23 Filogenetska.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filogenetska analiza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio import Entrez\n",
    "Entrez.email = 'moj.mail@naslovu.si'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "handle = Entrez.efetch(db='nucleotide', rettype='gb', id='NC_012920.1')\n",
    "\n",
    "rec = SeqIO.read(handle, \"gb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"green\"><b>Vaja</b></font>\n",
    "\n",
    "Preden začnemo s filogenetsko analizo, poskusite pridobiti še neandertalov mitohondrijski genom. Recimo, zaporedje [NC_011137.1](https://www.ncbi.nlm.nih.gov/nuccore/NC_011137.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "handle = Entrez.efetch(db='nucleotide', rettype='gb', id='NC_011137.1')\n",
    "\n",
    "rec_n = SeqIO.read(handle, \"gb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from reportlab.lib import colors\n",
    "from reportlab.lib.units import cm\n",
    "from Bio.Graphics import GenomeDiagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gd_diagram = GenomeDiagram.Diagram('Mitohondrijski genom neandertalca')\n",
    "gd_track_for_features = gd_diagram.new_track(1, name='geni')\n",
    "gd_feature_set = gd_track_for_features.new_set()\n",
    "\n",
    "for feature in rec_n.features:\n",
    "    if feature.type != \"gene\" and feature.type != 'D-loop':\n",
    "        #Exclude this feature\n",
    "        continue\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, color=color, label=True, sigil=\"ARROW\", \n",
    "                               arrowshaft_height=1.0, arrowhead_length=0.25,\n",
    "                               label_size=10, label_angle=40,\n",
    "                              )\n",
    "\n",
    "gd_diagram.draw(format=\"circular\", orientation=\"landscape\", pagesize='A4',\n",
    "                fragments=4, start=0, end=len(rec_n), circle_core = 0.5)\n",
    "gd_diagram.write(\"mt_lin_n.svg\", \"SVG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prikažimo oba genoma, človeškega in neandertalskega:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\t\t\t<g transform=\" matrix(0.637804,-0.770199,0.770199,0.637804,260.772025,491.059309)\">\n",
       "\t\t\t\t<text x=\"-32.23\" y=\"0\" style=\"font-family: Helvetica; font-size: 10px;fill: rgb(0%,0%,0%); stroke:none;\" transform=\"translate(0,0) scale(1,-1)\"> CYTB </text>\n",
       "\t\t\t</g>\n",
       "\t\t\t<g transform=\" matrix(0.256102,-0.96665,0.96665,0.256102,356.629622,540.394409)\">\n",
       "\t\t\t\t<text x=\"-32.22\" y=\"0\" style=\"font-family: Helvetica; font-size: 10px;fill: rgb(0%,0%,0%); stroke:none;\" transform=\"translate(0,0) scale(1,-1)\"> TRNT </text>\n",
       "\t\t\t</g>\n",
       "\t\t\t<g transform=\" matrix(0.231094,-0.972931,0.972931,0.231094,386.123818,444.238255)\">\n",
       "\t\t\t\t<text x=\"0\" y=\"0\" style=\"font-family: Helvetica; font-size: 10px;fill: rgb(0%,0%,0%); stroke:none;\" transform=\"translate(0,0) scale(1,-1)\"> TRNP </text>\n",
       "\t\t\t</g>\n",
       "\t\t</g>\n",
       "\t</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, SVG\n",
    "display(SVG(\"mt_lin_n.svg\"), SVG(\"mt_lin.svg\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"green\"><b>Vaja</b></font>\n",
    "Kako podobni sta regiji `HVR`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_Dloop(rec):\n",
    "    dloops = [f for f in rec.features if f.type == 'D-loop']\n",
    "    return dloops[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hvr = get_Dloop(rec)\n",
    "hvr_n = get_Dloop(rec_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hvr_seq = hvr.extract(rec)\n",
    "hvr_n_seq = hvr.extract(rec_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1122, 1118)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hvr_seq), len(hvr_n_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "885"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(n_hs==n_hn for n_hs, n_hn in zip(hvr_seq, hvr_n_seq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Praktično identično. Smo ljudje torej enaki neandertalcem?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"green\"><b>Vaja</b></font>\n",
    "\n",
    "Izračunaj podobnost celotnega mitohondrijskega genoma?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16569, 16565)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rec.seq), len(rec_n.seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4852"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(n_hs==n_hn for n_hs, n_hn in zip(rec.seq, rec_n.seq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uff, še dobro! Očitno si pa le nismo tako podobni."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poizvedbe baze NCBI\n",
    "\n",
    "Poiščimo še nekaj človeških mitohondrijskih genomov. Če želimo to storiti na [spletni strani NCBI](https://www.ncbi.nlm.nih.gov/nuccore), je potrebno zapisati v iskalno polje: [`\"Homo sapiens\"[Organism] AND mitochondrion[filter] AND complete_genome[All Fields]`](https://www.ncbi.nlm.nih.gov/nuccore?term=%22Homo%20sapiens%22%5BOrganism%5D%20AND%20mitochondrion%5Bfilter%5D%20AND%20complete_genome&cmd=DetailsSearch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('61048', 61048)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term = '\"Homo sapiens\"[Organism] AND mitochondrion[filter] AND complete_genome[All Fields]'\n",
    "handle = Entrez.esearch(db=\"nucleotide\", term=term, retmax=100000)\n",
    "record = Entrez.read(handle)\n",
    "id_list = sorted(set(record[\"IdList\"]))\n",
    "record[\"Count\"], len(id_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zapisov popolnih mitohondrijskih genomov v NCBI je preko 45000!\n",
    "\n",
    "### <font color=\"green\"><b>Vaja</b></font>\n",
    "\n",
    "Pridobite prvi 30 zapisov in preverite njihove dolžine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001886902\n",
      "1001887034\n",
      "1001887157\n",
      "1001887276\n",
      "1001887412\n",
      "1001887523\n",
      "1002819669\n",
      "1002819697\n",
      "1002819711\n",
      "1002819725\n",
      "1002819739\n",
      "1002819753\n",
      "1002819767\n",
      "1002819781\n",
      "1002819795\n",
      "1002819809\n",
      "1002819823\n",
      "1002819837\n",
      "1002819851\n",
      "1002819865\n",
      "1002819879\n",
      "1002819893\n",
      "1002819907\n",
      "1002819921\n",
      "1002819935\n",
      "1002819949\n",
      "1002819963\n",
      "1002819977\n",
      "1002819991\n",
      "1002820005\n"
     ]
    }
   ],
   "source": [
    "seqs = []\n",
    "for rid in id_list[:30]:\n",
    "    print(rid)\n",
    "    handle = Entrez.efetch(db=\"nucleotide\", rettype=\"gb\", id=rid)\n",
    "    rec = SeqIO.read(handle, \"gb\")\n",
    "    seqs.append(rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16571,\n",
       " 16570,\n",
       " 16570,\n",
       " 16568,\n",
       " 16569,\n",
       " 16569,\n",
       " 16567,\n",
       " 16569,\n",
       " 16568,\n",
       " 16567,\n",
       " 16567,\n",
       " 16566,\n",
       " 16568,\n",
       " 16570,\n",
       " 16567,\n",
       " 16567,\n",
       " 16559,\n",
       " 16563,\n",
       " 16561,\n",
       " 16554,\n",
       " 16559,\n",
       " 16560,\n",
       " 16560,\n",
       " 16561,\n",
       " 16562,\n",
       " 16558,\n",
       " 16562,\n",
       " 16561,\n",
       " 16562,\n",
       " 16564]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(x) for x in seqs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dolžine genomov se med posamezniki razlikujejo. Kaj pa dolžine regij HVR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "657\n",
      "657\n",
      "657\n",
      "657\n",
      "657\n",
      "657\n",
      "1121\n",
      "1123\n",
      "1122\n",
      "1121\n",
      "1121\n",
      "1120\n",
      "1122\n",
      "1124\n",
      "1121\n",
      "1121\n",
      "1122\n",
      "1126\n",
      "1124\n",
      "1117\n",
      "1122\n",
      "1123\n",
      "1123\n",
      "1124\n",
      "1125\n",
      "1121\n",
      "1125\n",
      "1124\n",
      "1125\n",
      "1127\n"
     ]
    }
   ],
   "source": [
    "for seq in seqs:\n",
    "    print(len(get_Dloop(seq).extract(seq)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ker se zaporedja med seboj očitno razlikujejo po dolžini, bi jih morali najprej poravnati.\n",
    "Ker pa to presega današnjo vajo, se osredotočimo le na sekvence dolžine 1121."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seqs_1121 = [get_Dloop(seq).extract(seq) for seq in seqs if len(get_Dloop(seq)) == 1121]\n",
    "len(seqs_1121)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"green\"><b>Vaja</b></font>\n",
    "\n",
    "Preverite variabilnost posameznih mest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "v = []\n",
    "for i in range(len(seqs_1121[0])):\n",
    "    tmpd = {}\n",
    "    for s in seqs_1121:\n",
    "        s = str(s.seq)\n",
    "        tmpd[s[i]] = tmpd.get(s[i], 0) + 1\n",
    "    v.append(tmpd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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