Day 12 - Multiple Alignment/Solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-9889a0a8d7a46373",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "# Day 12 notebook\n",
    "\n",
    "The objectives of this notebook are to practice\n",
    "\n",
    "* computing scores of multiple alignment columns\n",
    "* progressive multiple alignment\n",
    "* iterative refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-6c93ada6842a576c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Modules used in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-12d427fcb5be2ff7",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # for plotting\n",
    "import fasta                    # For reading in an alignment\n",
    "import submatrix                # For reading in a substitution matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-f7634a8cb5079638",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Multiple alignment of the CFTR protein"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-99a2a388e0eb6b4b",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "In this activity we will analyze a multiple alignment of the CFTR (Cystic fibrosis transmembrane conductance regulator) protein from a variety of vertebrate species.  We will implement the sum-of-pairs column scoring approaches and apply it to analyze the columns of this multiple alignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-42d6bb8c1577c3ec",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "We will first read in the CFTR multiple alignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-92d698980e71d063",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "cftr_aligned_sequences = fasta.read_sequences_from_fasta_file(\"cftr_alignment.fasta\")\n",
    "cftr_names, cftr_alignment = zip(*cftr_aligned_sequences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-4c29c6fe856f2534",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Here are the species of the CFTR proteins in this alignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-349c7cfc2573cf9c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mustela putorius furo (European domestic ferret) (Mustela furo)\n",
      "Papio anubis (Olive baboon)\n",
      "Atelerix albiventris (Middle-African hedgehog) (Four-toed hedgehog)\n",
      "Cavia porcellus (Guinea pig)\n",
      "Xenopus laevis (African clawed frog)\n",
      "Loxodonta africana (African elephant)\n",
      "Pan troglodytes (Chimpanzee)\n",
      "Canis lupus familiaris (Dog) (Canis familiaris)\n",
      "Plecturocebus moloch (Dusky titi monkey) (Callicebus moloch)\n",
      "Aotus nancymaae (Ma's night monkey)\n",
      "Saimiri boliviensis boliviensis (Bolivian squirrel monkey)\n",
      "Trichosurus vulpecula (Brush-tailed possum)\n",
      "Microcebus murinus (Lesser mouse lemur) (Gray Mouse Lemur)\n",
      "Macaca nemestrina (Pig-tailed macaque)\n",
      "Mus musculus (Mouse)\n",
      "Colobus guereza (Mantled guereza) (Eastern black-and-white colobus monkey)\n",
      "Homo sapiens (Human)\n",
      "Macaca fascicularis (Crab-eating macaque) (Cynomolgus monkey)\n",
      "Macaca fuscata fuscata (Japanese macaque)\n",
      "Sus scrofa (Pig)\n",
      "Squalus acanthias (Spiny dogfish)\n",
      "Chlorocebus aethiops (Green monkey) (Cercopithecus aethiops)\n",
      "Didelphis virginiana (North American opossum) (Didelphis marsupialis virginiana)\n",
      "Muntiacus reevesi (Reeve's muntjac) (Chinese muntjac)\n",
      "Ornithorhynchus anatinus (Duckbill platypus)\n",
      "Otolemur garnettii (Small-eared galago) (Garnett's greater bushbaby)\n",
      "Oryctolagus cuniculus (Rabbit)\n",
      "Ateles geoffroyi (Black-handed spider monkey) (Geoffroy's spider monkey)\n",
      "Callithrix jacchus (White-tufted-ear marmoset)\n",
      "Dasypus novemcinctus (Nine-banded armadillo)\n",
      "Equus caballus (Horse)\n",
      "Macaca mulatta (Rhesus macaque)\n",
      "Bos taurus (Bovine)\n",
      "Carollia perspicillata (Seba's short-tailed bat)\n",
      "Gorilla gorilla gorilla (Western lowland gorilla)\n",
      "Muntiacus muntjak (Barking deer) (Indian muntjac)\n",
      "Rhinolophus ferrumequinum (Greater horseshoe bat)\n",
      "Ovis aries (Sheep)\n",
      "Danio rerio (Zebrafish) (Brachydanio rerio)\n",
      "Nomascus leucogenys (Northern white-cheeked gibbon) (Hylobates leucogenys)\n",
      "Pongo abelii (Sumatran orangutan) (Pongo pygmaeus abelii)\n",
      "Rattus norvegicus (Rat)\n"
     ]
    }
   ],
   "source": [
    "print(*cftr_names, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-1d0dbf8c0db2c551",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "And here is a slice of this alignment in relatively interesting region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-9e19dce890aa159f",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NSIITETLRRFSLEG---DAPVSWNETKKQSFKQT----GEFGEKRKNS-ILNPVNSIRKFSVVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDGAEQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DGAVSWNETRKQSFKQT----GEFGEKRKNS-ILNSMNSARKFSIVQKTTL--QMNGIEA-NS-EEPLERRLSLVPDS-ELG',\n",
       " 'NSILTETLRRFSLEG---DPSVSFNETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVPKTPL--QISGIEE-DS-DDPVERRLSLVPDS-EQS',\n",
       " 'NSIITETLRRCSIDS---DPSAVRNEVKNKSFKQV----ADFTEKRKSS-IINPRKSSRKFSLMQKSQP--QMSGIEEEDMPAEQGERKLSLVPES-EQG',\n",
       " 'NSILTETLRRFSLEG---DT-VSWNETKKQSFKQA----GEFGEKRKNS-ILNSINSIRKFSVVQKTPL--QMNGIDE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DAAVPWNETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--PMNGIEG-ES-EVPVERRLSLFPDC-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVHKTPL--QMNGIEE-DS-DEPSERRLSLIPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLIPDS-EQG',\n",
       " 'SSILTETLRRFSIEG---DAAVSWNEGKMQSFKQT----GDFGERRKNS-VLSPLNSNRKFSVVQKGQQ--QMNGIEE-ND-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAAVSWNETKKQSFKQT----GEIGEKRKNS-ILNSINSLRKFSVVQKTPL--QMSGIEE-DP-DEPSERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'SSILTETLRRFSVDD----SSAPWSKP-KQSFRQT----GEVGEKRKNS-ILNSFSSVRKISIVQKTPL--CIDG----ES-DDLQEKRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPISRTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLHRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DASVSWNETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVQKTPL--QMNGFEE-DS-GEPLERRLSLVPDS-EHG',\n",
       " 'NSILTETFRRCSVSSGDGAGLGSYSETRKASFKQPP---PEFNEKRKSSLIVNPITSNKKFSLVQTAMSYPQTNGME--DATSEPGERHFSLIPEN-ELG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSVRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'SSILTETLRRFSIEG---DTAVSWNEGKKQSFKQT----GDFGERRKNS-ILNPLNSIRKFSVVQKGQP--QMNGIEE-ND-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DTSVSWNETKKPSFKQT----GEFGEKRKNS-ILNSINSKRKFSVAQKTSL--QMNGIEE-TS-DEPLERKLSLVPHS-EPG',\n",
       " 'NSILTETLRRFSLDG---DAAGSWNETKKQSFKQT----GEFGERRKNS-ILNPLNSIRKFSLVQKAPL--QMNCIDE-TL-DEPPERKLSLVPDS-EPG',\n",
       " 'NSILTETLRRFSLEG---DAAVSRNETKKQSFKQT----GEIGEKRKNS-ILNPINSIRKFSVVQKTPL--PMNGIDEEDS-EEPVERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DASISWNDTRKQSFKQN----GELGEKRKNS-ILNPVNSMRKFSIVPKTPL--QMNGIEE-DS-DASIERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GELGDKRKNS-ILNSINSIRKFSIVQKTPL--QMNGIEE-NS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNSINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DAAHSWNETKKQSFKQT----GEFGEKRKNS-ILNPINSLRRISITQKTPL--PMNGIDE-DS-GETLERRLSLVPDC-EQG',\n",
       " 'NSILTETLRRFSLEG---DATVSWNETKKQSFKQT----GEFGDKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DTSVSWNETKKPSFKQT----GEFGEKRKNS-ILSSINSIRKFSVVQKTSL--QMNGIEG-AA-DAPLERRLSLVPHS-EPG',\n",
       " 'NSILTETLRRFSLEG---DAAIPWNETKKQSFKQT----GEFGEKRKNS-ILNPINSAKKFSVVQKTPL--QMNGIEE-DS-EEPSERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DTSVSWNETKKPSFKQT----GEFGEKRKNS-ILNSINSKRKFSVAQKTSL--QMNGIEE-TS-DEPLERKLSLVPHS-EPG',\n",
       " 'NSILTETLRRFSFEG---DAAGPWNEPKKQSFKQT----GEFGEKRKSS-VLNPINAIKRFSIVQKTPL--QMNGIEE-DS-EESSERRLSLVPDS-EQG',\n",
       " 'NSIITETLRRFSLEG---DTSVSWNETKKPSFKQT----GEFGEKRKNS-ILNSINSIRKFSVVQKTSL--QMNGIDG-AS-DEPLERRLSLVPHS-EPG',\n",
       " 'CSILTETLHRVSVDESAGMQP------ERSAFRQVPPTKPMYIDERKASVIVNPLGVARKASFIQVPE----------EEVRRTLPDRKFSLVPENELVD',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQSFKQT----GEFGEKRKNS-ILNPINSIRKFSIVPKTPL--QMNGIEE-DS-DEPLERRLSLVPDS-EQG',\n",
       " 'NSILTETLRRFSLEG---DAPVSWTETKKQPFKQT----GEFGEKRKNS-ILNPINSIRKFSIVQKTPL--QMNGIEE-DS-DEPFERRVSLVPDS-EQG',\n",
       " 'SSILTETLRRFSVDD----ASTTWNKA-KQSFRQT----GEFGEKRKNS-ILSSFSSVKKISIVQKTPL--SIEG----ES-DDLQERRLSLVPDS-EHG']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def slice_alignment(alignment, start, length):\n",
    "    return [s[start: start + length] for s in alignment]\n",
    "\n",
    "slice_alignment(cftr_alignment, 660, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-71c59ee3f00b8d3f",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Test sequences for multiple alignment algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-23402d036daca5fd",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Below are three sets of homologous amino acid sequences, taken from the CFTR alignment. Each sequence is a short substring of a CFTR protein sequence.  These sets will be used in testing the multiple sequence alignment algorithms that you implement below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-bdf0376e7c7f73a5",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "tiny_set = {\n",
    "    'Zebrafish': 'SETPKPD',\n",
    "    'Human': 'TEGKPTKS',\n",
    "    'Mouse': 'TEESMYTQI'\n",
    "}\n",
    "tiny_seqs = sorted(tiny_set.values())\n",
    "\n",
    "small_set = {\n",
    "    'Guinea pig': 'EEGAPVKS',\n",
    "    'Zebrafish': 'SETPKPD',\n",
    "    'Human': 'TEGKPTKS',\n",
    "    'Mouse': 'TEESMYTQI',\n",
    "    'Spiny dogfish': 'PEGSETK'\n",
    "}\n",
    "small_seqs = sorted(small_set.values())\n",
    "\n",
    "large_set = {\n",
    "    'Guinea pig': 'FSIVPKTPLQISGIEEDSDDPVERRL',\n",
    "    'Zebrafish': 'ASFIQVPEEEVRRTLPDRKF',\n",
    "    'Human': 'FSIVQKTPLQMNGIEEDSDEPLERRL',\n",
    "    'Mouse': 'ISIVQKTPLCIDGESDDLQEKRL',\n",
    "    'Small-eared galago': 'FSVVQKTPLPMNGIDEEDSEEPVERRL',\n",
    "    'Rat': 'ISIVQKTPLSIEGESDDLQERRL',\n",
    "    'Spiny dogfish': 'FSLVQTAMSYPQTNGMEDATSEPGERHF'\n",
    "}\n",
    "large_seqs = sorted(large_set.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-f86769537a07fcf9",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Sum of pairs scoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-a79f064cc5ccc879",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "### PROBLEM 1: Computing the sum of pairs score of a column (1 POINT)\n",
    "\n",
    "Write a function `sum_of_pairs_column_score` below, which takes as input a column from a multiple alignment (represented as a string) and a substitution matrix, and computes the sum of pairs score of that column.  Recall that the sum of pairs score for a column is defined as:\n",
    "\n",
    "$S_{sum\\_of\\_pairs}(m_i) = \\sum_{k < l} s(m_i^k, m_i^l)$\n",
    "\n",
    "where $s(m_i^k, m_i^l)$ is the substitution score of the character in the $k$th row with the character in the $l$th row.\n",
    "\n",
    "For simplicity, we will simply treat a space ('-') like any other character in a column, and will add the space character to the substitution matrix, with the score of aligning a character to a space being the space score of a linear gap penalty function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-3c51c13c3843e4b8",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "First we will set up the substitution matrix, which will be the BLOSUM62 matrix with a space score of -2 added to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-59a701d7a78ad043",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def submatrix_with_spaces(matrix, space_score):\n",
    "    \"\"\"Returns an extended substitution matrix representing a linear gap penalty function.\"\"\"\n",
    "    extended_matrix = matrix.copy()\n",
    "    chars = {a for (a, b) in matrix}\n",
    "    for a in chars:\n",
    "        extended_matrix[a, '-'] = space_score\n",
    "        extended_matrix['-', a] = space_score\n",
    "    extended_matrix['-', '-'] = 0\n",
    "    return extended_matrix\n",
    "\n",
    "blosum62 = submatrix.read_substitution_matrix(\"BLOSUM62.txt\")\n",
    "space_score = -2\n",
    "blosum62_with_spaces = submatrix_with_spaces(blosum62, space_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-42a5b19afdc9c536",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def sum_of_pairs_column_score(column, matrix):\n",
    "    \"\"\"Returns the sum of pairs score of an alignment column.\n",
    "    \n",
    "    Args:\n",
    "        column: a column of a multiple alignment, represented as a string with the ith character\n",
    "                specifying the character from the ith sequence in the alignment.\n",
    "        matrix: a substitution matrix, represented as a dictionary\n",
    "    Returns:\n",
    "        The sum of pairs score of the column.\"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    return sum(matrix[column[k], column[l]] for l in range(len(column)) for k in range(l))\n",
    "    ### END SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "sum_of_pairs_column_score",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS: sum_of_pairs_column_score passed all tests!\n"
     ]
    }
   ],
   "source": [
    "# tests for sum_of_pairs_column_score\n",
    "assert sum_of_pairs_column_score('SQSQ', blosum62_with_spaces) == 9\n",
    "assert sum_of_pairs_column_score('WWWWWW', blosum62_with_spaces) == 165\n",
    "assert sum_of_pairs_column_score('ACDEFG', blosum62_with_spaces) == -27\n",
    "assert sum_of_pairs_column_score('GEGEKRK', blosum62_with_spaces) == 4\n",
    "assert sum_of_pairs_column_score('--S-', blosum62_with_spaces) == -6\n",
    "print(\"SUCCESS: sum_of_pairs_column_score passed all tests!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-b094675700e01d28",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "### Sum of pairs scores of columns in the CFTR alignment\n",
    "We will now use your `sum_of_pairs_column_score` to compute and examine the sum of pairs scores of the columns within the CFTR alignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-671ed1d3abd6f627",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def transpose_alignment(alignment):\n",
    "    \"\"\"Returns a column-based alignment from a row-based alignment or vice versa\"\"\"\n",
    "    return list(map(''.join, zip(*alignment)))\n",
    "\n",
    "cftr_alignment_columns = list(zip(*cftr_alignment))\n",
    "sum_of_pairs_scores = [sum_of_pairs_column_score(column, blosum62_with_spaces) for column in cftr_alignment_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-ec4800f42e2b600f",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2250x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 3), dpi=150)\n",
    "plt.plot(sum_of_pairs_scores, \".\")\n",
    "plt.xlabel(\"alignment column\")\n",
    "plt.ylabel(\"sum of pairs score\")\n",
    "plt.title(\"Sum of pairs scores of columns in the CFTR alignment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-5bc8b478c2f679f0",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "What does this plot tell you regarding which regions of the CFTR proteins are more or less conserved?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BEGIN SOLUTION TEMPLATE=*Your thoughts here*\n",
    "The regions where there are many positions with low scores are less conserved (i.e., there are more substitutions in these regions).  The region from roughly position 650 to position 950 is noticeably less conserved than the other regions, although there are some spikes of low conservation in other regions.\n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple alignment algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-2f9553f4b1341f75",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "### A core global pairwise alignment function\n",
    "Below is a function, `align_global`, that implements standard global pairwise alignment, but that generically supports aligning two alignments to each other.  You will use this function to implement both progressive alignment and iterative refinement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-0c8b21160a5de420",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def align_global(x, y, substitution_matrix):\n",
    "    \"\"\"Computes a global pairwise alignment of sequences/alignments x and y\n",
    "    \n",
    "    Uses a linear gap scoring function with the space score encoded in the given substitution matrix.\n",
    "    In the case of multiple optimal alignments, this function returns a \"middle-road\" \n",
    "    alignment (prefers match/mismatch first).\n",
    "    \n",
    "    Args:\n",
    "        x: an alignment/sequence represented as a list of strings. A single \n",
    "           unaligned sequence should be represented by a list with a single element (the sequence).\n",
    "        y: an alignment/sequence represented as a list of strings. A single \n",
    "           unaligned sequence should be represented by a list with a single element (the sequence).\n",
    "        substitution_matrix: the substitution matrix, represented as a dictionary \n",
    "    Returns:\n",
    "        An alignment, represented as a list of aligned strings of the same length.\n",
    "    \"\"\"\n",
    "    # create lists of the columns of the two alignments\n",
    "    xt, yt = transpose_alignment(x), transpose_alignment(y)\n",
    "    num_rows, num_cols = len(xt) + 1, len(yt) + 1\n",
    "    x_all_spaces, y_all_spaces = '-' * len(x), '-' * len(y)\n",
    "    m = matrix(num_rows, num_cols) # score matrix\n",
    "    t = matrix(num_rows, num_cols) # traceback pointer matrix\n",
    "\n",
    "    # constants representing the traceback pointers, with higher\n",
    "    # values representing higher priority for traceback in case of ties\n",
    "    M, IX, IY = 3, 2, 1\n",
    "    \n",
    "    # A function that scores the alignment of two columns\n",
    "    def score_column_pair(x_column, y_column):\n",
    "        return sum(substitution_matrix[a, b] for a in x_column for b in y_column)\n",
    "\n",
    "    # initialization\n",
    "    m[0][0] = 0\n",
    "    for i in range(1, num_rows):\n",
    "        m[i][0] = m[i - 1][0] + score_column_pair(xt[i - 1], y_all_spaces)\n",
    "        t[i][0] = IX\n",
    "    for j in range(1, num_cols):\n",
    "        m[0][j] = m[0][j - 1] + score_column_pair(x_all_spaces, yt[j - 1])\n",
    "        t[0][j] = IY\n",
    "  \n",
    "    # main fill\n",
    "    for i in range(1, num_rows):\n",
    "        for j in range(1, num_cols):\n",
    "            m[i][j], t[i][j] = max((m[i - 1][j - 1] + score_column_pair(xt[i - 1], yt[j - 1]),     M),\n",
    "                                   (m[i - 1][j]     + score_column_pair(xt[i - 1], y_all_spaces), IX),\n",
    "                                   (m[i][j - 1]     + score_column_pair(x_all_spaces, yt[j - 1]), IY))\n",
    "\n",
    "    # traceback\n",
    "    columns = []\n",
    "    i, j = num_rows - 1, num_cols - 1\n",
    "    while i > 0 or j > 0:\n",
    "        traceback = t[i][j]\n",
    "        if traceback == M:\n",
    "            i -= 1\n",
    "            j -= 1            \n",
    "            columns.append(xt[i] + yt[j])\n",
    "        elif traceback == IX:\n",
    "            i -= 1\n",
    "            columns.append(xt[i] + y_all_spaces)\n",
    "        elif traceback == IY:\n",
    "            j -= 1\n",
    "            columns.append(x_all_spaces + yt[j])\n",
    "        else:\n",
    "            assert False\n",
    "    \n",
    "    return transpose_alignment(reversed(columns))\n",
    "\n",
    "def matrix(num_rows, num_cols, initial_value=None):\n",
    "    \"\"\"Returns a matrix (a list of rows, each of which is a list) \n",
    "    with num_rows and num_cols and with initial_value in each entry\"\"\"\n",
    "    return [[initial_value] * num_cols for i in range(num_rows)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-28bc551441691e68",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## PROBLEM 2: Progressive alignment (1 POINT)\n",
    "Write a function that computes a multiple alignment of a set of sequences using the progressive alignment strategy.  Your function should take in an \"alignment order\" which specifies the ordering of the pairwise alignments to perform.  For example, if there are four sequences, the alignment order \n",
    "\n",
    "`[((1,),   (3,)),\n",
    " ((0,),   (2,)),\n",
    " ((1, 3), (0, 2))]`\n",
    " \n",
    " would specify the following sequence of pairwise alignments:\n",
    " \n",
    " 1. align sequence 1 to sequence 3\n",
    " 2. align sequence 0 to sequence 2\n",
    " 3. align the partial alignment of sequences 1 and 3 to the partial alignment of sequences 0 and 2\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-394b085441e5b9e1",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def align_progressive(sequences, alignment_order, substitution_matrix):\n",
    "    \"\"\"Computes a multiple alignment with the progressive alignment strategy\n",
    "    and a given ordering of pairwise alignments to perform.\n",
    "    \n",
    "    Args:\n",
    "        sequences: a list of strings\n",
    "        alignment_order: a list of two-element tuples, with each element\n",
    "                         of a tuple being a tuple of integer indices, \n",
    "                         specifying a partial alignment of a subset of\n",
    "                         the sequences (indicated by the indices). Each element\n",
    "                         of the alignment_order list specifies the next pair of\n",
    "                         partial alignments to align with each other.\n",
    "        substitution_matrix: a substitution matrix, represented as a dictionary\n",
    "    Returns:\n",
    "        A multiple alignment, represented by a list of aligned strings.\n",
    "    \"\"\"\n",
    "    # we will store the partial alignments that we construct in a dictionary with tuples\n",
    "    # of (sorted) indices of the sequences in each partial alignment as keys\n",
    "    # we start with each sequence as its own partial alignment\n",
    "    partial_alignments = {(i,): [seq] for i, seq in enumerate(sequences)}\n",
    "    \n",
    "    ### BEGIN SOLUTION\n",
    "    for aligned_set1, aligned_set2 in alignment_order:\n",
    "        combined_set = tuple(sorted(aligned_set1 + aligned_set2))\n",
    "        combined_alignment = align_global(partial_alignments[aligned_set1],\n",
    "                                          partial_alignments[aligned_set2],\n",
    "                                          substitution_matrix) \n",
    "        partial_alignments[combined_set] = combined_alignment\n",
    "    ### END SOLUTION\n",
    "    \n",
    "    # by this point there should be an entry in partial_alignments for the tuple\n",
    "    # containing the indices of all sequences\n",
    "    full_set = tuple(range(len(sequences)))\n",
    "    full_alignment = partial_alignments[full_set]\n",
    "    # return the full alignment with the sequences in the same order as in the input\n",
    "    return sorted(full_alignment, key=lambda s: sequences.index(s.replace('-', '')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "align_progressive",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS: align_progressive passed all tests!\n"
     ]
    }
   ],
   "source": [
    "# tests for align_progressive\n",
    "tiny_order1 = [((0,), (1,)), \n",
    "               ((0, 1), (2,))]\n",
    "tiny_result1 = ['S-E-TPKPD-', \n",
    "                'TEE-SMYTQI', \n",
    "                'T-EGKP-TKS']\n",
    "assert align_progressive(tiny_seqs, tiny_order1, blosum62_with_spaces) == tiny_result1\n",
    "\n",
    "tiny_order2 = [((0,), (2,)), \n",
    "               ((0, 2), (1,))]\n",
    "tiny_result2 = ['SE-TP--KPD', \n",
    "                'TEESMYTQ-I', \n",
    "                'TEGKP-TK-S']\n",
    "assert align_progressive(tiny_seqs, tiny_order2, blosum62_with_spaces) == tiny_result2\n",
    "\n",
    "tiny_order3 = [((1,), (2,)), \n",
    "               ((1, 2), (0,))]\n",
    "tiny_result3 = ['S-ET-P-KPD', \n",
    "                'TEESMYTQ-I', \n",
    "                'T-EGKPTK-S']\n",
    "assert align_progressive(tiny_seqs, tiny_order3, blosum62_with_spaces) == tiny_result3\n",
    "\n",
    "small_order1 = [((0,), (1,)),\n",
    "                ((2,), (3,)),\n",
    "                ((2, 3), (4,)),\n",
    "                ((0, 1), (2, 3, 4))]\n",
    "    \n",
    "small_result1 = ['E-EGAP-VKS', \n",
    "                 'P-EGSE-TK-', \n",
    "                 'S-E-TPKPD-', \n",
    "                 'TEE-SMYTQI', \n",
    "                 'T-EGKP-TKS']\n",
    "assert align_progressive(small_seqs, small_order1, blosum62_with_spaces) == small_result1\n",
    "print(\"SUCCESS: align_progressive passed all tests!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-9030879f44e08c85",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## PROBLEM 3: Iterative refinement (1 POINT)\n",
    "We will next write a function for performing iterative refinement on a multiple alignment.  The main function, `refine_alignment` is written for you below.  You are to write `realign_first_sequence` which takes as input an alignment and realigns the first sequence in the alignment back to the remainder of the alignment.  You will need to make sure that you\n",
    "\n",
    "1. Remove space characters from the first sequence before aligning it back\n",
    "2. Remove columns with all space characters in the partial alignment that results after removing the first sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-3ccfb3f70087db18",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def refine_alignment(alignment, substitution_matrix):\n",
    "    \"\"\"Performs iterative refinement on a multiple alignment.\n",
    "    \n",
    "    Args:\n",
    "        alignment: a multiple alignment, represented by a list of aligned strings.\n",
    "        substitution_matrix: a substitution matrix, represented by a dictionary.\n",
    "    Returns:\n",
    "        A multiple alignment, represented by a list of aligned strings.\"\"\"\n",
    "    previous_alignment = None\n",
    "    current_alignment = alignment\n",
    "    while current_alignment != previous_alignment:\n",
    "        previous_alignment = current_alignment\n",
    "        for i in range(len(current_alignment)):\n",
    "            # realign the first sequence\n",
    "            current_alignment = realign_first_sequence(current_alignment, substitution_matrix)\n",
    "            # move first sequence to the bottom of the alignment\n",
    "            current_alignment = current_alignment[1:] + current_alignment[:1]\n",
    "    return current_alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-a3c70d0b3b12f20a",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def realign_first_sequence(alignment, substitution_matrix):\n",
    "    \"\"\"Returns a new alignment that results from realigning the first\n",
    "    sequence of the given alignment back to the remaining alignment.\n",
    "    \n",
    "    Args:\n",
    "        alignment: a multiple alignment, represented by a list of aligned strings.\n",
    "        substitution_matrix: a substitution matrix, represented by a dictionary.\n",
    "    Returns:\n",
    "        A multiple alignment, represented by a list of aligned strings.\"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    first_seq = alignment[0].replace('-', '')\n",
    "    remaining_alignment = remove_all_space_cols(alignment[1:])\n",
    "    return align_global([first_seq], remaining_alignment, substitution_matrix)\n",
    "    ### END SOLUTION\n",
    "\n",
    "### BEGIN SOLUTION TEMPLATE=\n",
    "def remove_all_space_cols(alignment):\n",
    "    all_space_col = '-' * len(alignment)\n",
    "    return transpose_alignment(column for column in transpose_alignment(alignment) if column != all_space_col)\n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "realign_first_sequence",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS: realign_first_sequence passed all tests!\n"
     ]
    }
   ],
   "source": [
    "# tests for realign_first_sequence\n",
    "tiny_result1 = ['S-E-TPKPD-', \n",
    "                'TEE-SMYTQI', \n",
    "                'T-EGKP-TKS']\n",
    "tiny_refine1 = ['S-E-TP--KPD', \n",
    "                'TEE-SMYTQ-I', \n",
    "                'T-EGKP-TK-S']\n",
    "assert realign_first_sequence(tiny_result1, blosum62_with_spaces) == tiny_refine1\n",
    "\n",
    "tiny_result2 = ['SE-TP--KPD', \n",
    "                'TEESMYTQ-I', \n",
    "                'TEGKP-TK-S']\n",
    "tiny_refine2 = ['SE-TP--KPD', \n",
    "                'TEESMYTQ-I', \n",
    "                'TEGKP-TK-S']\n",
    "assert realign_first_sequence(tiny_result2, blosum62_with_spaces) == tiny_refine2\n",
    "\n",
    "# for the last test we will run refine_alignment rather than realign_first_sequence\n",
    "small_result1 = ['E-EGAP-VKS', \n",
    "                 'P-EGSE-TK-', \n",
    "                 'S-E-TPKPD-', \n",
    "                 'TEE-SMYTQI', \n",
    "                 'T-EGKP-TKS']\n",
    "\n",
    "small_refine1 = ['EEGAP-VK-S', \n",
    "                 'PEGSE-TK--', \n",
    "                 'SE-TP--KPD', \n",
    "                 'TEESMYTQ-I', \n",
    "                 'TEGKP-TK-S']\n",
    "assert refine_alignment(small_result1, blosum62_with_spaces) == small_refine1\n",
    "print(\"SUCCESS: realign_first_sequence passed all tests!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-7992089c7e7c05da",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## CHALLENGE: Find the best multiple alignment of the \"large\" test set\n",
    "Using your progressive alignment and iterative refinment functions above, try your hand at finding the best scoring alignment of the \"large\" test set.  Use the function below to compare the scores of your alignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-37de1582f617061e",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "def score_alignment_sum_of_pairs(alignment, substitution_matrix):\n",
    "    \"\"\"Returns the sum of pairs score for the given alignment using the given\n",
    "    substitution matrix, which must be extended for a linear gap penalty function.\"\"\"\n",
    "    return sum(sum_of_pairs_column_score(column, substitution_matrix) \n",
    "               for column in transpose_alignment(alignment))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Create Assignment",
  "kernelspec": {
   "display_name": "Python 3 [3.6]",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}