genome_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "genome 3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZf1GFcEQYX9"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt # plotting\n",
        "import numpy as np # linear algebra\n",
        "import os # accessing directory structure\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Y9Y6QCbQ1Fg",
        "outputId": "e621e15f-18dd-4cdd-e5de-8a1c1e704e9a"
      },
      "source": [
        "print(os.listdir('/content/'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'dog_data.txt', 'chimp_data.txt', 'human_data.txt', 'example.fa', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7hqeHMYQ986"
      },
      "source": [
        "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
        "    nunique = df.nunique()\n",
        "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
        "    nRow, nCol = df.shape\n",
        "    columnNames = list(df)\n",
        "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
        "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
        "    for i in range(min(nCol, nGraphShown)):\n",
        "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
        "        columnDf = df.iloc[:, i]\n",
        "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
        "            valueCounts = columnDf.value_counts()\n",
        "            valueCounts.plot.bar()\n",
        "        else:\n",
        "            columnDf.hist()\n",
        "        plt.ylabel('counts')\n",
        "        plt.xticks(rotation = 90)\n",
        "        plt.title(f'{columnNames[i]} (column {i})')\n",
        "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
        "    plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sc8GM1GZRPP6"
      },
      "source": [
        "# Correlation matrix\n",
        "def plotCorrelationMatrix(df, graphWidth):\n",
        "    filename = df.dataframeName\n",
        "    df = df.dropna('columns') # drop columns with NaN\n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
        "    if df.shape[1] < 2:\n",
        "        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n",
        "        return\n",
        "    corr = df.corr()\n",
        "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
        "    corrMat = plt.matshow(corr, fignum = 1)\n",
        "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
        "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
        "    plt.gca().xaxis.tick_bottom()\n",
        "    plt.colorbar(corrMat)\n",
        "    plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nph2JlQ7RZhX"
      },
      "source": [
        "def plotScatterMatrix(df, plotSize, textSize):\n",
        "    df = df.select_dtypes(include =[np.number]) # keep only numerical columns\n",
        "    # Remove rows and columns that would lead to df being singular\n",
        "    df = df.dropna('columns')\n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
        "    columnNames = list(df)\n",
        "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
        "        columnNames = columnNames[:10]\n",
        "    df = df[columnNames]\n",
        "    ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
        "    corrs = df.corr().values\n",
        "    for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):\n",
        "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
        "    plt.suptitle('Scatter and Density Plot')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6RYrJZ2Rise",
        "outputId": "128bb90b-8ffc-4eee-d6a4-2d40743fc25f"
      },
      "source": [
        "!pip install biopython"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: biopython in /usr/local/lib/python3.6/dist-packages (1.78)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from biopython) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPCNoBG1SO75",
        "outputId": "801e4e53-7b64-4dbd-b0cd-1619f7e544a4"
      },
      "source": [
        "from Bio import SeqIO\n",
        "for seq_record in SeqIO.parse('/content/example.fa', \"fasta\"):\n",
        "    print(seq_record.id)\n",
        "    print(seq_record.seq)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ENST00000435737.5\n",
            "ATGTTTCGCATCACCAACATTGAGTTTCTTCCCGAATACCGACAAAAGGAGTCCAGGGAATTTCTTTCAGTGTCACGGACTGTGCAGCAAGTGATAAACCTGGTTTATACAACATCTGCCTTCTCCAAATTTTATGAGCAGTCTGTTGTTGCAGATGTCAGCAACAACAAAGGCGGCCTCCTTGTCCACTTTTGGATTGTTTTTGTCATGCCACGTGCCAAAGGCCACATCTTCTGTGAAGACTGTGTTGCCGCCATCTTGAAGGACTCCATCCAGACAAGCATCATAAACCGGACCTCTGTGGGGAGCTTGCAGGGACTGGCTGTGGACATGGACTCTGTGGTACTAAATGAAGTCCTGGGGCTGACTCTCATTGTCTGGATTGACTGA\n",
            "ENST00000419127.5\n",
            "ATGTTTCGCATCACCAACATTGAGTTTCTTCCCGAATACCGACAAAAGGAGTCCAGGGAATTTCTTTCAGTGTCACGGACTGTGCAGCAAGTGATAAACCTGGTTTATACAACATCTGCCTTCTCCAAATTTTATGAGCAGTCTGTTGTTGCAGATGTCAGCAACAACAAAGGCGGCCTCCTTGTCCACTTTTGGATTGTTTTTGTCATGCCACGTGCCAAAGGCCACATCTTCTGTGAAGACTGTGTTGCCGCCATCTTGAAGGACTCCATCCAGACAAGCATCATAAACCGGACCTCTGTGGGGAGCTTGCAGGGACTGGCTGTGGACATGGACTCTGTGGTACTAAATGACAAAGGCTGCTCTCAGTACTTCTATGCAGAGCATCTGTCTCTCCACTACCCGCTGGAGATTTCTGCAGCCTCAGGGAGGCTGATGTGTCACTTCAAGCTGGTGGCCATAGTGGGCTACCTGATTCGTCTCTCAATCAAGTCCATCCAAATCGAAGCCGACAACTGTGTCACTGACTCCCTGACCATTTACGACTCCCTTTTGCCCATCCGGAGCAGCATCTTGTACAGAATTTGTGAACCCACAAGAACATTAATGTCATTTGTTTCTACAAATAATCTCATGTTGGTGACATTTAAGTCTCCTCATATACGGAGGCTCTCAGGAATCCGGGCATATTTTGAGGTCATTCCAGAACAAAAGTGTGAAAACACAGTGTTGGTCAAAGACATCACTGGCTTTGAAGGGAAAATTTCAAGCCCATATTACCCGAGCTACTATCCTCCAAAATGCAAGTGTACCTGGAAATTTCAGACTTCTCTATCAACTCTTGGCATAGCACTGAAATTCTATAACTATTCAATAACCAAGAAGAGTATGAAAGGCTGTGAGCATGGATGGTGGGAAATTAATGAGCACATGTACTGTGGCTCCTACATGGATCATCAGACAATTTTTCGAGTGCCCAGCCCTCTGGTTCACATTCAGCTCCAGTGCAGTTCAAGGCTTTCAGACAAGCCACTTTTGGCAGAATATGGCAGTTACAACATCAGTCAACCCTGCCCTGTTGGATCTTTTAGATGCTCCTCCGGTTTATGTGTCCCTCAGGCCCAGCGTTGTGATGGAGTAAATGACTGCTTTGATGAAAGTGATGAACTGTTTTGCGTGAGCCCTCAACCTGCCTGCAATACCAGCTCCTTCAGGCAGCATGGCCCTCTCATCTGTGATGGCTTCAGGGACTGTGAGAATGGCCGGGATGAGCAAAACTGCACTCAAAGTATTCCATGCAACAACAGAACTTTTAAGTGTGGCAATGATATTTGCTTTAGGAAACAAAATGCAAAATGTGATGGGACAGTGGATTGTCCAGATGGAAGTGATGAAGAAGGCTGCACCTGCAGCAGGAGTTCCTCCGCCCTTCACCGCATCATCGGAGGCACAGACACCCTGGAGGGGGGTTGGCCGTGGCAGGTCAGCCTCCACTTTGTTGGATCTGCCTACTGTGGTGCCTCAGTCATCTCCAGGGAGTGGCTTCTTTCTGCAGCCCACTGTTTTCATGGAAACAGGCTGTCAGATCCCACACCATGGACTGCACACCTCGGGATGTATGTTCAGGGGAATGCCAAGTTTGTCTCCCCGGTGAGAAGAATTGTGGTCCACGAGTACTATAACAGTCAGACTTTTGATTATGATATTGCTTTGCTACAGCTCAGTATTGCCTGGCCTGAGACCCTGAAACAGCTCATTCAGCCAATATGCATTCCTCCCACTGGTCAGAGAGTTCGCAGTGGGGAGAAGTGCTGGGTAACTGGCTGGGGGCGAAGACACGAAGCAGATAATAAAGGCTCCCTCGTTCTGCAGCAAGCGGAGGTAGAGCTCATTGATCAAACGCTCTGTGTTTCCACCTACGGGATCATCACTTCTCGGATGCTCTGTGCAGGCATAATGTCAGGCAAGAGAGATGCCTGCAAAGGAGATTCGGGTGGACCTTTATCTTGTCGAAGAAAAAGTGATGGAAAATGGATTTTGACTGGCATTGTTAGCTGGGGACATGGAAGTGGACGACCAAACTTTCCTGGTGTTTACACAAGGGTGTCAAACTTTGTTCCCTGGATTCATAAATATGTCCCTTCTCTTTTGTAA\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bu8Uf9ZlSaSy",
        "outputId": "cc0c0f32-b125-45bc-97fb-52808ecc346b"
      },
      "source": [
        "import numpy as np\n",
        "import re\n",
        "def string_to_array(my_string):\n",
        "    my_string = my_string.lower()\n",
        "    my_string = re.sub('[^acgt]', 'z', my_string)\n",
        "    my_array = np.array(list(my_string))\n",
        "    return my_array\n",
        "\n",
        "# create a label encoder with 'acgtn' alphabet\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "label_encoder.fit(np.array(['a','c','g','t','z']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2yHzCQqSowF"
      },
      "source": [
        "def ordinal_encoder(my_array):\n",
        "    integer_encoded = label_encoder.transform(my_array)\n",
        "    float_encoded = integer_encoded.astype(float)\n",
        "    float_encoded[float_encoded == 0] = 0.25 # A\n",
        "    float_encoded[float_encoded == 1] = 0.50 # C\n",
        "    float_encoded[float_encoded == 2] = 0.75 # G\n",
        "    float_encoded[float_encoded == 3] = 1.00 # T\n",
        "    float_encoded[float_encoded == 4] = 0.00 # anything else, z\n",
        "    return float_encoded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xeyfABzPStzX",
        "outputId": "b6779cc8-c7fc-4067-862b-19d4d09c405e"
      },
      "source": [
        "test_sequence = 'AACGCGCTTNN'\n",
        "ordinal_encoder(string_to_array(test_sequence))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.25, 0.25, 0.5 , 0.75, 0.5 , 0.75, 0.5 , 1.  , 1.  , 0.  , 0.  ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvzCaAmVSxY-"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "def one_hot_encoder(my_array):\n",
        "    integer_encoded = label_encoder.transform(my_array)\n",
        "    onehot_encoder = OneHotEncoder(sparse=False, dtype=int)\n",
        "    integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
        "    onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n",
        "    onehot_encoded = np.delete(onehot_encoded, -1, 1)\n",
        "    return onehot_encoded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbAXXU1yS3Zw",
        "outputId": "5e5a8701-fa0d-4f65-fc6f-c8c21e47bf87"
      },
      "source": [
        "test_sequence = 'AACGCGGTTNN'\n",
        "one_hot_encoder(string_to_array(test_sequence))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [0, 1, 0, 0],\n",
              "       [0, 0, 1, 0],\n",
              "       [0, 1, 0, 0],\n",
              "       [0, 0, 1, 0],\n",
              "       [0, 0, 1, 0],\n",
              "       [0, 0, 0, 1],\n",
              "       [0, 0, 0, 1],\n",
              "       [0, 0, 0, 0],\n",
              "       [0, 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ly7YeVshS6_L"
      },
      "source": [
        "def getKmers(sequence, size):\n",
        "    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDhsNgGETRkK",
        "outputId": "392c2c29-ad52-4a3f-aadd-9e5b8f3d4ce9"
      },
      "source": [
        "mySeq = 'CATGGCCATCCCCCCCCGAGCGGGGGGGGGG'\n",
        "getKmers(mySeq, size=6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['catggc',\n",
              " 'atggcc',\n",
              " 'tggcca',\n",
              " 'ggccat',\n",
              " 'gccatc',\n",
              " 'ccatcc',\n",
              " 'catccc',\n",
              " 'atcccc',\n",
              " 'tccccc',\n",
              " 'cccccc',\n",
              " 'cccccc',\n",
              " 'cccccc',\n",
              " 'cccccg',\n",
              " 'ccccga',\n",
              " 'cccgag',\n",
              " 'ccgagc',\n",
              " 'cgagcg',\n",
              " 'gagcgg',\n",
              " 'agcggg',\n",
              " 'gcgggg',\n",
              " 'cggggg',\n",
              " 'gggggg',\n",
              " 'gggggg',\n",
              " 'gggggg',\n",
              " 'gggggg',\n",
              " 'gggggg']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "R8i0ehX1TUk5",
        "outputId": "231c2a2a-79c6-4033-e56d-17543fc794d9"
      },
      "source": [
        "words = getKmers(mySeq, size=6)\n",
        "sentence = ' '.join(words)\n",
        "sentence"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'catggc atggcc tggcca ggccat gccatc ccatcc catccc atcccc tccccc cccccc cccccc cccccc cccccg ccccga cccgag ccgagc cgagcg gagcgg agcggg gcgggg cggggg gggggg gggggg gggggg gggggg gggggg'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryIOcBxTTX9r"
      },
      "source": [
        "mySeq2 = 'GATGGCCATCCCCGCCCGAGCGGGGGGGG'\n",
        "mySeq3 = 'CATGGCCATCCCCGCCCGAGCGGGCGGGG'\n",
        "sentence2 = ' '.join(getKmers(mySeq2, size=6))\n",
        "sentence3 = ' '.join(getKmers(mySeq3, size=6))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgcMdOf1TcE4"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "X = cv.fit_transform([sentence, sentence2, sentence3]).toarray()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGkcLdMjTfBb",
        "outputId": "e9dc3dc7-18cb-4763-abe0-3e8808fc44d6"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 1, 1, 1, 1, 1, 3, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
              "        0, 1, 1, 0, 0, 5, 1, 0, 1],\n",
              "       [1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "        0, 1, 1, 0, 0, 3, 0, 1, 1],\n",
              "       [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
              "        1, 1, 1, 1, 1, 0, 0, 1, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7BszAcGTiuW"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feG0eC0KT9ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8f6fa0a1-077a-411c-cd1f-711eea36a4ce"
      },
      "source": [
        "human = pd.read_table('/content/human_data.txt')\n",
        "human.head(60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>sequence</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ATGCCCCAACTAAATACTACCGTATGGCCCACCATAATTACCCCCA...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAG...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>GCCACCACAGGCCTGAAGATGAAGCACCTCCTTACTGGGCAAGAGA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>ATGCAGCCTTGGCACGGAAAGGCCATGCAGAGAGCTTCCGAGGCCG...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>ATGCAGCCTTGGCACGGAAAGGCCATGCAGAGAGCTTCCGAGGCCG...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>NGGCAAACAAGAGAGAAAGCAAAATTTTCAGATGGAGAAAAGTGCC...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>ATGGAGCCCGCGACCGCGCCCCGGCCCGACATGGCGCCGGAGCTGA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>GTAAAGCTGAAACCTCATGAGGAACCTCTTCGTTCTGAGATCCTCA...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>ATGGCGCCGGAGCTGACCCCGGAGGAGGAGCAGGCTACCAAGCAGT...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>ATGTTCTCTCGCGCGGGTGTCGCTGGGCTGTCGGCCTGGACCTTGC...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>ATGTTCTCTCGCGCGGGTGTCGCTGGGCTGTCGGCCTGGACCTTGC...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>ATGAGGCCCGAGCGTCCCCGGCCGCGCGGCAGCGCCCCCGGCCCGA...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>ATGAGGCCCGAGCGTCCCCGGCCGCGCGGCAGCGCCCCCGGCCCGA...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>NTGCAGGTGATTTTCAAAGCCAAGTCAAAATATTCTCCAGAATTAC...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>ATGAAGCTTGTCAACATCTGGCTGCTTCTGCTCGTGGTTTTGCTCT...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>NNGTTAAACCTCAGCATCGGCGGCCCGGACTTCATGGATCATCCGT...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>GTTGCTTCTCCAGTGGTTGCAGGTGCTGTCACCTTGTTAGTGAGCA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>ATGAGGGAGTGCCAAGGATTTGCTCCAGATGCAGAACTTCACATTT...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>ATGAAGCTTGTCAACATCTGGCTGCTTCTGCTCGTGGTTTTGCTCTGTG</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>ATGGGGGACGCTCCCAGCCCTGAAGAGAAACTGCACCTTATCACCC...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>ATGGGGGACGCTCCCAGCCCTGAAGAGAAACTGCACCTTATCACCC...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>GCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACGAGGAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>TCTCGGCCTCCCGACTCCTACAGTGGGCTAGGGCGAGCAGGTGCCA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>NGCCTCGCAGGCAGCACCAGCCTCATGCACAACCACGCGGCCCTCC...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>ATGAACCAGCCGCAGAGGATGGCGCCTGTGGGCACAGACAAGGAGC...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>ATGAACCAGCCGCAGAGGATGGCGCCTGTGGGCACAGACAAGGAGC...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>GCCACGGCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACG...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>GCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACGAGGAGA...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>NCCATGTCACTGGGCGGGCGGCACGCAGGCCTGGTTGGAGGCAGCC...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             sequence  class\n",
              "0   ATGCCCCAACTAAATACTACCGTATGGCCCACCATAATTACCCCCA...      4\n",
              "1   ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAG...      4\n",
              "2   ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "3   ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "4   ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...      3\n",
              "5   ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "6   ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...      3\n",
              "7   ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...      3\n",
              "8   ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...      3\n",
              "9   ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "10  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "11  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "12  ATGAAGATTGCACACAGAGGTCCAGATGCATTCCGTTTTGAGAATG...      3\n",
              "13  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "14  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "15  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
              "16  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "17  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "18  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "19  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "20  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "21  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "22  ATGGCGGATTCCAGCGAAGGCCCCCGCGCGGGGCCCGGGGAGGTGG...      5\n",
              "23  ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...      2\n",
              "24  GCCACCACAGGCCTGAAGATGAAGCACCTCCTTACTGGGCAAGAGA...      2\n",
              "25  ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...      2\n",
              "26  ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...      2\n",
              "27  ATGCCACTGCCATTTGGGTTGAAACTGAAACGCACCCGGCGCTACA...      2\n",
              "28  ATGCAGCCTTGGCACGGAAAGGCCATGCAGAGAGCTTCCGAGGCCG...      4\n",
              "29  ATGCAGCCTTGGCACGGAAAGGCCATGCAGAGAGCTTCCGAGGCCG...      4\n",
              "30  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "31  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "32  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "33  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "34  NGGCAAACAAGAGAGAAAGCAAAATTTTCAGATGGAGAAAAGTGCC...      6\n",
              "35  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "36  ATGGCCTCAAATGAGAGAGATGCTATATCGTGGTACCAAAAGAAGA...      6\n",
              "37  ATGGAGCCCGCGACCGCGCCCCGGCCCGACATGGCGCCGGAGCTGA...      2\n",
              "38  GTAAAGCTGAAACCTCATGAGGAACCTCTTCGTTCTGAGATCCTCA...      2\n",
              "39  ATGGCGCCGGAGCTGACCCCGGAGGAGGAGCAGGCTACCAAGCAGT...      2\n",
              "40  ATGTTCTCTCGCGCGGGTGTCGCTGGGCTGTCGGCCTGGACCTTGC...      4\n",
              "41  ATGTTCTCTCGCGCGGGTGTCGCTGGGCTGTCGGCCTGGACCTTGC...      4\n",
              "42  ATGAGGCCCGAGCGTCCCCGGCCGCGCGGCAGCGCCCCCGGCCCGA...      0\n",
              "43  ATGAGGCCCGAGCGTCCCCGGCCGCGCGGCAGCGCCCCCGGCCCGA...      0\n",
              "44  NTGCAGGTGATTTTCAAAGCCAAGTCAAAATATTCTCCAGAATTAC...      0\n",
              "45  ATGAAGCTTGTCAACATCTGGCTGCTTCTGCTCGTGGTTTTGCTCT...      6\n",
              "46  NNGTTAAACCTCAGCATCGGCGGCCCGGACTTCATGGATCATCCGT...      6\n",
              "47  GTTGCTTCTCCAGTGGTTGCAGGTGCTGTCACCTTGTTAGTGAGCA...      6\n",
              "48  ATGAGGGAGTGCCAAGGATTTGCTCCAGATGCAGAACTTCACATTT...      6\n",
              "49  ATGAAGCTTGTCAACATCTGGCTGCTTCTGCTCGTGGTTTTGCTCTGTG      6\n",
              "50  ATGGGGGACGCTCCCAGCCCTGAAGAGAAACTGCACCTTATCACCC...      3\n",
              "51  ATGGGGGACGCTCCCAGCCCTGAAGAGAAACTGCACCTTATCACCC...      3\n",
              "52  GCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACGAGGAGA...      6\n",
              "53  TCTCGGCCTCCCGACTCCTACAGTGGGCTAGGGCGAGCAGGTGCCA...      6\n",
              "54  NGCCTCGCAGGCAGCACCAGCCTCATGCACAACCACGCGGCCCTCC...      6\n",
              "55  ATGAACCAGCCGCAGAGGATGGCGCCTGTGGGCACAGACAAGGAGC...      6\n",
              "56  ATGAACCAGCCGCAGAGGATGGCGCCTGTGGGCACAGACAAGGAGC...      6\n",
              "57  GCCACGGCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACG...      6\n",
              "58  GCGGCCGCCAGCGAGATCAAGCGGGAGGAGAAGGAGGACGAGGAGA...      6\n",
              "59  NCCATGTCACTGGGCGGGCGGCACGCAGGCCTGGTTGGAGGCAGCC...      6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UQvAYY14VHDd",
        "outputId": "c6f317ef-febb-4d6c-a9f5-70df55151e4d"
      },
      "source": [
        "chimp = pd.read_table('/content/chimp_data.txt')\n",
        "dog = pd.read_table('/content/dog_data.txt')\n",
        "chimp.head()\n",
        "dog.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sequence</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ATGCCACAGCTAGATACATCCACCTGATTTATTATAATCTTTTCAA...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ATGAACGAAAATCTATTCGCTTCTTTCGCTGCCCCCTCAATAATAG...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ATGGAAACACCCTTCTACGGCGATGAGGCGCTGAGCGGCCTGGGCG...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ATGTGCACTAAAATGGAACAGCCCTTCTACCACGACGACTCATACG...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ATGAGCCGGCAGCTAAACAGAAGCCAGAACTGCTCCTTCAGTGACG...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            sequence  class\n",
              "0  ATGCCACAGCTAGATACATCCACCTGATTTATTATAATCTTTTCAA...      4\n",
              "1  ATGAACGAAAATCTATTCGCTTCTTTCGCTGCCCCCTCAATAATAG...      4\n",
              "2  ATGGAAACACCCTTCTACGGCGATGAGGCGCTGAGCGGCCTGGGCG...      6\n",
              "3  ATGTGCACTAAAATGGAACAGCCCTTCTACCACGACGACTCATACG...      6\n",
              "4  ATGAGCCGGCAGCTAAACAGAAGCCAGAACTGCTCCTTCAGTGACG...      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3BOgp4CVxdw"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image(\"../input/image2/Capture1.PNG\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjSqW-mIWQRT"
      },
      "source": [
        "def getKmers(sequence, size=6):\n",
        "    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfvp_jJoWQ8V"
      },
      "source": [
        "human['words'] = human.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
        "human = human.drop('sequence', axis=1)\n",
        "chimp['words'] = chimp.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
        "chimp = chimp.drop('sequence', axis=1)\n",
        "dog['words'] = dog.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
        "dog = dog.drop('sequence', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "GSzI763oWV2o",
        "outputId": "2b5b1b08-dd90-41d1-d515-99b450c33c26"
      },
      "source": [
        "human.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>class</th>\n",
              "      <th>words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>[atgccc, tgcccc, gcccca, ccccaa, cccaac, ccaac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>[atgaac, tgaacg, gaacga, aacgaa, acgaaa, cgaaa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>[atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>[atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>[atgcaa, tgcaac, gcaaca, caacag, aacagc, acagc...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   class                                              words\n",
              "0      4  [atgccc, tgcccc, gcccca, ccccaa, cccaac, ccaac...\n",
              "1      4  [atgaac, tgaacg, gaacga, aacgaa, acgaaa, cgaaa...\n",
              "2      3  [atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...\n",
              "3      3  [atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...\n",
              "4      3  [atgcaa, tgcaac, gcaaca, caacag, aacagc, acagc..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjrq5GXxWZA3"
      },
      "source": [
        "human_texts = list(human['words'])\n",
        "for item in range(len(human_texts)):\n",
        "    human_texts[item] = ' '.join(human_texts[item])\n",
        "y_h = human.iloc[:, 0].values "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "RZYdy83nWclh",
        "outputId": "ec92b5ec-29db-451f-ef54-36d14f7a1481"
      },
      "source": [
        "human_texts[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'atgccc tgcccc gcccca ccccaa cccaac ccaact caacta aactaa actaaa ctaaat taaata aaatac aatact atacta tactac actacc ctaccg taccgt accgta ccgtat cgtatg gtatgg tatggc atggcc tggccc ggccca gcccac cccacc ccacca caccat accata ccataa cataat ataatt taatta aattac attacc ttaccc tacccc accccc ccccca ccccat cccata ccatac catact atactc tactcc actcct ctcctt tcctta ccttac cttaca ttacac tacact acacta cactat actatt ctattc tattcc attcct ttcctc tcctca cctcat ctcatc tcatca catcac atcacc tcaccc caccca acccaa cccaac ccaact caacta aactaa actaaa ctaaaa taaaaa aaaaat aaaata aaatat aatatt atatta tattaa attaaa ttaaac taaaca aaacac aacaca acacaa cacaaa acaaac caaact aaacta aactac actacc ctacca taccac accacc ccacct caccta acctac cctacc ctacct tacctc acctcc cctccc ctccct tccctc ccctca cctcac ctcacc tcacca caccaa accaaa ccaaag caaagc aaagcc aagccc agccca gcccat cccata ccataa cataaa ataaaa taaaaa aaaaat aaaata aaataa aataaa ataaaa taaaaa aaaaaa aaaaat aaaatt aaatta aattat attata ttataa tataac ataaca taacaa aacaaa acaaac caaacc aaaccc aaccct accctg ccctga cctgag ctgaga tgagaa gagaac agaacc gaacca aaccaa accaaa ccaaaa caaaat aaaatg aaatga aatgaa atgaac tgaacg gaacga aacgaa acgaaa cgaaaa gaaaat aaaatc aaatct aatctg atctgt tctgtt ctgttc tgttcg gttcgc ttcgct tcgctt cgcttc gcttca cttcat ttcatt tcattc cattca attcat ttcatt tcattg cattgc attgcc ttgccc tgcccc gccccc ccccca ccccac cccaca ccacaa cacaat acaatc caatcc aatcct atccta tcctag'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3j1DSCCWgem",
        "outputId": "ef37310e-1c32-480d-8b45-cc9a50294eef"
      },
      "source": [
        "y_h"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 4, 3, ..., 6, 6, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5bTD0gfWqBS"
      },
      "source": [
        "chimp_texts = list(chimp['words'])\n",
        "for item in range(len(chimp_texts)):\n",
        "    chimp_texts[item] = ' '.join(chimp_texts[item])\n",
        "y_c = chimp.iloc[:, 0].values                       # y_c for chimp\n",
        "\n",
        "dog_texts = list(dog['words'])\n",
        "for item in range(len(dog_texts)):\n",
        "    dog_texts[item] = ' '.join(dog_texts[item])\n",
        "y_d = dog.iloc[:, 0].values    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ORmrxpGWtca"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(ngram_range=(4,4))\n",
        "X = cv.fit_transform(human_texts)\n",
        "X_chimp = cv.transform(chimp_texts)\n",
        "X_dog = cv.transform(dog_texts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47YSWP6nWxbx",
        "outputId": "dfa0d468-8113-4a72-aef0-0515502c9602"
      },
      "source": [
        "print(X.shape)\n",
        "print(X_chimp.shape)\n",
        "print(X_dog.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4380, 232414)\n",
            "(1682, 232414)\n",
            "(820, 232414)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "2bzw-8AvW--d",
        "outputId": "f6a7bb9e-3ae8-4495-c323-02f8bc1ffdcc"
      },
      "source": [
        "dog['class'].value_counts().sort_index().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78386285f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjrNMIAZXLDH"
      },
      "source": [
        "# Splitting the human dataset into the training set and test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    y_h, \n",
        "                                                    test_size = 0.20, \n",
        "                                                    random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgcNWQXZXPq4",
        "outputId": "0ea52f72-6567-4412-9d9a-a24aaa3bfe5d"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3504, 232414)\n",
            "(876, 232414)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4ricku0XTVI",
        "outputId": "ea4f53d3-a3d5-4b9f-b837-b0cb9adb197d"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "classifier = MultinomialNB(alpha=0.1)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCaiYPinXW4h"
      },
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugibkfsKXb86",
        "outputId": "5189309c-01e2-408b-d057-5d74b21d1476"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "print(\"Confusion matrix\\n\")\n",
        "print(pd.crosstab(pd.Series(y_test, name='Actual'), pd.Series(y_pred, name='Predicted')))\n",
        "def get_metrics(y_test, y_predicted):\n",
        "    accuracy = accuracy_score(y_test, y_predicted)\n",
        "    precision = precision_score(y_test, y_predicted, average='weighted')\n",
        "    recall = recall_score(y_test, y_predicted, average='weighted')\n",
        "    f1 = f1_score(y_test, y_predicted, average='weighted')\n",
        "    return accuracy, precision, recall, f1\n",
        "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)\n",
        "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix\n",
            "\n",
            "Predicted   0    1   2    3    4   5    6\n",
            "Actual                                   \n",
            "0          99    0   0    0    1   0    2\n",
            "1           0  104   0    0    0   0    2\n",
            "2           0    0  78    0    0   0    0\n",
            "3           0    0   0  124    0   0    1\n",
            "4           1    0   0    0  143   0    5\n",
            "5           0    0   0    0    0  51    0\n",
            "6           1    0   0    1    0   0  263\n",
            "accuracy = 0.984 \n",
            "precision = 0.984 \n",
            "recall = 0.984 \n",
            "f1 = 0.984\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YubSey-tXjLn"
      },
      "source": [
        "# Predicting the chimp, dog and worm sequences\n",
        "y_pred_chimp = classifier.predict(X_chimp)\n",
        "y_pred_dog = classifier.predict(X_dog)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRAfu0IuXyOO",
        "outputId": "83716bcc-e295-4f8d-9e36-39c54660de6c"
      },
      "source": [
        "print(\"Confusion matrix\\n\")\n",
        "print(pd.crosstab(pd.Series(y_c, name='Actual'), pd.Series(y_pred_chimp, name='Predicted')))\n",
        "accuracy, precision, recall, f1 = get_metrics(y_c, y_pred_chimp)\n",
        "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix\n",
            "\n",
            "Predicted    0    1    2    3    4    5    6\n",
            "Actual                                      \n",
            "0          232    0    0    0    0    0    2\n",
            "1            0  184    0    0    0    0    1\n",
            "2            0    0  144    0    0    0    0\n",
            "3            0    0    0  227    0    0    1\n",
            "4            2    0    0    0  254    0    5\n",
            "5            0    0    0    0    0  109    0\n",
            "6            0    0    0    0    0    0  521\n",
            "accuracy = 0.993 \n",
            "precision = 0.994 \n",
            "recall = 0.993 \n",
            "f1 = 0.993\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuCFlBVRX2bz",
        "outputId": "ac47c349-28f3-407e-8598-0161c2aceb55"
      },
      "source": [
        "print(\"Confusion matrix\\n\")\n",
        "print(pd.crosstab(pd.Series(y_c, name='Actual'), pd.Series(y_pred_chimp, name='Predicted')))\n",
        "accuracy, precision, recall, f1 = get_metrics(y_c, y_pred_chimp)\n",
        "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix\n",
            "\n",
            "Predicted    0    1    2    3    4    5    6\n",
            "Actual                                      \n",
            "0          232    0    0    0    0    0    2\n",
            "1            0  184    0    0    0    0    1\n",
            "2            0    0  144    0    0    0    0\n",
            "3            0    0    0  227    0    0    1\n",
            "4            2    0    0    0  254    0    5\n",
            "5            0    0    0    0    0  109    0\n",
            "6            0    0    0    0    0    0  521\n",
            "accuracy = 0.993 \n",
            "precision = 0.994 \n",
            "recall = 0.993 \n",
            "f1 = 0.993\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muVYuVMgX6Vz",
        "outputId": "71c73979-e496-4fbe-b19a-657f125e7268"
      },
      "source": [
        "%%sh\n",
        "pip install MulticoreTSNE pysam pyvcf;\n",
        "apt-get install tabix;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting MulticoreTSNE\n",
            "  Downloading https://files.pythonhosted.org/packages/2d/e8/2afa896fa4eebfa1d0d0ba2673fddac45582ec0f06b2bdda88108ced5425/MulticoreTSNE-0.1.tar.gz\n",
            "Collecting pysam\n",
            "  Downloading https://files.pythonhosted.org/packages/87/a1/73e80a7a873f3fb0e52d368a4343eb9882b737c932b95020d82251f1087e/pysam-0.16.0.1-cp36-cp36m-manylinux1_x86_64.whl (9.9MB)\n",
            "Collecting pyvcf\n",
            "  Downloading https://files.pythonhosted.org/packages/20/b6/36bfb1760f6983788d916096193fc14c83cce512c7787c93380e09458c09/PyVCF-0.6.8.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from MulticoreTSNE) (1.18.5)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.6/dist-packages (from MulticoreTSNE) (1.14.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pyvcf) (50.3.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi->MulticoreTSNE) (2.20)\n",
            "Building wheels for collected packages: MulticoreTSNE, pyvcf\n",
            "  Building wheel for MulticoreTSNE (setup.py): started\n",
            "  Building wheel for MulticoreTSNE (setup.py): finished with status 'done'\n",
            "  Created wheel for MulticoreTSNE: filename=MulticoreTSNE-0.1-cp36-cp36m-linux_x86_64.whl size=68503 sha256=2528f24f8f9fceb12da835fc3d8472c4de99d23d0940358927d8cc4a60269078\n",
            "  Stored in directory: /root/.cache/pip/wheels/27/59/53/3b52ee63add3692254c30d687fa4dff4d128d0557861fb028e\n",
            "  Building wheel for pyvcf (setup.py): started\n",
            "  Building wheel for pyvcf (setup.py): finished with status 'done'\n",
            "  Created wheel for pyvcf: filename=PyVCF-0.6.8-cp36-cp36m-linux_x86_64.whl size=121983 sha256=6438e739e16f7508cdeab87691b893cd26332e482b7b7f30f99da2a93b7c237b\n",
            "  Stored in directory: /root/.cache/pip/wheels/81/91/41/3272543c0b9c61da9c525f24ee35bae6fe8f60d4858c66805d\n",
            "Successfully built MulticoreTSNE pyvcf\n",
            "Installing collected packages: MulticoreTSNE, pysam, pyvcf\n",
            "Successfully installed MulticoreTSNE-0.1 pysam-0.16.0.1 pyvcf-0.6.8\n",
            "Reading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "The following NEW packages will be installed:\n",
            "  tabix\n",
            "0 upgraded, 1 newly installed, 0 to remove and 14 not upgraded.\n",
            "Need to get 326 kB of archives.\n",
            "After this operation, 1,733 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tabix amd64 1.7-2 [326 kB]\n",
            "Fetched 326 kB in 1s (350 kB/s)\n",
            "Selecting previously unselected package tabix.\r\n",
            "(Reading database ... \r(Reading database ... 5%\r(Reading database ... 10%\r(Reading database ... 15%\r(Reading database ... 20%\r(Reading database ... 25%\r(Reading database ... 30%\r(Reading database ... 35%\r(Reading database ... 40%\r(Reading database ... 45%\r(Reading database ... 50%\r(Reading database ... 55%\r(Reading database ... 60%\r(Reading database ... 65%\r(Reading database ... 70%\r(Reading database ... 75%\r(Reading database ... 80%\r(Reading database ... 85%\r(Reading database ... 90%\r(Reading database ... 95%\r(Reading database ... 100%\r(Reading database ... 144793 files and directories currently installed.)\r\n",
            "Preparing to unpack .../archives/tabix_1.7-2_amd64.deb ...\r\n",
            "Unpacking tabix (1.7-2) ...\r\n",
            "Setting up tabix (1.7-2) ...\r\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "7TXnUjqWYIQy",
        "outputId": "96149484-a9a6-4df6-ee89-f8195f9fc484"
      },
      "source": [
        "import bz2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.graph_objs as go\n",
        "import pysam\n",
        "import requests\n",
        "import seaborn as sns\n",
        "import umap\n",
        "import vcf\n",
        "\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "from MulticoreTSNE import MulticoreTSNE as mTSNE\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "init_notebook_mode(connected=True) #plotly"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNzpYI1xYZgy"
      },
      "source": [
        "def vcf2df(vcf_fname):\n",
        "    \"\"\"Convert a subsetted vcf file to pandas DataFrame\"\"\"\n",
        "    vcf_reader = vcf.Reader(filename=vcf_fname)\n",
        "    \n",
        "    df = pd.DataFrame(index=vcf_reader.samples)\n",
        "    for variant in vcf_reader:\n",
        "        df[variant.ID] = [call.gt_type if call.gt_type is not None else 3 for call in variant.samples]\n",
        "\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Tk_YjbtYfMf",
        "outputId": "5e93a4c1-2036-42eb-8209-b4f9231d887f"
      },
      "source": [
        "from Bio.Seq import Seq\n",
        "my_seq = Seq(\"AGTACACTGGT\")\n",
        "my_seq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seq('AGTACACTGGT')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNwzAIHIaDdS",
        "outputId": "fc74f82e-484c-4a5b-d328-06809a3ab957"
      },
      "source": [
        "my_seq.complement()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seq('TCATGTGACCA')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Opx55l_kaLuQ",
        "outputId": "080da7bc-c89a-4ba6-d1dd-3aef5b27317a"
      },
      "source": [
        "my_seq.reverse_complement()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seq('ACCAGTGTACT')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "nh1QZ5HjaPPS",
        "outputId": "8485d1c1-3484-4c6a-9dbe-7f0b408e1c87"
      },
      "source": [
        "from Bio import SeqIO\n",
        "for seq_record in list(SeqIO.parse(\"content/example.fa\", \"fasta\"))[:5]:\n",
        "    print(seq_record.id)\n",
        "    print(repr(seq_record.seq))\n",
        "    print(len(seq_record))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-34e0c1201639>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mBio\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSeqIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mseq_record\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeqIO\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"content/example.fa\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fasta\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_record\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_record\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/Bio/SeqIO/__init__.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(handle, format, alphabet)\u001b[0m\n\u001b[1;32m    605\u001b[0m     \u001b[0miterator_generator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_FormatToIterator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0miterator_generator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 607\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0miterator_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    608\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mAlignIO\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FormatToIterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m         \u001b[0;31m# Use Bio.AlignIO to read in the alignments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/Bio/SeqIO/FastaIO.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source, alphabet, title2ids)\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The alphabet argument is no longer supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle2ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtitle2ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Fasta\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/Bio/SeqIO/Interfaces.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source, alphabet, mode, fmt)\u001b[0m\n\u001b[1;32m     45\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The alphabet argument is no longer supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_close_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# not a path, assume we received a stream\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'content/example.fa'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oX2CfXba11q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}