Software Carpentry Workshop - Freiburg, 2017/03/10

python-102.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with Matplotlib and NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Matplotlib](http://matplotlib.org/) is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.\n",
    "\n",
    "Before we begin plotting, we must activate the PyLab module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to use matplotlib and numpy from your script and not from the ipython notebook you can:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some artifical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = linspace(0.01, 20, 20)\n",
    "y = x ** 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot is as easy as this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11acd2240>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7HVgW6n3f3ava8beISBJs3nWAuQvW8MqGSk4b3p+7P30O556kAZjdhcU+8HcP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\nUEIREZGk+P95dQRk44BnIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4810b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line style and labels are controlled in a way similar to Matlab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a6afa20>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gyZJ0gzsnoZrgRGRmncN778Hhh8Ozz8Jtt8HHP150RJY5EZlZ53DbbXD//XDt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\nurkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab89320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o')\n",
    "xlabel('x')\n",
    "ylabel('y')\n",
    "title('My first nice plot with dots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make it more fancy, like xkcd plots! http://xkcd.com/1299/\n",
    "Hint: For the xkcd style you need a recent matplotlib version, but it's worth it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GYZhn0dGjgFoNhIUB1arZVJUtsbNSk9+pU6fQpk0bqFQqdO3aFenp6YiKisLt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2da358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xkcd():\n",
    "    plot(x, y, 'r--o')\n",
    "    xlabel('x')\n",
    "    ylabel('y')\n",
    "    title('My first nice plot with dots.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot several individual lines at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a874898>,\n",
       " <matplotlib.lines.Line2D at 0x11aaed2b0>,\n",
       " <matplotlib.lines.Line2D at 0x11ad73748>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v8b77rrcTr1s3r96KCmjXbv8ecHODN0l6zM0V6eA+DRjjnDs/9PwBAOdcvddG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4bdc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o', x, y ** 1.1, 'bs', x, y ** 1.2, 'g^-' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Plot the above graph with the y-axis on a log scale.\n",
    "2. Limit the x axis range to between 2 and 6.\n",
    "3. Label the axes with something descriptive and give the plot a fun title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a9cf630>,\n",
       " <matplotlib.lines.Line2D at 0x11a9cf7b8>,\n",
       " <matplotlib.lines.Line2D at 0x11a9cc160>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CiEi0lJSXcMGkC1i8cTF/Pv/PtGvVzu+S4pZ63CISFx6d/SiLNy6mbXpb7hp8l9/lxDUF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\nS5s20Lbt3vupU+v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a733e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'r--o', x, y ** 1.1, 'bs', x, y ** 1.2, 'g^-' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SRgFzgBUVdVYAc1WYDmyPiK29tU3naLqcDKzLGIOZmQ1QthFMROyUdDZwPcWl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\nYMzMLIv/D15CTeA1LvNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119926048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(100000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "xlabel('Smarts')\n",
    "ylabel('Probability')\n",
    "title('Histogram of IQ')\n",
    "text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "axis([40, 160, 0, 0.04])\n",
    "grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you feel a bit playful (only in matplotlib > 1.3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j7ryLe+65B2q1GmPGjAHAxgAvX74cy5YtQzgcbvf8Cx6LxQIAqK2tFWN+fH74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\nGEIIIWIiA0MIIURM/gFCH0JkQQxPoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7112e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xkcd():\n",
    "    x = np.linspace(0, 1)\n",
    "    y = np.sin(4 * np.pi * x) * np.exp(-5 * x)\n",
    "\n",
    "    plt.fill(x, y, 'r')\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following example is from [matplotlib - 2D and 3D plotting in Python](http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-4-Matplotlib.ipynb) - great place to start for people interested in matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x11ad3fcf8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nHYc2/okMadrUKiJlt3n/ZnYc2sHMpplhh1IyVVeqkU/ZRblKF5J7LKdrA/fZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afa3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = array([0,1,2,3,4,5])\n",
    "xx = np.linspace(-0.75, 1., 100)\n",
    "x = linspace(0, 5, 10)\n",
    "\n",
    "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n",
    "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n",
    "axes[1].step(n, n**2, lw=2)\n",
    "axes[2].bar(n, n**2, align='center', width=0.5, alpha=0.5)\n",
    "axes[3].fill_between(x, x**2, x**3, color='green', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you going to plot something more or less complicated in Matplotlib, the first thing you do is open the [Matplotlib example gallery](http://matplotlib.org/gallery.html) and choose example closest to your case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can directly load python code (or basically any text file) to the notebook. This time we download code from the Matplotlib example gallery:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load http://matplotlib.org/mpl_examples/mplot3d/contour3d_demo2.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KK3zta1/j+vXrPPDAAwwPD/Nf/st/4YknnqhrrN/93d/lT//0T5EkiStXrvDlL3+ZTCbD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\nyZCKPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b355748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)\n",
    "ax.clabel(cset, fontsize=9, inline=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose one of the many code snippets on the [matplotlib gallery](http://matplotlib.org/examples/index.html) and load it into this notebook. Generate new data and have the code snippet visualize that data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Gene Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After having sequenced a genome and having obtained a high quality resolution of its DNA sequence,\n",
    "one of the initial tasks is to predict and annotate the protein-coding genes.\n",
    "\n",
    "Protein coding regions of a DNA sequence are first transcribed into messenger RNA (mRNA) and\n",
    "then translated into a protein. During this process, a codon of three DNA nucleotides is translated\n",
    "to a single amino acid and joined together to form a polypeptide, i.e. a protein. The mRNA contains\n",
    "signals for the ribosome telling it where to initiate and terminate translation, the sequence between\n",
    "these two signals is the coding sequence (CDS). These CDS can also be called an open reading\n",
    "frame (ORF). Such an open reading frame is a sequence that contains a start and a stop codon and\n",
    "could be read by the ribosome, only if it is located on an mRNA. A single-stranded DNA sequence\n",
    "contains of three reading frames, this means three possibilities of reading codons off the sequence,\n",
    "starting from position one, two, and three. The codons are then just read as a consecutive sequence\n",
    "of three nucleotides.\n",
    "\n",
    "Given a fragment of DNA sequence ``S`` of n nucleotides, let S[i] denote the i-th nucleotide of sequence\n",
    "S, for $$1 ≤ i ≤ n$$ \n",
    "Let also $$ S[i, . . . , j], i ≤ j$$\n",
    "denote the fragment of S containing nucleotides\n",
    "$$S[i], S[i + 1], . . . , S[j]$$\n",
    "and \n",
    "$$S[i, . . . , i] = S[i]$$\n",
    "With this notation, an open reading frame ORF is a fragment S[i, . . . , j], of length l = j − i + 1, such that S[i, . . . , i + 2] is the start codon ATG and S[j − 2, . . . , j] is one of the stop codons {TAA, TAG, TGA}.\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "u_t(x,t) & = f(x,t) \\\\\n",
    "u(x,0) & = u_0\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "The shortest known protein has 8 amino acids and so ORFs with fewer than 3 + 24 + 3 = 30 nucleotides cannot encode for a protein,\n",
    "therefore l ≥ 30. Furthermore, there are no other stop codons in the ORF except for the one at\n",
    "the end.\n",
    "\n",
    "Bacteriophage φ-X174 was the first genome to be sequenced in 1977 by Fred Sanger and his team.\n",
    "It is a small genome, consisting only of 5,386 nucleotides and is contained in a single-stranded\n",
    "circular chromosome. You shall download its genome sequence from NCBI and use it as an example\n",
    "to identify all ORFs and to identify which of these actually encode for real proteins. As this genome\n",
    "is single stranded you only have to look at the 3 reading frames, of course you would have to look at\n",
    "a further 3 reading frames in the reverse complement sequence if the genome were double-stranded,\n",
    "as genes could lie on either strand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the genome from NCBI, the accession number is NC 001422.1. How many genes are annotated?\n",
    "\n",
    "Hint: You can download and ``open()`` the FASTA file like a normal text file or reading it directly from the FTP server with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'>tr|Q7T2N8|Q7T2N8_DANRE Foxi one OS=Danio rerio GN=foxi1 PE=4 SV=1\\n'\n",
      "b'MSPFTACAPDRLNNPPLKRTRTLSLRNPSETSTLSRSDRRNSMFLEGERIMNAFGQQPSS\\n'\n",
      "b'QQTSPLQQQDILDMTVYCDSNFSMYQQNLHHHHHHHHHQRPPAHPSGYGLGEYSSPSTNP\\n'\n",
      "b'YLWMNSPGITSTPYLSSPNGGSYIQSGFGSNQRQFLPPPTGFGSADLGWLSISSQQELFK\\n'\n",
      "b'MVRPPYSYSALIAMAIQNAQDKKLTLSQIYQYVADNFPFYKKSKAGWQNSIRHNLSLNDC\\n'\n",
      "b'FKKVARDEDDPGKGNYWTLDPNCEKMFDNGNFRRKRKRRADGNAMSVKSEDALKLADTSS\\n'\n",
      "b'LMSASPPSLQNSPTSSDPKSSPSPSAEHSPCFSNFIGNMNSIMSGNAVRSRDGSSAHLGD\\n'\n",
      "b'FTQHGMSGHEISPPSEPGHLNTNRLNYYSASHNNSGLINSISNHFSVNNLIYNRDGSEV\\n'\n"
     ]
    }
   ],
   "source": [
    "from urllib import request\n",
    "url = \"http://www.uniprot.org/uniprot/Q7T2N8.fasta\"\n",
    "data = request.urlopen( url )\n",
    "for line in data:\n",
    "    print(line),"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a method that reads the genome sequence in fasta format, calculates all possible\n",
    "ORFs, and returns a fasta file that contains the ORF DNA sequences and the reading frame\n",
    "number, the starting position i, the ending position j and the length l of each in the header.\n",
    "How many ORFs do you find in total and in each reading frame separately?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why have we asked you to only look in 3 reading frames and not in 6 as is usually done (Hint:\n",
    "please read our introduction)? What would it mean to look in 6 reading frames, how would you\n",
    "define the other 3 reading frames?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download all protein sequences annotated for Bacteriophage φ-X174 in one fasta file. Which\n",
    "part of the genes correspond to the respective CDS? Can you find untranslated regions (UTRs)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a method to translate all ORFs that are currently saved as DNA sequences\n",
    "into protein sequences. Parse the real protein sequences and compare them to your predicted\n",
    "sequences. Your output shall be a table of ORFs that correspond to real proteins giving the\n",
    "protein accession number (NP...) they correspond to, the reading frame they are located on\n",
    "with starting and ending positions (w.r.t. the genome) and length. Do you find all CDS? If not,\n",
    "why is this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "codon_table = {\n",
    "    'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L', 'TCT': 'S',\n",
    "    'TCC': 'S', 'TCA': 'S', 'TCG': 'S', 'TAT': 'Y', 'TAC': 'Y',\n",
    "    'TGT': 'C', 'TGC': 'C', 'TGG': 'W', 'CTT': 'L', 'CTC': 'L',\n",
    "    'CTA': 'L', 'CTG': 'L', 'CCT': 'P', 'CCC': 'P', 'CCA': 'P',\n",
    "    'CCG': 'P', 'CAT': 'H', 'CAC': 'H', 'CAA': 'Q', 'CAG': 'Q',\n",
    "    'CGT': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', 'ATT': 'I',\n",
    "    'ATC': 'I', 'ATA': 'I', 'ATG': 'M', 'ACT': 'T', 'ACC': 'T',\n",
    "    'ACA': 'T', 'ACG': 'T', 'AAT': 'N', 'AAC': 'N', 'AAA': 'K',\n",
    "    'AAG': 'K', 'AGT': 'S', 'AGC': 'S', 'AGA': 'R', 'AGG': 'R',\n",
    "    'GTT': 'V', 'GTC': 'V', 'GTA': 'V', 'GTG': 'V', 'GCT': 'A',\n",
    "    'GCC': 'A', 'GCA': 'A', 'GCG': 'A', 'GAT': 'D', 'GAC': 'D',\n",
    "    'GAA': 'E', 'GAG': 'E', 'GGT': 'G', 'GGC': 'G', 'GGA': 'G',\n",
    "    'GGG': 'G'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe how you would extend your algorithm to be able to identify all protein sequences. From\n",
    "this description it should be clear exactly how you can program this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the location of the genes on the chromosome, what do you notice? What does\n",
    "this observation have to do with the size? Name an advantage of reading frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use your new plotting skills to plot the Codon usage of all predicted genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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