1-sequence-analysis-exercises.ipynb

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    "In this chapter, we will use BioPython to read in some genomic sequences. We will see how BioPython reads from files, and we'll inspect the resulting `SeqRecord` objects. We'll use the genome of e. coli as our leading example, as it is very well understood and not too large.\n",
    "\n",
    "APIs covered:\n",
    "- `Bio.Seq`\n",
    "- `Bio.SeqIO`\n",
    "- `Bio.SeqUtils`\n",
    "\n",
    "We'll focus mostly on reading in data from FASTA files, but reading data from other formats will be similar.\n"
   ]
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   "source": [
    "The _genome_ is the collection of all the genetic material from an organism. The _genome sequence_ is a long string in the alphabet A, C, G, T. Depending on organism, the genome sequence differs in length, ranging from the very short (160 000 base pairs, a bacterium called Carsonella ruddi) to the very long (133 billion basepairs in the marbled lungfish). The human genome is about 3 billion base pairs long. "
   ]
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   "source": [
    "Genomic sequences are commonly (but not always) saved as text files, and [FASTA](https://en.wikipedia.org/wiki/FASTA_format) is a very common file format. A FASTA file can contain one or several records, and each record consists of a header line, followed by the sequence itself. Each description is a single line starting with a `>` sign and followed by a unique identifier for the sequence. The sequence can either be a nucleotide sequence (A, C, G, T, ..) or a protein sequence."
   ]
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     "text": [
      ">NR_132798.1 Roccella elisabethae S L14846 ITS region; from TYPE material\r\n",
      "GAACCTGCGGAAGGATCATTATTAAGACAGGGCCTGTTAGGCCCGACCTCCAACCCCCTGTCTACCCATCTATTGTTGCT\r\n",
      "TTGGCGGTGCGTCTGGTACTTGCCGCACGCGTTACGGCTAAGAACCGCCAGTGGCCCGGCGCGCGCGCGGGGCCGCTGAG\r\n",
      "TCGCCGTCAAGGGGCTACCACTAAATTCTCCGCACTAGTAAGCAGTCTAAGAGGAACTTTTAAAATAATCGCTTTTAAAA\r\n",
      "AAAAAAAAACTTTCAACAACGGATCTCTTGGTTCTGGCATCGATGAAGAACGCAGCGAAATGCGATAAGTAATGTGAATT\r\n",
      "GCAGAATTCAGTGAATCATCGAATCTTTGAACGCATCTTGCGCCCTCCGGTATCCCCGGTGGGCATGCCTGTTCGAGCGT\r\n",
      "CATTCGTTAAAATCATCAAGCACCGCTTGGTATTGGGTCTGACGTCCCTGCACCCGCAGAGGGACGTACCCTAAACCCCT\r\n",
      "CGGCAACGTCTCGTGGCCGTAGGCGTAGCGGAACATAATCTTTACCACGTCTACGAGGCGCGCGAGGTCGTCCAGCCCCT\r\n",
      "CTGAATCTAACGTAGAACCTTACCGTTGACCTCGGATCAGGTAGGAGTACCCGCTGAACTTAAGCATATCAATAAGCGGA\r\n",
      "GGAA\r\n"
     ]
    }
   ],
   "source": [
    "!head data/fungi.ITS.fna"
   ]
  },
  {
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   "source": [
    "A FASTA file can contain one or more records. Usually, counting the number of sequences in a file is equivalent to counting the number of lines that start with `>`."
   ]
  },
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   "source": [
    "1. Write a small snippet of code that counts the number of sequences in the file `data/fungi.ITS.fna`."
   ]
  },
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   "source": [
    "# Your implementation here\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "When dealing with sequences in text format, it is useful to keep in mind that one base is one character, so that genome sizes can easily be estimated:\n",
    "\n",
    "- The genome of e. coli is about 4.6 million base pairs in length, and takes up 4.6MB of disk space.\n",
    "- A human genome (3 billion base pairs) is about 3 GB in size.\n",
    "\n",
    "\n"
   ]
  },
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    "For most of this tutorial we will work with bacterial or fungal genomes, since they have several advantages:\n",
    "\n",
    "- The genomes are fairly short: on the order of a few million base pairs, so that each bacterial genome is a few megabytes in size.\n",
    "- There is a subset of bacteria and fungi whose role in nature, medicine, industry, ... is very well understood. These organisms also have a well-sequenced genomic sequence."
   ]
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     "text": [
      "1.70\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import Bio\n",
    "\n",
    "print(Bio.__version__)"
   ]
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   "source": [
    "Reading and writing sequence records\n",
    "----"
   ]
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   "source": [
    "In this first part, we will explore how BioPython allows us to easily read and write sequences. The data that we will use for this lives in the `data/` folder of this repo. We will use FASTA files exclusively, but BioPython's [SeqIO](https://biopython.org/wiki/SeqIO) module has support for many more file formats. Usually, reading a different file format is as easy as changing a single parameter.\n",
    "\n",
    "SeqIO has several different ways of reading files:\n",
    "\n",
    "- `SeqIO.read`: for files that contain a single sequence.\n",
    "- `SeqIO.parse`: reads a file and returns an _iterator_ over SeqRecord instances\n",
    "- `SeqIO.index`: reads a file and returns a _dict-like_ object.\n",
    "\n",
    "Both `SeqIO.parse` and `SeqIO.index` don't read all data into memory at once (useful when you have a 100GB sequence file!) but they differ in what they return. `SeqIO.parse` opens the file for reading, and returns an iterator over sequences. This allows you to process sequences in _streaming_ fashion, e.g.\n",
    "```python\n",
    "seqs = SeqIO.parse(\"my-fasta-file.fa\", \"fasta\")\n",
    "for seq in seqs:\n",
    "    print(seq)\n",
    "\n",
    "seqs.close()\n",
    "```\n",
    "\n",
    "`SeqIO.index` does a little more work. It scans through the file and records for each sequence the ID of the sequence and the location of the sequence in the file. This allows you to retrieve a single sequence record at a time by specifying the ID:\n",
    "\n",
    "```python\n",
    "index = SeqIO.index(\"my-fasta-file.fa\", \"fasta\")\n",
    "print(index[\"NR_132798.1\"])\n",
    "\n",
    "```"
   ]
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   "source": [
    "One last note: reading a different file format in BioPython is as easy as changing the format string. For example, to read an AB1 file (a file format used in Sanger sequencing), you can do \n",
    "```python\n",
    "abi_seqs = SeqIO.parse(\"fname.ab1\", \"ab1\")\n",
    "```"
   ]
  },
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   "source": [
    "from Bio import SeqIO"
   ]
  },
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   "source": [
    "In the data folder, we have a file called `data/fungi.ITS.fna`.\n",
    "\n",
    "1. Open the file for reading with `SeqIO.parse`.\n",
    "\n",
    "1. Extract the first record and print it. What data type is this record?\n",
    "\n",
    "1. Close the sequence file.\n",
    "\n",
    "1. Open the file for reading with `SeqIO.index`. Extract the record for the sequence with ID `NR_111007.1`. How many records are there in the entire file?"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Your implementation here\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Lastly, run some experiments to compare timings for `SeqIO.parse` and `SeqIO.index`. Which one is faster?\n",
    "\n",
    "Hint: you can use the `%timeit` IPython magic command to record the execution time for a Python function call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Aside: it is good form to _close_ the sequence index or iterator explicitly when you are done with it. This will free up whatever resources were associated with it, and signals to the reader of your code that you are \"done\" with reading sequences. Moreover, Python 3 will warn you explicitly when an open file is garbage collected.\n",
    "\n",
    "To avoid having to pepper your code with explicit calls to `index.close()`, you can pass in an explicit file handle, or use the `contextlib.closing` context manager."
   ]
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     "name": "stdout",
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     "text": [
      "ID: NR_132798.1\n",
      "Name: NR_132798.1\n",
      "Description: NR_132798.1 Roccella elisabethae S L14846 ITS region; from TYPE material\n",
      "Number of features: 0\n",
      "Seq('GAACCTGCGGAAGGATCATTATTAAGACAGGGCCTGTTAGGCCCGACCTCCAAC...GAA', SingleLetterAlphabet())\n",
      "ID: NR_132798.1\n",
      "Name: NR_132798.1\n",
      "Description: NR_132798.1 Roccella elisabethae S L14846 ITS region; from TYPE material\n",
      "Number of features: 0\n",
      "Seq('GAACCTGCGGAAGGATCATTATTAAGACAGGGCCTGTTAGGCCCGACCTCCAAC...GAA', SingleLetterAlphabet())\n"
     ]
    }
   ],
   "source": [
    "from contextlib import closing\n",
    "\n",
    "with closing(SeqIO.parse(\"data/fungi.ITS.fna\", \"fasta\")) as fungi0:\n",
    "    print(next(fungi0))\n",
    "    \n",
    "with open(\"data/fungi.ITS.fna\") as fp:\n",
    "    fungi1 = SeqIO.parse(fp, \"fasta\")\n",
    "    print(next(fungi1))"
   ]
  },
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   "source": [
    "Exploring sequence records\n",
    "----"
   ]
  },
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   "source": [
    "Now let's explore the `SeqRecord` class a little more in detail. In the repository we have a FASTA file `data/GCF_000005845.2_ASM584v2_genomic.fna` containing a reasonably up-to-date reference genome for the bacterium e. coli.\n",
    "\n",
    "1. Read this file into a single sequence record.\n",
    "1. Find the length of this sequence record.\n",
    "1. What is the description for the sequence record?\n",
    "1. Call the `repr` function on the \"sequence\" itself (accessible via the `.seq` attribute). What type is this object?\n",
    "\n",
    "For the last part, do not attempt to print the sequence directly (it's long)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "As you can see, the `SeqRecord` contains the full sequence together with some metadata. The `Seq` object contains just the sequence, with some information about the alphabet. Both the sequence record and the sequence itself are object that can be sliced, iterated over, ... and therefore they interact quite naturally with the rest of the Python universe.\n",
    "\n",
    "For example, let's count the number of bases A, C, G, T across the entire sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'A': 1142742, 'C': 1180091, 'G': 1177437, 'T': 1141382})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "record = SeqIO.read(\"data/GCF_000005845.2_ASM584v2_genomic.fna\", \"fasta\")\n",
    "\n",
    "seq = record.seq\n",
    "Counter(record.seq)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Let's slice out a particular region of the sequence and assign it to a variable `cds`. The reason for the particular indices will become clear later on..."
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('ATGTCTGAACCACGTTTCGTACACCTGCGGGTGCACAGCGACTACTCGATGATC...TAA', SingleLetterAlphabet())"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, stop = 205125, 208608  # dnaE, dna polymerase III, alpha subunit\n",
    "\n",
    "cds = seq[start:stop]\n",
    "cds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "3483"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cds)"
   ]
  },
  {
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   "source": [
    "Last, sequences aren't just plain old containers, they also have some specific genomic functionality."
   ]
  },
  {
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   "source": [
    "We can ask for the _reverse complement_..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('TTAGTCAAACTCCAGTTCCACCTGCTCCGAACCAATGAGGCCACGGAGATCGTT...CAT', SingleLetterAlphabet())"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cds.reverse_complement()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "... or the RNA sequence for a given DNA sequence ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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    "collapsed": false,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('AUGUCUGAACCACGUUUCGUACACCUGCGGGUGCACAGCGACUACUCGAUGAUC...UAA', RNAAlphabet())"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cds.transcribe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "... and even the protein that is expressed by a given DNA sequence, according to the \"central dogma\" of molecular biology. \n",
    "\n",
    "By setting `cds=True` we ask BioPython to check that the length of the DNA sequence is a multiple of 3, since each amino acid is encoded by 3 bases, and that the sequence starts with a _start codon_ and ends with a _stop codon_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('MSEPRFVHLRVHSDYSMIDGLAKTAPLVKKAAALGMPALAITDFTNLCGLVKFY...EFD', ExtendedIUPACProtein())"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein = cds.translate(cds=True)\n",
    "protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSEPRFVHLRVHSDYSMIDGLAKTAPLVKKAAALGMPALAITDFTNLCGLVKFYGAGHGAGIKPIVGADFNVQCDLLGDELTHLTVLAANNTGYQNLTLLISKAYQRGYGAAGPIIDRDWLIELNEGLILLSGGRMGDVGRSLLRGNSALVDECVAFYEEHFPDRYFLELIRTGRPDEESYLHAAVELAEARGLPVVATNDVRFIDSSDFDAHEIRVAIHDGFTLDDPKRPRNYSPQQYMRSEEEMCELFADIPEALANTVEIAKRCNVTVRLGEYFLPQFPTGDMSTEDYLVKRAKEGLEERLAFLFPDEEERLKRRPEYDERLETELQVINQMGFPGYFLIVMEFIQWSKDNGVPVGPGRGSGAGSLVAYALKITDLDPLEFDLLFERFLNPERVSMPDFDVDFCMEKRDQVIEHVADMYGRDAVSQIITFGTMAAKAVIRDVGRVLGHPYGFVDRISKLIPPDPGMTLAKAFEAEPQLPEIYEADEEVKALIDMARKLEGVTRNAGKHAGGVVIAPTKITDFAPLYCDEEGKHPVTQFDKSDVEYAGLVKFDFLGLRTLTIINWALEMINKRRAKNGEPPLDIAAIPLDDKKSFDMLQRSETTAVFQLESRGMKDLIKRLQPDCFEDMIALVALFRPGPLQSGMVDNFIDRKHGREEISYPDVQWQHESLKPVLEPTYGIILYQEQVMQIAQVLSGYTLGGADMLRRAMGKKKPEEMAKQRSVFAEGAEKNGINAELAMKIFDLVEKFAGYGFNKSHSAAYALVSYQTLWLKAHYPAEFMAAVMTADMDNTEKVVGLVDECWRMGLKILPPDINSGLYHFHVNDDGEIVYGIGAIKGVGEGPIEAIIEARNKGGYFRELFDLCARTDTKKLNRRVLEKLIMSGAFDRLGPHRAALMNSLGDALKAADQHAKAEAIGQADMFGVLAEEPEQIEQSYASCQPWPEQVVLDGERETLGLYLTGHPINQYLKEIERYVGGVRLKDMHPTERGKVITAAGLVVAARVMVTKRGNRIGICTLDDRSGRLEVMLFTDALDKYQQLLEKDRILIVSGQVSFDDFSGGLKMTAREVMDIDEAREKYARGLAISLTDRQIDDQLLNRLRQSLEPHRSGTIPVHLYYQRADARARLRFGATWRVSPSDRLLNDLRGLIGSEQVELEFD\n"
     ]
    }
   ],
   "source": [
    "print(protein)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
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   },
   "source": [
    "In the above, we have glossed over one important detail: how does BioPython know which _codons_ correspond to each amino acid? The answer is that there are different codon tables for the different branches of life, and by default BioPython uses table #1, the standard table for Eukarya."
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 1 Standard, SGC0\n",
      "\n",
      "  |  T      |  C      |  A      |  G      |\n",
      "--+---------+---------+---------+---------+--\n",
      "T | TTT F   | TCT S   | TAT Y   | TGT C   | T\n",
      "T | TTC F   | TCC S   | TAC Y   | TGC C   | C\n",
      "T | TTA L   | TCA S   | TAA Stop| TGA Stop| A\n",
      "T | TTG L(s)| TCG S   | TAG Stop| TGG W   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "C | CTT L   | CCT P   | CAT H   | CGT R   | T\n",
      "C | CTC L   | CCC P   | CAC H   | CGC R   | C\n",
      "C | CTA L   | CCA P   | CAA Q   | CGA R   | A\n",
      "C | CTG L(s)| CCG P   | CAG Q   | CGG R   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "A | ATT I   | ACT T   | AAT N   | AGT S   | T\n",
      "A | ATC I   | ACC T   | AAC N   | AGC S   | C\n",
      "A | ATA I   | ACA T   | AAA K   | AGA R   | A\n",
      "A | ATG M(s)| ACG T   | AAG K   | AGG R   | G\n",
      "--+---------+---------+---------+---------+--\n",
      "G | GTT V   | GCT A   | GAT D   | GGT G   | T\n",
      "G | GTC V   | GCC A   | GAC D   | GGC G   | C\n",
      "G | GTA V   | GCA A   | GAA E   | GGA G   | A\n",
      "G | GTG V   | GCG A   | GAG E   | GGG G   | G\n",
      "--+---------+---------+---------+---------+--\n"
     ]
    }
   ],
   "source": [
    "from Bio.Data import CodonTable\n",
    "standard_table = CodonTable.unambiguous_dna_by_id[1]\n",
    "\n",
    "print(standard_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Recall that we are working with e. coli, a bacterium, so this is not the correct translation table. \n",
    "\n",
    "1. Take a look at `CodonTable.unambiguous_dna_by_name` to find a translation table that is appropriate for e. coli.\n",
    "\n",
    "2. Run the translation again, and compare the resulting protein to the one that we obtained using the standard table. Are the two proteins identical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    " Fortunately, the translated protein ends up being identical in this case. \n",
    " \n",
    " 1. Check this explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "More genomic information with SeqUtils\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "SeqUtils is a BioPython module to compute some quantities of interest for genomic sequences, such as GC content, molecular weights, etc. It is not strictly necessary to use this module; many of these quantities can also be computed with straightforward Numpy (or a little Cython)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Bio import SeqUtils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here we focus on computing the _GC skew_ along a sliding window. That is, we break the genome up into pieces of a fixed size (1000 bases in the example) and for each piece we compute the quantity\n",
    "\n",
    "$$\n",
    "    \\text{skew} = \\frac{G - C}{G + C}\n",
    "$$\n",
    "\n",
    "If there are as many Gs as there are Cs, the skew is 0, and if G is more/less abundant, the skew is positive/negative.\n",
    "\n",
    "Given that we saw earlier that there are globally about 25% As, 25% Cs, 25% Gs, and 25% Ts, should we conclude that the skew is always zero? Let's find out!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "1. Use `SeqUtils.GC_skew` to compute the GC skew with a 1000-base window. Hint: you should get a list of 4642 numbers.\n",
    "1. The result of the first step is a list of values, which is not very informative. Make a plot of the GC skew. Does it look like the skew is always roughly 0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If all goes well, the previous step should have given you a fairly noisy plot. We can get a clearer signal by instead plotting the _cumulative GC skew_.\n",
    "\n",
    "1. Make a plot of the cumulative GC skew. (_Hint_: you can use `numpy.cumsum` to compute the cumulative sum of a list of values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is quite surprising: we may have assumed that Gs and Cs would be distributed more or less randomly throughout the genome. Instead we see that the amount of Gs steadily increases until it reaches a maximum, and then decreases again. \n",
    "\n",
    "\n",
    "There is a good biological reason for this. Normally DNA is quite stable, and in equilibrium you expect to see about as many G as there is C (and similarly for A and T). However, during replication DNA is not in equilibrium. Replication of bacterial chromosomes usually starts at a single, well-defined location. The DNA strand is opened into two strands of single-stranded DNA, and for a circular chromosome (like that of e. coli) replication then proceeds in two directions. However, DNA polymerase, the enzyme that replicates DNA, can only move from the 5' to the 3' end of a DNA strand. That means that one strand, the _leading_ strand, is replicated continuously, while the other one (the _lagging_ strand) is replicated in fits and starts. It is thought that this asymetry ultimately leads to the imbalance between G and C that we observe.\n",
    "\n",
    "This provides a mechanism to find the _origin of replication_ in a genome (the place where DNA replication starts): it corresponds roughly to the _minimum_ of the cumulative GC skew. (This mechanism is not always appropriate).\n",
    "\n",
    "More information in [Rocha, _The replication-related organization of bacterial genomes._](https://www.ncbi.nlm.nih.gov/pubmed/15184548)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Using sequence databases to find more information about genomes\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We are not limited to writing our own algorithms to find e.g. the origin of replication. For well-studied organisms such as e. coli a vast array of information is available (e.g. genes and coding sequences, repeats, CRISPR arrays, ...) that can be queried from more-or-less standard databases (available online).\n",
    "\n",
    "One such database is Genbank, XYZ.\n",
    "\n",
    "We will look at the Genbank record for e. coli. To avoid having to download this, the entire record has been provided with this repository. It is about 12MB in size (5MB for the sequence, and 7MB for the remaining metadata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOCUS       NC_000913            4641652 bp    DNA     circular CON 08-AUG-2016\r\n",
      "DEFINITION  Escherichia coli str. K-12 substr. MG1655, complete genome.\r\n",
      "ACCESSION   NC_000913\r\n",
      "VERSION     NC_000913.3\r\n",
      "DBLINK      BioProject: PRJNA57779\r\n",
      "            BioSample: SAMN02604091\r\n",
      "            Assembly: GCF_000005845.2\r\n",
      "KEYWORDS    RefSeq.\r\n",
      "SOURCE      Escherichia coli str. K-12 substr. MG1655\r\n",
      "  ORGANISM  Escherichia coli str. K-12 substr. MG1655\r\n"
     ]
    }
   ],
   "source": [
    "!head data/GCF_000005845.2_ASM584v2_genomic.gbff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's start by reading the Genbank record from the file `data/GCF_000005845.2_ASM584v2_genomic.gbff` into a `SeqRecord`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "gbk_record = SeqIO.read(\"data/GCF_000005845.2_ASM584v2_genomic.gbff\", \"genbank\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Like our previous `SeqRecord` instances, this one has a `.seq`, a `.id`, a `.description`, etc. It also has some more information that's specific to Genbank. Let's look at the `.features` attribute. This is a list of genomic features (genes, coding sequences, origin of replication, ...)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeqFeature(FeatureLocation(ExactPosition(5233), ExactPosition(5530), strand=1), type='CDS')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = gbk_record.features[10]\n",
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CDS'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Each feature has an `.extract` method to slice out the region of the sequence that corresponds to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCC...TAA', IUPACAmbiguousDNA())"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.extract(gbk_record.seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Features also have a `.qualifiers` dictionary with metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('gene', ['yaaX']),\n",
       "             ('locus_tag', ['b0005']),\n",
       "             ('gene_synonym', ['ECK0005; JW0004']),\n",
       "             ('codon_start', ['1']),\n",
       "             ('transl_table', ['11']),\n",
       "             ('product', ['DUF2502 family putative periplasmic protein']),\n",
       "             ('protein_id', ['NP_414546.1']),\n",
       "             ('db_xref',\n",
       "              ['ASAP:ABE-0000015',\n",
       "               'UniProtKB/Swiss-Prot:P75616',\n",
       "               'EcoGene:EG14384',\n",
       "               'GeneID:944747']),\n",
       "             ('translation',\n",
       "              ['MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDHGWWKQHYEWRGNRWHLHGPPPPPRHHKKAPHDHHGGHGPGKHHR'])])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.qualifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CDS',\n",
       " 'gene',\n",
       " 'misc_feature',\n",
       " 'mobile_element',\n",
       " 'ncRNA',\n",
       " 'rRNA',\n",
       " 'rep_origin',\n",
       " 'repeat_region',\n",
       " 'source',\n",
       " 'tRNA',\n",
       " 'tmRNA'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(feature.type for feature in gbk_record.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's put our knowledge to good use.\n",
    "\n",
    "1. Iterate over the features, and count the number of _genes_ in E. Coli. \n",
    "1. Find the location of the `rep_origin` feature. Does this agree with the answer we got from GC skew?\n",
    "1. Find the feature that corresponds to the coding sequence for the `dnaE` gene.\n",
    "1. What percentage of the genome consists of coding regions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Bonus question: let's compare the GC content in coding regions versus non-coding regions. With what we have seen of features so far, plus some Matplotlib, this is quite achievable.\n",
    "\n",
    "1. Divide the genome into coding and non-coding sequences. Hint: we already know the CDS regions; the non-CDS regions are the regions in between CDS regions.\n",
    "2. Use the `SeqUtils.GC` function to find the GC content in each region.\n",
    "3. Make a plot with two histograms: one with the GC content for CDS regions, and one with the GC content for non-CDS regions.\n",
    "\n",
    "Do you find that the GC content is significantly different for CDS and non-CDS regions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Your implementation here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Getting information from protein databases\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "At this point, we come quite far. We know what genes e. coli posseses, as well as their locations, and we even know the protein sequences (either by inspecting the Genbank record, or by doing the translation ourselves). What we don't know so far is the _why_: what is the function of all these proteins? \n",
    "\n",
    "Luckily, BioPython allows us to answer that question as well. If you inspect the protein metadata, you'll notice identifiers for that protein in _protein databases_, such as swiss-prot. Querying this information allows you to find more information about the function and structure of proteins."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Note: the following cell assumes that you have a working internet connection. If you cannot download the record for some reason, you can also use a downloaded copy that comes with this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Bio import ExPASy\n",
    "from Bio import SwissProt\n",
    "\n",
    "# P10443 is the protein made by dnaE\n",
    "with ExPASy.get_sprot_raw(\"P10443\") as handle:\n",
    "    record = SwissProt.read(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# # Use this version if downloading the record doesn't work\n",
    "# with open('data/P10443.txt') as handle:\n",
    "#     record = SwissProt.read(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Records have a few useful properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DPO3A_ECOLI\n"
     ]
    }
   ],
   "source": [
    "print(record.entry_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['83333']\n"
     ]
    }
   ],
   "source": [
    "print(record.taxonomy_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Escherichia coli (strain K12).\n"
     ]
    }
   ],
   "source": [
    "print(record.organism)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bacteria', 'Proteobacteria', 'Gammaproteobacteria', 'Enterobacterales', 'Enterobacteriaceae', 'Escherichia']\n"
     ]
    }
   ],
   "source": [
    "print(record.organism_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"FUNCTION: DNA polymerase III is a complex, multichain enzyme responsible for most of the replicative synthesis in bacteria (PubMed:2932432). This DNA polymerase also exhibits 3' to 5' exonuclease activity. The alpha chain is the DNA polymerase catalytic subunit (PubMed:2932432). It is tethered to replicating DNA by the beta sliding clamp (dnaN), which confers extremely high processivity to the catalytic subunit, copying a 5.4 kb genome in 11 seconds, a speed of at least 500 nucleotides/second at 30 degrees Celsius (PubMed:2413035). {ECO:0000269|PubMed:2413035, ECO:0000269|PubMed:2932432}.\", 'CATALYTIC ACTIVITY: Deoxynucleoside triphosphate + DNA(n) = diphosphate + DNA(n+1). {ECO:0000269|PubMed:2932432}.', \"SUBUNIT: The DNA polymerase III holoenzyme complex contains at least 10 different subunits organized into 3 functionally essential subassemblies: the Pol III core, the beta sliding clamp processivity factor and the clamp-loading complex. The Pol III core (subunits alpha, epsilon and theta) contains the polymerase and the 3'-5' exonuclease proofreading activities (PubMed:2040637). The polymerase is tethered to the template via the dimeric beta sliding clamp processivity factor. The clamp loader (also called gamma complex) assembles the beta sliding clamp onto the primed template and plays a central role in the organization and communication at the replication fork. The clamp- loading complex contains delta, delta', psi and chi, and 3 copies of either or both of two different DnaX proteins, gamma and tau. The DNA replisome complex has a single clamp loader (3 tau and 1 each of delta, delta', psi and chi subunits) which binds 3 Pol III cores (1 core on the leading strand and 2 on the lagging strand) each with a beta sliding clamp dimer. Additional proteins in the replisome are other copies of gamma, psi and chi, Ssb, DNA helicase and RNA primase (PubMed:20413500, PubMed:22157955). Interacts with the beta sliding-clamp subunit via the peptide Gln- Ala-Asp-Met-Phe (residues 920-924) (PubMed:11573000). {ECO:0000269|PubMed:11573000, ECO:0000269|PubMed:2040637, ECO:0000269|PubMed:20413500, ECO:0000269|PubMed:22157955}.\", 'INTERACTION: P0A988:dnaN; NbExp=17; IntAct=EBI-549111, EBI-542385; P03007:dnaQ; NbExp=22; IntAct=EBI-549111, EBI-549131; P06710:dnaX; NbExp=11; IntAct=EBI-549111, EBI-549140; P28630:holA; NbExp=5; IntAct=EBI-549111, EBI-549153; P0A7K2:rplL; NbExp=3; IntAct=EBI-549111, EBI-543702; P77806:ybdL; NbExp=3; IntAct=EBI-549111, EBI-543661;', 'SUBCELLULAR LOCATION: Cytoplasm.', 'SIMILARITY: Belongs to the DNA polymerase type-C family. DnaE subfamily. {ECO:0000305}.', 'SEQUENCE CAUTION: Sequence=AAA70369.1; Type=Erroneous initiation; Note=Translation N-terminally shortened.; Evidence={ECO:0000305};']\n"
     ]
    }
   ],
   "source": [
    "print(record.comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('EMBL', 'M19334', 'AAC36920.1', '-', 'Genomic_DNA'), ('EMBL', 'U70214', 'AAB08613.1', '-', 'Genomic_DNA'), ('EMBL', 'U00096', 'AAC73295.1', '-', 'Genomic_DNA'), ('EMBL', 'AP009048', 'BAA77859.1', '-', 'Genomic_DNA'), ('EMBL', 'S52931', 'AAB24889.1', '-', 'mRNA'), ('EMBL', 'M96394', 'AAA70369.1', 'ALT_INIT', 'Genomic_DNA'), ('EMBL', 'D49445', 'BAA08424.1', '-', 'Genomic_DNA'), ('PIR', 'C28390', 'DJEC3A'), ('RefSeq', 'NP_414726.1', 'NC_000913.3'), ('RefSeq', 'WP_001294757.1', 'NZ_LN832404.1'), ('PDB', '2HNH', 'X-ray', '2.30 A', 'A=1-910'), ('PDB', '2HQA', 'X-ray', '2.60 A', 'A=1-917'), ('PDB', '4GX8', 'X-ray', '1.70 A', 'A/B/C/D=1-270'), ('PDB', '4GX9', 'X-ray', '2.15 A', 'A/B/C/D=1-270'), ('PDB', '4JOM', 'X-ray', '2.90 A', 'A=1-917'), ('PDB', '5FKU', 'EM', '8.34 A', 'A=1-1160'), ('PDB', '5FKV', 'EM', '8.00 A', 'A=1-1160'), ('PDB', '5FKW', 'EM', '7.30 A', 'A=1-1160'), ('PDB', '5M1S', 'EM', '6.70 A', 'A=1-927'), ('PDBsum', '2HNH', '-'), ('PDBsum', '2HQA', '-'), ('PDBsum', '4GX8', '-'), ('PDBsum', '4GX9', '-'), ('PDBsum', '4JOM', '-'), ('PDBsum', '5FKU', '-'), ('PDBsum', '5FKV', '-'), ('PDBsum', '5FKW', '-'), ('PDBsum', '5M1S', '-'), ('ProteinModelPortal', 'P10443', '-'), ('SMR', 'P10443', '-'), ('BioGrid', '4262228', '218'), ('ComplexPortal', 'CPX-1925', 'DNA polymerase III proofreading complex'), ('DIP', 'DIP-9458N', '-'), ('IntAct', 'P10443', '70'), ('MINT', 'P10443', '-'), ('STRING', '316385.ECDH10B_0164', '-'), ('BindingDB', 'P10443', '-'), ('ChEMBL', 'CHEMBL4621', '-'), ('PaxDb', 'P10443', '-'), ('PRIDE', 'P10443', '-'), ('EnsemblBacteria', 'AAC73295', 'AAC73295', 'b0184'), ('EnsemblBacteria', 'BAA77859', 'BAA77859', 'BAA77859'), ('GeneID', '944877', '-'), ('KEGG', 'ecj:JW0179', '-'), ('KEGG', 'eco:b0184', '-'), ('PATRIC', 'fig|1411691.4.peg.2095', '-'), ('EchoBASE', 'EB0234', '-'), ('EcoGene', 'EG10238', 'dnaE'), ('eggNOG', 'ENOG4105C0B', 'Bacteria'), ('eggNOG', 'COG0587', 'LUCA'), ('HOGENOM', 'HOG000021784', '-'), ('InParanoid', 'P10443', '-'), ('KO', 'K02337', '-'), ('OMA', 'KSHSTCY', '-'), ('PhylomeDB', 'P10443', '-'), ('BioCyc', 'EcoCyc:EG10238-MONOMER', '-'), ('BioCyc', 'MetaCyc:EG10238-MONOMER', '-'), ('EvolutionaryTrace', 'P10443', '-'), ('PRO', 'PR:P10443', '-'), ('Proteomes', 'UP000000318', 'Chromosome'), ('Proteomes', 'UP000000625', 'Chromosome'), ('GO', 'GO:0005737', 'C:cytoplasm', 'IDA:EcoliWiki'), ('GO', 'GO:0005829', 'C:cytosol', 'IDA:EcoCyc'), ('GO', 'GO:0044776', 'C:DNA polymerase III, core complex', 'IDA:EcoCyc'), ('GO', 'GO:0003677', 'F:DNA binding', 'IEA:UniProtKB-KW'), ('GO', 'GO:0003887', 'F:DNA-directed DNA polymerase activity', 'IDA:EcoliWiki'), ('GO', 'GO:0006260', 'P:DNA replication', 'IEA:UniProtKB-KW'), ('InterPro', 'IPR011708', 'DNA_pol3_alpha'), ('InterPro', 'IPR029460', 'DNAPol_HHH'), ('InterPro', 'IPR004365', 'NA-bd_OB_tRNA'), ('InterPro', 'IPR004013', 'PHP_dom'), ('InterPro', 'IPR003141', 'Pol/His_phosphatase_N'), ('InterPro', 'IPR016195', 'Pol/histidinol_Pase-like'), ('InterPro', 'IPR004805', 'PolC_alpha'), ('PANTHER', 'PTHR32294', 'PTHR32294', '1'), ('Pfam', 'PF07733', 'DNA_pol3_alpha', '1'), ('Pfam', 'PF14579', 'HHH_6', '1'), ('Pfam', 'PF02811', 'PHP', '1'), ('Pfam', 'PF01336', 'tRNA_anti-codon', '1'), ('SMART', 'SM00481', 'POLIIIAc', '1'), ('SUPFAM', 'SSF89550', 'SSF89550', '1'), ('TIGRFAMs', 'TIGR00594', 'polc', '1')]\n"
     ]
    }
   ],
   "source": [
    "print(record.cross_references)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The `.cross_references` attribute contains accessions for records in other databases. Here, we extract the PDB references. PDB (Protein Data Bank) is a databank of molecular structures for proteins, and we can query it for information about the molecular structure of DNA polymerase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('PDB', '2HNH', 'X-ray', '2.30 A', 'A=1-910')\n",
      "('PDB', '2HQA', 'X-ray', '2.60 A', 'A=1-917')\n",
      "('PDB', '4GX8', 'X-ray', '1.70 A', 'A/B/C/D=1-270')\n",
      "('PDB', '4GX9', 'X-ray', '2.15 A', 'A/B/C/D=1-270')\n",
      "('PDB', '4JOM', 'X-ray', '2.90 A', 'A=1-917')\n",
      "('PDB', '5FKU', 'EM', '8.34 A', 'A=1-1160')\n",
      "('PDB', '5FKV', 'EM', '8.00 A', 'A=1-1160')\n",
      "('PDB', '5FKW', 'EM', '7.30 A', 'A=1-1160')\n",
      "('PDB', '5M1S', 'EM', '6.70 A', 'A=1-927')\n"
     ]
    }
   ],
   "source": [
    "pdb_refs = []\n",
    "for ref in record.cross_references:\n",
    "    if ref[0] == 'PDB':\n",
    "        print(ref)\n",
    "        pdb_refs.append(ref[1])\n",
    "        \n",
    "# Let's analyze one PDB record\n",
    "pdb_ref = pdb_refs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now that we have a single PDB record, let's view the molecular structure. There are many molecular viewers in Python, and some even play well with the notebook.\n",
    "\n",
    "- https://github.com/arose/nglview\n",
    "- https://github.com/avirshup/py3dmol\n",
    "- https://pymol.org/2/\n",
    "\n",
    "Here we use `nglview`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2HNH\n"
     ]
    }
   ],
   "source": [
    "print(pdb_ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3ccf9f917254f1592d6b7a59a1add14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nglview\n",
    "\n",
    "view = nglview.show_pdbid(pdb_ref)\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The default download format has changed from PDB to PDBx/mmCif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Structure exists: '/Users/jvankerschaver/src/jvkersch/bio-tutorial/hn/2hnh.cif' \n",
      "/Users/jvankerschaver/src/jvkersch/bio-tutorial/hn/2hnh.cif\n"
     ]
    }
   ],
   "source": [
    "from Bio import PDB\n",
    "\n",
    "pdbl = PDB.PDBList()\n",
    "pdb_fname = pdbl.retrieve_pdb_file(pdb_ref)\n",
    "\n",
    "print(pdb_fname)\n",
    "\n",
    "parser = PDB.MMCIFParser()\n",
    "structure = parser.get_structure(pdb_ref, pdb_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Structure id=2HNH>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " <Atom CD>,\n",
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       " <Atom CZ>,\n",
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       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom SG>,\n",
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       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG1>,\n",
       " <Atom CG2>,\n",
       " <Atom N>,\n",
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       " <Atom C>,\n",
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       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
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       " <Atom CE2>,\n",
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       " <Atom O>,\n",
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       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom OE1>,\n",
       " <Atom NE2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom NE>,\n",
       " <Atom CZ>,\n",
       " <Atom NH1>,\n",
       " <Atom NH2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom CE1>,\n",
       " <Atom CE2>,\n",
       " <Atom CZ>,\n",
       " <Atom OH>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
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       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG1>,\n",
       " <Atom CG2>,\n",
       " <Atom CD1>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG1>,\n",
       " <Atom CG2>,\n",
       " <Atom CD1>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom OD1>,\n",
       " <Atom OD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom NE>,\n",
       " <Atom CZ>,\n",
       " <Atom NH1>,\n",
       " <Atom NH2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom OD1>,\n",
       " <Atom OD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom NE1>,\n",
       " <Atom CE2>,\n",
       " <Atom CE3>,\n",
       " <Atom CZ2>,\n",
       " <Atom CZ3>,\n",
       " <Atom CH2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG1>,\n",
       " <Atom CG2>,\n",
       " <Atom CD1>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom OE1>,\n",
       " <Atom OE2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom OD1>,\n",
       " <Atom ND2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD>,\n",
       " <Atom OE1>,\n",
       " <Atom OE2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG1>,\n",
       " <Atom CG2>,\n",
       " <Atom CD1>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom CG>,\n",
       " <Atom CD1>,\n",
       " <Atom CD2>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom CB>,\n",
       " <Atom OG>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " <Atom O>,\n",
       " <Atom N>,\n",
       " <Atom CA>,\n",
       " <Atom C>,\n",
       " ...]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(structure.get_atoms())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Making a genome diagram\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So far we've studied genes and coding sequences as separate features. It is often useful, however, to have a global representation of the entire genome. This can help us figure out, for example, where regions of interest are.\n",
    "\n",
    "BioPython offers a way to achieve this by means of a genome diagram: a picture of a genome with regions of interest indicated. BioPython has a convenient API to add new features to the diagram, which we explore here.\n",
    "\n",
    "Instead of working with the full e. coli genome (which is far too large for a meaningful genome diagram), here we work with a single plasmid, which is much smaller. A plasmid is a piece of circular DNA; the one that we have in the repository is 7395 bases long and contains 10 (putative) genes.\n",
    "\n",
    "Adapted from http://biopython.org/DIST/docs/tutorial/Tutorial.html#htoc262"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plasmid = SeqIO.read(\"data/e_coli_plasmid.gb\", \"genbank\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Bio.Graphics import GenomeDiagram\n",
    "from reportlab.lib import colors\n",
    "\n",
    "gd_diagram = GenomeDiagram.Diagram(\"Escherichia coli str. K-12 substr. MG1655\")\n",
    "\n",
    "gd_track_for_features = gd_diagram.new_track(1, name=\"Annotated Features\")\n",
    "\n",
    "gd_feature_set = gd_track_for_features.new_set()\n",
    "for feature in plasmid.features:\n",
    "    if feature.type != 'gene':\n",
    "        continue\n",
    "    \n",
    "    if len(gd_feature_set) % 2 == 0:\n",
    "        color = colors.blue\n",
    "    else:\n",
    "        color = colors.lightblue\n",
    "    gd_feature_set.add_feature(feature, sigil=\"ARROW\",\n",
    "                               color=color, label=True,\n",
    "                               label_size = 14, label_angle=0)\n",
    "    \n",
    "gd_diagram.draw(format=\"circular\", circular=True,\n",
    "                start=0, end=len(plasmid), circle_core=0.5)\n",
    "gd_diagram.write(\"diagram.png\", \"PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uwYMHDxs2bN68eeXKlSu8f/PmzefOndu7dy+PuQEAAECphR6eAAD6kZmZOXr06J07d/bt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\nt7OOAwAAANC8/R+NPBNBmbW8NwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='diagram.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}