practica_val.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tema 2. Anàlisi de seqüències\n",
    "\n",
    "## Anàlisi de qualitat de seqüències curtes\n",
    "\n",
    "Les seqüències curtes que utilitzarem en esta pràctica provenen d'un experiment de seqüenciació d'un aïllat de coronavirus. Es troben als arxius `data/ERR4423464_1.fastq` i `data/ERR4423464_2.fastq`, que són arxius de text amb format **fastq**. Hi ha dos perquè es tracta de lectures aparellades (*paired end*): cada fragment original de ADN ha sigut sequenciat primer des d'un extrem i després des de l'altre. Les dues lectures poden arribar a solapar-se, si el fragment original no era massa llarg. Primer, dóna-li una ullada a l'arxiu. Hauries de poder observar alguna cosa així:\n",
    "\n",
    "    @ERR4423464.1 M00289:305:000000000-J7B3T:1:1101:18085:1894 length=109\n",
    "    CTGCTACACGCTGCGAAGCTCCCAATTTGTAATAAGAAAGCGTTCGTGATGTAGCAACAGTGATTTCTTTAGGC...\n",
    "    +ERR4423464.1 M00289:305:000000000-J7B3T:1:1101:18085:1894 length=109\n",
    "    BC@CCDE9F-@C7C+++6@CEFF8,CFFCFA9F9,,,,,,C:FFFCF@,C9F99C,6C8<C9,EFFFFEE9,,9...\n",
    "\n",
    "\n",
    "Pots aprendre més sobre el format **fastq** [aquí](https://en.wikipedia.org/wiki/FASTQ_format). Pots identificar les diferents parts del format? Per a què serveix la quarta línia de cada registre?\n",
    "\n",
    "A continuació utilitzarem el paquet *ShortReads* de R per comprovar la qualitat i característiques de les seqüències. Aquest paquet ha estat creat per Martin Morgan, Michael Lawrence i Simon Anders, i és distribuït amb llicència *Artistic-2.0* en [Bioconductor](http://www.bioconductor.org/packages/release/bioc/html/ShortRead.html).\n",
    "\n",
    "Recorda que per executar el codi mostrat a continuació (o aquell que tu escrigues) has de seleccionar el bloc de codi i fer clic al botó *Run*, al menú de la capçalera. El codi està distribuït en diferents blocs per facilitar la comprensió de cada pas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per poder utilitzar funcions d'anàlisi de seqüències curtes\n",
    "# cal carregar algun paquet específic, com ara \"ShortReads\":\n",
    "suppressMessages(library('ShortRead', logical.return = TRUE))\n",
    "\n",
    "# library('ShortRead')  # faria el mateix, però amb un llarg missatge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# La funció qa() aplica una anàlisi estàndard als arxius indicats. En aquest\n",
    "# cas, els únics dos arxius amb extensió \"fastq\" de la carpeta \"data\":\n",
    "ResumQualitat <- qa('data', type = 'fastq', pattern = '*.fastq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: FastqQA(10)\n",
       "QA elements (access with qa[[\"elt\"]]):\n",
       "  readCounts: data.frame(2 3)\n",
       "  baseCalls: data.frame(2 5)\n",
       "  readQualityScore: data.frame(1024 4)\n",
       "  baseQuality: data.frame(190 3)\n",
       "  alignQuality: data.frame(2 3)\n",
       "  frequentSequences: data.frame(100 4)\n",
       "  sequenceDistribution: data.frame(72 4)\n",
       "  perCycle: list(2)\n",
       "    baseCall: data.frame(1626 4)\n",
       "    quality: data.frame(8719 5)\n",
       "  perTile: list(2)\n",
       "    readCounts: data.frame(0 4)\n",
       "    medianReadQualityScore: data.frame(0 4)\n",
       "  adapterContamination: data.frame(2 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Podem veure el contingut de l'objecte \"ResumQualitat\" així:\n",
    "ResumQualitat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>read</th><th scope=col>filter</th><th scope=col>aligned</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ERR4423464_1.fastq</th><td>65135</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>ERR4423464_2.fastq</th><td>65135</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & read & filter & aligned\\\\\n",
       "  & <int> & <lgl> & <lgl>\\\\\n",
       "\\hline\n",
       "\tERR4423464\\_1.fastq & 65135 & NA & NA\\\\\n",
       "\tERR4423464\\_2.fastq & 65135 & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 3\n",
       "\n",
       "| <!--/--> | read &lt;int&gt; | filter &lt;lgl&gt; | aligned &lt;lgl&gt; |\n",
       "|---|---|---|---|\n",
       "| ERR4423464_1.fastq | 65135 | NA | NA |\n",
       "| ERR4423464_2.fastq | 65135 | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "                   read  filter aligned\n",
       "ERR4423464_1.fastq 65135 NA     NA     \n",
       "ERR4423464_2.fastq 65135 NA     NA     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Així com accedir als seus elements individuals:\n",
    "ResumQualitat[['readCounts']]\n",
    "\n",
    "# Edita aquest bloc per mostrar altres elements del \"ResumQualitat\",\n",
    "# com per exemple \"baseCalls\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>score</th><th scope=col>count</th><th scope=col>lane</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> </td><td> 0</td><td>ERR4423464_1.fastq</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>!</td><td> 0</td><td>ERR4423464_1.fastq</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>\"</td><td> 0</td><td>ERR4423464_1.fastq</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>#</td><td>33</td><td>ERR4423464_1.fastq</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>$</td><td> 0</td><td>ERR4423464_1.fastq</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>%</td><td> 0</td><td>ERR4423464_1.fastq</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & score & count & lane\\\\\n",
       "  & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 &   &  0 & ERR4423464\\_1.fastq\\\\\n",
       "\t2 & ! &  0 & ERR4423464\\_1.fastq\\\\\n",
       "\t3 & \" &  0 & ERR4423464\\_1.fastq\\\\\n",
       "\t4 & \\# & 33 & ERR4423464\\_1.fastq\\\\\n",
       "\t5 & \\$ &  0 & ERR4423464\\_1.fastq\\\\\n",
       "\t6 & \\% &  0 & ERR4423464\\_1.fastq\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | score &lt;chr&gt; | count &lt;dbl&gt; | lane &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | <!----> |  0 | ERR4423464_1.fastq |\n",
       "| 2 | ! |  0 | ERR4423464_1.fastq |\n",
       "| 3 | \" |  0 | ERR4423464_1.fastq |\n",
       "| 4 | # | 33 | ERR4423464_1.fastq |\n",
       "| 5 | $ |  0 | ERR4423464_1.fastq |\n",
       "| 6 | % |  0 | ERR4423464_1.fastq |\n",
       "\n"
      ],
      "text/plain": [
       "  score count lane              \n",
       "1        0    ERR4423464_1.fastq\n",
       "2 !      0    ERR4423464_1.fastq\n",
       "3 \"      0    ERR4423464_1.fastq\n",
       "4 #     33    ERR4423464_1.fastq\n",
       "5 $      0    ERR4423464_1.fastq\n",
       "6 %      0    ERR4423464_1.fastq"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per mostrar sols una petita part dels elements més grans,\n",
    "# pots utilitzar les funcions head() i tail():\n",
    "head(ResumQualitat[['baseQuality']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'ResumQualitat.html/index.html'"
      ],
      "text/latex": [
       "'ResumQualitat.html/index.html'"
      ],
      "text/markdown": [
       "'ResumQualitat.html/index.html'"
      ],
      "text/plain": [
       "[1] \"ResumQualitat.html/index.html\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per últim, podem generar un arxiu html amb tota la informació\n",
    "# generada per la funció qa().\n",
    "report(ResumQualitat, dest = 'ResumQualitat.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Després d'experimentar amb els resultat de la funció `qa()` del paquet *ShortRead*, hauràs observat que en alguns aspectes no s'ajusta a les nostres dades. Per exemple, les seqüències en un arxiu **fastq** no estan alineades, i per tant `qa()` no ha pogut informar sobre la qualitat dels *alineaments*. A més, una anàlisi estàndard pot no respondre a totes les nostres necessitats.\n",
    "\n",
    "Com les longituds de les seqüències no semblen estar registrades a l'informe de `qa()`, cal llegir de nou les dades originals. Per fer-ho, disposem de diferents funcions. Normalment, per tal de comprovar la qualitat de les dades s'utilitza només una fracció aleatòria de les seqüències. Aleshores s'utilitza la funció `FastqSampler()`. Però en aquest cas particular es tracta de dos arxius relativament curts, i podem utilitzar la funció `readFastq()`, que carregarà en memòria la totalitat de les seqüències."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DNAStringSet object of length 6:\n",
       "    width seq\n",
       "[1]   184 \u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m...\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\n",
       "[2]   200 \u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m...\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\n",
       "[3]   183 \u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m...\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\n",
       "[4]    85 \u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m...\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\n",
       "[5]    84 \u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m...\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\n",
       "[6]   201 \u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m...\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mT\u001b[39m\u001b[49m\u001b[47m\u001b[30mC\u001b[39m\u001b[49m\u001b[47m\u001b[30mG\u001b[39m\u001b[49m\u001b[47m\u001b[30mA\u001b[39m\u001b[49m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lectures.1 <- readFastq('data/ERR4423464_1.fastq')\n",
    "lectures.2 <- readFastq('data/ERR4423464_2.fastq')\n",
    "\n",
    "# Per mostrar les primeres seqüències de cada mostra:\n",
    "head(sread(lectures.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: FastqQuality\n",
       "quality:\n",
       "BStringSet object of length 6:\n",
       "    width seq\n",
       "[1]   184 CCCCCGGGGGGGGGGGGGGGGGGGGGGGGGGGGG...GGGGGGGGGGGGGGGGGGGGGFGGGGGGGGGGF\n",
       "[2]   200 CCCCCCFFGAFGGGGGEEFFFFGGEFDGEGGGGF...GGGGGGGFGGGGGGGGGGGFGGGGGGGGGGGGG\n",
       "[3]   183 CCCCCGGGGGGGGGGGGGGGGGGGGGGGGGGGGG...GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\n",
       "[4]    85 CCCCCGGGGGGGGGGGGGGGGGGGGGGGGGGGGG...GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG\n",
       "[5]    84 BCCCCFECFFFFGGGCF9<FFFGC<FGAFDC8<F...6<FE9,,CFFGFA9CE@CA<<,EF8F,C@CF<F\n",
       "[6]   201 ?CCCCFCFCF<-@FFG<,CE@FEFGGGCC@FD;F...GGFGGGGGGFGGGFCGGGGCFG9FGGCFEGGGB"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Podem mostrar també les seues qualitats:\n",
    "head(quality(lectures.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>184</li><li>200</li><li>183</li><li>85</li><li>84</li><li>201</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 184\n",
       "\\item 200\n",
       "\\item 183\n",
       "\\item 85\n",
       "\\item 84\n",
       "\\item 201\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 184\n",
       "2. 200\n",
       "3. 183\n",
       "4. 85\n",
       "5. 84\n",
       "6. 201\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 184 200 183  85  84 201"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Així com extraure directament les seues longituds:\n",
    "head(width(lectures.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xfqs0QICUpNSCwRQpJSExJLhJCk1ITEEiEkKTUhsUQISUpNSCwRQpKMDenASZZe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\nASEBGvw//t6bYU22YzAAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Histogram of width(lectures.1)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I per fer un histograma, podem utilitzar la funció hist():\n",
    "hist(width(lectures.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intenta usar la funció `mean()` per calcular la longitud mitjana de les seqüències de cada arxiu.\n",
    "\n",
    "L'anàlisi de qualitat de les seqüències obtingudes d'un experiment de seqüenciació és un pas fonamental, en el qual s'han d'eliminar les seqüències de baixa qualitat, o retallar-se, si en algun extrem la seqüenciació ha produït resultats pobres.\n",
    "\n",
    "## Mapatge de lectures curtes\n",
    "El pas següent és normalment identificar la posició d'origen, en un genoma de referència, de totes les seqüències curtes obtingudes per seqüenciació. Existeixen molts programes disponibles per fer-ho. En l'entorn d'R, podem utilitzar una implementació de Bowtie-2, anomenada *Rbowtie2*, creada per Zheng Wei i Wei Zhang, i disponible en [Bioconductor](https://bioconductor.org/packages/release/bioc/html/Rbowtie2.html) sota llicència *GPL (>= 3)*.\n",
    "\n",
    "El genoma de referència és el del SARS-CoV-2, i es troba en l'arxiu `data/referencia.fna`. Aquestes són les seues primeres línies.\n",
    "\n",
    "    >NC_045512.2 Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome\n",
    "    ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAA\n",
    "    CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTGCACTCACGCAGTATAATTAATAAC\n",
    "    TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGCAGGCTGCTTACGGTTTCGTCCGTG\n",
    "    TTGCAGCCGATCATCAGCACATCTAGGTTTCGTCCGGGTGTGACCGAAAGGTAAGATGGAGAGCCTTGTC\n",
    "    CCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTTTACAGGTTCGCGACGTGCTCGTAC\n",
    "    GTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGCACGTCAACATCTTAAAGATGGCACTTGTGG\n",
    "    CTTAGTAGAAGTTGAAAAAGGCGTTTTGCCTCAACTTGAACAGCCCTATGTGTTCATCAAACGTTCGGAT\n",
    "\n",
    "Reconeixes el format? Abans de mapar o alinear les seqüències curtes al genoma de referència és necessari preparar (de fet, *indexar*) el genoma de referència. L'índex es composarà d'uns pocs arxius, amb el mateix nom i terminacions diferents. Per tenir els arxius ordenats, crearem una carpeta on guardar l'índex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in dir.create(\"index\"):\n",
      "“'index' already exists”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".1.bt2\"), \"bt2Index\", overwrite):\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.1.bt2. It will be overwrited”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".2.bt2\"), \"bt2Index\", overwrite):\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.2.bt2. It will be overwrited”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".3.bt2\"), \"bt2Index\", overwrite):\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.3.bt2. It will be overwrited”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".4.bt2\"), \"bt2Index\", overwrite):\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.4.bt2. It will be overwrited”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".rev.1.bt2\"), \"bt2Index\", :\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.rev.1.bt2. It will be overwrited”\n",
      "Warning message in checkFileCreatable(paste0(bt2Index, \".rev.2.bt2\"), \"bt2Index\", :\n",
      "“For argument bt2Index, file exist: index/SARS-Cov2.rev.2.bt2. It will be overwrited”\n",
      "arguments 'show.output.on.console', 'minimized' and 'invisible' are for Windows only\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(Rbowtie2))\n",
    "dir.create('index')\n",
    "\n",
    "# Observa com podem utilitzar més d'una línia per escriure una ordre més clara:\n",
    "build_output <- bowtie2_build(references = 'data/referencia.fna',\n",
    "                              bt2Index = 'index/SARS-Cov2',\n",
    "                              '--quiet',\n",
    "                              overwrite = TRUE)\n",
    "\n",
    "# En aquesta variable hem guardat els missatges d'eixida del procés. Vegem si hi\n",
    "# ha cap error:\n",
    "build_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La construcció de l'índex del genoma de referència accepta moltes opcions. Les úniques que són imprescindibles són la *direcció* de l'arxiu **fasta** amb la seqüència del genoma de referència i el nom base que volem donar als arxius de l'índex. Comprova que l'índex ha sigut creat en la carpeta designada. Els arxius que el composen no són arxius de text.\n",
    "\n",
    "Ara podem mapar les seqüències curtes al genoma del coronavirus SARS-Cov-2. Si has donat un nom diferent a la carpeta de l'índex, no oblides canviar-lo ací també. També pots triar el nom de l'arxiu on es guardaran els resultats del mapatge en format SAM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arguments 'show.output.on.console', 'minimized' and 'invisible' are for Windows only\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'65135 reads; of these:'</li><li><span style=white-space:pre-wrap>'  65135 (100.00%) were paired; of these:'</span></li><li><span style=white-space:pre-wrap>'    2477 (3.80%) aligned concordantly 0 times'</span></li><li><span style=white-space:pre-wrap>'    62658 (96.20%) aligned concordantly exactly 1 time'</span></li><li><span style=white-space:pre-wrap>'    0 (0.00%) aligned concordantly &gt;1 times'</span></li><li><span style=white-space:pre-wrap>'    ----'</span></li><li><span style=white-space:pre-wrap>'    2477 pairs aligned concordantly 0 times; of these:'</span></li><li><span style=white-space:pre-wrap>'      714 (28.83%) aligned discordantly 1 time'</span></li><li><span style=white-space:pre-wrap>'    ----'</span></li><li><span style=white-space:pre-wrap>'    1763 pairs aligned 0 times concordantly or discordantly; of these:'</span></li><li><span style=white-space:pre-wrap>'      3526 mates make up the pairs; of these:'</span></li><li><span style=white-space:pre-wrap>'        3258 (92.40%) aligned 0 times'</span></li><li><span style=white-space:pre-wrap>'        268 (7.60%) aligned exactly 1 time'</span></li><li><span style=white-space:pre-wrap>'        0 (0.00%) aligned &gt;1 times'</span></li><li>'97.50% overall alignment rate'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '65135 reads; of these:'\n",
       "\\item '  65135 (100.00\\%) were paired; of these:'\n",
       "\\item '    2477 (3.80\\%) aligned concordantly 0 times'\n",
       "\\item '    62658 (96.20\\%) aligned concordantly exactly 1 time'\n",
       "\\item '    0 (0.00\\%) aligned concordantly >1 times'\n",
       "\\item '    ----'\n",
       "\\item '    2477 pairs aligned concordantly 0 times; of these:'\n",
       "\\item '      714 (28.83\\%) aligned discordantly 1 time'\n",
       "\\item '    ----'\n",
       "\\item '    1763 pairs aligned 0 times concordantly or discordantly; of these:'\n",
       "\\item '      3526 mates make up the pairs; of these:'\n",
       "\\item '        3258 (92.40\\%) aligned 0 times'\n",
       "\\item '        268 (7.60\\%) aligned exactly 1 time'\n",
       "\\item '        0 (0.00\\%) aligned >1 times'\n",
       "\\item '97.50\\% overall alignment rate'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '65135 reads; of these:'\n",
       "2. <span style=white-space:pre-wrap>'  65135 (100.00%) were paired; of these:'</span>\n",
       "3. <span style=white-space:pre-wrap>'    2477 (3.80%) aligned concordantly 0 times'</span>\n",
       "4. <span style=white-space:pre-wrap>'    62658 (96.20%) aligned concordantly exactly 1 time'</span>\n",
       "5. <span style=white-space:pre-wrap>'    0 (0.00%) aligned concordantly &gt;1 times'</span>\n",
       "6. <span style=white-space:pre-wrap>'    ----'</span>\n",
       "7. <span style=white-space:pre-wrap>'    2477 pairs aligned concordantly 0 times; of these:'</span>\n",
       "8. <span style=white-space:pre-wrap>'      714 (28.83%) aligned discordantly 1 time'</span>\n",
       "9. <span style=white-space:pre-wrap>'    ----'</span>\n",
       "10. <span style=white-space:pre-wrap>'    1763 pairs aligned 0 times concordantly or discordantly; of these:'</span>\n",
       "11. <span style=white-space:pre-wrap>'      3526 mates make up the pairs; of these:'</span>\n",
       "12. <span style=white-space:pre-wrap>'        3258 (92.40%) aligned 0 times'</span>\n",
       "13. <span style=white-space:pre-wrap>'        268 (7.60%) aligned exactly 1 time'</span>\n",
       "14. <span style=white-space:pre-wrap>'        0 (0.00%) aligned &gt;1 times'</span>\n",
       "15. '97.50% overall alignment rate'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"65135 reads; of these:\"                                                \n",
       " [2] \"  65135 (100.00%) were paired; of these:\"                              \n",
       " [3] \"    2477 (3.80%) aligned concordantly 0 times\"                         \n",
       " [4] \"    62658 (96.20%) aligned concordantly exactly 1 time\"                \n",
       " [5] \"    0 (0.00%) aligned concordantly >1 times\"                           \n",
       " [6] \"    ----\"                                                              \n",
       " [7] \"    2477 pairs aligned concordantly 0 times; of these:\"                \n",
       " [8] \"      714 (28.83%) aligned discordantly 1 time\"                        \n",
       " [9] \"    ----\"                                                              \n",
       "[10] \"    1763 pairs aligned 0 times concordantly or discordantly; of these:\"\n",
       "[11] \"      3526 mates make up the pairs; of these:\"                         \n",
       "[12] \"        3258 (92.40%) aligned 0 times\"                                 \n",
       "[13] \"        268 (7.60%) aligned exactly 1 time\"                            \n",
       "[14] \"        0 (0.00%) aligned >1 times\"                                    \n",
       "[15] \"97.50% overall alignment rate\"                                         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapeo_output <- bowtie2(bt2Index = 'index/SARS-Cov2',\n",
    "                        samOutput = 'ERR4423464.sam',\n",
    "                        seq1 = 'data/ERR4423464_1.fastq',\n",
    "                        seq2 = 'data/ERR4423464_2.fastq',\n",
    "                        overwrite = TRUE)\n",
    "mapeo_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dedica un temps a llegir el missatge d'eixida del mapatge. Et sembla un resultat satisfactori? Pots interpretar tota la informació?\n",
    "\n",
    "L'arxiu d'eixida, `ERR4423464.sam`, sí és un arxiu de text en format **SAM**, que pots obrir per explorar-lo. Observaràs que les línies són més llargues que l'espai disponible en la pantalla, la qual cosa dificulta reconéixer el format. Per identificar millor les columnes, pots utilitzar el comandament següent en una *shell* de Unix:\n",
    "\n",
    "    less -S ERR4423464.sam\n",
    "\n",
    "Visita [aquest enllaç](https://en.wikipedia.org/wiki/SAM_(file_format)) o bé la [publicació original](https://academic.oup.com/bioinformatics/article/25/16/2078/204688) on es descriu el format. De totes maneres, és més pràctic (i ocupa menys) la versió *binària* del format **SAM**, anomenada **BAM**. Anem a crear una versió *bam* dels nostres alineaments per utilitzar-la en el pròxim pas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'ERR4423464.bam'"
      ],
      "text/latex": [
       "'ERR4423464.bam'"
      ],
      "text/markdown": [
       "'ERR4423464.bam'"
      ],
      "text/plain": [
       "[1] \"ERR4423464.bam\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                group |    nb of |    nb of | mean / max\n",
      "                                   of |  records |   unique | records per\n",
      "                              records | in group |   QNAMEs | unique QNAME\n",
      "All records........................ A |   130270 |    65135 |    2 / 2\n",
      "  o template has single segment.... S |        0 |        0 |   NA / NA\n",
      "  o template has multiple segments. M |   130270 |    65135 |    2 / 2\n",
      "      - first segment.............. F |    65135 |    65135 |    1 / 1\n",
      "      - last segment............... L |    65135 |    65135 |    1 / 1\n",
      "      - other segment.............. O |        0 |        0 |   NA / NA\n",
      "\n",
      "Note that (S, M) is a partitioning of A, and (F, L, O) is a partitioning of M.\n",
      "Indentation reflects this.\n",
      "\n",
      "Details for group M:\n",
      "  o record is mapped.............. M1 |   127012 |    63640 |    2 / 2\n",
      "      - primary alignment......... M2 |   127012 |    63640 |    2 / 2\n",
      "      - secondary alignment....... M3 |        0 |        0 |   NA / NA\n",
      "  o record is unmapped............ M4 |     3258 |     1763 | 1.85 / 2\n",
      "\n",
      "Details for group F:\n",
      "  o record is mapped.............. F1 |    63510 |    63510 |    1 / 1\n",
      "      - primary alignment......... F2 |    63510 |    63510 |    1 / 1\n",
      "      - secondary alignment....... F3 |        0 |        0 |   NA / NA\n",
      "  o record is unmapped............ F4 |     1625 |     1625 |    1 / 1\n",
      "\n",
      "Details for group L:\n",
      "  o record is mapped.............. L1 |    63502 |    63502 |    1 / 1\n",
      "      - primary alignment......... L2 |    63502 |    63502 |    1 / 1\n",
      "      - secondary alignment....... L3 |        0 |        0 |   NA / NA\n",
      "  o record is unmapped............ L4 |     1633 |     1633 |    1 / 1\n"
     ]
    }
   ],
   "source": [
    "suppressMessages(library(Rsamtools))\n",
    "asBam('ERR4423464.sam', destination='ERR4423464', overwrite = TRUE)\n",
    "\n",
    "# Açò ens ofereix un resum dels tipus d'alineaments continguts a l'arxiu:\n",
    "quickBamFlagSummary('ERR4423464.bam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El format **SAM** conté molta informació, però no ens ofereix una visió panoràmica al llarg del genoma de referència. iPer poder visualitzar la distribució de les seqüències curtes al llarg del genoma de referència necessitem *amuntegar-les*. La funció `pileup()` permet comptar quantes vegades ha sigut *llegida* cada posició (és a dir, la seua **cobertura**), a més de poder registrar quins nucleòtids diferents s'han trobat en cada posició i amb quina freqüència cada un, entre altres coses. Nota, al codi següent, que podem triar la qualitat mínima d'una base llegida per tal de comptar-la en la seua cobertura:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>seqnames</th><th scope=col>pos</th><th scope=col>count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>NC_045512.2</td><td>39</td><td>25</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>NC_045512.2</td><td>40</td><td>32</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>NC_045512.2</td><td>41</td><td>32</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>NC_045512.2</td><td>42</td><td>34</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>NC_045512.2</td><td>43</td><td>34</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>NC_045512.2</td><td>44</td><td>35</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & seqnames & pos & count\\\\\n",
       "  & <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & NC\\_045512.2 & 39 & 25\\\\\n",
       "\t2 & NC\\_045512.2 & 40 & 32\\\\\n",
       "\t3 & NC\\_045512.2 & 41 & 32\\\\\n",
       "\t4 & NC\\_045512.2 & 42 & 34\\\\\n",
       "\t5 & NC\\_045512.2 & 43 & 34\\\\\n",
       "\t6 & NC\\_045512.2 & 44 & 35\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | seqnames &lt;fct&gt; | pos &lt;int&gt; | count &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | NC_045512.2 | 39 | 25 |\n",
       "| 2 | NC_045512.2 | 40 | 32 |\n",
       "| 3 | NC_045512.2 | 41 | 32 |\n",
       "| 4 | NC_045512.2 | 42 | 34 |\n",
       "| 5 | NC_045512.2 | 43 | 34 |\n",
       "| 6 | NC_045512.2 | 44 | 35 |\n",
       "\n"
      ],
      "text/plain": [
       "  seqnames    pos count\n",
       "1 NC_045512.2 39  25   \n",
       "2 NC_045512.2 40  32   \n",
       "3 NC_045512.2 41  32   \n",
       "4 NC_045512.2 42  34   \n",
       "5 NC_045512.2 43  34   \n",
       "6 NC_045512.2 44  35   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cobertura <- pileup('ERR4423464.bam',\n",
    "                    pileupParam = PileupParam(max_depth = 10000,\n",
    "                                              min_base_quality = 13,\n",
    "                                              min_mapq = 13,\n",
    "                                              min_nucleotide_depth = 1,\n",
    "                                              min_minor_allele_depth = 0,\n",
    "                                              distinguish_strands = FALSE,\n",
    "                                              distinguish_nucleotides = FALSE))\n",
    "head(cobertura)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abans de veure com varia la cobertura al llarg del genoma en este experiment de seqüenciació, considerem què cap esperar. Hem utilitzat 65.135 parells de seqüències curtes, la longitud mitjana de les quals era 154. Aproximadament, esperaríem unes $65.135 \\times 2 \\times 154 = 20.061.580$ bases seqüenciades. Com que el genoma del coronavirus SARS-Cov-2 té unes 30000 bases, en principi tenim suficients seqüències curtes com per cobrir el genoma de referència $20.061.580 / 30000 = 668,72 vegades. A la gràfica que generarem a continuació, la línia roja marcarà la cobertura mitjana obtinguda (676) i la blava, la mediana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jgcFl0WABDPNcEOS4oBACTiK0/oT/MJ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FEoC7RvjRqnyd5QoAW8JoGooAAcy5FWTH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\nWlFRsW/fPlxzRUUFSVVA9QHzi1lGlshLmr6Yt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pOam0strjF55HIKQqP7qTGj0IoEIIo777zzNm3axP7vzp07v/nNbyKEHnzwQdubfuSR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\nAJ5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UsIEQgE//znPxMSEuRyOf2rXC4v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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Els dos primers arguments de la funció plot() són les coordenades.\n",
    "# \n",
    "plot(cobertura$pos, cobertura$count, type = 'l',\n",
    "     xlab = 'Posició', ylab = 'Cobertura')\n",
    "abline(h = mean(cobertura$count),   col = 'red')\n",
    "abline(h = median(cobertura$count), col = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si la seqüenciació haguera sigut realment aleatòria, de manera que totes les posicions tingueren la mateixa probabilitat de ser seqüenciades, la cobertura seguiria una distribució de Poisson, on la mitjana és igual a la variància. En este cas, la variància (calcula-la amb `var(cobertura$count)`), és molt major que la mitjana. Quin motiu se t'ocorre que podria haver provocat que algunes posicions hagen sigut seqüenciades tant més que altres?\n",
    "\n",
    "## Detecció de variants\n",
    "\n",
    "L'objectiu de molts projectes de seqüenciació és detectar i analitzar les posicions (i fragments) variables: aquelles on no tots els individus seqüenciats tenen la mateixa informació genètica. Un tipus bàsic de variació són les mutacions puntuals o els SNPs. Diferenciar les posicions variables de les que no ho són és un problema de classificació que pot resultar difícil, perquè les seqüències curtes inclouen alguns **errors de seqüenciació**.\n",
    "\n",
    "Fora de l'entorn d'R, existeixen molts programes per detecgtar variants. [GATK](https://gatk.broadinstitute.org/hc/en-us), desenvolupat pel Broad Institute, és una de les eines més populars per detectar variants a gran escala. Es distribueix com a programari lliure, sota llicència BSD modificada de 3 clàusules. Un altre programa interessant és [Freebayes](https://github.com/freebayes/freebayes) d'Erik Garrisson i altres, distribuït sota llicència MIT.\n",
    "\n",
    "Sense entrar en els detalls del procés, l'exemple bàsic següent ens dóna una idea dels pasos a seguir en R i ens permetrà observar el format **VCF** dels resultats. Anem a utilitzar els paquets [VariantTools](https://bioconductor.org/packages/release/bioc/html/VariantTools.html) i [gmapR](https://bioconductor.org/packages/release/bioc/html/gmapR.html).\n",
    "\n",
    "El primer pas és indexar de nou el genoma de referència. El paquet `gmapR` és independent de `Rbowtie2` i no utilitza el mateix tipus d'índex. La funció `GmapGenome()` pot crear un índex a partir d'un arxiu **fasta**, sempre que se li indique la direcció (o *path*) d'eixe arxiu de la manera adequada. Així doncs, abans de crear l'índex, cal crear la variable que indica on es troba la seqüència del genoma de referència en format **fasta**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressMessages(library('VariantTools'))\n",
    "suppressMessages(library('gmapR'))\n",
    "\n",
    "# Creem un objecte 'FastaFile' a partir de l'arxiu data/referencia.fna\n",
    "referencia.fasta  <- rtracklayer::FastaFile(\n",
    "    paste(getwd(), 'data/referencia.fna', sep = '/'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: genome 'referencia' already exists, not overwriting\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Ací s'indexa el genoma de referència.\n",
    "referencia.genoma <- GmapGenome(referencia.fasta, create = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta expressió haurà generat un error. Llig-lo i intenta desxifrar-ne el significat. Davant situacions així, és recomanable buscar en internet el text o les paraules clau de l'error. En aquest cas, el problema és que la màquina virtual on s'executen aquestes ordres no disposa de suficient memòia RAM per generar els arxius de l'índex. Per solucionar-ho, utilitzarem un índex ja creat i contingut a l'arxiu `data/gmap.tar.gz`. Abans d'utilitzar-lo cal *desempaquetar-lo*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "untar('data/gmap.tar.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I ara podem informar la funció `GmapGenome()` de la ubicació de l'índex que acabem de desempaquetar. Observaràs que la forma de fer-ho és una mica complicada, perquè a l'argument  `directory` se li assigna el resultat de combinar tres funcions diferents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "referencia.genoma <- GmapGenome(genome = 'referencia',\n",
    "                               directory = GmapGenomeDirectory(file.path(getwd(),'gmap')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ara sí que tenim el genoma de referència en el format adequat i estem en condicions d'utilitzar-lo, junt amb els alineaments de l'arxiu `ERR4423464.bam` per detectar variants.\n",
    "\n",
    "La funció `TallyVariantsParam()` permet ajustar alguns paràmetres que afecten la sensibilitat i l'especificitat del procés de detecció de variants. Pots esbrinar quins paràmetres són aquests llegint l'ajuda de la funció: `help(TallyVariantsParam)`. Si no especifiquem cap més paràmetre que el genoma de referència, s'apliquen els valors per defecte de la resta de paràmetres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "parametres <- TallyVariantsParam(referencia.genoma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalment es detecten variants entre un conjunt de mostres o individus seqüenciats. En aquest exercici estem utilitzant només una mostra, representada per l'arxiu `ERR4423464.bam`. Ací, les *variants* que busquem són diferències entre el genoma seqüenciat i el de referència. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'VRanges object with 40 ranges and 17 metadata columns'"
      ],
      "text/latex": [
       "'VRanges object with 40 ranges and 17 metadata columns'"
      ],
      "text/markdown": [
       "'VRanges object with 40 ranges and 17 metadata columns'"
      ],
      "text/plain": [
       "[1] \"VRanges object with 40 ranges and 17 metadata columns\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preliminar <- callVariants('ERR4423464.bam', parametres)\n",
    "summary(preliminar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les posicions variables estan indicades com a *rangs*, perquè podrien abastar més d'un nucleòtid. Sempre poden haver **falsos positius** entre els resultats d'un procés de detecció de variants. El pas següent consisteix en filtrar els resultats, per tal de quedar-nos únicament amb les variants més probablement vertaderes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'VRanges object with 34 ranges and 17 metadata columns'"
      ],
      "text/latex": [
       "'VRanges object with 34 ranges and 17 metadata columns'"
      ],
      "text/markdown": [
       "'VRanges object with 34 ranges and 17 metadata columns'"
      ],
      "text/plain": [
       "[1] \"VRanges object with 34 ranges and 17 metadata columns\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtrades  <- postFilterVariants(preliminar)\n",
    "summary(filtrades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantes posicions variables hem trobat? Aconsegueixes extraure i interpretar la informació continguda en l'objecte `filtrades`? Existeixen moltes funcions per treballar amb aquesta classe d'objecte, però aprendre-les no és el nostre objectiu ara. Potser el més fàcil i pràctic siga guardar la informació sobre les variants filtrades en un arxiu de text amb format **VCF**.\n",
    "\n",
    "El format **VCF** necessita els noms de les mostres (en el nostre cas només una), que normalment coincideixen amb els noms dels arxius `.bam`. Com esta informació no està encara continguda en l'objecte `filtrades`, la introduïm ara:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleNames(filtrades) <- 'ERR4423464'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I finalment, creem l'arxiu **VCF**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .Call(.make_vcf_geno, filename, fixed, names(geno), as.list(geno), :\n",
      "“converting NULL pointer to R NULL”\n"
     ]
    }
   ],
   "source": [
    "writeVcf(filtrades, filename = 'ERR4423464.vcf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El format **VCF** està descrit en [aquet document](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwicz7ih05zuAhUHExoKHQB-DY8QFjAIegQIARAC&url=https%3A%2F%2Fsamtools.github.io%2Fhts-specs%2FVCFv4.2.pdf&usg=AOvVaw3UrlHdXnBVzm0df9OE90Rm). En obrir l'arxiu `ERR4423464.vcf` amb un editor de text, normalment les línies més llargues que la pantalla acaben ocupant més d'una línia, la qual cosa difivulta la identificació del format. Per poder observar millor el format és recomanable obrir-lo, per exemple, des d'una terminal (o *shell*) amb el comandament:\n",
    "\n",
    "    less -S ERR4423464.vcf"
   ]
  }
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