{
 "metadata": {
  "name": "kmer-abundance"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Remember to go to 'File' and 'Make a copy'\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cd /mnt",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "/mnt\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Change the name below to something unique to you"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "NetID='titus'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mkdir -p $NetID",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cd $NetID",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "/mnt/titus\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You should now be in '/mnt/USERNAME', where USERNAME is *not* CHANGEME"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Make two random genomes with a 10:1 abundance"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# configure some stuff...\nimport sys\nsys.path.insert(0, '/usr/local/share/khmer/python')\nimport os\nos.environ['PYTHONPATH'] = '/usr/local/share/khmer/python'\nimport khmer",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import random\nrandom.seed(1)\n\nx = [\"A\"] + [\"G\"] + [\"C\"] + [\"T\"]\nx = x*1000\nrandom.shuffle(x)\nx = \"\".join(x)\n\ny = [\"A\"] + [\"G\"] + [\"C\"] + [\"T\"]\ny = y*1000\nrandom.shuffle(y)\ny = \"\".join(y)\n\nprint 'x is', x[:100]\nprint 'y is', y[:100]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "x is CAGACTTGGAAGCTGAGAGTCCGACGTCACTGCCTCAACTCGCGCAAATGTTCCCGCCAAATTGTATCCTAGGGATCTTCCATAAGCTTATATACGGGGG\ny is ATTAGACCACATGTTGCTAGCTATTCGGAGGAAGGCAGTTTAGCTAAATATGTGAGGGCTGTGGGACGAATATCGGGGGATGACGGTCCCATAGCTAGTT\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "outfp = open('metagenome.fa', 'w')\nprint >>outfp, \">x 1\"\nprint >>outfp, x\nprint >>outfp, \">y 2\"\nprint >>outfp, y\noutfp.close()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!python /usr/local/share/2012-paper-diginorm/pipeline/make-biased-reads.py metagenome.fa | head -100000 > reads.fa",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Traceback (most recent call last):\r\n  File \"/usr/local/share/2012-paper-diginorm/pipeline/make-biased-reads.py\", line 56, in <module>\r\n    print '>read%d\\n%s' % (i, read)\r\nIOError: [Errno 32] Broken pipe\r\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "(Yes, you should see an error.)\n\n## Now, calculate a k-mer abundance histogram for k=20"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!/usr/local/share/khmer/scripts/abundance-dist-single.py -k 20 -x 1e8 reads.fa reads.hist",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\r\nPARAMETERS:\r\n - kmer size =    20 \t\t(-k)\r\n - n hashes =     4 \t\t(-N)"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\r\n - min hashsize = 1e+08 \t(-x)\r\n\r\nEstimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n--------\r\nmaking hashtable\r\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "building tracking ht\r\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "K: 20\r\nHT sizes: [100000007, 100000037, 100000039, 100000049]\r\noutputting to reads.hist\r\nconsuming input, round 1 -- reads.fa\r\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "preparing hist from reads.fa...\r\nconsuming input, round 2 -- reads.fa\r\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "histdata = numpy.loadtxt('reads.hist')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(histdata[:,0], histdata[:,1])\nxlabel(\"k-mer abundance\")\nylabel(\"N of k-mers with that abundance\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "<matplotlib.text.Text at 0x7fdfac1fdc10>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEKCAYAAADEovgeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x2ad88d0>"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(histdata[:,0], histdata[:,1])\naxis(ymax=500)\nxlabel(\"k-mer abundance\")\nylabel(\"N of k-mers with that abundance\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "<matplotlib.text.Text at 0x7fdfac1f3450>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEKCAYAAADn+anLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBGRSUwSRERkEpMEERGZ9P8IWSVRRUoYKQAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7fdfac21bfd0>"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(histdata[:,0], histdata[:,1])\naxis(xmax=10)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "(0.0, 10, 0.0, 160000.0)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAD9CAYAAABN7FvjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbZKwEEII0SYJCyGEEG36/5eIXTUiDQZSAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x2acad10>"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Questions to answer\n\nHow do the 3 peaks (1, 100, 800) shift relative to each other with (a) changing coverage and (b) different k values?\n\nThe '-k' parameter is set up above in the abundance-dist-single command; the coverage is set by how many sequences you keep ('head -10000') in the make-biased-reads.py command."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}