DONOTUSE_GIGANTIC_FastTreeFamily.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/000generic/GIGANTIC-FHL2022/blob/stable-branch/TEMPLATE_GIGANTIC_FastTreeFamily.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe7df2b2",
      "metadata": {
        "id": "fe7df2b2"
      },
      "source": [
        "# **GIGANTIC Phylogenomic Pipeline: FastTreeFamily**\n",
        "# GIGANTIC - FHL2022 - Gene Tree Core - FastTree - Species313 - FHL Ted\n",
        "![octopus lowres.jpeg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "lAx0qs6ISGX4",
      "metadata": {
        "id": "lAx0qs6ISGX4"
      },
      "source": [
        "## GIGANTIC Team is [Eric Edsinger](https://github.com/000generic \" 🧬\"), [Jan Hsiao](https://github.com/jannxiao \"🧫\"), [Lola Chenxi Deng](https://github.com/LolaDeng \"🧪\"), and 🐌 🦑 🐙 "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "033559d4",
      "metadata": {
        "id": "033559d4"
      },
      "source": [
        "<br>\n",
        "\n",
        "# Welcome :)\n",
        "<br>\n",
        "\n",
        "\n",
        "**OVERVIEW**\n",
        "\n",
        "GIGANTIC is a phylogenomic pipeline that scans genes across genomes to produce phylogenetic trees for genes and species. This notebook runs a variant of GIGANTIC's core tree building pipeline for genes. The notebook is intended for collaborators and students and provides ready access to GIGANTIC's core functionality in gene tree generation. The notebook and GIGANTIC are under active development - new collaborations to use it are very welcome :) GIGANTIC and all associated notebooks are unpublished - please do not share or use outside the 2022 FHL Biodiversity Genomics course.\n",
        "\n",
        "This notebook takes a user-provided reference gene set, identifies homologs in a user-selected GIGANTIC Species313 species subset, and rapidly (using FastTree) produces maximum likelihood-based gene tree and associated output. The notebook pipeline installs and uses powerful phylogenomic tools, including BUSCO, MAFFT, ClipKit, and FastTree, that are run and integrated using Bash and Python scripts. The notebook can be run in the Google Colab cloud or elsewhere.  Thus, GIGANTIC's core tree building pipeline is made available to others.\n",
        "\n",
        "#### A Benchling protocol for USERS to prepare INPUT and explore OUTPUT is provided:\n",
        "\n",
        "[Benchling GIGANTIC Protocol 001](https://benchling.com/s/prt-C6ePxNC4AuGTuCq3XQLz?m=slm-ceHLb7PJqO26qpBZNEZl)\n",
        "\n",
        "Below is a visual overview of GIGANTIC (coming soon) - this notebook leverages Blocks 0-3, 5, and 10 to prouduce a phylogenetic gene tree:\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67ab91d3",
      "metadata": {
        "id": "67ab91d3"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "# OK - GO!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3da61fd1",
      "metadata": {
        "id": "3da61fd1"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create upper level directories for GIGANTIC pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "23184d22",
      "metadata": {
        "id": "23184d22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77034b5c-e38c-41f1-aeb6-ac6ff2718599"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your GIGANTIC run ID is GIGANTIC20220510082314\n",
            "\n",
            "Upper level directory structure is:\n",
            "\n",
            "gigantic-run-GIGANTIC20220510082314\n",
            "gigantic-run-GIGANTIC20220510082314/gigantic-run\n",
            "\n",
            "Top directories within gigantic-run represent major code blocks in GIGANTIC:\n",
            "\n",
            "0-start\n",
            "1-genomesdb\n",
            "2-othersdb\n",
            "3-projectdb\n",
            "4-homologs-busco\n",
            "5-homologs-genes\n",
            "6-trees-species\n",
            "7-trees-genes\n",
            "8-trees-clades\n",
            "9-trees-groups\n",
            "10-auto-annotation\n",
            "\n",
            "This notebook runs GIGANTIC Blocks 0-3, 5, and 10 to produce a gene family tree\n",
            "\n",
            "\n",
            "COMPLETED Create upper level directories for GIGANTIC pipeline\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "\n",
        "date_time=$(date '+%Y%m%d%H%M%S')\n",
        "echo gigantic-run-GIGANTIC$date_time > date_time-directory-info \n",
        "echo 'Your GIGANTIC run ID is GIGANTIC'$date_time\n",
        "echo\n",
        "echo 'Upper level directory structure is:'\n",
        "echo\n",
        "\n",
        "mkdir gigantic-run-GIGANTIC$date_time; echo gigantic-run-GIGANTIC$date_time\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run; echo gigantic-run-GIGANTIC$date_time/gigantic-run\n",
        "echo\n",
        "echo 'Top directories within gigantic-run represent major code blocks in GIGANTIC:'\n",
        "echo\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start; echo 0-start\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/1-genomesdb; echo 1-genomesdb\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/2-othersdb; echo 2-othersdb\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/3-projectdb; echo 3-projectdb\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/4-homologs-busco; echo 4-homologs-busco\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/5-homologs-genes; echo 5-homologs-genes\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/6-trees-species; echo 6-trees-species\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/7-trees-genes; echo 7-trees-genes\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/8-trees-genes; echo 8-trees-clades\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/9-trees-genes; echo 9-trees-groups\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/10-auto-annotation; echo 10-auto-annotation\n",
        "\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/software\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/project-species\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/source-rgs\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/source-ogs\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/others-genomes\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/species313\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/species-colors\n",
        "mkdir gigantic-run-GIGANTIC$date_time/gigantic-run/0-start/user-input/busco\n",
        "echo\n",
        "echo 'This notebook runs GIGANTIC Blocks 0-3, 5, and 10 to produce a gene family tree'\n",
        "echo\n",
        "echo\n",
        "# Completed\n",
        "echo 'COMPLETED Create upper level directories for GIGANTIC pipeline'\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CWX6MaJ7tc-m",
      "metadata": {
        "id": "CWX6MaJ7tc-m"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Read in GIGANTIC run top directory name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "XWB7AzLisnv6",
      "metadata": {
        "id": "XWB7AzLisnv6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc4a95c0-4aa5-4aae-b987-6914e3db170c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COMPLETED Read in GIGANTIC run top directory name\n"
          ]
        }
      ],
      "source": [
        "input_info = open( 'date_time-directory-info', 'r' )\n",
        "\n",
        "for info in input_info:\n",
        "\n",
        "  gigantic_run_directory = info[ :-1 ]\n",
        "\n",
        "input_info.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Read in GIGANTIC run top directory name'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22f5c157",
      "metadata": {
        "id": "22f5c157"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "# GIGANTIC Block 0 Start"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f593f8d8",
      "metadata": {
        "id": "f593f8d8"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## 🛑 💬 Interactive 1: User input details on gene family and species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "515626dc",
      "metadata": {
        "id": "515626dc",
        "tags": [
          "parameters"
        ],
        "outputId": "fc8e1f79-09c1-4e1c-d63a-2e04e218fc9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\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--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\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    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\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/python3.7/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\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--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\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    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\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/python3.7/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    624\u001b[0m         \"\"\"\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\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    626\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-97ae298b89ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0moutput_command\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'001-wget-user-species'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mgene_family_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'What is the name of the gene family?\\n'\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      6\u001b[0m \u001b[0mrgs_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'How many genes are in your Reference Gene Set?\\n'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mspecies_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'How many species are in your Species Gene Set?\\n'\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/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "#! python\n",
        "\n",
        "output_command = open( '001-wget-user-species', 'w')\n",
        "\n",
        "gene_family_name = input( 'What is the name of the gene family?\\n' )\n",
        "rgs_count = input( 'How many genes are in your Reference Gene Set?\\n' )\n",
        "species_count = input( 'How many species are in your Species Gene Set?\\n')\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED User input details on gene family and species'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7994bcb6",
      "metadata": {
        "id": "7994bcb6"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Move into directory Block 0 Start : User Input : Project Species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6ed3c6f",
      "metadata": {
        "id": "a6ed3c6f"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "new_directory = gigantic_run_directory + '/gigantic-run/0-start/user-input/project-species'\n",
        "os.chdir( new_directory )\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' ) [ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b639013",
      "metadata": {
        "id": "1b639013"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## 🛑 💬 Interactive 2: Input  a Google Drive link to your SSS Species308 Subset Set list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c34fa8bb",
      "metadata": {
        "id": "c34fa8bb",
        "tags": [
          "parameters"
        ]
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "output_command = open( '001-wget-user-species', 'w')\n",
        "\n",
        "google_link_species = input( 'Please copy and paste a Google Drive shared link to your 000-project-species file:\\n' )\n",
        "print( '\\nThank you :) \\n')\n",
        "info_id_1 = google_link_species.split( 'file/d/')\n",
        "info_id_2 = info_id_1[ 1 ].split( '/view' )\n",
        "file_id = info_id_2[ 0 ]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "QxccU-NKo-f0",
      "metadata": {
        "id": "QxccU-NKo-f0"
      },
      "outputs": [],
      "source": [
        "# wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1JFn9ORGZciO0RRxviAb1kl7opOG9DERm' -O 000-project-species\n",
        "output = 'wget --no-check-certificate \\'https://docs.google.com/uc?export=download&id=\\'' + file_id + ' -O 000-project-species \\n'\n",
        "output_command.write( output)    \n",
        "\n",
        "output = 'head -n 6 000-project-species\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'COMPLETED Download of your 000-project-species file from Google Drive\\'\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output_command.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Input  a Google Drive link to your SSS Species308 Subset Set list'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4df3fc1a",
      "metadata": {
        "id": "4df3fc1a"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Download of your 000-project-species file from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "821357b6",
      "metadata": {
        "id": "821357b6"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 001-wget-user-species\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b8b4bc66",
      "metadata": {
        "id": "b8b4bc66"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Move into directory Block 0 Start : User Input : Source RGS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdf187d2",
      "metadata": {
        "id": "cdf187d2"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.chdir( '../source-rgs')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c570c877",
      "metadata": {
        "id": "c570c877"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## 🛑 💬 Interactive 3: Input  a Google Drive link to your RGS Reference Gene Set fasta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c6386ba",
      "metadata": {
        "id": "6c6386ba",
        "tags": [
          "parameters"
        ]
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "output_command = open( '002-wget-user-RGS', 'w')\n",
        "\n",
        "# https://drive.google.com/file/d/12MdBfhV2UskQuGeORWJFdaRvqt521NAS/view?usp=sharing\n",
        "\n",
        "google_link_rgs = input( 'Please copy and paste a Google Drive shared link to your Reference Gene Set fasta:\\n' )\n",
        "print( '\\nThank you :) \\n')\n",
        "info_id_1 = google_link_rgs.split( 'file/d/')\n",
        "info_id_2 = info_id_1[ 1 ].split( '/view' )\n",
        "file_id = info_id_2[ 0 ]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "_dtw3nMypBkN",
      "metadata": {
        "id": "_dtw3nMypBkN"
      },
      "outputs": [],
      "source": [
        "# wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1JFn9ORGZciO0RRxviAb1kl7opOG9DERm' -O 000-project-species\n",
        "output = 'wget --no-check-certificate \\'https://docs.google.com/uc?export=download&id=\\'' + file_id + ' -O user_rgs.fasta \\n'\n",
        "output_command.write( output)    \n",
        "\n",
        "output = 'head -n 6 user_rgs.fasta\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'COMPLETED Download of your Reference Gene Set fasta from Google Drive\\'\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output_command.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Interactive input of a Google Drive link to your RGS Reference Gene Set fasta'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc8144f5",
      "metadata": {
        "id": "dc8144f5"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Download of your Reference Gene Set fasta from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f7f3b58",
      "metadata": {
        "id": "4f7f3b58"
      },
      "outputs": [],
      "source": [
        "%%bash \n",
        "\n",
        "bash 002-wget-user-RGS\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b63897d6",
      "metadata": {
        "id": "b63897d6"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Move into directory Block 0 Start : User Input : Species Colors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c52b7e6",
      "metadata": {
        "id": "9c52b7e6"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../species-colors')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fb412dba",
      "metadata": {
        "id": "fb412dba"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## 🛑 💬 Interactive 4: Input a Google Drive link to your ACS Annotation Species Colors list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9ee1bc07",
      "metadata": {
        "id": "9ee1bc07",
        "tags": [
          "parameters"
        ]
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "output_command = open( '004-wget-user-species-colors', 'w')\n",
        "\n",
        "# https://drive.google.com/file/d/12MdBfhV2UskQuGeORWJFdaRvqt521NAS/view?usp=sharing\n",
        "\n",
        "google_link_color = input( 'Please copy and paste a Google Drive shared link to your Species Colors file:\\n' )\n",
        "print( '\\nThank you :) \\n')\n",
        "info_id_1 = google_link_color.split( 'file/d/')\n",
        "info_id_2 = info_id_1[ 1 ].split( '/view' )\n",
        "file_id = info_id_2[ 0 ]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2_x6FUi4pGMS",
      "metadata": {
        "id": "2_x6FUi4pGMS"
      },
      "outputs": [],
      "source": [
        "# wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1JFn9ORGZciO0RRxviAb1kl7opOG9DERm' -O 000-project-species\n",
        "output = 'wget --no-check-certificate \\'https://docs.google.com/uc?export=download&id=\\'' + file_id + ' -O species-colors.txt \\n'\n",
        "output_command.write( output)    \n",
        "\n",
        "output = 'head -n 6 species-colors.txt\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'COMPLETED Download of your Species Colors file from Google Drive\\'\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "output_command.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Input a Google Drive link to your ACS Annotation Species Colors list'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f62b6b7d",
      "metadata": {
        "id": "f62b6b7d"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Download of your Species Colors file from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8491727",
      "metadata": {
        "id": "f8491727"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 004-wget-user-species-colors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "155c61c9",
      "metadata": {
        "id": "155c61c9"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Move into directory Block 0 Start : Eric-Jan Provided Input : Species313"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "41cc590e",
      "metadata": {
        "id": "41cc590e"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../species313')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5b010e0",
      "metadata": {
        "id": "f5b010e0"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Download Species313 map phylofasta to Google link"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0446eaec",
      "metadata": {
        "id": "0446eaec"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "# To download all of Species313\n",
        "# https://drive.google.com/file/d/1SVZHsBwrbWFnFrpTVA7Q-rF65YC4uKQJ/view?usp=sharing\n",
        "#wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1SVZHsBwrbWFnFrpTVA7Q-rF65YC4uKQJ' -O wget-for-copy-paste-into-notebook\n",
        "\n",
        "# Map of phylofastas to google shared links\n",
        "# https://drive.google.com/file/d/1Heq45vbnWNM4z9kB6wbi2wc0IPprH5Sd/view?usp=sharing\n",
        "wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Heq45vbnWNM4z9kB6wbi2wc0IPprH5Sd' -O map-phyloname-to-google-drive-shared-link\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Download Species313 map phylofasta to Google link'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dac9d476",
      "metadata": {
        "id": "dac9d476"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create Bash script to download user-requested species genomes in Species313"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2bc098f6",
      "metadata": {
        "id": "2bc098f6"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "input_user_gspp = open( '../project-species/000-project-species', 'r')\n",
        "input_map = open( 'map-phyloname-to-google-drive-shared-link', 'r' )\n",
        "output_wget = open( '001-wget-user-requested-gspp', 'w' )\n",
        "\n",
        "gspps = []\n",
        "for next_gspp in input_user_gspp:\n",
        "    \n",
        "    gspps.append( next_gspp[ :-1 ] )\n",
        "\n",
        "for next_line in input_map:\n",
        "    print( next_line )\n",
        "    info = next_line.split( '\\t' )\n",
        "    \n",
        "    phylofasta = info[ 0 ]\n",
        "    new_phylo_fasta = '-'.join( phylofasta.split( '-' )[ :-2] ) + '.aa' \n",
        "    info_phylofasta = phylofasta.split( '-' )\n",
        "    gspp = info_phylofasta[ 5 ] + ' ' + info_phylofasta[ 6 ]\n",
        "    \n",
        "    google_link = info[ 1 ]\n",
        "\n",
        "    if gspp in gspps:\n",
        "    \n",
        "        info_id_1 = google_link.split( 'file/d/')\n",
        "        info_id_2 = info_id_1[ 1 ].split( '/view' )\n",
        "        file_id = info_id_2[ 0 ]\n",
        "        output = 'wget --no-check-certificate \\'https://docs.google.com/uc?export=download&id=\\'' + file_id + ' -O ' + new_phylo_fasta + '\\n'\n",
        "        output_wget.write( output)  \n",
        "        \n",
        "    else:\n",
        "        pass\n",
        "    \n",
        "output = '# Completed\\necho \\'COMPLETED Download user-requested species genomes in Species313\\''\n",
        "output_wget.write( output)\n",
        "\n",
        "input_user_gspp.close()    \n",
        "input_map.close()\n",
        "output_wget.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Create Bash script to download user-requested species genomes in Species313'\n",
        "print( output )    \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4d23a23",
      "metadata": {
        "id": "e4d23a23"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Download user-requested species genomes in Species313"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68442b79",
      "metadata": {
        "id": "68442b79"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 001-wget-user-requested-gspp\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "# GIGANTIC Block 3 ProjectDB"
      ],
      "metadata": {
        "id": "PEG9rY-3MrJY"
      },
      "id": "PEG9rY-3MrJY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Change working directory"
      ],
      "metadata": {
        "id": "mPgxY8ZAM1K1"
      },
      "id": "mPgxY8ZAM1K1"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir( '../../../3-projectdb')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ],
      "metadata": {
        "id": "KnHAGN7TMvDw"
      },
      "id": "KnHAGN7TMvDw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create directories in GIGANTIC Block 3 OthersDB"
      ],
      "metadata": {
        "id": "cjcO9T0vM-WL"
      },
      "id": "cjcO9T0vM-WL"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "mkdir projectdb-project\n",
        "mkdir projectdb-fastas\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Create directories in GIGANTIC Block 3 OthersDB'\n"
      ],
      "metadata": {
        "id": "-h8Z1iOKND86"
      },
      "id": "-h8Z1iOKND86",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Change working directory"
      ],
      "metadata": {
        "id": "d6GSNSL_NIPA"
      },
      "id": "d6GSNSL_NIPA"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir( 'projectdb-project')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ],
      "metadata": {
        "id": "1j1FBWmzNMgJ"
      },
      "id": "1j1FBWmzNMgJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create directories in GIGANTIC Block 3 ProjectDB ProjectDB Project"
      ],
      "metadata": {
        "id": "TTIIpSkoNP2t"
      },
      "id": "TTIIpSkoNP2t"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "# mkdir projectdb-buscos\n",
        "mkdir output\n",
        "mkdir output/1-output\n",
        "mkdir output/3-output\n",
        "mkdir output/8-output\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Create directories in GIGANTIC Block 3 ProjectDB ProjectDB Project'\n"
      ],
      "metadata": {
        "id": "WOXaOsKcNTqt"
      },
      "id": "WOXaOsKcNTqt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "LytyujfgNWVF"
      },
      "id": "LytyujfgNWVF"
    },
    {
      "cell_type": "markdown",
      "id": "4cb31c68",
      "metadata": {
        "id": "4cb31c68"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate a list of ProjectDB fastas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e4496bd6",
      "metadata": {
        "id": "e4496bd6"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "pwd\n",
        "cp ../../0-start/user-input/species313/*aa ../projectdb-fastas/\n",
        "sed -i 's/*/X/g' ../projectdb-fastas/*aa\n",
        "ls ../projectdb-fastas/* > output/6-list-fastas\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Generate a list of ProjectDB fastas'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "345390cd",
      "metadata": {
        "id": "345390cd"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate a Bash script to build ProjectDB Blast database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33defdc7",
      "metadata": {
        "id": "33defdc7"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "input_list = open( 'output/6-list-fastas', 'r' )\n",
        "output_makeblastdb = open( '011-makeblastdb', 'w' )\n",
        "\n",
        "for next_line in input_list:\n",
        "\n",
        "    fasta = next_line[ :-1 ]\n",
        "    output = '/usr/local/bin/makeblastdb -in ' + fasta + ' -parse_seqids  -dbtype prot\\n'\n",
        "    output_makeblastdb.write( output )\n",
        "    \n",
        "output = '# Completed\\necho \\'COMPLETED Build ProjectDB Blast database\\''\n",
        "output_makeblastdb.write( output )\n",
        "\n",
        "input_list.close()\n",
        "output_makeblastdb.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Generate a Bash script to build ProjectDB Blast database'\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fcd75f60",
      "metadata": {
        "id": "fcd75f60"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Build ProjectDB Blast database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "911e5f51",
      "metadata": {
        "id": "911e5f51"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 011-makeblastdb\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "469f2149",
      "metadata": {
        "id": "469f2149"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "# GIGANTIC Block 5 Homologs Genes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2af0d9b3",
      "metadata": {
        "id": "2af0d9b3"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Change working directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a972c9cf",
      "metadata": {
        "id": "a972c9cf"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../../5-homologs-genes')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1890e9eb",
      "metadata": {
        "id": "1890e9eb"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create directories for Block 5 Homologs Genes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "866f3c7d",
      "metadata": {
        "id": "866f3c7d"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "/bin/bash\n",
        "mkdir output\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Create directories for Block 5 Homologs Genes'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4866cf4",
      "metadata": {
        "id": "f4866cf4"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate lists for ProjectDB and Reference Gene Set fastas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "507946f7",
      "metadata": {
        "id": "507946f7"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "ls -1 ../3-projectdb/projectdb-fastas/*aa > output/1-list-projectdb-fastas\n",
        "ls -1 ../0-start/user-input/source-rgs/user_rgs.fasta > output/1-list-rgs\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Generate lists for ProjectDB and Reference Gene Set fastas'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b9f8cdd",
      "metadata": {
        "id": "9b9f8cdd"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate a Bash script to run Blastp ( RGS ) x ( Each ProjectDB Genome )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "161ac098",
      "metadata": {
        "id": "161ac098"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "# input_list\n",
        "# localdb/projectdb-Metazoa-Nematoda-Chromadorea-Rhabditida-Onchocercidae-Brugia-malayi-6279-Bmal-4.0.fasta\n",
        "\n",
        "input_list = open( 'output/1-list-projectdb-fastas', 'r' )\n",
        "input_rgs = open( 'output/1-list-rgs', 'r' )\n",
        "output_command = open( '003-blastp_X_projectDB', 'w' )\n",
        "\n",
        "for next_rgs in input_rgs:\n",
        "\n",
        "    rgs_fasta = next_rgs[ :-1 ] # ends up assigning to pore region in this case\n",
        "\n",
        "for next_line in input_list:\n",
        "    db_path = next_line[ :-1 ]\n",
        "    info_1 = next_line.split( '.a' )[ 0 ]\n",
        "    info_2 = info_1.split( '-' )\n",
        "    genus = info_2[ 6 ]\n",
        "    species = info_2[ 7 ]\n",
        "    gspp = genus + '-' + species\n",
        "    \n",
        "    output = '/usr/local/bin/blastp -db ' + db_path  + ' -outfmt 6 -out output/3-blast-report-rgs_X_projectdb-' + gspp + ' -max_hsps 1 -query ' + rgs_fasta + ' -evalue 1e-3\\n'\n",
        "    output_command.write( output )\n",
        "    \n",
        "output = '# Completed\\necho \\'COMPLETED Run Blastp ( RGS ) x ( Each ProjectDB Genome )\\'\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "input_list.close()\n",
        "input_rgs.close()\n",
        "output_command.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Generate a Bash script to run Blastp ( RGS ) x ( Each ProjectDB Genome )'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5fcd79b2",
      "metadata": {
        "id": "5fcd79b2"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Run Blastp ( RGS ) x ( Each ProjectDB Genome )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a048ca2",
      "metadata": {
        "id": "0a048ca2"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 003-blastp_X_projectDB\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "kHQSKICHgqbC",
      "metadata": {
        "id": "kHQSKICHgqbC"
      },
      "outputs": [],
      "source": [
        "!pwd\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6122975",
      "metadata": {
        "id": "c6122975"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Generate a list of Blast reports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1223c9a1",
      "metadata": {
        "id": "1223c9a1"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "ls output/3-blast-report-rgs_X_projectdb-* > output/4-list-reports\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Generate a list of Blast reports'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62a319eb",
      "metadata": {
        "id": "62a319eb"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Parse Blast reports to identify top hit per RGS per ProjectDB genome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8561fb67",
      "metadata": {
        "id": "8561fb67"
      },
      "outputs": [],
      "source": [
        "#! python \n",
        "# rgs133-worm-TRPA_Cele_TRPA-TRPA2_1_481-uniprotQ21517-extraction_51_366   Metazoa-Chordata-Coelacanthimorpha-Coelacanthiformes-Coelacanthidae-Latimeria-chalumnae-pdb0000292542   26.036  169     98      4       140     299     846     996     3.39e-05        44.4\n",
        "\n",
        "input_fastas = open( 'output/1-list-projectdb-fastas', 'r' )\n",
        "input_reports = open( 'output/4-list-reports', 'r' )\n",
        "output_geneset_gspp = open( 'output/5-blastp-hits.fasta', 'w')\n",
        "output_geneset_gspp_sub = open( 'output/5-blastp-pore-region-hits.fasta', 'w')\n",
        "\n",
        "gene_seq = {}\n",
        "gene_coordinates = {}\n",
        "for next_fasta in input_fastas:\n",
        "    \n",
        "    fasta = next_fasta[ :-1 ]\n",
        "    input_fasta = open( fasta, 'r' )\n",
        "    \n",
        "    for next_line in input_fasta:\n",
        "\n",
        "        if next_line[ 0 ] == '>':\n",
        "            \n",
        "            header = next_line[ 1:-1 ]\n",
        "            gene_seq[ header ] = ''\n",
        "\n",
        "        else:\n",
        "\n",
        "            sequence = next_line[ :-1 ]\n",
        "            gene_seq[ header ] = gene_seq[ header ] + sequence\n",
        "\n",
        "    input_fasta.close()\n",
        "\n",
        "for next_report in input_reports:\n",
        "\n",
        "    all_hits = []\n",
        "    report = next_report[ :-1 ]\n",
        "    input_report = open( report, 'r' )\n",
        " \n",
        "    for next_line in input_report:\n",
        "\n",
        "        report_info = next_line.split( '\\t' )\n",
        "        gene_id = report_info[ 1 ]\n",
        "        all_hits.append( gene_id )\n",
        "        coordinate_1 = int( report_info[ 8 ] ) - 1\n",
        "        coordinate_2 = int( report_info[ 9 ] ) - 1\n",
        "        gene_coordinates[ gene_id ] = ( coordinate_1, coordinate_2 )\n",
        "        \n",
        "    unique_hits = list( set( all_hits ) )\n",
        "\n",
        "    for next_hit in unique_hits:\n",
        "        \n",
        "        gene_id = next_hit\n",
        "        sequence = gene_seq[ gene_id ]\n",
        "        coordinate_1 = gene_coordinates[ gene_id ][ 0 ]\n",
        "        coordinate_2 = gene_coordinates[ gene_id ][ 1 ]\n",
        "        subsequence = sequence[ coordinate_1 : coordinate_2 ] \n",
        "\n",
        "        output = '>' + gene_id + '\\n' + sequence + '\\n'\n",
        "        output_geneset_gspp.write( output )\n",
        "        \n",
        "        output_sub = '>' + gene_id + '\\n' + subsequence + '\\n'\n",
        "        output_geneset_gspp_sub.write( output_sub )\n",
        "\n",
        "    input_report.close()\n",
        "\n",
        "input_fastas.close()\n",
        "input_reports.close()\n",
        "output_geneset_gspp.close()\n",
        "output_geneset_gspp_sub.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Parse Blast reports to identify top hit per RGS per ProjectDB genome'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "275c837e",
      "metadata": {
        "id": "275c837e"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Run Blastp ( RGS ) x ( Each RGS Species ProjectDB Genome )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc757b37",
      "metadata": {
        "id": "bc757b37"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "\n",
        "/usr/local/bin/blastp -db ../3-projectdb/projectdb-fastas/Metazoa-Chordata-Mammalia-Primates-Hominidae-Homo-sapiens*aa -query ../0-start/user-input/source-rgs/user_rgs.fasta -out output/6-blast-report-rgs_X_human-genome -outfmt 6  -max_hsps 1\n",
        "/usr/local/bin/blastp -db ../3-projectdb/projectdb-fastas/Metazoa-Arthropoda-Insecta-Diptera-Drosophilidae-Drosophila-melanogaster*aa -query ../0-start/user-input/source-rgs/user_rgs.fasta -out output/6-blast-report-rgs_X_fly-genome -outfmt 6  -max_hsps 1\n",
        "# Completed\n",
        "echo 'COMPLETED Run Blastp ( RGS ) x ( Each RGS Species ProjectDB Genome )'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d4db6a0b",
      "metadata": {
        "id": "d4db6a0b"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate a list of Blast reports and RGS species ProjectDB genomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "de73dd14",
      "metadata": {
        "id": "de73dd14"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "\n",
        "ls output/6*genome* > output/7-list-reports\n",
        "ls ../3-projectdb/projectdb-fastas/*Homo-sapiens*aa > output/7-list-rgs-projectdb-fastas\n",
        "ls ../3-projectdb/projectdb-fastas/*Drosophila-melanogaster*aa >> output/7-list-rgs-projectdb-fastas\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Generate a list of Blast reports and RGS species ProjectDB genomes'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f708d925",
      "metadata": {
        "id": "f708d925"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Parse Blast reports to identify and annotate top hit per RGS sequence per RGS genome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7763e89e",
      "metadata": {
        "id": "7763e89e"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "##### USER INPUT\n",
        "input_reports = open( 'output/7-list-reports', 'r' )\n",
        "input_fastas = open( 'output/7-list-rgs-projectdb-fastas', 'r' )\n",
        "input_rgsfasta = open( '../0-start/user-input/source-rgs/user_rgs.fasta', 'r' )\n",
        "output_map = open( 'output/8-map-source-to-reference-identifiers', 'a' )\n",
        "\n",
        "model_species = [ 'human','fly', 'worm', 'mouse', 'fish'  ]\n",
        "\n",
        "###### BEING SCRIPT \n",
        "# read in reference gene header identifier and sequence into dictionary\n",
        "rgs_seq = {}\n",
        "for next_line in input_rgsfasta:\n",
        "\n",
        "    if next_line[ 0 ] == '>':\n",
        "\n",
        "        identifier = next_line[ 1:-1 ].split( ' ' )[ 0 ]\n",
        "        rgs_seq[ identifier ] = ''\n",
        "\n",
        "    else:\n",
        "\n",
        "        rgs_seq[ identifier ] = rgs_seq[ identifier ] + next_line[ :-1 ]\n",
        "\n",
        "# read rgs query and rgs genome top hit into dictionary\n",
        "gengene_refgene = {}\n",
        "rgs_genes = []\n",
        "gengenes = []\n",
        "\n",
        "for next_report in input_reports:\n",
        "    \n",
        "    input_report = open( next_report[ :-1 ], 'r' )\n",
        "    \n",
        "    model_name = \"\"\n",
        "    for model in model_species:\n",
        "        if model in next_report:\n",
        "            model_name = model\n",
        "            break\n",
        "\n",
        "    for next_hit in input_report:\n",
        "\n",
        "        info = next_hit.split( '\\t' )\n",
        "        refgene = info[ 0 ]\n",
        "        gengene = info[ 1 ]\n",
        "        name = refgene.split( '-' )[ 1 ]\n",
        "        \n",
        "        if name == model_name:\n",
        "\n",
        "            if (refgene in rgs_genes) or (gengene in gengenes):\n",
        "\n",
        "                pass\n",
        "\n",
        "            else:\n",
        "\n",
        "                gengene_refgene[ gengene ] = refgene\n",
        "                rgs_genes.append( refgene )\n",
        "                gengenes.append( gengene )\n",
        "\n",
        "        else:\n",
        "\n",
        "            pass\n",
        "            \n",
        "    input_report.close()\n",
        "\n",
        "\n",
        "# read in RGS genome and replace rgs genes (top hit in blast of rgs _X_ rgs genome) with rgs header and sequence\n",
        "header_seq = {}\n",
        "for next_fasta in input_fastas:\n",
        "\n",
        "    input_fasta = open( next_fasta[ :-1 ], 'r' )\n",
        "    output_name = 'output/8-' + next_fasta[ :-1 ].split( '/' )[ -1 ] + '-rgs'\n",
        "    output_fasta = open( output_name, 'w' )\n",
        "\n",
        "    for next_line in input_fasta:\n",
        "\n",
        "        if next_line[ 0 ] == '>':\n",
        "\n",
        "            count = 0\n",
        "            \n",
        "            header_info = next_line[ 1:-1 ]\n",
        "\n",
        "            if header_info in gengene_refgene.keys():\n",
        "                \n",
        "                count = 1\n",
        "                gengene = header_info\n",
        "                refgene = gengene_refgene[ gengene ]\n",
        "                \n",
        "                header = '>' + refgene + '\\n'\n",
        "                output_fasta.write( header )\n",
        "\n",
        "                \n",
        "                output = gengene + '\\t' + refgene + '\\n'\n",
        "                output_map.write( output )                \n",
        "                \n",
        "            else:\n",
        "\n",
        "                header = next_line\n",
        "                output_fasta.write( header )\n",
        "                \n",
        "        else:\n",
        "\n",
        "            if count == 0:\n",
        "\n",
        "                sequence = next_line\n",
        "                output_fasta.write( sequence )\n",
        "\n",
        "            else:\n",
        "\n",
        "                sequence = rgs_seq[ refgene ] + '\\n'\n",
        "                output_fasta.write( sequence )\n",
        "\n",
        "    input_fasta.close()\n",
        "    output_fasta.close()\n",
        "\n",
        "input_reports.close()\n",
        "input_fastas.close()\n",
        "input_rgsfasta.close()\n",
        "output_map.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Parse Blast reports to identify and annotate top hit per RGS sequence per RGS genome'\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8265dce8",
      "metadata": {
        "id": "8265dce8"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Combine all RGS genomes and list the combined RGS genomes fasta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa2db1e1",
      "metadata": {
        "id": "fa2db1e1"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "cat output/8*rgs > output/9-rgs-all-genomes-combined.fasta-rgs\n",
        "ls -1 output/9*rgs > output/9-list-RGS-header-reference-genome-fastas\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Combine all RGS genomes and listed the combined RGS genomes fasta'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2b51c1b7",
      "metadata": {
        "id": "2b51c1b7"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Create a Bash script to generate a Blast database for combined RGS genomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7f04d27",
      "metadata": {
        "id": "f7f04d27"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "input_list = open( 'output/9-list-RGS-header-reference-genome-fastas', 'r' )\n",
        "output_makedb = open( '011-blastp-makedb', 'w' )\n",
        "\n",
        "for next_fasta in input_list:\n",
        "\n",
        "    fasta_path = next_fasta[ :-1 ]\n",
        "    fasta = fasta_path.split( '/' )[ -1 ]\n",
        "    \n",
        "    db_name = fasta.split( '.' )[ 0 ] + '-AA'\n",
        "    \n",
        "    command = '/usr/local/bin/makeblastdb -in '  + fasta_path + ' -parse_seqids -dbtype prot'\n",
        "    output = command + '\\n'\n",
        "    output_makedb.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'Generate a Blast database for combined RGS genomes\\''\n",
        "    \n",
        "input_list.close()\n",
        "output_makedb.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Create a Bash script to generate a Blast database for combined RGS genomes'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d3928187",
      "metadata": {
        "id": "d3928187"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Generate a Blast database for combined RGS genomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1cf03d82",
      "metadata": {
        "id": "1cf03d82"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 011-blastp-makedb\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1c443cb",
      "metadata": {
        "id": "d1c443cb"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Generate a list of the combined RGS genomes Blast database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e393e89",
      "metadata": {
        "id": "7e393e89"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "ls -1 output/9-rgs-all-genomes-combined.fasta-rgs > output/12-list-blastp-annotated-rgs-genomes\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Generate a list of the combined RGS genomes Blast database'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a538fe0b",
      "metadata": {
        "id": "a538fe0b"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Create a Bash script to run Blastp ( RGS-ProjectDB Hits ) x ( Combined RGS Genomes )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e93f3340",
      "metadata": {
        "id": "e93f3340"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "# input_list\n",
        "input_list = open( 'output/12-list-blastp-annotated-rgs-genomes', 'r' )\n",
        "output_command = open( '014-blastp-hits_X_RGS-genomes', 'w' )\n",
        "\n",
        "output = '\\n'\n",
        "output_command.write( output )\n",
        "\n",
        "for next_line in input_list:\n",
        "\n",
        "    db_path = next_line[ :-1 ]\n",
        "    info = db_path.split( '-' )\n",
        "    genome = info[ -3 ] + '-' + info[ -2 ]\n",
        "\n",
        "    output =  '/usr/local/bin/blastp -db ' + db_path  + ' -outfmt 6 -out output/14-blastp-report-blastp_hits_X_RGS-genome-' + genome + '  -max_target_seqs 1 -max_hsps 1 -query output/5-blastp-hits.fasta -matrix BLOSUM45 -evalue 1e-3 -num_threads 60 &\\n'\n",
        "    output_command.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'Run Blastp ( RGS-ProjectDB Hits ) x ( Combined RGS Genomes )\\''\n",
        "output_command.write( output )\n",
        "\n",
        "input_list.close()\n",
        "output_command.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Create a Bash script to run Blastp ( RGS-ProjectDB Hits ) x ( Combined RGS Genomes )'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9260052d",
      "metadata": {
        "id": "9260052d"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Run Blastp ( RGS-ProjectDB Hits ) x ( Combined RGS Genomes )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d613db75",
      "metadata": {
        "id": "d613db75"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 014-blastp-hits_X_RGS-genomes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9195cc0",
      "metadata": {
        "id": "f9195cc0"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Parse Blast report to identify RGS-ProjectDB Hits having a top hit to RGS sequence in  Combined RGS Genomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae015a5b",
      "metadata": {
        "id": "ae015a5b"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "##### USER INPUT\n",
        "\n",
        "input_fastas = open( 'output/1-list-projectdb-fastas', 'r' )\n",
        "input_hits = open( 'output/14-blastp-report-blastp_hits_X_RGS-genome-genomes-combined.fasta', 'r' )\n",
        "input_rgs_ids = open( 'output/8-map-source-to-reference-identifiers', 'r' )\n",
        "\n",
        "output_fasta = open( 'output/15-CGS-sequences-blastp-rbf.fasta', 'w' )\n",
        "output_filtered = open( 'output/15-dropped-queries-no-rgs-top-hit-in-rgs-genome', 'w' )\n",
        "\n",
        "model_species = [ 'human','fly', 'Sepia_pharaonis' ]\n",
        "\n",
        "##### BEGIN SCRIPT\n",
        "\n",
        "rgs_ids = []\n",
        "\n",
        "for next_line in input_rgs_ids:\n",
        "\n",
        "        info = next_line[ :-1 ].split( '\\t' )\n",
        "        projectdb_id = info[ 0 ]\n",
        "        rgs_id = info[ 1 ]\n",
        "        rgs_ids.append( rgs_id )\n",
        "        rgs_ids.append( projectdb_id )\n",
        "\n",
        "keepers = []\n",
        "queries = []\n",
        "\n",
        "for next_hit in input_hits:\n",
        "\n",
        "        info = next_hit.split( '\\t' )\n",
        "        query = info[ 0 ]\n",
        "        queries.append( query )\n",
        "        \n",
        "        hit = info[ 1 ]        \n",
        "        hit_info = hit.split( '-' )\n",
        "        name = hit_info[ 1 ]\n",
        "\n",
        "        if name in model_species:\n",
        "                \n",
        "                # drop RGS genes\n",
        "                if query in rgs_ids:\n",
        "\n",
        "                        pass\n",
        "\n",
        "                else:\n",
        "                        keepers.append( query )\n",
        "\n",
        "        else:\n",
        "                output = query + '\\t' + hit + '\\n'\n",
        "                output_filtered.write( output )\n",
        "\n",
        "# produce post-filtered keepers diamond fasta NO RGS\n",
        "count = 0\n",
        "\n",
        "for next_fasta in input_fastas:\n",
        "\n",
        "        input_fasta = open( next_fasta[ :-1 ], 'r' )\n",
        "\n",
        "        for next_line in input_fasta:\n",
        "                \n",
        "                if next_line[ 0 ] == '>':\n",
        "                        count = 0\n",
        "                        identifier = next_line[ 1:-1 ]\n",
        "\n",
        "                        if identifier in keepers:\n",
        "                                count = 1\n",
        "                                header = '>' + identifier + '\\n'\n",
        "                                output_fasta.write( header )\n",
        "                                \n",
        "                        else:\n",
        "                                pass\n",
        "\n",
        "                else:\n",
        "\n",
        "                        if count == 0:\n",
        "                                pass\n",
        "\n",
        "                        else:\n",
        "\n",
        "                                sequence = next_line\n",
        "                                output_fasta.write( sequence )\n",
        "                                \n",
        "        input_fasta.close()\n",
        "            \n",
        "input_hits.close()\n",
        "input_fastas.close()\n",
        "output_fasta.close()\n",
        "input_rgs_ids.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Parse Blast report to identify RGS-ProjectDB Hits having a top hit to RGS sequence in  Combined RGS Genomes'\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2dfbcff3",
      "metadata": {
        "id": "2dfbcff3"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Combine Candidate Gene Set - Reference Gene Set - Outside Gene Set sequences to produce the Final Gene Set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b502424",
      "metadata": {
        "id": "7b502424"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "\n",
        "cp ../../ogs-outgroup-sequences.fasta ../0-start/user-input/ogs-outgroup-sequences/\n",
        "cat output/15-CGS-sequences-blastp-rbf.fasta ../0-start/user-input/source-rgs/user_rgs.fasta ../0-start/user-input/ogs-outgroup-sequences/ogs-outgroup-sequences.fasta > output/16-blastp-final-gene-set.aa\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Combine Candidate Gene Set - Reference Gene Set - Outside Gene Set sequences to produce the Final Gene Set'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af7b4ce3",
      "metadata": {
        "id": "af7b4ce3"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Clean up FGS fasta to make each sequence one line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "426ef236",
      "metadata": {
        "id": "426ef236"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "input_fasta = open( 'output/16-blastp-final-gene-set.aa', 'r' )\n",
        "output_fasta = open( 'output/17-blastp-final-gene-set.aa', 'w' )\n",
        "\n",
        "header_seq = {}\n",
        "for next_line in input_fasta:\n",
        "\n",
        "    if next_line[ 0 ] == '>':\n",
        "\n",
        "        header = next_line[ :-1 ]\n",
        "        header_seq[ header ] = ''\n",
        "\n",
        "    else:\n",
        "\n",
        "        sequence = next_line[ :-1 ]\n",
        "        header_seq[ header ] = header_seq[ header ] + sequence\n",
        "\n",
        "for next_header in header_seq:\n",
        "\n",
        "    output = next_header + '\\n' + header_seq[ next_header ] + '\\n'\n",
        "    output_fasta.write( output)\n",
        "\n",
        "input_fasta.close()\n",
        "output_fasta.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Clean up FGS fasta to make each sequence one line'\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c64e1ff",
      "metadata": {
        "id": "3c64e1ff"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create a Bash script to count number of homologs per ProjectDB species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96957316",
      "metadata": {
        "id": "96957316"
      },
      "outputs": [],
      "source": [
        "#! python\n",
        "\n",
        "input_gspp = open( '../0-start/user-input/project-species/000-project-species', 'r' )\n",
        "output_grep = open( '019-grep-species-gene-family-counts', 'w' )\n",
        "\n",
        "ags_fasta = 'output/17-blastp-final-gene-set.aa'\n",
        "\n",
        "clean = False\n",
        "\n",
        "for next_line in input_gspp:\n",
        "\n",
        "    info = next_line[ :-1 ].split( ' ' )\n",
        "    gspp = info[ 0] + '-' + info[ 1]\n",
        "    gspp_short = gspp \n",
        "\n",
        "    if gspp == 'Homo-sapiens':\n",
        "        gspp_short = 'human'\n",
        "    elif gspp == 'Drosophila-melanogaster':\n",
        "        gspp_short = 'fly'\n",
        "    elif gspp == 'Caenorhabditis-elegans':\n",
        "        gspp_short = 'worm'\n",
        "    elif gspp == 'Sepia-pharaonis':\n",
        "        gspp_short = 'Sepia_pharaonis'\n",
        "    else:\n",
        "        pass\n",
        "\n",
        "    if clean == False:\n",
        "        output = 'echo \\'' + gspp + '\\' > output/19-gene-family-counts\\ngrep -c \\'' + gspp_short + '\\' ' + ags_fasta + '>> output/19-gene-family-counts\\n'\n",
        "        clean = True\n",
        "        \n",
        "    else:\n",
        "        output = 'echo \\'' + gspp + '\\' >> output/19-gene-family-counts\\ngrep -c \\'' + gspp_short + '\\' ' + ags_fasta + ' >> output/19-gene-family-counts\\n'\n",
        "\n",
        "    output_grep.write( output )\n",
        "\n",
        "output = '# Completed\\necho \\'COMPLETED Count number of homologs per ProjectDB species\\''\n",
        "output_grep.write( output )\n",
        "\n",
        "input_gspp.close()\n",
        "output_grep.close()\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Create a Bash script to count number of homologs per ProjectDB species'\n",
        "print( output )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42f6085e",
      "metadata": {
        "id": "42f6085e"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Count number of homologs per ProjectDB species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51bc783f",
      "metadata": {
        "id": "51bc783f"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "bash 019-grep-species-gene-family-counts\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d19a3e8d",
      "metadata": {
        "id": "d19a3e8d"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "# GIGANTIC Block 7 Trees Genes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1c1889e",
      "metadata": {
        "id": "b1c1889e"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Change working directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68176de8",
      "metadata": {
        "id": "68176de8"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../7-trees-genes')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8527f11",
      "metadata": {
        "id": "e8527f11"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Create directories for GIGANTIC Block 7 Trees Genes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59854683",
      "metadata": {
        "id": "59854683"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "mkdir output\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Create directories for GIGANTIC Block 7 Trees Genes'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cab7e71",
      "metadata": {
        "id": "0cab7e71"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Clean up Final Gene Set fasta file for MAFFT (due to bug in Mafft in Aug 2021 - may no longer be needed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5d5476f7",
      "metadata": {
        "id": "5d5476f7"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "sed -e 's/-/_/g' ../5-homologs-genes/output/17-blastp-final-gene-set.aa > output/1-blastp-final-gene-set.aa\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Clean up Final Gene Set fasta file for MAFFT (due to bug in Mafft in Aug 2021 - may no longer be needed)'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c54687bb",
      "metadata": {
        "id": "c54687bb"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Clean up Final Gene Set fasta file for ???"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "439b7a99",
      "metadata": {
        "id": "439b7a99"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "sed -e 's/U/X/g' output/1-blastp-final-gene-set.aa  > output/2-blastp-final-gene-set.aa\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Clean up Final Gene Set fasta file for ???'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7cad7838",
      "metadata": {
        "id": "7cad7838"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Run MAFFT on Final Gene Set fasta to generate a multiple sequence alignment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a158f5b8",
      "metadata": {
        "id": "a158f5b8"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "mafft --originalseqonly --maxiterate 1000 --reorder  --bl 45 --thread 40 output/2-blastp-final-gene-set.aa > output/3-blastp-final-gene-set.mafft\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Run MAFFT on Final Gene Set fasta to generate a multiple sequence alignment'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d04d0d72",
      "metadata": {
        "id": "d04d0d72"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Run ClipKit on MAFFT multiple sequence alignment to remove low quality regions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "087324f1",
      "metadata": {
        "id": "087324f1"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "clipkit -l -m smart-gap output/3-blastp-final-gene-set.mafft -o output/4-blastp-final-gene-set.clipkit-smartgap\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Run ClipKit on MAFFT multiple sequence alignment to remove low quality regions'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9357956",
      "metadata": {
        "id": "c9357956"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Run FastTree on trimmed multiple sequence alignment to generate a phylogenetic gene tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "297fd2eb",
      "metadata": {
        "id": "297fd2eb"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "fasttree -spr 4 -mlacc 2 -slownni -gamma -boot 2000 -log output/6-log-fasttree output/4-blastp-final-gene-set.clipkit-smartgap > output/5-blastp-final-gene-set.fasttree\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Run FastTree on trimmed multiple sequence alignment to generate a phylogenetic gene tree'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "559d94b0",
      "metadata": {
        "id": "559d94b0"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "# GIGANTIC Block 10 Auto Annotation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f883597d",
      "metadata": {
        "id": "f883597d"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Change working directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3134340",
      "metadata": {
        "id": "e3134340"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../0-start/software')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Install FigTree"
      ],
      "metadata": {
        "id": "MhQKhQtxIPSz"
      },
      "id": "MhQKhQtxIPSz"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash \n",
        "# FigTree\n",
        "wget https://github.com/rambaut/figtree/releases/download/v1.4.4/FigTree.v1.4.4.zip --quiet\n",
        "unzip FigTree.v1.4.4.zip\n",
        "mv FigTree\\ v1.4.4 FigTree\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Installed FigTree'\n"
      ],
      "metadata": {
        "id": "mxy8S3WCIMrI"
      },
      "id": "mxy8S3WCIMrI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Change working directory"
      ],
      "metadata": {
        "id": "oR57KJ1zLtfy"
      },
      "id": "oR57KJ1zLtfy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fUvIyds4IM-X"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir( '../../10-auto-annotation')\n",
        "\n",
        "# Completed\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "output = 'COMPLETED Change working directory\\nCurrent directory is: ' + relative_path\n",
        "print( output )"
      ],
      "id": "fUvIyds4IM-X"
    },
    {
      "cell_type": "markdown",
      "id": "a42aa117",
      "metadata": {
        "id": "a42aa117"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        " ## Create directories for GIGANTIC Block 10 Auto Annotation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8eb4b0eb",
      "metadata": {
        "id": "8eb4b0eb"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "mkdir output\n",
        "\n",
        "# Completed\n",
        "echo 'COMPLETED Create directories for GIGANTIC Block 10 Auto Annotation'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e07ad67",
      "metadata": {
        "id": "5e07ad67"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Color annotation gene family tree\n",
        "#### Jan script from AutoAno in Ctenophore book chapter notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "zENBYDLV40GK",
      "metadata": {
        "id": "zENBYDLV40GK"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "sed -e 's/-/_/g' ../0-start/user-input/species-colors/species-colors.txt > ../0-start/user-input/species-colors/species-colors.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfc8a992",
      "metadata": {
        "id": "dfc8a992"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "color_annotation = \"../0-start/user-input/species-colors/species-colors.txt\" #color-annotation.txt\n",
        "before_tree = \"../7-trees-genes/output/5-blastp-final-gene-set.fasttree\"\n",
        "after_tree = \"output/blastp-final-gene-set.fasttree\"\n",
        "itol_map = \"output/itol-map.color\" #itol-friendly-annotated-identifier-to-color-map.txt\n",
        "\n",
        "\n",
        "#make random colors for elements based on input map\n",
        "element_colors = []\n",
        "input_map = open( color_annotation, 'r')\n",
        "for nextline in input_map:\n",
        "    info = nextline[:-1].split('\\t')\n",
        "    if len(info) == 2:\n",
        "        element = info[0]\n",
        "        color = info[1]\n",
        "    elif len(info) == 1:\n",
        "        element = info[0]\n",
        "        color = \"#\"+''.join([random.choice('ABCDEF0123456789') for i in range(6)])\n",
        "    else:\n",
        "        print('map should be two column, tab separated, element followed by hex color')\n",
        "        break\n",
        "    element_colors.append( (element, color) )\n",
        "input_map.close()\n",
        "\n",
        "#parse newick tree\n",
        "infile = open(before_tree,'r')\n",
        "tree = infile.readline()\n",
        "infile.close()\n",
        "\n",
        "leftremoved = tree.replace('(','')\n",
        "commasplit = leftremoved.split(',')\n",
        "\n",
        "nodes = []\n",
        "for item in commasplit:\n",
        "    colonsplit = item.split(':')\n",
        "    nodes.append(colonsplit[0])\n",
        "\n",
        "#sort nodes by first character\n",
        "nodes.sort()\n",
        "\n",
        "#annotate color\n",
        "annotated_map = []\n",
        "nodes_annotated = []\n",
        "for node in nodes:\n",
        "    annotated = False\n",
        "    for element, color in element_colors:\n",
        "        if element in node:\n",
        "            nodes_annotated.append(node + '[&!color='+color+']')\n",
        "            annotated = True\n",
        "            annotated_map.append( (node, color) )\n",
        "    if not annotated:\n",
        "        nodes_annotated.append(node)\n",
        "\n",
        "#write nexus tree\n",
        "outfile = open(after_tree,'w')\n",
        "outfile.write('#NEXUS\\n'+'begin taxa;\\n')\n",
        "outfile.write('\\tdimensions ntax='+str(len(nodes))+';\\n'+ '\\ttaxlabels\\n')\n",
        "for node in nodes_annotated:\n",
        "    outfile.write('\\t'+node+'\\n')\n",
        "outfile.write(';\\n'+'end;\\n'+'\\n'+'begin trees;\\n')\n",
        "outfile.write('\\ttree tree_1 = '+tree+'\\n'+'end;\\n')\n",
        "outfile.close()\n",
        "\n",
        "output_map = open(itol_map,'w')\n",
        "output_map.write('TREE_COLORS\\n'+ 'SEPARATOR TAB\\n'+'DATA\\n')\n",
        "for node, color in annotated_map:\n",
        "    output_map.write(node + '\\t' + 'label' + '\\t')\n",
        "    r = int(color[1:3], 16)\n",
        "    g = int(color[3:5], 16)\n",
        "    b = int(color[5:7], 16)\n",
        "    rgb = 'rgb(' + str(r) + ', ' + str(g) + ', ' + str(b) + ')'\n",
        "    output_map.write(rgb + '\\t' + 'normal' + '\\t' + '1\\n')\n",
        "output_map.write('\\n')\n",
        "output_map.close()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "036db32a",
      "metadata": {
        "id": "036db32a"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "  \n",
        "## Generate pdf of gene tree\n",
        "\n",
        "#### Jan script from AutoAno in Ctenophore book chapter notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88fb37fc",
      "metadata": {
        "id": "88fb37fc"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "\n",
        "count=$(grep -c '>' ../7-trees-genes/output/2-blastp-final-gene-set.aa)\n",
        "\n",
        "height=200\n",
        "if [[ $count -gt 20 ]]\n",
        "then\n",
        "  height=$(($count * 10))\n",
        "fi\n",
        "\n",
        "\n",
        "java -jar ../0-start/software/FigTree/lib/figtree.jar -graphic PDF -height $height output/blastp-final-gene-set.fasttree output/blastp-final-gene-set-annotated-tree.pdf\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08e5544f",
      "metadata": {
        "id": "08e5544f"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "## Add gene and species to tree file pdf name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4817a724",
      "metadata": {
        "id": "4817a724"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "# os.rename(src, dst)\n",
        "# shutil.copyfile('file1.txt', 'file2.txt')\n",
        "\n",
        "gigantic_id = 'GIGANTIC' + gigantic_run_directory.split( 'GIGANTIC')[ -1]\n",
        "new_file_name = 'output/gigantic-blastp-rgs' + rgs_count + '-' + gene_family_name + '_X_species' + species_count +  '-auto-annotated-tree-' + gigantic_id + '.pdf'\n",
        "\n",
        "shutil.copyfile('output/blastp-final-gene-set-annotated-tree.pdf', new_file_name )\n",
        "\n",
        "# Completed\n",
        "output = 'COMPLETED Final gene tree pdf is named: ' + new_file_name.split( '/' )[ -1 ]\n",
        "print( output)\n",
        "absolute_path = os.getcwd()\n",
        "relative_path = '/'.join( absolute_path.split( '/' )[ 3:] )\n",
        "\n",
        "output = 'COMPLETED The pdf is located here: ' + relative_path + '/output/'\n",
        "print( output)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93fe5102",
      "metadata": {
        "id": "93fe5102"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        " \n",
        "# GIGANTIC run complete! Great job everyone   🐌  🦑  🐙  💯 🎉 :) \n"
      ]
    }
  ],
  "metadata": {
    "celltoolbar": "Tags",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "name": "TEMPLATE GIGANTIC shortshort iqtree FHLTed",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}