Assembler.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import networkx as nx\n",
        "from networkx import algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Monta o grafo no formato pathGraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "###pathGraph###\n",
        "def pathGraph(entrada,mostraCadaAresta = False):\n",
        "    blocos = entrada.split(\",\")\n",
        "    g = nx.MultiDiGraph()\n",
        "    for bloco in blocos:\n",
        "        g.add_edge(bloco[:-1],bloco[1:],label = bloco)\n",
        "        if mostraCadaAresta: print(bloco[:-1],bloco[1:],bloco)\n",
        "    return g,len(blocos[0])\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Retorna o caminho Euleriano dado o pathGraph como entrada"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def deBruijn(G,mostraCadaAresta = False) -> str:\n",
        "    caminhoEuleriano = list(algorithms.euler.eulerian_path(G))\n",
        "    sequenciaFinal = \"\"\n",
        "    for index,(origem,destino) in enumerate(caminhoEuleriano):\n",
        "        if index != len(caminhoEuleriano) - 1: \n",
        "            sequenciaFinal +=origem[0]\n",
        "            if mostraCadaAresta : print(origem + \" -> \",end=\"\")\n",
        "        else: \n",
        "            sequenciaFinal +=origem[0] + destino\n",
        "            if mostraCadaAresta : print(origem + \" -> \" +destino)\n",
        "    return sequenciaFinal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Arquivo de Entrada"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "SrYGLTPjG7en"
      },
      "outputs": [],
      "source": [
        "#Passe o caminho abaixo.. Deixe o arquivo no mesmo diretório!\n",
        "diretorio = \"k50mer.txt\"\n",
        "arquivo = open(diretorio,mode=\"r\") \n",
        "entrada = arquivo.read()\n",
        "if entrada[-1]==\",\": entrada = entrada[:-1]\n",
        "arquivo.close()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Monta o pathGraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MultiDiGraph with 29952 nodes and 29951 edges\n"
          ]
        }
      ],
      "source": [
        "G,tamanhoDoBloco = pathGraph(entrada)\n",
        "print(G)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Gera a sequência remontada em um arquivo fasta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Resultado Gerado no diretório corrente!\n"
          ]
        }
      ],
      "source": [
        "resultado = deBruijn(G)\n",
        "arquivoGerado = open(\"João Lucas-\"+str(tamanhoDoBloco)+\"kmer-resposta.fasta\",mode=\"w\")\n",
        "arquivoGerado.write(\">k=\"+str(tamanhoDoBloco)+\"\\n\"+resultado)\n",
        "arquivoGerado.close()\n",
        "print(\"Resultado Gerado no diretório corrente!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plota o Grafo completo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 573,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pos=nx.spring_layout(G,scale=1000)\n",
        "nx.draw(G,pos,with_labels = False)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "deBruijnGraph.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}