bioinformatics_intro_python.ipynb

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   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pprint import pprint\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import YouTubeVideo"
   ]
  },
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   "source": [
    "# The Source Code of Life: Using Python to Explore Our DNA\n",
    "![xkcd_dna](images/dna.png)\n",
    "Researchers just found the gene responsible for mistakenly thinking we've found the gene for specific things. It's the region between the start and the end of every chromosome, plus a few segments in our mitochondria."
   ]
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   "source": [
    "Every good presentation starts with xkcd, right?"
   ]
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   "source": [
    "## Bioinformatics\n",
    "HINT: If you're viewing this notebook as slides, press the \"s\" key to see a bunch of extra notes.\n",
    "![dna_code](images/dna_code.jpg)\n",
    "Image: http://www.sciencemag.org/sites/default/files/styles/article_main_large/public/images/13%20June%202014.jpg?itok=DPBy5nLZ"
   ]
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   "source": [
    "Today we're going to talk about part off the wonderful field of study known as bioinformatics. What is bioinformatics? According to [Wikipedia](https://en.wikipedia.org/wiki/Bioinformatics), its \"an interdisciplinary field that develops methods and software tools for understanding biological data.\" Here's another definition that more fits my experiences: \"The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.\" ([Fredj Tekaia](http://slideplayer.com/slide/4800050/), Institut Pasteur)"
   ]
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   "source": [
    "## What is DNA?\n",
    "A long string of A, C, G, and T (bases)"
   ]
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   "source": [
    "    ACGTTCATGG           <- Ten bases\n",
    "\n",
    "    ACGTTCATGGATGTGACCAG <- Twenty bases\n",
    "\n",
    "    etc..."
   ]
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   "source": [
    "So what is it about biology that needs such computationally intensive stuff? I thought biology was a \"squishy\" science? Well guess what, nature has been playing with big data long before it became the buzzword it is now. Let's start by talking about DNA. DNA is your \"source code,\" but we'll get to more about how that works in a minute. First we're just going to talk about what it is. It's a string of four different types of molecules with long names that we're not going to worry about right now, so we'll just call them by their abbreviations (which is what everyone uses most of the time anyway): `A`, `C`, `G`, and `T`. And instead of molecules, we'll call them bases, because reasons. So if you have just an `A`, that's one base, or if you have `ACGTTCATGG`, that is ten bases."
   ]
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   "source": [
    "Well, not really a string...\n",
    "<img src=\"images/double_helix.jpg\" alt=\"Double helix\" style=\"width: 200px;\"/>\n",
    "Two strings stuck together? But if you know one you know the other..."
   ]
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   "source": [
    "Ok, when I said it was a string that was a bit of a simplification, it's actually two strings stuck together. That's why pictures of DNA look like a twisted ladder (that's the \"double helix\" you may hear about): each side of the ladder is a string, and the \"rungs\" are where they stick together. But the nice thing is, each base has only one other base it can stick to. `A` always sticks to `T` and vice versa, same with `C` and `G`. So if you know that one side of the ladder is `ACGTTCATGG`, then we know the other side is `TGCAAGTACC`. This is really nice because it cuts the amount of information we actually have to know in half."
   ]
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   "source": [
    "## The Human Genome\n",
    "It's big. How big?"
   ]
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   "source": [
    "So now that you know everything (just kidding) about DNA, it's time for a question: How many bases long would you guess the human genome (fancy word for all the DNA) is? It ends up being about 3.2 billion base pairs long. (Remember the double helix? That's why it's _pairs_)"
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   "source": [
    "About 3,200,000,000bp (bp is base pairs). Actual file format commonly used in bioinformatics (FASTA):\n",
    "\n",
    "```\n",
    ">Sequence0\n",
    "TTTCTGACTAACACTACAATTACCACTTGATGTTACCGACTAAGTGGTACGACTTGCTAGAACCGACTCTCGTACGTAT\n",
    "CGCAGACTAGTGCGCGCGCTTAGTGACTATACTAGAATATACCTGGGGCCCAAGGAGTGTCGGGCGATCGTCCTTGAAA\n",
    "TAAATATCTCAACCATCGTCATCTAGGGGGAACAGAGCGGTGGGCAGGTCCCAACCTGTTTATTTGTGTTGCTAACACT\n",
    "ACGGCGCAGCTGCTCAAGTAGGTGCGATTATCGAGTAGAGGCTCCACCGGCTCTATGTGCCACGCATCTACTGAACCGA\n",
    "ATTCTATCCCTGATACTCCAGAAGGTCGCAGGTTTACAGACACGTTTCAGCTCGAGAGGCCATCGATTATCTTAATATA\n",
    "CCACACTGCCGAATAGCATGCCCGTAGAATCCAAGCCACGAGATAGCGTTACTTAATGAGTACCCAACGCAAATGAGGT\n",
    "TGATTATCCCTAACCTGCAATCTAGGCCTTGTTCTGGAGGGGGTTATCCTTTATAGTTGATTACTTACACTCACCATGT\n",
    "TCGTAGTCGGAACTCACCGATTAAGACCGATTTTACTATGGGAAGGCCAGGTTACACCTGTTTCGGGGGGGCCGCGGCG\n",
    "GGTTACTTTAACCTGTCCATCCATCAGTCACTGGGCGCCAAGATTCTCCTATAGTTATATCCGCCCTTTGATTTAAACC\n",
    "TAGGCCTACCTCAACGAACTGGGCCATGGGGTTCACACAGAAACAAGGGGGATAGACAGTCTTATTGAGCGCTTCTGAA\n",
    "CAGCGTGTGTTCACGGTACGGCAATACCACCAGTAAACCGAGAACAGTGTTGAAGGTGATCGAACACGTGTTTTCTTCA\n",
    "CCGTAGGGCTTCTAGGGAGTATCGCCCCCATATAGGCAGACGAGAAGGACTGTCACGCGCGGAGATCGATAATACGTAT\n",
    "AACACAAGCACAGTAACTGCCCCGACCGGCTAAAGGACGTGGCCCAGTGTACCCAACGTACGTAATTGCAAGAGGTCTG\n",
    "TCTGTCATCCCGAGGACTGCTTCTATAACTCGTTGAGGGCACTAGGCTTGAGACAATCAGCTTCGCTCGTCACGATTTT\n",
    "ACTTTTTTCCTGGAAAAGCCCCCCCACAGACTATCAGGTCGCGCTTACCATACCAGTCCTTCTTGATAAGCCAATCCGT\n",
    "ATTAGGTAGATTAAGCTGACAGTCGGGGCGACTCTTTGGAAACAGTATTCCCGTTTCGGGCACCTAGGATTCAGGCTTG\n",
    "TACAACGATCATAGACGTCGCGGAAAGAAATAGCACAGTGTAGGAGCTGGTCGTGACCCGTGCTGTCAAGTTTATTGCA\n",
    "CGGCTTGCTAAAAGGTACAGTGTAACGTTTCACAAACAAGCGAGACCCATTGTTGGTCTAACGCTATCGTACTTGATAC\n",
    "CAGCCTGTGACGTCACGCGAAATCGTCTGTATAACTAGTTCTTCCCCGACTGCCACGGTATCCCAAAATTACATACTGA\n",
    "CAGGACCTCTTCCATATTCATCAGGACTCGACGAAGCGCGCCCCGTGTAGTACGCGAAAATTATACCGTCCGTAGGTAC\n",
    "\n",
    "```\n",
    "Now picture this 2 million more times. "
   ]
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   "source": [
    "If you wrapped it in PEP 8-guideline adhering lines of 79 characters, it would still be over 40 million lines (although granted DNA is about as un-Pythonic of code as you can get). And we're not even all that special when it comes to genome length. A lot of plants have us beat, actually, with _paris japonica_ being a [top contender](http://blogs.biomedcentral.com/on-biology/2014/03/20/worlds-largest-sequenced-genome-unlocking-the-loblolly-pine/) for longest genome at 150 billion base pairs.\n",
    "\n",
    "So what does all of that code even do? Well, most of it doesn't appear to \"do\" much. (How much code in your codebase is like that? Although I'd be willing to bet there's a lot of it that we just don't understand what it does yet) But we'll stick with the ~1.5% of it that we have a pretty good idea about."
   ]
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   },
   "source": [
    "![central_dogma](images/Central_Dogma_of_Molecular_Biochemistry_with_Enzymes.jpg)\n",
    "https://en.wikipedia.org/wiki/Central_dogma_of_molecular_biology"
   ]
  },
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   "source": [
    "It's time to learn something called \"The Central Dogma of Molecular Biology,\" since that's a lot cooler-sounding than \"what DNA does.\" Don't worry, we'll keep to the very basics. There are two steps, \"transcription\" and \"translation.\" Transcription basically involves making a copy of a chunk of DNA, except that you only copy one side of it (it looks like half a strand of DNA). We call this copy RNA. Translation involves reading the pattern of the DNA into protein. What is protein? Basically most of what we're made of is either protein or is made by proteins (excluding water, of course). So that's basically how your source code works: your DNA tells your body how to make proteins, which are like parts of a machine. These little parts make little machines, which are part of bigger machines, etc. etc. until all the parts fit together to make you! (But how do the parts know how to fit together you ask? Umm...great question...that we're not going to cover today. Next slide!)"
   ]
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   },
   "source": [
    "## Sequencing DNA"
   ]
  },
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   "outputs": [
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TTu1m7wrtLbtU1ajFmzOzFj2knW8xsdtOogNWmxSonTV1NmVl1DAjcbi/qnoL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\n"
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f00bbbe56a0>"
      ]
     },
     "metadata": {}
    }
   ],
   "source": [
    "YouTubeVideo('fCd6B5HRaZ8', start=135)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Ok, now you know something about DNA, so we can start getting into some of the fun puzzles that this leads to. So how do we find out what someone's DNA sequence is? There are several methods, including some newer ones that I'm really excited about, but we'll stick with the most popular here: Illuimina's sequencing by synthesis. It's probably so popular because it's fast and keeps getting cheaper. The reason it's so fast is because it's parallelized. It works by breaking the DNA up into little chunks and then looking at all the chunks at the same time. Basically you have a whole bunch of these fragments on a slide and then flood the slide with a whole bunch of one base (say A). The base is modified slightly so that it fluoresces a certain color when illuminated by a certain wavelength and also so that no other bases can attach after it. Then the excess bases are washed off, the slide is imaged to see which fragments got a base added, and then the fluorescent part of the new addition as well as the part that blocks the next base are removed and the process is repeated with another base (say C). Repeat until you've got most of the fragments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Sequence Alignment\n",
    "Now things start to get fun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "That's a nice-looking lake...\n",
    "![lake](images/lake.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Until now!\n",
    "![lake_puzzle](images/lake_puzzle.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "So now you've got a whole bunch of little pieces of DNA that you have to match up to a reference sequence. A quick analogy (credit for the analogy/pics to Aaron Quinlan, who has [all his slides](https://github.com/quinlan-lab/applied-computational-genomics) for a course in applied computational genomics freely available). What makes this lake puzzle so hard? So much blue and white! So we need to find a way to determine how well the piece we have actually matches the picture at that point. We'll call this aligning sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### First step: Assign scores\n",
    "If a base matches with itself, it gets a score of one. Otherwise it gets a score of zero.\n",
    "\n",
    "||A|C|G|T|\n",
    "|-|-|-|-|-|\n",
    "|__A__|1|0|0|0|\n",
    "|__C__|0|1|0|0|\n",
    "|__G__|0|0|1|0|\n",
    "|__T__|0|0|0|1|\n",
    "\n",
    "So these two sequences:\n",
    "```\n",
    "AACTGTGGTAC\n",
    "ACTTGTGGAAC\n",
    "10011111011\n",
    "```\n",
    "have an alignment score of eight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "This is going to seem like complete overkill at first, but you'll understand the advantages to doing it this way after a bit. Each base that aligns with itself gets a score of one, any other alignment is zero. But what if these are long segments, could we maybe get a better score by shifting one in reference to another? That's a valid question, but before we can tackle that, I need to introduce you to another unfortunate aspect of sequencing..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What about gaps?\n",
    "Yes, unfortunately there are going to be gaps.\n",
    "\n",
    "We'll add a \"gap penalty\" of -3.\n",
    "\n",
    "So the score for this alignment:\n",
    "```\n",
    "ACTACA-ACGTTGAC\n",
    "A-TAGAAACGCT-AC\n",
    "1 1101 11101 11\n",
    "-3   -3    -3\n",
    "```\n",
    "is just one\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Sequencing DNA is great, but it's also kind of messy. You may end up with extra bases or missing bases in your sequences. Also, people don't all have the same DNA (unless you're identical twins!) so you may have bases that are actually missing or extra with respect to the \"reference\" sequence. But, gaps are problematic because when they're in a place that codes for a protein (remember earlier?) they are pretty good at making the protein not work. So we want to introduce gaps only when it's a lot better than the alternative. For now we'll have a \"gap penalty\" of -3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scoring matrix\n",
    "\n",
    "||-|A|C|G|T|T|T|G|T|C|G|C|\n",
    "|-|\n",
    "|__-__|0|-3|-6|-9|-12|-15|-18|-21|-24|-27|-30|-33|\n",
    "|__A__|-3||||||||||||\n",
    "|__C__|-6||||||||||||\n",
    "|__T__|-9||||||||||||\n",
    "|__T__|-12||||||||||||\n",
    "|__T__|-15||||||||||||\n",
    "|__C__|-18||||||||||||\n",
    "|__T__|-21||||||||||||\n",
    "|__G__|-24||||||||||||\n",
    "|__C__|-27||||||||||||\n",
    "\n",
    "$$\n",
    "S_{m,n}=\\left\\{\n",
    "  \\begin{array}{ll}\n",
    "    S_{m-1,n} + gap\\\\\n",
    "    S_{m,n-1} + gap\\\\\n",
    "    S_{m-1,n-1} + B(a,b)\\\\\n",
    "  \\end{array}\n",
    "\\right.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "||-|A|C|G|T|T|T|G|T|C|G|C|\n",
    "|-|\n",
    "|__-__|0|-3|-6|-9|-12|-15|-18|-21|-24|-27|-30|-33|\n",
    "|__A__|-3|1|-2|-5|-8|-11|-14|-17|-20|-23|-26|-29|\n",
    "|__C__|-6||||||||||||\n",
    "|__T__|-9||||||||||||\n",
    "|__T__|-12||||||||||||\n",
    "|__T__|-15||||||||||||\n",
    "|__C__|-18||||||||||||\n",
    "|__T__|-21||||||||||||\n",
    "|__G__|-24||||||||||||\n",
    "|__C__|-27||||||||||||"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We need to keep track of where that score came from."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "||-|A|C|G|T|T|T|G|T|C|G|C|\n",
    "|-|\n",
    "|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
    "|__A__|↑-3|↖1|←-2|←-5|←-8|←-11|←-14|←-17|←-20|←-23|←-26|←-29|\n",
    "|__C__|↑-6||||||||||||\n",
    "|__T__|↑-9||||||||||||\n",
    "|__T__|↑-12||||||||||||\n",
    "|__T__|↑-15||||||||||||\n",
    "|__C__|↑-18||||||||||||\n",
    "|__T__|↑-21||||||||||||\n",
    "|__G__|↑-24||||||||||||\n",
    "|__C__|↑-27||||||||||||"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "||-|A|C|G|T|T|T|G|T|C|G|C|\n",
    "|-|\n",
    "|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
    "|__A__|↑-3|↖1|←-2|←-5|←-8|←-11|←-14|←-17|←-20|←-23|←-26|←-29|\n",
    "|__C__|↑-6|↑-2|↖2|←1|←-4|←-7|←-10|←-13|←-16|↖-19|←-22|↖-25|\n",
    "|__T__|↑-9|↑-5|↑-1|↖2|↖0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|\n",
    "|__T__|↑-12|↑-8|↑-4|↑-1|↖3|↖1|←-2|←-5|↖-8|←-11|←-14|←-17|\n",
    "|__T__|↑-15|↑-11|↑-7|↖-4|↑0|↖4|↖2|←-1|↖-4|←-7|←-10|←-13|\n",
    "|__C__|↑-18|↑-14|↖-10|↖-7|↑-3|↑1|↖4|↖2|↖-1|↖-3|←-6|←-9|\n",
    "|__T__|↑-21|↑-17|↑-13|↑-10|↑-6|↑-2|↖2|↖4|↖3|←0|↖-3|←-6|\n",
    "|__G__|↑-24|↑-20|↑-16|↖-12|↑-9|↑-5|↑-1|↖3|↖4|↖3|↖1|←-2|\n",
    "|__C__|↑-27|↑-23|↖-19|↑-15|↖-12|↑-8|↑-4|↑0|↖3|↖5|↖3|↖2|\n",
    "\n",
    "Now just follow the arrows from that bottom-right corner back to the top-left zero.\n",
    "```\n",
    "ACGTTTGTCGC\n",
    "|| ||| | ||\n",
    "AC-TTTCT-GC\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "So how do we keep track of where we actually want gaps? We will use a scoring matrix. We start out with one sequence at the top and one on the side with an extra space added at the beginning of each. The two spaces align with a score of zero and we start form there. Each cell gets filled in with whatever ends up the highest of three possibilities:\n",
    "1. The score above plus the gap penalty (remember the gap penalty is negative)\n",
    "2. The score to the left plus the gap penalty\n",
    "3. The score to the upper left plus the alignment score\n",
    "\n",
    "We can fill in the top and left columns right away since they don't have a score to their upper-left, so their only possible score is the gap penalty.\n",
    "\n",
    "But our choice affects the score of everything down and to the right, so besides just the score, we need to keep track of where that score came from.\n",
    "\n",
    "Once it's all filled out, just follow the arrows from the bottom-right corner to the top-left zero. Every time you go straight up, it's a gap in the left sequence. Every time you go left it's a gap in the top sequence. Every time you go up-left, the two bases align."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's code this!\n",
    "See _Python for Bioinformatics_ for the inspiration for this demo.\n",
    "\n",
    "Here is the \"substitution matrix\" and its corresponding \"alphabet\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dna_sub_mat = np.array(\n",
    "    [[ 1, 0, 0, 0],\n",
    "     [ 0, 1, 0, 0],\n",
    "     [ 0, 0, 1, 0],\n",
    "     [ 0, 0, 0, 1]])\n",
    "\n",
    "dbet = 'ACGT'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Same as we defined above, with `dbet` being the \"dna alphabet\" used for this matrix (four-letter alphabet, could be in a different order, this is just the one we chose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And here are we calculate the scores and arrows as separate matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def nw_alignment(sub_mat, abet, seq1, seq2, gap=-8):\n",
    "    # Get the lengths of the sequences\n",
    "    seq1_len, seq2_len = len(seq1), len(seq2)\n",
    "    # Create the scoring and arrow matrices\n",
    "    score_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
    "    arrow_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
    "    # Fill first column and row of score matrix with scores based on gap penalty\n",
    "    score_mat[0] = np.arange(seq2_len+1) * gap\n",
    "    score_mat[:,0] = np.arange(seq1_len+1) * gap\n",
    "    # Fill top row of arrow matrix with ones (left arrow)\n",
    "    arrow_mat[0] = np.ones(seq2_len+1)\n",
    "    for seq1_pos, seq1_letter in enumerate(seq1):\n",
    "        for seq2_pos, seq2_letter in enumerate(seq2):\n",
    "            f = np.zeros(3)\n",
    "            # Cell above + gap penalty\n",
    "            f[0] = score_mat[seq1_pos,seq2_pos+1] + gap\n",
    "            # Cell to left + gap penalty\n",
    "            f[1] = score_mat[seq1_pos+1,seq2_pos] + gap\n",
    "            n1 = abet.index(seq1_letter)\n",
    "            n2 = abet.index(seq2_letter)\n",
    "            # Cell to upper-left + alignment score\n",
    "            f[2] = score_mat[seq1_pos,seq2_pos] + sub_mat[n1,n2]\n",
    "            score_mat[seq1_pos+1, seq2_pos+1] = f.max()\n",
    "            arrow_mat[seq1_pos+1, seq2_pos+1] = f.argmax()\n",
    "    return score_mat, arrow_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "I'm calling this `nw_align` after the Needleman-Wunsch algorithm. It's hard to put score and directional information in just one matrix, so we make two matrices. We start with a matrix of zeros for each and then fill them in concurrently as we run through the possibilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What does our result look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  -3  -6  -9 -12 -15 -18 -21 -24 -27 -30 -33]\n",
      " [ -3   1  -2  -5  -8 -11 -14 -17 -20 -23 -26 -29]\n",
      " [ -6  -2   2  -1  -4  -7 -10 -13 -16 -19 -22 -25]\n",
      " [ -9  -5  -1   2   0  -3  -6  -9 -12 -15 -18 -21]\n",
      " [-12  -8  -4  -1   3   1  -2  -5  -8 -11 -14 -17]\n",
      " [-15 -11  -7  -4   0   3   1  -2  -5  -7 -10 -13]\n",
      " [-18 -14 -10  -7  -3   1   4   1  -1  -4  -7 -10]\n",
      " [-21 -17 -13  -9  -6  -2   1   5   2  -1  -3  -6]\n",
      " [-24 -20 -16 -12  -9  -5  -2   2   5   3   0  -2]]\n",
      "[[1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [0 2 1 1 1 1 1 1 1 1 1 1]\n",
      " [0 0 2 1 1 1 1 1 1 1 1 1]\n",
      " [0 0 0 2 2 1 1 1 1 1 1 1]\n",
      " [0 0 0 0 2 2 1 1 1 1 1 1]\n",
      " [0 0 0 0 0 2 2 1 1 2 1 1]\n",
      " [0 0 0 0 0 2 2 1 2 1 1 1]\n",
      " [0 0 0 2 0 0 0 2 1 1 2 1]\n",
      " [0 0 0 0 0 0 0 0 2 2 1 2]]\n"
     ]
    }
   ],
   "source": [
    "s1 = 'ACTTCTGC'\n",
    "\n",
    "s2 = 'ACGTTTGTCGC'\n",
    "\n",
    "score_mat, arrow_mat = nw_alignment(dna_sub_mat, dbet, s1, s2, gap=-3)\n",
    "print(score_mat)\n",
    "print(arrow_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Looks good, but not all that useful by itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now we need a way to get the sequences back out of our scoring matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def backtrace(arrow_mat, seq1, seq2):\n",
    "    align1, align2 = '', ''\n",
    "    align1_pos, align2_pos = arrow_mat.shape\n",
    "    align1_pos -= 1\n",
    "    align2_pos -= 1\n",
    "    selected = []\n",
    "    while True:\n",
    "        selected.append((align1_pos, align2_pos))\n",
    "        if arrow_mat[align1_pos, align2_pos] == 0:\n",
    "            # Up arrow, add gap to align2\n",
    "            align1 += seq1[align1_pos-1]\n",
    "            align2 += '-'\n",
    "            align1_pos -= 1\n",
    "        elif arrow_mat[align1_pos, align2_pos] == 1:\n",
    "            # Left arrow, add gap to align2\n",
    "            align1 += '-'\n",
    "            align2 += seq2[align2_pos-1]\n",
    "            align2_pos -= 1\n",
    "        elif arrow_mat[align1_pos, align2_pos] == 2:\n",
    "            # Up-arrow arrow, no gap\n",
    "            align1 += seq1[align1_pos-1]\n",
    "            align2 += seq2[align2_pos-1]\n",
    "            align1_pos -= 1\n",
    "            align2_pos -= 1\n",
    "        if align1_pos==0 and align2_pos==0:\n",
    "            break\n",
    "    # reverse the strings\n",
    "    return align1[::-1], align2[::-1], selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AC-TTC-T-GC\n",
      "ACGTTTGTCGC\n"
     ]
    }
   ],
   "source": [
    "a1, a2, selected = backtrace(arrow_mat, s1, s2)\n",
    "print(a1)\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Sometimes it's nice to see the scoring matrix, though, so here's a function to visualize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def visual_scoring_matrix(seq1, seq2, score_mat, arrow_mat):\n",
    "    visual_mat = []\n",
    "    for i, row in enumerate(arrow_mat):\n",
    "        visual_mat_row = []\n",
    "        for j, col in enumerate(row):\n",
    "            if col == 0:\n",
    "                arrow = '↑'\n",
    "            elif col == 1:\n",
    "                arrow = '←'\n",
    "            else:\n",
    "                arrow = '↖'\n",
    "            visual_mat_row.append(arrow + ' ' + str(score_mat[i,j]))\n",
    "        visual_mat.append(visual_mat_row)\n",
    "    visual_mat = np.array(visual_mat, object)\n",
    "\n",
    "    tab = plt.table(\n",
    "        cellText=visual_mat,\n",
    "        rowLabels=['-'] + list(s1),\n",
    "        colLabels=['-'] + list(s2),\n",
    "        loc='center')\n",
    "    tab.scale(2, 3)\n",
    "    tab.set_fontsize(30)\n",
    "    plt.axis('tight')\n",
    "    plt.axis('off')\n",
    "    \n",
    "    align1, align2, selected = backtrace(arrow_mat, seq1, seq2)\n",
    "    for pos in selected:\n",
    "        y, x = pos\n",
    "        tab._cells[(y+1, x)]._text.set_color('green')\n",
    "        tab._cells[(y+1, x)]._text.set_weight(1000)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Yv78+Zg4cSLmzJlj9o/xJBIJ6tWrB0dHR7P2q6+Ssv/mmfHPGaBwQr+W/Qzu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A1j23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\nAAAAF8UxAAAA4KI4BgAAAFz/AuRWqpteGOQSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0096cc7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_scoring_matrix(s1, s2, score_mat, arrow_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's generate some sequences and see how fast this is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def random_dna_seq(length=1000):\n",
    "    seq = [random.choice(dbet) for x in range(length)]\n",
    "    return ''.join(seq)\n",
    "\n",
    "def mutate_dna_seq(seq, chance=1/5):\n",
    "    mut_seq_base = [random.choice(dbet) if random.random() < chance else x for x in seq]\n",
    "    mut_seq_indel = [random.choice(('', x + random.choice(dbet))) if random.random() < chance else x for x in mut_seq_base]\n",
    "    return ''.join(mut_seq_indel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AATCTACTTTCCTTAGTCTTCATGTAGCCCCTCGGTCAAGTCCTCTTATCCCTATCGTAAATGCCATCGTGAGTTTAGTTCATGTGCGTTGTGGTAACATGTATACGTATGCGGGAGCTGAGTCTCACGAAAACGAGTTCAAGCATTTCAATGAGTCCAATGTGCTTTATATTGACTGTTCTCAGTCTGTCATATCGCTGCGCCGCCTCTTATCAATGTAGGTAACGGCGTAGAGTGCACACGAATCGTCAAGTTACTCCCATCTCCATTTTTACGCGGTAATATGTCATGCACGCCGATACAGATAAAAGGGGCGTGGACGGGCCACGAATGACTACCCTGTGGGCGTATCGCAAGTTGCGTAAATCCGCTCACGAGTCCCATCGAGCATTCACCCATATGACCAGCCCGATAATTATGGACGTAATATAACCTCCCATTGGGGACGTACGCGACCTCCATTTGTGGAAAGATGGTTCGGTTGCTGACTCAAATGATAAAACTCCTGTACGGCGTTGACACAAACCTCCTTGCCCTGTCTCTATGGCATCCAACGTTGTTCTTCAACTGCGCCGTCTCGTAATGCAGCTATAAGTTCCACCTTGAACCGCGTTCAGCGGTTCTTCTTTCCACTGTGAATTTTAAAAAAGTTTCATCCTCGATGTATAGCGTTCCGACGGGCACTTAAGAAAGGGGCTTCGAGCCGGCCCATCGATAAAGACGATTAAGTACGGTAACGAGGCCCCGGGGGATGTATGTATAGCACGCTACTGCGTGGATGTTCCAGAACATGGGTTCGTAGTCCCGACGCGCTGCCGGCCCCTGTTTTCGTTCTTGCTATCTGAGCGCTTGTTTTCGCGTGTATCAGCTTTATGCACATAATAAAAAGTGATGCAGAGATAAGTTCTTCTAATCTGACAGGGAATAAATGAGTGCCCGATGGACTGGGTCACTTAATGGCATCTGCCTTCCGAGAGTAAAGGTGCCATTTCCACTCTTT\n",
      "TATTATTTCCTCAGTCGTTCTATGTTTCCCCCGCGGTCAAGTCGCTTTAGTGCTATGCGTAAAGGTTACGTGAGTTTATTTAGGCGGTTGGTGGTAACAGTATCGTATTGCGGGAGCTGTAGCTCCACCAAATACGTTCAGCATATACAATGGCCAATTGCTTTTACAGTGGACTTCGCCCAGATGATACATACTCCTGCGCCATCTTATCTATGTGAGACGGCGTGAGTGCGCGACAATCGATCAGAATACTGCATCCCAGTTTACGCTGCATAGTCAGCACGCTGGTAACAGCGAAAAGTGCTATCGACCCCCAGAGATGCATGCCTGTTGGGCGATCATCGCAAGTGCGTAAATCCGCTCACGGGTCCAACGATGACTTCACCTAATATGACTGCGATAAAATATAGGACGAGAAGATCTTAATTGGGGGACGGACGCGAGCCTCGATTTGTGAAAAAATGGGGTTCGTTTGCGGACCCAAAAGATAAGCACACCTTGTACGGTTGGACTCATCCTCACTTGCCTCGTCACGTGGCATACCAACGTTGGTCACTTCAACGCGCCGTAGCCGTCAATGCCTGAATAGTACCATCGTGAATCCTCTTCAAGGTTCTAATCCTTGGGATTTTAAAAAGTTCATGGCTACGAGTAAGGTTCCGCTCGGGCACTTATAGTAGTGTGCTCGAACCGCCATCGAGGAAAGACCCATTAAGTACGGAACGTGGCCCCGGGGGAATGTATATATAGCACGCTAACTGCGTGGCTTTCCAAGCACGCGGTCATAGTTCGCCAGAGGGCTGTTTGCCCTTTTCGTTATACCTGATCTTCGCGCCTGTTTCGCTGCTGTTATCTAGCTTAATTACACTATGATGAAAACGGTGCATGCAGGAGATAGTTCGTTCAAATCGGAGGATCTTCGTGCCGTCATGGGTGCCTTAATGGCTCCGCACTTCAGAGTAAGGCCGGATTTCCTCTAT\n",
      "11.7 s ± 3.06 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "852.0 years for the whole genome\n"
     ]
    }
   ],
   "source": [
    "s1 = random_dna_seq()\n",
    "s2 = mutate_dna_seq(s1)\n",
    "print(s1)\n",
    "print(s2)\n",
    "a = %timeit -o nw_alignment(dna_sub_mat, dbet, s1, s2, gap=-3)\n",
    "print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "If we wanted to shift this one position at a time along the whole genome and check the alignments, how long would it take?\n",
    "\n",
    "That's a long time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's make it faster! (Just because)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "So in reality, we don't actually want to use this algorithm to align our fragments to the whole genome. It's too slow, and there's no good way to decide which alignment is \"best.\" It's still a good introduction to thinking about these types of problems, though. And since it's actually fairly easy and demonstrates how you can improve your code by understanding your algorithm, we'll do something to make it a bit faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "||-|A|C|G|T|T|T|G|T|C|G|C|\n",
    "|-|\n",
    "|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
    "|__A__|↑-3|↖1|←-2||||||||||\n",
    "|__C__|↑-6|↑-2|||||||||||\n",
    "|__T__|↑-9||||||||||||\n",
    "|__T__|↑-12||||||||||||\n",
    "|__T__|↑-15||||||||||||\n",
    "|__C__|↑-18||||||||||||\n",
    "|__T__|↑-21||||||||||||\n",
    "|__G__|↑-24||||||||||||\n",
    "|__C__|↑-27||||||||||||\n",
    "\n",
    "We can't calculate a whole row or column at a time because the values depend on those in the same row/column. But what about diagonals?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "If you look at the diagonals, you know the values above and to the left, so you have everything you need to calculate your score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We'll \"get rid of\" our nested loop (really just abstract it into a faster numpy \"loop\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This is going to take a couple more steps, but it will be worth it in the end.\n",
    "\n",
    "First we pre-calculate the \"upper-left score\" for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sub_values(sub_mat, abet, seq1, seq2):\n",
    "    # convert the sequences to numbers\n",
    "    seq1_ind = [abet.index(i) for i in seq1]\n",
    "    seq2_ind = [abet.index(i) for i in seq2]\n",
    "    sub_vals = np.array([[0] * (len(seq2)+1)] + [[0] + [sub_mat[y, x] for x in seq2_ind] for y in seq1_ind], int)\n",
    "    return sub_vals\n",
    "\n",
    "sub_values(dna_sub_mat, dbet, 'AACGTTA', 'AAGCTTAAAAAAAA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Then we get a list of all the diagonals in the matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[array([1]), array([1])],\n",
       " [array([2, 1]), array([1, 2])],\n",
       " [array([3, 2, 1]), array([1, 2, 3])],\n",
       " [array([4, 3, 2]), array([1, 2, 3])],\n",
       " [array([5, 4, 3]), array([1, 2, 3])],\n",
       " [array([6, 5, 4]), array([1, 2, 3])],\n",
       " [array([6, 5]), array([2, 3])],\n",
       " [array([6]), array([3])]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diags(l1, l2):\n",
    "    ys = np.array([np.arange(l1) + 1 for i in np.arange(l2)])\n",
    "    xs = np.array([np.arange(l2) + 1 for i in np.arange(l1)])\n",
    "    diag_ys = [np.flip(ys.diagonal(i), 0) for i in range(1-l2, l1)]\n",
    "    diag_xs = [xs.diagonal(i) for i in range(1-l1, l2)]\n",
    "    index_list = []\n",
    "    for y, x in zip(diag_ys, diag_xs):\n",
    "        index_list.append([y, x])\n",
    "    return index_list\n",
    "\n",
    "diags(6, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And here's the actual function. It takes the same arguments and produces the same matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[    0,    -8,   -16, ..., -7824, -7832, -7840],\n",
       "        [   -8,     0,    -7, ..., -7815, -7823, -7831],\n",
       "        [  -16,    -8,     1, ..., -7806, -7814, -7822],\n",
       "        ..., \n",
       "        [-7984, -7975, -7966, ...,   321,   328,   337],\n",
       "        [-7992, -7983, -7974, ...,   313,   321,   329],\n",
       "        [-8000, -7991, -7982, ...,   305,   313,   322]]),\n",
       " array([[1, 1, 1, ..., 1, 1, 1],\n",
       "        [0, 2, 2, ..., 1, 1, 1],\n",
       "        [0, 0, 2, ..., 1, 1, 1],\n",
       "        ..., \n",
       "        [0, 0, 0, ..., 2, 0, 2],\n",
       "        [0, 0, 0, ..., 0, 2, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 2]]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def FastNW(sub_mat, abet, seq1, seq2, gap=-8):\n",
    "    sub_vals = sub_values(sub_mat, abet, seq1, seq2)\n",
    "    # Get the lengths of the sequences\n",
    "    seq1_len, seq2_len = len(seq1), len(seq2)\n",
    "    # Create the scoring and arrow matrices\n",
    "    score_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
    "    arrow_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
    "    # Fill first column and row of score matrix with scores based on gap penalty\n",
    "    score_mat[0] = np.arange(seq2_len+1) * gap\n",
    "    score_mat[:,0] = np.arange(seq1_len+1) * gap\n",
    "    # Fill top row of arrow matrix with ones (left arrow)\n",
    "    arrow_mat[0] = np.ones(seq2_len+1)\n",
    "    # Get the list of diagonals\n",
    "    diag_list = diags(seq1_len, seq2_len)\n",
    "    # fill in the matrix\n",
    "    for diag in diag_list:\n",
    "        # Matrix to hold all three possible scores for every element in the diagonal\n",
    "        f = np.zeros((3, len(diag[0])), float)\n",
    "        # Cell above + gap penalty for every cell in the diagonal\n",
    "        x, y = diag[0]-1, diag[1]\n",
    "        f[0] = score_mat[x, y] + gap\n",
    "        # Cell to the left + gap penalty for every cell in the diagonal\n",
    "        x, y = diag[0], diag[1]-1\n",
    "        f[1] = score_mat[x, y] + gap\n",
    "        # Cell to the upper left + alignment score for every cell in the diagonal\n",
    "        x, y = diag[0]-1, diag[1]-1\n",
    "        f[2] = score_mat[x,y] + sub_vals[diag]\n",
    "        max_score = (f.max(0))\n",
    "        max_score_pos = f.argmax(0)\n",
    "        score_mat[diag] = max_score\n",
    "        arrow_mat[diag] = max_score_pos\n",
    "    return score_mat, arrow_mat\n",
    "\n",
    "FastNW(dna_sub_mat, dbet, s1, s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "So how much faster is it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.04 s ± 369 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "658.5 years for the whole genome\n",
      "589 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "43.0 years for the whole genome\n"
     ]
    }
   ],
   "source": [
    "s1 = random_dna_seq()\n",
    "s2 = mutate_dna_seq(s1)\n",
    "a = %timeit -o nw_alignment(dna_sub_mat, dbet, s1, s2)\n",
    "print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))\n",
    "a = %timeit -o FastNW(dna_sub_mat, dbet, s1, s2)\n",
    "print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Now why did we use the substitution matrix?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here's how DNA translates to protein:\n",
    "\n",
    "[Wikipedia](https://en.wikipedia.org/wiki/DNA_codon_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "So we can align proteins, too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "blosum50 = np.array(\n",
    "    [[ 5,-2,-1,-2,-1,-1,-1, 0,-2,-1,-2,-1,-1,-3,-1, 1, 0,-3,-2, 0],\n",
    "     [-2, 7,-1,-2,-1, 1, 0,-3, 0,-4,-3, 3,-2,-3,-3,-1,-1,-3,-1,-3],\n",
    "     [-1,-1, 7, 2,-2, 0, 0, 0, 1,-3,-4,-0,-2,-4,-2,-1, 0,-4,-2,-3],\n",
    "     [-2,-2, 2, 8,-4, 0, 2,-1,-1,-4,-4,-1,-4,-5,-1, 0,-1,-5,-3,-4],\n",
    "     [-1,-4,-2,-4,13,-3,-3,-3,-3,-2,-2,-3,-2,-2,-4,-1,-1,-5,-3,-1],\n",
    "     [-1,-1, 0, 0,-3, 7, 2,-2, 1,-3,-2, 2, 0,-4,-1,-0,-1,-1,-1,-3],\n",
    "     [-1, 0, 0, 2,-3, 2, 6,-3, 0,-4,-3, 1,-2,-3,-1,-1,-1,-3,-2,-3],\n",
    "     [ 0,-3, 0,-1,-3,-2,-3, 8,-2,-4,-4,-2,-3,-4,-2, 0,-2,-3,-3,-4],\n",
    "     [-2, 0, 1,-1,-3, 1, 0,-2,10,-4,-3, 0,-1,-1,-2,-1,-2,-3,-1, 4],\n",
    "     [-1,-4,-3,-4,-2,-3,-4,-4,-4, 5, 2,-3, 2, 0,-3,-3,-1,-3,-1, 4],\n",
    "     [-2,-3,-4,-4,-2,-2,-3,-4,-3, 2, 5,-3, 3, 1,-4,-3,-1,-2,-1, 1],\n",
    "     [-1, 3, 0,-1,-3, 2, 1,-2, 0,-3,-3, 6,-2,-4,-1, 0,-1,-3,-2,-3],\n",
    "     [-1,-2,-2,-4,-2, 0,-2,-3,-1, 2, 3,-2, 7, 0,-3,-2,-1,-1, 0, 1],\n",
    "     [-3,-3,-4,-5,-2,-4,-3,-4,-1, 0, 1,-4, 0, 8,-4,-3,-2, 1, 4,-1],\n",
    "     [-1,-3,-2,-1,-4,-1,-1,-2,-2,-3,-4,-1,-3,-4,10,-1,-1,-4,-3,-3],\n",
    "     [ 1,-1, 1, 0,-1, 0,-1, 0,-1,-3,-3, 0,-2,-3,-1, 5, 2,-4,-2,-2],\n",
    "     [ 0,-1, 0,-1,-1,-1,-1,-2,-2,-1,-1,-1,-1,-2,-1, 2, 5,-3,-2, 0],\n",
    "     [-3,-3,-4,-5,-5,-1,-3,-3,-3,-3,-2,-3,-1, 1,-4,-4,-3,15, 2,-3],\n",
    "     [-2,-1,-2,-3,-3,-1,-2,-3, 2,-1,-1,-2, 0, 4,-3,-2,-2, 2, 8,-1],\n",
    "     [ 0,-3,-3,-4,-1,-3,-3,-4,-4, 4, 1,-3, 1,-1,-3,-2, 0,-3,-1, 5]])\n",
    "pbet = 'ARNDCQEGHILKMFPSTWYV'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NQ1pamqxtAEhISEBYWBj8/Pxw48YNfPLJJwgPD8esWbPwxRdfyN43XOtCCGg0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0093c93550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1 = [random.choice(pbet) for _ in range(10)]\n",
    "s2 = [random.choice(pbet) if random.random() < .25 else x for x in s1] + [random.choice(pbet) for _ in range(10)]\n",
    "score_mat, arrow_mat = FastNW(blosum50, pbet, s1, s2)\n",
    "visual_scoring_matrix(s1, s2, score_mat, arrow_mat)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other things we can do with this algorithm:\n",
    "* Local alignment\n",
    "* Affine gap penalties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other useful bioinformatics Python packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "853.3953523635864 MiB\n",
      "83257441 bp\n",
      "TGTTATTCTTTTGCGCCTGTTGCTGTCCCTAGGATACCGGAGATACTCAGCCTGTGCTTGCCAAGGGTATACATATGTATTCAAATACGCACATCCTGTAAACAGCTGTGGTGGAAACCCAAGACCAGACTTGTAGAGACCTGGATCTATTTTGCAGGCCACAGTGAAGGGCTGTGTGAGCTTGAGTCCTCTCTGAGCCTCACGTTTTACATCTAAAACATGAGGGGCTGGTCCAACTGGTTTCCAAAAGTCCTTCCAACTGGGACAGCCTGTGGATCTGCCAGTGCTCCAGCTGCACCAGCATGCAGCACATGTGCCCACACGTGTACACACCTGCTTACGCACGGCCACCCACCATGCTGAAGAACCAGCCCTCGGCATCCTTGCAGCTCTTCTCCACCAATGTCTTGTCCTGGTCACGCATCTCATTCAGGATGCAGCTCAGGTTCACTCCAGGCACAGTGTCCATCTTCACACTGACATCCTCATCCACCTGACCT\n"
     ]
    }
   ],
   "source": [
    "from pysam import FastaFile\n",
    "from os.path import getsize\n",
    "\n",
    "print(getsize('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz')/1024**2, 'MiB')\n",
    "\n",
    "\n",
    "with FastaFile('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz') as myfasta:\n",
    "    chr17len = myfasta.get_reference_length('17')\n",
    "    print(chr17len, 'bp')\n",
    "    seq = myfasta.fetch('17', int(chr17len/2), int(chr17len/2)+500)\n",
    "print(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203 µs ± 1.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "with FastaFile('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz') as myfasta:\n",
    "    chr17len = myfasta.get_reference_length('17')\n",
    "    %timeit myfasta.fetch('17', int(chr17len/2), int(chr17len/2)+500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())\n",
      "Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))\n"
     ]
    }
   ],
   "source": [
    "from Bio.Seq import Seq\n",
    "from Bio.Alphabet import IUPAC\n",
    "coding_dna = Seq(\"ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG\", IUPAC.unambiguous_dna)\n",
    "print(coding_dna.__repr__())\n",
    "print(coding_dna.translate().__repr__())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "## Any questions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other possible topics:\n",
    "How do you actually get from reads to something usable?\n",
    "![gatk_call_variants_old](images/gatk_call_variants_old.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Why does bioinformatics use flat files and not databases for everything?\n",
    "The best answer I can give is that of Heng Li when talking about Tabix:\n",
    "> It is straightforward to achieve overlap queries using the standard B-tree index (with or without binning) implemented in all SQL databases, or the R-tree index in PostgreSQL and Oracle. But there are still many reasons to use tabix. Firstly, tabix directly works with a lot of widely used TAB-delimited formats such as GFF/GTF and BED. We do not need to design database schema or specialized binary formats. Data do not need to be duplicated in different formats, either. Secondly, tabix works on compressed data files while most SQL databases do not. The GenCode annotation GTF can be compressed down to 4%. Thirdly, tabix is fast. The same indexing algorithm is known to work efficiently for an alignment with a few billion short reads. SQL databases probably cannot easily handle data at this scale. Last but not the least, tabix supports remote data retrieval. One can put the data file and the index at an FTP or HTTP server, and other users or even web services will be able to get a slice without downloading the entire file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "source": [
    "What things can you do in bioinformatics?\n",
    "* Look at variants - which ones matter?\n",
    "* Assemble genomes for new organisms\n",
    "* Compare genomes of different organisms\n",
    "* Biomarker studies:\n",
    " * Compare gene expression levels across case and control samples, see if you can find significantly different ones. (It's messy. Also, R is better for this than Python (shhh...))\n",
    "* Molecular interaction networks"
   ]
  }
 ],
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