{
 "metadata": {
  "celltoolbar": "Slideshow",
  "name": "",
  "signature": "sha256:846002efa317708cd8c8b3b3413ad15490655a94d48b418b5af431b3a21b5081"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Example of reproducible analysis pipeline"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "We are going to reproduce a couple of figures (2A and 8A) of the following paper:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import IFrame\n",
      "url = \"http://www.ncbi.nlm.nih.gov/pubmed/22219510\"\n",
      "IFrame(url, 800, 400)\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"800\"\n",
        "            height=400\"\n",
        "            src=\"http://www.ncbi.nlm.nih.gov/pubmed/22219510\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.lib.display.IFrame at 0x110b26c10>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "I select this paper because:  \n",
      "* The article is open access  \n",
      "* The reference organism is yeast (12Mb)  \n",
      "* The data are freely available  \n",
      "* I know it very well...  "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Required software:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "* [SRA](http://www.ncbi.nlm.nih.gov/Traces/sra/?view=software): The Sequence Read Archive (SRA) stores raw sequencing data from the next generation of sequencing platforms including Roche 454 GS System\u00ae, Illumina Genome Analyzer\u00ae, Applied Biosystems SOLiD\u00ae System, Helicos Heliscope\u00ae, Complete Genomics\u00ae, and Pacific Biosciences SMRT\u00ae. **In the analysis version: 2.3.5-2 was used.**\n",
      "\n",
      "\n",
      "* [Bowtie](http://bowtie-bio.sourceforge.net/index.shtml): Bowtie is an ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: typically about 2.2 GB for the human genome (2.9 GB for paired-end). **In the analysis version 1.0.1 was used.**"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Required Python libraries:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "This is extracted from the command `pip freeze`. On the right of each library there is the version I have been using in the analysis:  \n",
      "* ipython==2.0.0  \n",
      "* numpy==1.8.1  \n",
      "* matplotlib==1.3.1  \n",
      "* pandas==0.13.1  \n",
      "* Cython==0.20.1  \n",
      "* weblogo==3.3  \n",
      "* patsy==0.2.1  \n",
      "* scipy==0.13.3  "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Import modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pandas as pd\n",
      "import tarfile\n",
      "import urllib\n",
      "import os\n",
      "import weblogolib\n",
      "import random"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Define the path of the required software"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SRA = \"bin/fastq-dump\"\n",
      "BOWTIE = \"bin\""
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "PIPELINE"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Download the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Download one of the set of raw reads of the paper\n",
      "sra_url = \"ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/\" + \\\n",
      "            \"SRP%2FSRP009%2FSRP009462/SRR384975/SRR384975.sra\"\n",
      "sra_file = \"reads/SRR384975.sra\"\n",
      "# Download the yeast genomic sequences. The release used in this analysis is: R64-1-1\n",
      "yeast_url = \"http://downloads.yeastgenome.org/sequence/S288C_reference/\" + \\\n",
      "            \"genome_releases/S288C_reference_genome_Current_Release.tgz\"\n",
      "yeast_file = \"data/yeast_genome.tgz\"\n",
      "genome_file = \"data/yeast_genome.fa\"\n",
      "# Download the yeast genomic annotations\n",
      "features_url = \"http://downloads.yeastgenome.org/curation/chromosomal_feature/SGD_features.tab\"\n",
      "features_file = \"data/yeast_features.tsv\""
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create the directories \"reads\" and \"data\"\n",
      "folders = [\"data\", \"reads\", \"index\", \"sequences\", \"figures\"]\n",
      "[os.mkdir(folder) for folder in folders if not os.path.isdir(folder)]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[None, None, None, None, None]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit -n 1 -r 1\n",
      "# Download the files and save it into the local directory\n",
      "urllib.urlretrieve(sra_url, sra_file)\n",
      "urllib.urlretrieve(yeast_url, yeast_file)\n",
      "urllib.urlretrieve(features_url, features_file)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 13min 50s per loop\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "analysis.ipynb \u001b[1m\u001b[34mdata\u001b[m\u001b[m           \u001b[1m\u001b[34mindex\u001b[m\u001b[m          \u001b[1m\u001b[34msequences\u001b[m\u001b[m\r\n",
        "\u001b[1m\u001b[34mbin\u001b[m\u001b[m            \u001b[1m\u001b[34mfigures\u001b[m\u001b[m        \u001b[1m\u001b[34mreads\u001b[m\u001b[m\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Extract the SRA file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!time $SRA $sra_file --outdir reads"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Read 7990112 spots for reads/SRR384975.sra\r\n",
        "Written 7990112 spots for reads/SRR384975.sra\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "real\t0m53.784s\r\n",
        "user\t0m51.446s\r\n",
        "sys\t0m1.265s\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Extract the sequence of the yeast genome"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tar = tarfile.open(yeast_file)\n",
      "tar.getnames()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "['S288C_reference_genome_R64-1-1_20110203',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/S288C_reference_sequence_R64-1-1_20110203.fsa',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/gene_association_R64-1-1_20110205.sgd',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/saccharomyces_cerevisiae_R64-1-1_20110208.gff',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/other_features_genomic_R64-1-1_20110203.fasta',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/rna_coding_R64-1-1_20110203.fasta',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/NotFeature_R64-1-1_20110203.fasta',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/orf_trans_all_R64-1-1_20110203.fasta',\n",
        " 'S288C_reference_genome_R64-1-1_20110203/orf_coding_all_R64-1-1_20110203.fasta']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "The right file to extract is **S288C_reference_sequence_R64-1-1_20110203.fsa**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract the file\n",
      "tar.extract('S288C_reference_genome_R64-1-1_20110203/S288C_reference_sequence_R64-1-1_20110203.fsa',\n",
      "            'data')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls data/"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[1m\u001b[34mS288C_reference_genome_R64-1-1_20110203\u001b[m\u001b[m yeast_genome.tgz\r\n",
        "yeast_features.tsv\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rename the reference_sequence file\n",
      "!mv data/S288C_reference_genome_R64-1-1_20110203/S288C_reference_sequence_R64-1-1_20110203.fsa $genome_file\n",
      "!rm -r data/S288C_reference_genome_R64-1-1_20110203/\n",
      "!rm $yeast_file"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls data/"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "yeast_features.tsv yeast_genome.fa\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Build a bowtie index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index_name = \"index/yeast_genome\"\n",
      "!$BOWTIE/bowtie-build -q $genome_file $index_name"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Trim and align the reads to the yeast genome"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "The reads should start with the `Ty1 LTR` (Long Terminal Repeat), so I check for its presence. I also get rid of reads with _undefined (N)_ nucleotides. In the alignment step, in order to align only the genomic sequence downstream the insertion site, I trim the first 10bp."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reads should start with the Ty1 LTR (\"TATT\"). \n",
      "# I also discard those reads that have ambiguous nucleotides.\n",
      "fastq_file = os.path.splitext(sra_file)[0] + \".fastq\"\n",
      "trimmed_file = os.path.splitext(sra_file)[0] + \".trimmed\"\n",
      "\n",
      "LTR = \"TATT\"\n",
      "MAX_MISMATCHES = 0\n",
      "\n",
      "output = file(trimmed_file, 'w')\n",
      "\n",
      "class ReadFastq(object):\n",
      "    def __init__(self, line):\n",
      "        self.lines = [line]\n",
      "        self.output = \"\"\n",
      "    \n",
      "    def add_line(self, line):\n",
      "        self.lines.append(line)        \n",
      "        \n",
      "    def get_output(self):\n",
      "        if self.lines[1].startswith(LTR) and self.lines[1].count(\"N\") <= MAX_MISMATCHES:\n",
      "            self.output = \"\".join(self.lines)\n",
      "            return True\n",
      "        else:\n",
      "            return False\n",
      "        \n",
      "for n, line in enumerate(file(fastq_file, 'r')):\n",
      "    if n % 4 == 0:\n",
      "        if n != 0 and obj.get_output():\n",
      "            output.write(obj.output)\n",
      "        obj = ReadFastq(line)\n",
      "    else:\n",
      "        obj.add_line(line)\n",
      "            \n",
      "output.close()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# I align the reads to the yeast genome with Bowtie\n",
      "# I trim the first 10bp of the reads and then I require \n",
      "# unique (-m 1) and perfect (-v 0) alignments.\n",
      "mapped_file = os.path.splitext(sra_file)[0] + \".mapped\"\n",
      "!$BOWTIE/bowtie -t $index_name -m 1 -v 0 -5 10 -p 2 -q $trimmed_file $mapped_file"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time loading forward index: 00:00:00\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time for 0-mismatch search: 00:00:29\r\n",
        "# reads processed: 7504175\r\n",
        "# reads with at least one reported alignment: 1187781 (15.83%)\r\n",
        "# reads that failed to align: 5885730 (78.43%)\r\n",
        "# reads with alignments suppressed due to -m: 430664 (5.74%)\r\n",
        "Reported 1187781 alignments to 1 output stream(s)\r\n",
        "Time searching: 00:00:29\r\n",
        "Overall time: 00:00:29\r\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Get rid of potential clonal reads"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_file = mapped_file + \".unique\"\n",
      "%timeit -n 1 -r 1 !cut -f2-5 $mapped_file | sort -u > $unique_file"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 47.5 s per loop\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head $unique_file"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+\tref|NC_001133|\t101400\tGCATTATCTATCATTATAAATTCTTTTATT\r\n",
        "+\tref|NC_001133|\t11050\tTAAAGTCACTGAGATATTAGAGGTTATAAA\r\n",
        "+\tref|NC_001133|\t11333\tAAACTCTATGTAAACACTTATTTTATTGTG\r\n",
        "+\tref|NC_001133|\t115808\tCCTCTCAGATACCACGATGCATAAGGCTCA\r\n",
        "+\tref|NC_001133|\t118483\tATATGTGTATATATACATAGGTTAGTATGT\r\n",
        "+\tref|NC_001133|\t118488\tTGTATATATACATAGGTTAGTATGTATAGC\r\n",
        "+\tref|NC_001133|\t125037\tAGGGTAGAAGCCATGAAAAAACTAGATACC\r\n",
        "+\tref|NC_001133|\t125957\tGATCTTACCTGCTGTGCAGAATTTGAGTAT\r\n",
        "+\tref|NC_001133|\t127779\tGATCTCAGTACTCGCATTCTAGCGTATGTT\r\n",
        "+\tref|NC_001133|\t1289\tATTTCTAGTTACAGTTACACAAAAAACTAT\r\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Extract the regions upstream and downstream the Ty1 insertion sites"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the yeast genome in memory\n",
      "dict_genome = {}\n",
      "sequence = \"\"\n",
      "for line in file(genome_file, 'r'):\n",
      "    if line.startswith(\">\"):\n",
      "        if sequence != \"\":\n",
      "            dict_genome[head] = sequence.upper()\n",
      "        head = line.split()[0][1:]\n",
      "        sequence = \"\"\n",
      "        continue\n",
      "    sequence += line.strip()\n",
      "if sequence != \"\":\n",
      "    dict_genome[head] = sequence.upper()\n",
      "    "
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define a class to read the alignment file\n",
      "class ReadAlignment(object):\n",
      "    def __init__(self, line):\n",
      "        self.line = line.strip().split(\"\\t\")\n",
      "        self.strand = self.line[0]\n",
      "        self.chromosome = self.line[1]\n",
      "        self.position = int(self.line[2])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define a function to extract the genomic sequences\n",
      "# upstream and downstream a given position.\n",
      "def reverse_complement(sequence):\n",
      "    d = {\"A\":\"T\", \"T\":\"A\", \"C\":\"G\", \"G\":\"C\", \"N\":\"N\"}\n",
      "    sequence = sequence[::-1]\n",
      "    return \"\".join([d[i] for i in sequence])\n",
      "    \n",
      "def get_sequences(input_file, output_file, window=10):\n",
      "    output = file(output_file, 'w')\n",
      "    for n, line in enumerate(file(input_file, 'r')):\n",
      "        obj = ReadAlignment(line)\n",
      "        if obj.strand == \"+\":\n",
      "            position = obj.position + 3\n",
      "        else:\n",
      "            position = obj.position - 3\n",
      "        sequence = dict_genome[obj.chromosome][position - window: position + window]\n",
      "        if obj.strand == \"-\":\n",
      "            sequence = reverse_complement(sequence)\n",
      "        output.write(\">sequence_{}\\n{}\\n\".format(n, sequence))\n",
      "    output.close()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract sequences of 10bp and 1Kb\n",
      "get_sequences(input_file=unique_file, output_file=\"sequences/fasta_10bp.fa\")\n",
      "get_sequences(input_file=unique_file, output_file=\"sequences/fasta_1kb.fa\", window=1000)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Seqlogos"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "First we will check the presence of the insertion site pattern. We will do a seqlogo of the flanking regions of the Ty1 insertion sites."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def do_seqlogo(input_fasta, output_seqlogo):\n",
      "    fin = open(input_fasta)\n",
      "    seqs = weblogolib.read_seq_data(fin) \n",
      "    data = weblogolib.LogoData.from_seqs(seqs)\n",
      "    options = weblogolib.LogoOptions()\n",
      "    options.yaxis_scale = 0.2\n",
      "    options.yaxis_tic_interval  = 0.2\n",
      "    format_logo = weblogolib.LogoFormat(data, options)\n",
      "    fout = open(output_seqlogo, 'w') \n",
      "    weblogolib.png_formatter(data, format_logo, fout)\n",
      "    fout.close()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create the seqlogos\n",
      "do_seqlogo(\"sequences/fasta_10bp.fa\", \"figures/fasta_10bp.png\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "<img src=\"example/figures/fasta_10bp.png\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "To verify that the above seqlogo is meaninful, we can compare it with a seqlogo obtained with 10000 random sequences."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Select 10000 random position in the yeast genome\n",
      "N = 10000\n",
      "genome_length = sum([len(i) for i in dict_genome.values()])\n",
      "sample = random.sample(xrange(1, genome_length + 1), N)\n",
      "\n",
      "# This function map a random number to a chromosome and position in the yeast genome\n",
      "def get_coordinates(number):\n",
      "    for chromosome, sequence in dict_genome.items():\n",
      "        if (number - len(sequence)) > 0:\n",
      "            number -= len(sequence)\n",
      "        # Do not consider position that are too close to the start/end of the chromosome\n",
      "        elif number < 10 or number > len(sequence) - 10:\n",
      "            continue\n",
      "        else:\n",
      "            return (chromosome, number)\n",
      "  \n",
      "# Open the output file\n",
      "random_file = \"reads/random.mapped\"\n",
      "output = file(random_file, \"w\")\n",
      "\n",
      "for n in sample:\n",
      "    chromosome, position = get_coordinates(n) \n",
      "    output.write(\"+\\t{}\\t{}\\n\".format(chromosome, position))\n",
      "    \n",
      "# Close the output file\n",
      "output.close()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract sequences of 10bp and 1Kb\n",
      "get_sequences(input_file=random_file, output_file=\"sequences/random_10bp.fa\")\n",
      "\n",
      "# Create the seqlogos\n",
      "do_seqlogo(\"sequences/random_10bp.fa\", \"figures/random_10bp.png\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "<img src=\"example/figures/random_10bp.png\">"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Ty1 insertions around tRNA"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Second, we will verify that the Ty1 retrotrnsposons have a strong preference for inserting upstream tRNA genes.  \n",
      "First of all lets look at the structure of the features file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a dataframe with the yeast features\n",
      "df = pd.read_csv(features_file, sep=\"\\t\", header=None)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(3)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "      <th>13</th>\n",
        "      <th>14</th>\n",
        "      <th>15</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> S000028864</td>\n",
        "      <td> telomeric_repeat</td>\n",
        "      <td>     NaN</td>\n",
        "      <td> TEL01L-TR</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> chromosome 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>  62</td>\n",
        "      <td>   1</td>\n",
        "      <td> C</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2003-09-09</td>\n",
        "      <td> 2003-09-09</td>\n",
        "      <td> Terminal telomeric repeats on the left arm of ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> S000002143</td>\n",
        "      <td>              ORF</td>\n",
        "      <td> Dubious</td>\n",
        "      <td>   YAL069W</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> chromosome 1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 335</td>\n",
        "      <td> 649</td>\n",
        "      <td> W</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1996-07-31</td>\n",
        "      <td> 1996-07-31</td>\n",
        "      <td> Dubious open reading frame; unlikely to encode...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> S000031098</td>\n",
        "      <td>              CDS</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      YAL069W</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 335</td>\n",
        "      <td> 649</td>\n",
        "      <td> W</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1996-07-31</td>\n",
        "      <td> 1996-07-31</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "           0                 1        2          3    4    5             6   \\\n",
        "0  S000028864  telomeric_repeat      NaN  TEL01L-TR  NaN  NaN  chromosome 1   \n",
        "1  S000002143               ORF  Dubious    YAL069W  NaN  NaN  chromosome 1   \n",
        "2  S000031098               CDS      NaN        NaN  NaN  NaN       YAL069W   \n",
        "\n",
        "    7  8    9    10 11  12          13          14  \\\n",
        "0  NaN  1   62    1  C NaN  2003-09-09  2003-09-09   \n",
        "1  NaN  1  335  649  W NaN  1996-07-31  1996-07-31   \n",
        "2  NaN  1  335  649  W NaN  1996-07-31  1996-07-31   \n",
        "\n",
        "                                                  15  \n",
        "0  Terminal telomeric repeats on the left arm of ...  \n",
        "1  Dubious open reading frame; unlikely to encode...  \n",
        "2                                                NaN  \n",
        "\n",
        "[3 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "The **feature** column is the second. Lets look at which features are in the file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.unique(df[1])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "array(['ARS', 'ARS consensus sequence', 'CDEI', 'CDEII', 'CDEIII', 'CDS',\n",
        "       'ORF', 'W_region', 'X_element_combinatorial_repeats',\n",
        "       'X_element_core_sequence', 'X_region', \"Y'_element\", 'Y_region',\n",
        "       'Z1_region', 'Z2_region', 'binding_site', 'centromere',\n",
        "       'external_transcribed_spacer_region', 'five_prime_UTR_intron',\n",
        "       'gene_cassette', 'insertion', 'internal_transcribed_spacer_region',\n",
        "       'intron', 'long_terminal_repeat', 'mating_locus', 'multigene locus',\n",
        "       'ncRNA', 'non_transcribed_region', 'noncoding_exon',\n",
        "       'not in systematic sequence of S288C', 'not physically mapped',\n",
        "       'plus_1_translational_frameshift', 'pseudogene', 'rRNA',\n",
        "       'repeat_region', 'retrotransposon', 'snRNA', 'snoRNA', 'tRNA',\n",
        "       'telomere', 'telomeric_repeat', 'transposable_element_gene'], \n",
        "      dtype='|S35')"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "We will extract the the rows that contain the **tRNA** feature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_trna = df[df[1] == \"tRNA\"]\n",
      "trna = pd.DataFrame({\"Chromosome\": df_trna[8], \"Start\": df_trna[9], \n",
      "                     \"End\": df_trna[10], \"Strand\": df_trna[11]})"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "The chromosomes in the features file are named from 1 to 17 but in the mapped file have different names. \n",
      "Here we create a dictionary that convert one format into the other."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict_chromosomes = {\"NC_001133\": \"1\", \"NC_001134\": \"2\", \"NC_001135\": \"3\", \n",
      "                    \"NC_001136\": \"4\", \"NC_001137\": \"5\", \"NC_001138\": \"6\", \n",
      "                    \"NC_001139\": \"7\", \"NC_001140\": \"8\", \"NC_001141\": \"9\", \n",
      "                    \"NC_001142\": \"10\", \"NC_001143\": \"11\", \"NC_001144\": \"12\", \n",
      "                    \"NC_001145\": \"13\", \"NC_001146\": \"14\", \"NC_001147\": \"15\", \n",
      "                    \"NC_001148\": \"16\", \"NC_001224\": \"17\"}"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Lets look at the first lines of the dataframe:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trna.head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Chromosome</th>\n",
        "      <th>End</th>\n",
        "      <th>Start</th>\n",
        "      <th>Strand</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>176</th>\n",
        "      <td>  1</td>\n",
        "      <td> 139254</td>\n",
        "      <td> 139152</td>\n",
        "      <td> W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>222</th>\n",
        "      <td>  1</td>\n",
        "      <td> 166339</td>\n",
        "      <td> 166267</td>\n",
        "      <td> W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>239</th>\n",
        "      <td>  1</td>\n",
        "      <td> 181254</td>\n",
        "      <td> 181141</td>\n",
        "      <td> W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>243</th>\n",
        "      <td>  1</td>\n",
        "      <td> 182522</td>\n",
        "      <td> 182603</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>376</th>\n",
        "      <td> 10</td>\n",
        "      <td>  59100</td>\n",
        "      <td>  59172</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "    Chromosome     End   Start Strand\n",
        "176          1  139254  139152      W\n",
        "222          1  166339  166267      W\n",
        "239          1  181254  181141      W\n",
        "243          1  182522  182603      C\n",
        "376         10   59100   59172      C\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "The strand column uses **W (Whatson)** and **C (Crick)** to indicate the positive and the negative\n",
      "strands. Lets use **+** and **-** instead."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trna.Strand = trna.Strand.apply(lambda x: \"+\" if x == \"W\" else \"-\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Look at the first lines of the dataframe\n",
      "trna.head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Chromosome</th>\n",
        "      <th>End</th>\n",
        "      <th>Start</th>\n",
        "      <th>Strand</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>176</th>\n",
        "      <td>  1</td>\n",
        "      <td> 139254</td>\n",
        "      <td> 139152</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>222</th>\n",
        "      <td>  1</td>\n",
        "      <td> 166339</td>\n",
        "      <td> 166267</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>239</th>\n",
        "      <td>  1</td>\n",
        "      <td> 181254</td>\n",
        "      <td> 181141</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>243</th>\n",
        "      <td>  1</td>\n",
        "      <td> 182522</td>\n",
        "      <td> 182603</td>\n",
        "      <td> -</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>376</th>\n",
        "      <td> 10</td>\n",
        "      <td>  59100</td>\n",
        "      <td>  59172</td>\n",
        "      <td> -</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "    Chromosome     End   Start Strand\n",
        "176          1  139254  139152      +\n",
        "222          1  166339  166267      +\n",
        "239          1  181254  181141      +\n",
        "243          1  182522  182603      -\n",
        "376         10   59100   59172      -\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Now we create a dataframe with the **Ty1** insertion positions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "insertions = []\n",
      "for line in file(unique_file, 'r'):\n",
      "    obj = ReadAlignment(line)\n",
      "    chromosome = dict_chromosomes[obj.chromosome.split(\"|\")[1]]\n",
      "    insertions.append([chromosome, obj.position, obj.strand])\n",
      "    \n",
      "# We create a dataframew ith the Ty1 insertion positions.\n",
      "insertions = pd.DataFrame(insertions, columns=[\"Chromosome\", \"Position\", \"Strand\"])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Look at the first lines of the dataframe\n",
      "insertions.head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Chromosome</th>\n",
        "      <th>Position</th>\n",
        "      <th>Strand</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 101400</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td>  11050</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td>  11333</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 115808</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 118483</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "  Chromosome  Position Strand\n",
        "0          1    101400      +\n",
        "1          1     11050      +\n",
        "2          1     11333      +\n",
        "3          1    115808      +\n",
        "4          1    118483      +\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "We calculate the distances of the insertions 1Kb upstream and downstream tRNA genes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_distances(feature, insertions, window=1000):\n",
      "    \n",
      "    feature_grouped = feature.groupby([\"Chromosome\"])\n",
      "    insertions_grouped = insertions.groupby([\"Chromosome\"])\n",
      "    insertions_chromosomes = np.unique(insertions.Chromosome)\n",
      "\n",
      "    distances_from_feature = []\n",
      "    for chromosome, feature_group in feature_grouped:\n",
      "        if chromosome not in insertions_chromosomes:\n",
      "            continue\n",
      "        insertions_group = insertions_grouped.get_group(chromosome)\n",
      "        for n, line in feature_group.iterrows():\n",
      "            start, end, strand = line[1:]\n",
      "            insertions_around = insertions_group[(insertions_group.Position >= start - window) &\n",
      "                                                 (insertions_group.Position <= start + window)]\n",
      "            if len(insertions_around) == 0:\n",
      "                continue\n",
      "            # Calculate the distance of the insertions from the feature\n",
      "            distances = insertions_around.Position.apply(lambda x: x - start)\n",
      "            # Correct for the strand of the feature\n",
      "            distances = distances.apply(lambda x: x * -1 if strand == \"-\" else x)\n",
      "            distances_from_feature.extend(distances)\n",
      "    return distances_from_feature"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "distances = calculate_distances(trna, insertions)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Represent with a histogram the distribution of the Ty1 insertions aroung tRNA TSS "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure = plt.figure(figsize=(10, 10))\n",
      "\n",
      "ax = plt.subplot2grid((1, 1), (0, 0))\n",
      "n, bins, patches = ax.hist(distances, bins=100, normed=1, alpha=0.6)\n",
      "ax.set_xlabel(\"Distance from tRNA TSS\", fontsize=14)\n",
      "ax.plot([0, 0], [0, ax.axis()[3]], 'k--', lw=2, alpha=0.5)\n",
      "\n",
      "# Fit polynomial of orders 20 using the Numpy \"polyfit\" function.\n",
      "xnew = np.linspace(min(bins), max(bins), 100)\n",
      "# 30th order polynomial fit\n",
      "fit = np.polyfit(xnew, n, 30)\n",
      "# Create a functions from the fit\n",
      "f = np.poly1d(fit)\n",
      "plt.plot(xnew, f(xnew),'-', color=\"red\", linewidth=3)\n",
      "plt.ylim(0, ax.get_ylim()[1])\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAJiCAYAAACVXfVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEJqAAraCCGEEEJqgP8DKUlUFOLi1I0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113734ed0>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "We got a peak of insertions just upstream the tRNA TSS, as expected. This is the position where \n",
      "the nucleosome is located.  \n",
      "Also notice that there are other two smaller peaks that overlap with the positions of other nucleosomes."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Now we can check if this happens with every TSS. So first we extract the rows of the features file\n",
      "that contain the **ORF** feature and are **not marked as Dubious**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_orf = df[(df[1] == \"ORF\") & (df[2] != \"Dubious\")]\n",
      "orf = pd.DataFrame({\"Chromosome\": df_orf[8], \"Start\": df_orf[9], \n",
      "                    \"End\": df_orf[10], \"Strand\": df_orf[11]})\n",
      "orf.Strand = orf.Strand.apply(lambda x: \"+\" if x == \"W\" else \"-\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Look at the first lines of the dataframe\n",
      "orf.head()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Chromosome</th>\n",
        "      <th>End</th>\n",
        "      <th>Start</th>\n",
        "      <th>Strand</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 1</td>\n",
        "      <td>  1807</td>\n",
        "      <td>  2169</td>\n",
        "      <td> -</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 1</td>\n",
        "      <td>  2707</td>\n",
        "      <td>  2480</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 1</td>\n",
        "      <td>  7235</td>\n",
        "      <td>  9016</td>\n",
        "      <td> -</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 1</td>\n",
        "      <td> 11565</td>\n",
        "      <td> 11951</td>\n",
        "      <td> -</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> 1</td>\n",
        "      <td> 12426</td>\n",
        "      <td> 12046</td>\n",
        "      <td> +</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "   Chromosome    End  Start Strand\n",
        "11          1   1807   2169      -\n",
        "13          1   2707   2480      +\n",
        "16          1   7235   9016      -\n",
        "20          1  11565  11951      -\n",
        "22          1  12426  12046      +\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "source": [
      "Calculate the distances of the insertions from the ORF TSS"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "distances = calculate_distances(orf, insertions)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Represent with a histogram the distribution of the Ty1 insertions aroung ORF TSS.  \n",
      "In this case we use the module **xkcd**. This library is useful when you want to show an expected \n",
      "result vs the observed result."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with plt.xkcd():\n",
      "    figure = plt.figure(figsize=(10, 10))\n",
      "\n",
      "    ax = plt.subplot2grid((1, 1), (0, 0))\n",
      "    n, bins, patches = ax.hist(distances, bins=100, normed=1, alpha=0.5)\n",
      "    ax.set_xlabel(\"Distance from ORF TSS\", fontsize=14)\n",
      "    ax.plot([0, 0], [0, ax.axis()[3]], 'k--', lw=2, alpha=0.5)\n",
      "    \n",
      "    # Fit polynomial of orders 20 using the Numpy \"polyfit\" function.\n",
      "    xnew = np.linspace(min(bins), max(bins), 100)\n",
      "    # 30th order polynomial fit\n",
      "    fit = np.polyfit(xnew, n, 30)\n",
      "    # Create a functions from the fit\n",
      "    f = np.poly1d(fit)\n",
      "    plt.plot(xnew, f(xnew),'-', color=\"red\", linewidth=3)\n",
      "    plt.ylim(0, ax.get_ylim()[1])\n",
      "\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAJhCAYAAADMh/3aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSLATQgghhPASfwDqi1ccgbLOtQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115b61610>"
       ]
      }
     ],
     "prompt_number": 83
    }
   ],
   "metadata": {}
  }
 ]
}