Módulo_Biopython_003.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Curso Introdução à Programação para Bioinformática com Python\n",
    "\n",
    "\n",
    "## Módulo - Biopython\n",
    "\n",
    "\n",
    "### Visualização de dados\n",
    "\n",
    "Visualização de dados é uma importante área de estudo para Data Science\n",
    "Nesta seção abordaremos alguns conceitos importantes como criação de visualizações para bioinformática com Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estudo de caso: comparando sequências de RNA ribossomal entre humanos e bactéria\n",
    "\n",
    "![imagem.svg](https://se.wikipedia.org/wiki/Fiila:Dna-base-flipping.svg)\n",
    "Estrutura do DNA. Fonte: https://se.wikipedia.org/wiki/Fiila:Dna-base-flipping.svg\n",
    "\n",
    "Neste estudo de caso, faremos a comparação entre duas sequências de DNA: (1) **ser humano**; vs. (2) **bactéria**. \n",
    "\n",
    "> DNA é uma molécula presente em todos os seres vivos, que é responsável por armazenar as características hereditárias. Ela é composta por sequências de nucleotídeos, que podem de quatro tipos: adenina, timina, citosina ou guanina.\n",
    "\n",
    "\"Computacionalmente\" falando podemos representá-los através de 4 letras: A, T, C ou G.\n",
    "\n",
    "\n",
    "Neste estudo de caso, queremos avaliar se estruturas com funções parecidas (estamos usando sequências de RNA ribossomal) de organismos diferentes têm diferenças. Para isso vamos avaliar a quantidade de pares de nucleotídeos.\n",
    "\n",
    "**Dados utilizados**:\n",
    "\n",
    "- Human 18S rRNA gene, complete\n",
    "\n",
    "Fonte: https://www.ncbi.nlm.nih.gov/nuccore/M10098.1?report=fasta\n",
    "\n",
    "\n",
    "- Escherichia coli strain U 5/41 16S ribosomal RNA gene, partial sequence\n",
    "\n",
    "Fonte: https://www.ncbi.nlm.nih.gov/nuccore/NR_024570.1?report=fasta\n",
    "\n",
    "**Resultado final**\n",
    "Como resultado final, geraremos uma figura que compara os DNAs visualmente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualização de dados gerando html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AA': 105, 'AT': 64, 'AC': 86, 'AG': 111, 'TA': 64, 'TT': 57, 'TC': 60, 'TG': 108, 'CA': 83, 'CT': 75, 'CC': 74, 'CG': 97, 'GA': 113, 'GT': 93, 'GC': 109, 'GG': 147}\n"
     ]
    }
   ],
   "source": [
    "entrada = open(\"16s_bacteria.fasta\").read()\n",
    "saida = open(\"16s_bacteria.html\",\"w\")\n",
    "\n",
    "cont_b = {}\n",
    "\n",
    "#Laço para preencher o dicionário cont_b com os dinucleotídeos\n",
    "for i in ['A', 'T', 'C', 'G']:\n",
    "    for j in ['A', 'T', 'C', 'G']:\n",
    "        cont_b[i+j] = 0\n",
    "\n",
    "# Para formatar a entrada dos dados que veio do arquivo FASTA\n",
    "entrada = entrada.replace(\"\\n\",\"\")\n",
    "\n",
    "#Laço para contar a frequência dos dinucleotídeos\n",
    "for k in range(len(entrada)-1):\n",
    "    cont_b[entrada[k]+entrada[k+1]] += 1\n",
    "print(cont_b)\n",
    "\n",
    "# html\n",
    "\n",
    "saida.write(\"<div>\")\n",
    "\n",
    "i = 1\n",
    "for k in cont_b:\n",
    "    transparencia = cont_b[k]/max(cont_b.values())\n",
    "    saida.write(\"<div style='width:100px; border:1px solid #111; color:#fff; height:100px; float:left; background-color:rgba(0, 0, 0, \"+str(transparencia)+\"')>\"+k+\"</div>\")\n",
    "\n",
    "    if i%4 == 0:\n",
    "        saida.write(\"<div style='clear:both'></div>\")\n",
    "\n",
    "    i+=1\n",
    "\n",
    "saida.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AA': 116, 'AT': 95, 'AC': 97, 'AG': 113, 'TA': 85, 'TT': 113, 'TC': 109, 'TG': 111, 'CA': 87, 'CT': 105, 'CC': 184, 'CG': 170, 'GA': 134, 'GT': 105, 'GC': 155, 'GG': 184}\n"
     ]
    }
   ],
   "source": [
    "entrada = open(\"18s_humano.fasta\").read()\n",
    "saida = open(\"18s_humano.html\",\"w\")\n",
    "\n",
    "cont_h = {}\n",
    "\n",
    "#Laço para preencher o dicionário cont_h com os dinucleotídeos\n",
    "for i in ['A', 'T', 'C', 'G']:\n",
    "    for j in ['A', 'T', 'C', 'G']:\n",
    "        cont_h[i+j] = 0\n",
    "\n",
    "#Para formatar a entrada dos dados que veio do arquivo FASTA\n",
    "entrada = entrada.replace(\"\\n\",\"\")\n",
    "\n",
    "#Laço para contar a frequência dos dinucleotídeos\n",
    "for k in range(len(entrada)-1):\n",
    "    cont_h[entrada[k]+entrada[k+1]] += 1\n",
    "print(cont_h)\n",
    "\n",
    "# html\n",
    "saida.write(\"<div>\")\n",
    "\n",
    "i = 1\n",
    "for k in cont_h:\n",
    "    transparencia = cont_h[k]/max(cont_h.values())\n",
    "    saida.write(\"<div style='width:100px; border:1px solid #111; color:#fff; height:100px; float:left; background-color:rgba(0, 0, 0, \"+str(transparencia)+\"')>\"+k+\"</div>\")\n",
    "\n",
    "    if i%4 == 0:\n",
    "        saida.write(\"<div style='clear:both'></div>\")\n",
    "\n",
    "    i+=1\n",
    "\n",
    "saida.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualização de dados gerando gráfico Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bactéria</th>\n",
       "      <th>humano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>105</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>64</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>86</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AG</th>\n",
       "      <td>111</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TT</th>\n",
       "      <td>57</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TC</th>\n",
       "      <td>60</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TG</th>\n",
       "      <td>108</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>83</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>75</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC</th>\n",
       "      <td>74</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CG</th>\n",
       "      <td>97</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>113</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GT</th>\n",
       "      <td>93</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GC</th>\n",
       "      <td>109</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GG</th>\n",
       "      <td>147</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bactéria  humano\n",
       "AA       105     116\n",
       "AT        64      95\n",
       "AC        86      97\n",
       "AG       111     113\n",
       "TA        64      85\n",
       "TT        57     113\n",
       "TC        60     109\n",
       "TG       108     111\n",
       "CA        83      87\n",
       "CT        75     105\n",
       "CC        74     184\n",
       "CG        97     170\n",
       "GA       113     134\n",
       "GT        93     105\n",
       "GC       109     155\n",
       "GG       147     184"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "obj = {'bactéria': cont_b, 'humano': cont_h} # Utilizando as variáveis cont_b e cont_h dos scripts anteriores\n",
    "df = pd.DataFrame(data=obj)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup ok!\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\")\n",
    "print(\"Setup ok!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18458f32988>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparação de sequências de RNA ribossomal entre humanos e bactéria\n",
    "plt.subplots(figsize=(16, 8))\n",
    "sns.heatmap(df, annot=True, fmt=\"d\", linewidths=.10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}