths[0:chr_num-1]) + (chr_num-1)*600000 + H11022.node[x.lstrip('B')]['aln_end']\n",
    "        \n",
    "    except:\n",
    "        color_map11022[x] = 5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NAgRMy6YlVV15KMEx0NOMX4LdDBmJFq3jKGFMFY8QJYTg6AKLg1I9Za4Eh7/p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\n93+lyXHbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0517e21050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_max = max(color_map11022.values())\n",
    "values11022 = [color_max - color_map11022.get(node, 1) for node in G11022.nodes()]\n",
    "nx.draw_networkx(G11022, l11022, with_labels=False, node_color=values11022,cmap = 'hsv',vmin = 0,vmax = color_max, node_size=8,linewidths=0.1) \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}