lexnederbragt/PacBio_longest_subread_plots.ipynb

## PacBio_longest_subread_plots.ipynb
{
 "metadata": {
  "name": "PacBio_longest_subread_plots"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Code belonging to the blog post 'Longing for the longest reads: PacBio and BluePippin'  \n",
      "Link: http://flxlexblog.wordpress.com/2013/06/19/longing-for-the-longest-reads-pacbio-and-bluepippin  \n",
      "Download the raw notebook here: https://gist.github.com/lexnederbragt/5814241\n",
      "\n",
      "Released under CC BY-SA 2.0\n",
      "\n",
      "Lex Nederbragt, June 2013"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter\n",
      "from matplotlib import pylab as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following cell sets up the project, and allows for using the notebook on different datasets  \n",
      "Input files are filtered subreads.  These are provided by default with software version 2.0 and higher.  \n",
      "For older version, reads need to be filtered and split into subreads."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataset_name = 'some_name'\n",
      "dataset_outfolder = 'path/to/some/folder'\n",
      "\n",
      "# older software versions\n",
      "files1 = !find /path/to/rundata -name \"*filtered_subreads.fasta\"\n",
      "# 2.0 and up\n",
      "files2 = !find /path/to/rundata -name \"*subreads.fasta\"\n",
      "\n",
      "inputs = []\n",
      "inputs.append([\"Dataset name 1\", files1])\n",
      "inputs.append([\"Dataset name 2\", files2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def N50(numlist):\n",
      "    \"\"\"\n",
      "Abstract: Returns the N50 value of the passed list of numbers.\n",
      "Usage: N50(numlist)\n",
      "\n",
      "Based on the definition from this SEQanswers post\n",
      "http://seqanswers.com/forums/showpost.php?p=7496&postcount=4\n",
      "(modified Broad Institute's definition\n",
      "https://www.broad.harvard.edu/crd/wiki/index.php/N50)\n",
      "\n",
      "See SEQanswers threads for details:\n",
      "http://seqanswers.com/forums/showthread.php?t=2857\n",
      "http://seqanswers.com/forums/showthread.php?t=2332\n",
      "\"\"\"\n",
      "    numlist.sort(reverse = True)\n",
      "    s = sum(numlist)\n",
      "    limit = s * 0.5\n",
      "    for l in numlist:\n",
      "        s -= l\n",
      "        if s <= limit:\n",
      "            return l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_data(files, max_reads):\n",
      "    \"\"\"Parses the subreads fasta file and collects the length of the longest subread for each ZMW ('read of insert')\"\"\"\n",
      "    lengths = {}\n",
      "    i = 0\n",
      "    for fname in files:\n",
      "        with open(fname, 'r') as fh:\n",
      "            for line in fh:\n",
      "                line = line.strip().split()[0]\n",
      "                if line.startswith('>'):\n",
      "                    i += 1\n",
      "                    if i > max_reads:\n",
      "                        break\n",
      "                    [name, zmw, coord] = line[1:].split('/')\n",
      "                    start, stop = coord.split('_')\n",
      "                    read_len = int(stop) - int(start)\n",
      "                    read_name = name + '_' + zmw\n",
      "                    # add length if no entry yet, otherwise use longest length\n",
      "                    # 'lengths.get(read_name,0)' returns either the previously recorded length for this read, or 0\n",
      "                    lengths[read_name] = max(lengths.get(read_name,0), read_len)\n",
      "    return lengths.values()          "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def rel_cumsum(lengths):\n",
      "    \"\"\"Determines the lengths of the reads sorted from short to long,\n",
      "       and the fracttion of the cumualtive sum of reads at that length.\n",
      "       Needed for plotting \"\"\"\n",
      "    c = Counter(lengths)\n",
      "    total = sum(lengths)\n",
      "    csum = 0\n",
      "    cum_len = []\n",
      "    sort_len = []\n",
      "    for k in sorted(c.iterkeys()):\n",
      "        csum = csum + k * c[k]\n",
      "        cum_len.append((total_len - csum)/float(total_len))\n",
      "        sort_len.append(k)\n",
      "    return sort_len, cum_len"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_reads =  1E30 #allows for loading a subset by using a smaller number\n",
      "all_lengths = {}\n",
      "for indata in inputs:\n",
      "    libname, fnames = indata\n",
      "    print libname\n",
      "    lengths = load_data(fnames, max_reads)\n",
      "    all_lengths[libname] = lengths"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Metrics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N50s = {}\n",
      "print \"Library\\t# of reads\\tAve. length\\tLongest subread N50\\tLongest\"\n",
      "for indata in inputs:\n",
      "    libname, fname = indata\n",
      "    lib_N50 = N50(all_lengths[libname])\n",
      "    N50s[libname] = lib_N50\n",
      "    print \"%s\\t%i\\t%i\\t%i\\t%i\" %(libname, \n",
      "                             len(all_lengths[libname]), \n",
      "                             sum(all_lengths[libname])/len(all_lengths[libname]), \n",
      "                             lib_N50,\n",
      "                             max(all_lengths[libname]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Library\t# of reads\tAve. length\tLongest subread N50\tLongest\n",
        "Regular library\t693275\t3009\t4041\t22298"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Blue Pippin library\t325407\t6045\t8820\t25931"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting length distributions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_points = -1 # '-1' for all\n",
      "for indata in inputs:\n",
      "    libname, fname = indata\n",
      "    y,binEdges=np.histogram(all_lengths[libname][0:num_points],bins=100)\n",
      "    bincenters = 0.5*(binEdges[1:]+binEdges[:-1])\n",
      "    plt.plot(bincenters,y,'-', label = libname)\n",
      "legend()\n",
      "plt.xlim(0,20000)\n",
      "plt.xlabel('Longest subread length')\n",
      "plt.ylabel('Count')\n",
      "plt.savefig(dataset_outfolder + '/' + dataset_name + '_length_dist.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kgwYNolu3bkRGRrJmzRoA5s+fj7u7O4GBgQQGBvL9998bjlm8eDFdunQhICCA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\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting fractional distribution:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for indata in inputs:\n",
      "    libname, fname = indata\n",
      "    total_len = sum(all_lengths[libname])\n",
      "    x, y = rel_cumsum(all_lengths[libname])\n",
      "    plt.plot(x, [100*i for i in y], label = libname)\n",
      "    plt.plot([N50s[libname],N50s[libname]],[0,50], ls='dashed', color = '0.75')\n",
      "    plt.plot([0,N50s[libname]],[50,50], ls='dashed', color = '0.75')\n",
      "legend()\n",
      "plt.xlim(0,20000)\n",
      "plt.title('Fraction of bases at different length cutoffs')\n",
      "plt.xlabel('Minimum length')\n",
      "plt.ylabel('Percentage of total bases')\n",
      "plt.savefig(dataset_outfolder + '/' + dataset_name + '_cumul_length_dist.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ywc0NzM3V0+AqqWC+gnh38qazbWcclzly6fElZQNJOVbSKW2PHz/O3bt3+eOP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\nmTdvHmvXrr0zWDkDsWjl5fD889q9kS+/hA4d9I5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      }
     ],
     "prompt_number": 29
    }
   ],
   "metadata": {}
  }
 ]
}