Unit4_FinalProject.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TanzinaS/Brigde-up/blob/master/Unit4_FinalProject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LB1yGDW7NjCW",
        "colab_type": "text"
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      "source": [
        "# Unit 4 Final Projects: Calculating evolutionary distance\n",
        "\n",
        "In this notebook you will compare the sequence of a particular gene from 3 different species to find out which two are most closely related. \n",
        "\n",
        "You will need to do the following:\n",
        "1. Open and read in your files\n",
        "2. Decide which sequences to compare (DNA, RNA, or protein), (you may have to look at all of them)\n",
        "3. Write code that prints out the percent similarity of each pair of sequences\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkIUxVO8NjCZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "whale= open(\"/content/drive/My Drive/Colab Notebooks/1a_CSN2_DNA_txt.txt\").read()\n",
        "hippo= open(\"/content/drive/My Drive/Colab Notebooks/1b_CSN2_DNA_txt.txt\").read()\n",
        "boar= open(\"/content/drive/My Drive/Colab Notebooks/1c_CSN2_DNA.txt\").read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_fp-LQ6ORBS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3462fe61-eca5-4cf4-fdbf-1d80775d70bd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86ikdynONjCe",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "5f23271e-0742-4ecf-bf86-7712db444d67"
      },
      "source": [
        "trDict = {\"A\":\"U\", \"T\":\"A\", \"G\":\"C\", \"C\":\"G\"}\n",
        "RNAwhale = \"\"\n",
        "RNAhippo = \"\"\n",
        "RNAboar = \"\"\n",
        "\n",
        "for i in whale:\n",
        "    RNAwhale += trDict[i]\n",
        "    \n",
        "for i in hippo:\n",
        "    RNAhippo += trDict[i]\n",
        "\n",
        "for i in boar:\n",
        "    RNAboar += trDict[i]\n",
        "\n",
        "print(RNAwhale)\n",
        "print(RNAhippo)\n",
        "print(RNAboar)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UUAGGGGUUUCGAUUCCUCUGAUAGGAAGGAUUCGUAUUUCUUUACGGGAAGGGAUAUAGAGGUCAUCUGGGAAAUAACUUUCGGUCUCGGACGGAGAGUGACUAGAACUUUUAGACGUGGAAGGAGACAGAGACGAGGUCAGGAACUACGUGGUCGGAGGGGUCGGAGAAGGAGGAUGGGGGUACAAAGGAGGAGUCGGGGACGGCAGGGAAAGAGUCGGGUUUCAGGACGGAUAAGGGGUCGUUCACCACGGGGUGUAGGUCUCUGUAUACGGGUAGGU\n",
            "UCAGGGGUUUCGUUUCCUCUGAUAGGAAGGAUUCGUAUUUCUUUACGGGAAGAGAUUUAGAGGUCAUCUCGGGAAAUGACUUUCCGUCUCGGACUGAGAAUGACUAGAACUUUUAGACGUGAAGGGGACGGAGACGAAGUCAGGACCUACGUGGGUGAAGGGUCGGAGAAAGAGGAUGGGAGUACAAAGGAAGAGUCGGGAACGUCAGGGAAGGUGUCGGGCAAAGAGUCGGGGUUCGGGACGGAUAAGGGGUCGUUCACCAUGGGAUGGGUGUCUCUCU\n",
            "UCAGGGGUUUAACAGGUUUUAAAUUAACCCGUAUAAAGUUAUUGUUUCCGUUGAUUAUAUUAUCACGUGUUCGUUAUAGAAACUUACCCGUGAAGUGGUUUCUCCAAUAAAAUUAACAGUUAUUUAUAUACUUUUCUACAAGUUAUGGUAAUAAAGAGACCUUUUACGUUCAAUUUUUAUGGUCUAUAGUACUGUGUUGGAGGUCUUAUCGACUUUACUCUUCUUAACCAUUUUGGUUCAUAAAUGUUUUUAUUCGUUUGACUUAUUUACGAUCCAUUACAA\n"
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          "name": "stdout"
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      "source": [
        "codonDict = {\"UUU\":\"F\",\"UUC\":\"F\",\"UUA\":\"L\",\"UUG\":\"L\", \"CUU\":\"L\",\"CUC\":\"L\", \n",
        "             \"CUA\":\"L\",\"CUG\":\"L\",\"AUU\":\"I\",\"AUC\":\"I\",\"AUA\":\"I\",\"AUG\":\"M\",\n",
        "             \"GUU\":\"V\",\"GUC\":\"V\",\"GUA\":\"V\",\"GUG\":\"V\",\"UCU\":\"S\",\"UCC\":\"S\",\n",
        "             \"UCA\":\"S\",\"UCG\":\"S\",\"CCU\":\"P\",\"CCC\":\"P\",\"CCA\":\"P\",\"CCG\":\"P\",\n",
        "             \"ACU\":\"T\",\"ACC\":\"T\",\"ACA\":\"T\",\"ACG\":\"T\",\"GCU\":\"A\",\"GCC\":\"A\",\n",
        "             \"GCA\":\"A\",\"GCG\":\"A\",\"UAU\":\"Y\",\"UAC\":\"Y\",\"UAA\":\"STOP\",\"UAG\":\"STOP\",\n",
        "             \"UGA\":\"STOP\",\"CAU\":\"H\",\"CAC\":\"H\",\"CAA\":\"Q\",\"CAG\":\"Q\",\"AAU\":\"N\",\n",
        "             \"AAC\":\"N\",\"AAA\":\"K\",\"AAG\":\"K\",\"GAU\":\"D\",\"GAC\":\"D\",\"GAA\":\"E\",\n",
        "             \"GAG\":\"E\",\"UGU\":\"C\",\"UGC\":\"C\",\"UGG\":\"W\",\"CGU\":\"R\",\"CGC\":\"R\",\n",
        "             \"CGA\":\"R\",\"CGG\":\"R\",\"AGU\":\"S\",\"AGC\":\"S\",\"AGA\":\"R\",\"AGG\":\"R\",\n",
        "             \"GGU\":\"G\",\"GGC\":\"G\",\"GGA\":\"G\",\"GGG\":\"G\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kklqyc5RNjDM",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "proteinwhale = \"\" #we will be adding protein sequences to this\n",
        "codon = \"\" #keeps track of a block of 3 at a time\n",
        "\n",
        "for i in RNAwhale: #goes through the entire RNA sequence\n",
        "    if len(codon) == 3: #in the case that the codon has 3 characters....\n",
        "        proteinwhale += codonDict[codon] #matches the codon(key) with the amino acid(value), and appends it to the proteinA string\n",
        "        codon = \"\" #empties out the codon so we can move to the next block of 3\n",
        "        codon += i #takes i into account before moving on to next iteration.  \n",
        "    else:\n",
        "        codon += i\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiFu9iB_PMQ5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "proteinhippo = \"\"\n",
        "codon = \"\"\n",
        "\n",
        "for i in RNAhippo:\n",
        "    if len(codon) == 3:\n",
        "        proteinhippo += codonDict[codon]\n",
        "        codon = \"\"\n",
        "        codon += i\n",
        "    else:\n",
        "        codon += i"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCfpiF_rRmVE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "proteinboar = \"\"\n",
        "codon = \"\"\n",
        "\n",
        "for i in RNAboar:\n",
        "    if len(codon) == 3:\n",
        "        proteinboar += codonDict[codon]\n",
        "        codon = \"\"\n",
        "        codon += i\n",
        "    else:\n",
        "        codon += i"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXIvCAlpPMxo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "3ccd91df-663c-4032-ff3f-374b40e86b20"
      },
      "source": [
        "print(proteinwhale)\n",
        "print(proteinhippo)\n",
        "print(proteinboar)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LGVSIPLIGRIRISLREGISTOPRSSGKSTOPLSVSDGESTOPLELLDVEGDRDEVRNYVVGGVGEGGWGYKGGVGDGRERVGFQDGSTOPGVVHHGVSTOPVSVYGSTOP\n",
            "SGVSFPLIGRIRISLREEISTOPRSSREMTFRLGLRMTRTFRREGDGDEVRTYVGEGSEKEDGSTKEESGTSGKVSGKESGFGTDKGSFTMGWVSL\n",
            "SGVSTOPQVLNSTOPPVSTOPSYCFRSTOPLYYHVFVIETYPSTOPSGFSNKINSYLYTFLQVMVIKRPFTFNFYGLSTOPYCVGGLIDFTLLNHFGSSTOPMFLFVSTOPLIYDPL\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gy8-66-tPNF3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b7af07b-4adb-42ba-ff6b-b30cf2845f9b"
      },
      "source": [
        "#hippo to whale protein\n",
        "similar = 0 #stores the number of similarities\n",
        "for counter, valuehippo in enumerate(proteinhippo): \n",
        "  valuewhale = proteinwhale[counter]\n",
        "  if valuewhale == valuehippo:\n",
        "    similar += 1\n",
        "\n",
        "\n",
        "print(similar)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IItLqYLihRf6",
        "colab_type": "text"
      },
      "source": [
        "## **DnA PERCENt**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Edh_tgJYPNdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbbd0982-f9cd-4223-f607-ec722d96a5d9"
      },
      "source": [
        "#hippo to whale dna\n",
        "\n",
        "similardna = 0 #stores the number of similarities\n",
        "for counter, valuehippo in enumerate(hippo): \n",
        "  valuewhale = whale[counter]\n",
        "  if valuewhale == valuehippo:\n",
        "    similardna += 1\n",
        " \n",
        "\n",
        "print(similardna)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "145\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2hWiGC6eXFK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fdf25c07-272e-479e-89e6-4fad455c21a5"
      },
      "source": [
        "#boar to whale dna\n",
        "\n",
        "similardna = 0 #stores the number of similarities\n",
        "for counter, valuewhale in enumerate(whale): \n",
        "  valueboar = boar[counter]\n",
        "  if valueboar == valuewhale:\n",
        "    similardna += 1\n",
        " \n",
        "print(similardna)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "70\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWT8m7sxf5kK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5cf793e2-5bcf-4b95-8eaf-68e576349404"
      },
      "source": [
        "#boar to hippo dna\n",
        "\n",
        "similardna = 0 #stores the number of similarities\n",
        "for counter, valuehippo in enumerate(hippo): \n",
        "  valueboar = boar[counter]\n",
        "  if valueboar == valuehippo:\n",
        "    similardna += 1\n",
        " \n",
        "\n",
        "print(similardna)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuIRplFDdeQ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "efce25f4-817d-4209-ee7d-460791ffc151"
      },
      "source": [
        "percenthtw = 145*100/(len(hippo))\n",
        "print(percenthtw)\n",
        "percentbtw = 70*100/(len(whale))\n",
        "print(percentbtw)\n",
        "percentbth = 69*100/(len(hippo))\n",
        "print(percentbth)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51.785714285714285\n",
            "24.91103202846975\n",
            "24.642857142857142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62t1O_j-NjDS",
        "colab_type": "text"
      },
      "source": [
        "## Bonus Challenge\n",
        "\n",
        "Visualize your results! Choose a graph type that you think will communicate your conclusions effectively and create it using matplotlib:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "au9WtKMdNjDT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "09ec99c0-2e49-4158-e6ee-4cd0ea7d7685"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#boar to whale\n",
        "\n",
        "plt.bar([1,2,3],[percentbtw,percenthtw,percentbth], color =[\"blue\", \"pink\",\"purple\"])\n",
        "plt.xticks([1,2,3], [\"Boar to Whale\", \"hippo to whale\", \"boar to hippo\"])\n",
        "plt.xlabel(\"Animals\")\n",
        "plt.ylabel(\"Similarity Percentage\")\n",
        "plt.title(\"Comparing DNA SImilarity\")\n",
        "plt.scatter([],[], color = \"blue\", label = \"25%\")\n",
        "plt.scatter([],[], color = \"pink\", label = \"52%\")\n",
        "plt.scatter([],[], color = \"purple\", label = \"25%\")\n",
        "plt.legend(bbox_to_anchor=[1.0,1.0])\n",
        "plt.show\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_K9S0FvNjDX",
        "colab_type": "text"
      },
      "source": [
        "## Bonus Challenge 2\n",
        "If you finish your project and want to try a challenge, come up with a hypothesis about which of the following species are most closely related to humans (carp, chicken, chimp, dog, eel, elephant, frog, gorilla, macaque monkey, mouse, orangutan, pig). Write out a ranked list with the species you think is most related to humans at the top.\n",
        "\n",
        "Then calculate the percent difference between the hemoglobin amino acid sequences of humans and each species to find out how close you came to the right ranking!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVe4Av-CNjDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}