SARS_CoV_2_Analysis_Workflow.ipynb

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      "name": "SARS-CoV-2 Analysis Workflow",
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        "<a href=\"https://colab.research.google.com/github/epi2me-labs/tutorials/blob/master/SARS_CoV_2_Analysis_Workflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "<h1>SARS-Cov-2 Analysis Workflow</h1>\n",
        "\n",
        "This tutorial implements the best-practices bioinformatics workflow for the assembly of an SARS-CoV-2 viral genomes. The workflow in the document implements the [ARTIC Nanopore bioinformatics SOP](https://https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html).\n",
        "\n",
        "Computational requirements for this tutorial include:\n",
        "\n",
        "- Computer running the **EPI2ME Labs** notebook Server\n",
        "- At least 16 Gb RAM\n",
        "- An internet connection\n",
        "\n",
        "> ⚠️ **Warning:** Before starting a new analysis it is recommeded to select `Edit > Clear all outputs` from the menu above before using the notebook.\n",
        "\n"
      ]
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        "## Getting Started\n",
        "\n",
        "\n",
        "\n",
        "⚠️ ***Reminder:*** Connect to your locally running EPI2ME Labs Server before running any of the code below. See Run a tutorial on the EPI2ME Labs Server help page for more information."
      ]
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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vniqalF0V1Uq",
        "colab_type": "text"
      },
      "source": [
        "Having connected to the EPI2ME Labs Server, before anything else we will create and set a working directory:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWl8M6TCV1fN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec94e9f0-9110-4ae0-fa04-c093c1b8347a"
      },
      "source": [
        "from epi2melabs import ping\n",
        "tutorial_name = 'ncov_tutorial'\n",
        "pinger = ping.Pingu()\n",
        "pinger.send_notebook_ping('start', tutorial_name)\n",
        "\n",
        "# create a work directory and move into it\n",
        "working_dir = '/epi2melabs/{}/'.format(tutorial_name)\n",
        "!mkdir -p \"$working_dir\"\n",
        "%cd \"$working_dir\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/epi2melabs/ncov_tutorial\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0L95Ko61LtN",
        "colab_type": "text"
      },
      "source": [
        "### Install additional software\n",
        "\n",
        "The default **EPI2MELabs** environment does not contain the ARTIC software. In this section we will prepare the enviroment with the necessary ARTIC software installation.\n",
        "\n",
        "*Please note that the software installed is not persistent and this step will need to be re-run if you stop and restart the EPI2ME Labs server*\n",
        "\n",
        "Having connected to the EPI2ME Labs Server, we will install the necessary software. Press the play button below to the left hand side (it may appear as `[ ]`):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_O1igexGLZ-",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "1ce1da36-1955-41e1-fd1d-be6522787a00"
      },
      "source": [
        "#@markdown **ARTIC Bioinformatics installation** *(click the play button to install)*\n",
        "import os\n",
        "%cd \"$working_dir\"\n",
        "if os.path.exists(\"artic-ncov2019\"):\n",
        "    print(\"Found existing install, removing.\")\n",
        "    !rm -rf artic-ncov2019\n",
        "print(\"Downloading artic-ncov\")\n",
        "# we need this just for primerset data\n",
        "!git clone https://github.com/artic-network/artic-ncov2019.git\n",
        "\n",
        "# install artic analysis pipeline\n",
        "if os.path.exists(\"fieldbioinformatics\"):\n",
        "    print(\"Found existing install, removing.\")\n",
        "    !rm -rf fieldbioinformatics\n",
        "!git clone https://github.com/artic-network/fieldbioinformatics\n",
        "%cd fieldbioinformatics\n",
        "!conda env remove -n artic\n",
        "!conda env create -f environment.yml\n",
        "!source /opt/conda/etc/profile.d/conda.sh \\\n",
        "    && conda activate artic \\\n",
        "    && pip install . && pip install ipykernel\n",
        "!echo \"\\nTesting install\"\n",
        "!run artic --version\n",
        "\n",
        "%cd \"$working_dir\""
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/epi2melabs/ncov_tutorial\n",
            "Found existing install, removing.\n",
            "Downloading artic-ncov\n",
            "Cloning into 'artic-ncov2019'...\n",
            "remote: Enumerating objects: 7, done.\u001b[K\n",
            "remote: Counting objects: 100% (7/7), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 588 (delta 2), reused 0 (delta 0), pack-reused 581\u001b[K\n",
            "Receiving objects: 100% (588/588), 8.01 MiB | 1.34 MiB/s, done.\n",
            "Resolving deltas: 100% (295/295), done.\n",
            "/epi2melabs/ncov_tutorial/fieldbioinformatics\n",
            "/epi2melabs/ncov_tutorial\n",
            "Testing install\n",
            "artic 1.1.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5uwBJCVECBg",
        "colab_type": "text"
      },
      "source": [
        "### Sample Data\n",
        "\n",
        "This tutorial is provided with a sample dataset. This dataset contains a range of multiplexed samples, some from real clinical samples others and synthetic control preparations.\n",
        "\n",
        "To download the sample file we run the `linux` command `wget`. To execute the command click on the cell and then press Command/Ctrl-Enter, or click the Play symbol to the left-hand side."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bAE0vYvEia0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bucket = \"ont-exd-int-s3-euwst1-epi2me-labs\"\n",
        "domain = \"s3-eu-west-1.amazonaws.com\"\n",
        "site = \"https://{}.{}\".format(bucket, domain)\n",
        "\n",
        "!wget -O sample_ncov.tar.gz \"$site/ncov_tutorial/sample_ncov.tar.gz\"\n",
        "print(\"Extracting data...\")\n",
        "!rm -rf sample_ncov\n",
        "!tar -xzf sample_ncov.tar.gz\n",
        "print(\"Done.\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nh7-4mZeXHRA",
        "colab_type": "text"
      },
      "source": [
        "### Using your own data\n",
        "\n",
        "If you have your own sequencing, you can edit the value of the input variables below in the **Data Entry** section. To find the correct full path of a file you can navigate to it in the **Files** browser to the left-hand side, right-click on the file and select **Copy path**:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDCEs7E3Zy60",
        "colab_type": "text"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h43P8CBYzV0e",
        "colab_type": "text"
      },
      "source": [
        "The location shared with the EPI2ME labs server from your computer will show as `/epi2melabs`, for example a file located at `/data/my_gridion_run/fastq_pass` on your computer will appear as `/epi2melabs/my_gridion_run/fastq_pass` when it is the `/data` folder that is shared."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0y2JPI9SlxM",
        "colab_type": "text"
      },
      "source": [
        "### Data Entry\n",
        "\n",
        "The workflow requires `.fastq` files from an Oxford Nanopore Technologies' sequencing device. These should not have previously been trimmed of barcodes as this will be performed as part of the workflow.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fI18AeJtIXAO",
        "colab_type": "text"
      },
      "source": [
        "The input folder should be the top level MinKNOW (or Guppy) output folder containing the `fastq_pass` folder and `sequencing_summary.txt` file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d4yns3wIfpW",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@markdown **Analysis Parameters** *(fill in the required values and then press play)*\n",
        "import glob\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "from epi2melabs import notebook\n",
        "\n",
        "debug = True\n",
        "ref_name = \"MN908947.3\"\n",
        "ref_len = 29903\n",
        "\n",
        "input_folder = \"/epi2melabs/ncov_tutorial/sample_ncov\" #@param {type:\"string\"}\n",
        "output_folder = \"/epi2melabs/ncov_tutorial/analysis/\" #@param {type:\"string\"}\n",
        "run_name = \"run0\" #@param {type:\"string\"}\n",
        "overwrite = False #@param {type:\"boolean\"}\n",
        "threads =  64#@param {type:\"integer\"}\n",
        "primer_set = \"nCoV-2019/V3\" #@param [\"nCoV-2019/V1\", \"nCoV-2019/V2\", \"nCoV-2019/V3\"]\n",
        "minimum_reads = 30000 #@param {type:\"integer\"}\n",
        "min_read_length = 400 #@param {type:\"integer\"}\n",
        "max_read_length = 700 #@param {type:\"integer\"}\n",
        "barcode_arrangements = \"barcode_arrs_nb12.cfg barcode_arrs_nb24.cfg\" #@param {type:\"string\"}\n",
        "\n",
        "success = True\n",
        "msg_lines = []\n",
        "\n",
        "# check input folder\n",
        "if not os.path.exists(input_folder):\n",
        "    msg = \"`{}` does not exist.\".format(input_folder)\n",
        "    success = False\n",
        "else:\n",
        "    msg = input_folder\n",
        "msg_lines.append(\"Input folder:  {}\".format(msg))\n",
        "\n",
        "# check fastq_pass folder\n",
        "fastq_pass = os.path.join(input_folder, 'fastq_pass')\n",
        "if not os.path.exists(fastq_pass):\n",
        "    msg = notebook.error(\"fastq_pass folder not found under {}.\".format(input_folder))\n",
        "    success = False\n",
        "else:\n",
        "    msg = fastq_pass\n",
        "msg_lines.append(\"fastq folder:  {}\".format(msg))\n",
        "\n",
        "# check summary file\n",
        "seqsum_glob = \"sequencing_summary_*_*.txt\"\n",
        "seqsums = glob.glob(os.path.join(input_folder, seqsum_glob))\n",
        "if len(seqsums) == 0:\n",
        "    msg = notebook.error(\"No file matching {} under {}.\".format(seqsum_glob, input_folder))\n",
        "    success = False\n",
        "else:\n",
        "    seq_summary_file = seqsums[0]\n",
        "    msg = seq_summary_file\n",
        "msg_lines.append(\"seq. summary:  {}\".format(msg))\n",
        "\n",
        "# check output\n",
        "if os.path.exists(output_folder) and not overwrite:\n",
        "    msg = notebook.error(\"{} exists, overwrite not selected\".format(output_folder))\n",
        "    success = False\n",
        "else:\n",
        "    if os.path.exists(output_folder):\n",
        "        print(\"- Previous output folder exists, removing\")\n",
        "        try:\n",
        "            shutil.rmtree(output_folder)\n",
        "        except:\n",
        "            print(notebook.error('- Error while deleting directory'))\n",
        "    os.mkdir(output_folder)\n",
        "    msg = output_folder\n",
        "msg_lines.append(\"Output folder: {}\".format(msg))\n",
        "\n",
        "print()\n",
        "print(\"Configuration\")\n",
        "print(\"-------------\")\n",
        "for line in msg_lines:\n",
        "    print(line)\n",
        "if success:\n",
        "    !cecho success \"Inputs setup correctly.\"\n",
        "else:\n",
        "    !cecho error \"One of more errors occurred finding inputs.\"\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWIv_lakSnbh",
        "colab_type": "text"
      },
      "source": [
        "## Analysis\n",
        "\n",
        "With our software environment set up and our inputs specified it is time to move on the the analysis. The following workflow begins with demultiplexing the reads using strict settings to avoid misidentification, followed by running of the Artic analysis, and finally creation of Quality control plots to determine if tha analysis is valid.\n",
        "\n",
        "### Demultiplexing and Read Quality Control\n",
        "\n",
        "In this section we will run sample demultiplexing using the `guppy_barcoder` software. The results of this will appear in the output folder under the `demultiplex` folder. After demultiplexing a report is produced by merging results of the demultiplexing with the \"sequencing summary\" file from the original MinKNOW output.\n",
        "\n",
        "Use the code cell below to run the demultiplexing:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfyYmjGyJsJ2",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "382a75bf-8c85-4919-a986-3c80db8ff673"
      },
      "source": [
        "#@markdown **Running Guppy demultiplexing** *(press play)*\n",
        "!rm -rf $output_folder/demultiplex\n",
        "!guppy_barcoder \\\n",
        "    --require_barcodes_both_ends --arrangements_files \"$barcode_arrangements\" \\\n",
        "    --compress_fastq --records_per_fastq 0 --recursive --worker_threads $threads \\\n",
        "    --save_path $output_folder/demultiplex \\\n",
        "    --input_path $input_folder \\\n",
        "    && cecho success \"Guppy finished successfully\" \\\n",
        "    || cecho error \"Guppy failed\""
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "ONT Guppy barcoding software version 3.5.2+5b7a51b\r\n",
            "input path:         /epi2melabs/ncov_tutorial/sample_ncov\r\n",
            "save path:          /epi2melabs/ncov_tutorial/analysis//demultiplex\r\n",
            "arrangement files:  barcode_arrs_nb12.cfg barcode_arrs_nb24.cfg\r\n",
            "min. score front:   60\r\n",
            "min. score rear:    60\r\n",
            "\r\n",
            "\r\n",
            "Found 371 fastq files.\r\n",
            "\r\n",
            "0%   10   20   30   40   50   60   70   80   90   100%\r\n",
            "|----|----|----|----|----|----|----|----|----|----|\n",
            "***************************************************\n",
            "Done in 141014 ms.\n",
            "\u001b[32m\u001b[1mGuppy finished successfully\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23xFjHXYXWXG",
        "colab_type": "text"
      },
      "source": [
        "The demultiplexing produces a summary file recording the barcode found in each read, or \"unclassified\" if the barcodes could not be confidently identified."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flXQBuCIVrre",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e10d9e02-6534-4099-8ce1-5b81279061d8"
      },
      "source": [
        "#@markdown **Merging data files** *(press play)*\n",
        "import pandas as pd\n",
        "\n",
        "success = False\n",
        "bc_summary, seq_summary = None, None\n",
        "barcode_summary_file = os.path.join(output_folder, \"demultiplex\", \"barcoding_summary.txt\")\n",
        "if not os.path.exists(barcode_summary_file):\n",
        "    !cecho error \"Could not find barcode summary: $barcode_summary_file\"\n",
        "else:\n",
        "    print(\"Reading demultiplex summary: {}.\".format(barcode_summary_file))\n",
        "    try:\n",
        "        bc_summary = pd.read_csv(barcode_summary_file, sep='\\t')\n",
        "    except Exception as e:\n",
        "        !cecho error \"Could not read barcode summary: $barcode_summary\"\n",
        "        if debug:\n",
        "            raise e\n",
        "\n",
        "print(\"Reading sequencing summary: {}\".format(seq_summary_file))\n",
        "try:\n",
        "    seq_summary = pd.read_csv(seq_summary_file, sep='\\t')\n",
        "except Exception as e:\n",
        "    !cecho error \"Could not read barcode summary: $seq_summary_file\"\n",
        "    if debug:\n",
        "        raise e\n",
        "\n",
        "if not any(x is None for x in (bc_summary, seq_summary)):\n",
        "    print(\"Merging barcode and sequencing summaries.\")\n",
        "    to_drop = [x for x in seq_summary.columns if x.startswith('barcode')]\n",
        "    seq_summary = seq_summary.drop(columns=to_drop)\n",
        "    try:\n",
        "        summary = pd.merge(bc_summary, seq_summary, on='read_id')\n",
        "    except Exception as e:\n",
        "        !cecho error \"Could not merge files.\"\n",
        "        if debug:\n",
        "            print(e)\n",
        "    else:\n",
        "        success = True\n",
        "if success:\n",
        "    !cecho success \"Sequencing and barcode summaries read and merged.\"\n",
        "else:\n",
        "    !cecho error \"One or more errors occurred reading input files.\""
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading demultiplex summary: /epi2melabs/ncov_tutorial/analysis/demultiplex/barcoding_summary.txt.\n",
            "Reading sequencing summary: /epi2melabs/ncov_tutorial/sample_ncov/sequencing_summary_FAL50547_8e49b54f.txt\n",
            "Merging barcode and sequencing summaries.\n",
            "\u001b[32m\u001b[1mSequencing and barcode summaries read and merged.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSZK6-YH016f",
        "colab_type": "text"
      },
      "source": [
        "To generate a QC report, run the cell below:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FGLFpM600sz",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "ffeb0dad-1573-4967-eb0e-bfa6c6730b15"
      },
      "source": [
        "#@markdown **Reads QC Report** *(press play)*\n",
        "import aplanat\n",
        "from aplanat import bars, hist, annot\n",
        "from bokeh.models import Span\n",
        "from bokeh.layouts import gridplot\n",
        "import numpy as np\n",
        "\n",
        "plots = list()\n",
        "\n",
        "total_bases = seq_summary['sequence_length_template'].sum()\n",
        "mean_length = total_bases / len(seq_summary)\n",
        "median_length = np.median(seq_summary['sequence_length_template'])\n",
        "\n",
        "# read length plot\n",
        "datas = [summary['sequence_length_template']]\n",
        "length_hist = hist.histogram(\n",
        "    datas, bins=400,\n",
        "    title=\"Read length distribution.\",\n",
        "    x_axis_label='Read Length / bases',\n",
        "    y_axis_label='Number of reads',\n",
        "    xlim=(0, 2000))\n",
        "length_hist = annot.subtitle(\n",
        "    length_hist,\n",
        "    \"Mean: {:.0f}. Median: {:.0f}\".format(\n",
        "        mean_length, median_length))\n",
        "plots.append(length_hist)\n",
        "\n",
        "# barcode count plot\n",
        "barcode_counts = pd.DataFrame(summary['barcode_arrangement'].value_counts()) \\\n",
        "    .sort_index().reset_index().rename(\n",
        "        columns={'index':'barcode', 'barcode_arrangement':'count'})\n",
        "bc_counts = bars.simple_bar(\n",
        "    barcode_counts['barcode'], barcode_counts['count'],\n",
        "    title='Number of reads per barcode')\n",
        "bc_counts = annot.subtitle(\n",
        "    bc_counts,\n",
        "    'Barcodes with fewer than {} reads will not be analysed further.'.format(\n",
        "        minimum_reads))\n",
        "bc_counts.xaxis.major_label_orientation = 3.14/2\n",
        "bc_counts = annot.marker_hline(bc_counts, minimum_reads)\n",
        "plots.append(bc_counts)\n",
        "\n",
        "# determine which barcode datasets are good\n",
        "barcode_counts['sufficient data'] = barcode_counts['count'] > minimum_reads\n",
        "valid_barcodes = set(\n",
        "    barcode_counts.loc[barcode_counts['sufficient data']]['barcode'])\n",
        "\n",
        "aplanat.show(gridplot(plots, ncols=2))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
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              "  function display_loaded() {\n",
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              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
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              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
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              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
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              "\n",
              "    function on_load() {\n",
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              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
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              "\n",
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              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"d441d7d3-dcb8-4942-85dc-3366c95a3dcd\" data-root-id=\"1104\"></div>\n"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = {\"694bd6f2-6463-4d42-b237-e9a9705249a4\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"1103\",\"type\":\"ToolbarBox\"},{\"id\":\"1101\",\"type\":\"GridBox\"}]},\"id\":\"1104\",\"type\":\"Column\"},{\"attributes\":{\"axis_label\":\"Number of reads\",\"formatter\":{\"id\":\"1084\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1067\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1098\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{\"text\":\"Number of reads per barcode\"},\"id\":\"1044\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1068\",\"type\":\"HelpTool\"},{\"attributes\":{\"text\":\"Mean: 527. 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      "cell_type": "markdown",
      "metadata": {
        "id": "ZMhjoLNaP6Ku",
        "colab_type": "text"
      },
      "source": [
        "### Run ARTIC for each sample\n",
        "\n",
        "With demultiplexed reads, we are in a position to analyse each dataset independently using the ARTIC workflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CiJvixCiXQVt",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b715b99a-15b4-458d-9705-9f38113f016f"
      },
      "source": [
        "#@markdown **Run ARTIC analysis for each barcode** *(press play)*\n",
        "def run_artic(\n",
        "        barcode, directory, artic_output,\n",
        "        min_len=400, max_len=700, run_name=\"run0\", threads=8,\n",
        "        scheme=\"V3\"):\n",
        "    !mkdir -p $artic_output/$barcode\n",
        "    prefix = os.path.join(artic_output, barcode, run_name)\n",
        "    log_file = \"{}_artic.log\".format(prefix)\n",
        "    read_file = \"{}_{}.fastq\".format(prefix, barcode)\n",
        "    print(\"Writing log file to: {}.\".format(log_file))\n",
        "    # read filtering\n",
        "    !cecho ok \"Running artic guppyplex to filter reads\"\n",
        "    # note, artic commands seem to exit 0 even if they fail,\n",
        "    # so our && || below don't function as intended\n",
        "    !run artic guppyplex --skip-quality-check \\\n",
        "        --min-length $min_len --max-length $max_len \\\n",
        "        --directory $directory --prefix $prefix >>$log_file 2>&1 \\\n",
        "        && echo \" - artic guppyplex finished\"\n",
        "    #    && cecho success \"artic guppyplex run successfully\" \\\n",
        "    #    || cecho error \"artic guppyplex failed\"\n",
        "    # run everything else\n",
        "    !cecho ok \"Running artic minion --medaka to call variants\"\n",
        "    !run artic minion --medaka --normalise 200 --threads $threads \\\n",
        "        --scheme-directory $working_dir/artic-ncov2019/primer_schemes \\\n",
        "        --read-file $read_file $scheme \\\n",
        "        $prefix >>$log_file 2>&1 \\\n",
        "        && echo \" - artic minion finished\"\n",
        "    #    && cecho success \"artic minion run successfully\" \\\n",
        "    #    || cecho error \"artic minion failed\"\n",
        "    !cecho ok \"Running alignment QC\"\n",
        "    for i in (1,2):\n",
        "        bam = \"{}.primertrimmed.nCoV-2019_{}.sorted.bam\".format(prefix, i)\n",
        "        if os.path.isfile(bam):\n",
        "            !stats_from_bam $bam > $bam\".stats\"\n",
        "            !coverage_from_bam -s 50 -p $bam $bam\n",
        "        else:\n",
        "            !echo error \" - No Artic output bam file found for primer set $i, Artic failed.\"\n",
        "    return prefix\n",
        "\n",
        "# setup artic output\n",
        "artic_output = os.path.join(output_folder, \"artic\")\n",
        "\n",
        "# run analysis for each barcode\n",
        "valid_barcodes = [\n",
        "    x for x in barcode_counts.loc[barcode_counts['sufficient data']]['barcode']\n",
        "    if x != \"unclassified\"]\n",
        "print(notebook.warning(\"Running Artic for:\"))\n",
        "for barcode in valid_barcodes:\n",
        "    print(\"   {}\".format(barcode))\n",
        "print()\n",
        "for barcode in valid_barcodes:\n",
        "    msg = \"Running ARTIC analysis for: {}\".format(barcode)\n",
        "    print(notebook.warning(msg))\n",
        "    print(notebook.warning(\"-\" * len(msg)))\n",
        "    print()\n",
        "    artic_input = os.path.join(output_folder, \"demultiplex\", barcode)\n",
        "    !rm -rf $artic_output/$barcode\n",
        "    out_prefix = run_artic(\n",
        "        barcode, artic_input, artic_output,\n",
        "        min_len=min_read_length, max_len=max_read_length, run_name=run_name, \n",
        "        threads=threads, scheme=primer_set)\n",
        "    print(\"Results for {} can be found at {}\".format(barcode, out_prefix))\n",
        "    msg = \"ARTIC finished for: {}\".format(barcode)\n",
        "    print(msg)\n",
        "    print(\"=\" * len(msg))\n",
        "    print()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[35m\u001b[1mRunning Artic for:\u001b[0m\n",
            "   barcode01\n",
            "   barcode02\n",
            "   barcode03\n",
            "   barcode05\n",
            "\n",
            "\u001b[35m\u001b[1mRunning ARTIC analysis for: barcode01\u001b[0m\n",
            "\u001b[35m\u001b[1m-------------------------------------\u001b[0m\n",
            "\n",
            "Writing log file to: /epi2melabs/ncov_tutorial/analysis/artic/barcode01/run0_artic.log.\n",
            "\u001b[34m\u001b[1mRunning artic guppyplex to filter reads\u001b[0m\n",
            " - artic guppyplex finished\n",
            "\u001b[34m\u001b[1mRunning artic minion --medaka to call variants\u001b[0m\n",
            " - artic minion finished\n",
            "\u001b[34m\u001b[1mRunning alignment QC\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 14312/0/0/0\n",
            "[14:32:20 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 13711/0/0/0\n",
            "[14:32:22 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Results for barcode01 can be found at /epi2melabs/ncov_tutorial/analysis/artic/barcode01/run0\n",
            "ARTIC finished for: barcode01\n",
            "=============================\n",
            "\n",
            "\u001b[35m\u001b[1mRunning ARTIC analysis for: barcode02\u001b[0m\n",
            "\u001b[35m\u001b[1m-------------------------------------\u001b[0m\n",
            "\n",
            "Writing log file to: /epi2melabs/ncov_tutorial/analysis/artic/barcode02/run0_artic.log.\n",
            "\u001b[34m\u001b[1mRunning artic guppyplex to filter reads\u001b[0m\n",
            " - artic guppyplex finished\n",
            "\u001b[34m\u001b[1mRunning artic minion --medaka to call variants\u001b[0m\n",
            " - artic minion finished\n",
            "\u001b[34m\u001b[1mRunning alignment QC\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 14433/0/0/0\n",
            "[14:34:58 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 15756/0/0/0\n",
            "[14:35:00 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Results for barcode02 can be found at /epi2melabs/ncov_tutorial/analysis/artic/barcode02/run0\n",
            "ARTIC finished for: barcode02\n",
            "=============================\n",
            "\n",
            "\u001b[35m\u001b[1mRunning ARTIC analysis for: barcode03\u001b[0m\n",
            "\u001b[35m\u001b[1m-------------------------------------\u001b[0m\n",
            "\n",
            "Writing log file to: /epi2melabs/ncov_tutorial/analysis/artic/barcode03/run0_artic.log.\n",
            "\u001b[34m\u001b[1mRunning artic guppyplex to filter reads\u001b[0m\n",
            " - artic guppyplex finished\n",
            "\u001b[34m\u001b[1mRunning artic minion --medaka to call variants\u001b[0m\n",
            " - artic minion finished\n",
            "\u001b[34m\u001b[1mRunning alignment QC\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 14229/0/0/0\n",
            "[14:37:15 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 14271/0/0/0\n",
            "[14:37:17 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Results for barcode03 can be found at /epi2melabs/ncov_tutorial/analysis/artic/barcode03/run0\n",
            "ARTIC finished for: barcode03\n",
            "=============================\n",
            "\n",
            "\u001b[35m\u001b[1mRunning ARTIC analysis for: barcode05\u001b[0m\n",
            "\u001b[35m\u001b[1m-------------------------------------\u001b[0m\n",
            "\n",
            "Writing log file to: /epi2melabs/ncov_tutorial/analysis/artic/barcode05/run0_artic.log.\n",
            "\u001b[34m\u001b[1mRunning artic guppyplex to filter reads\u001b[0m\n",
            " - artic guppyplex finished\n",
            "\u001b[34m\u001b[1mRunning artic minion --medaka to call variants\u001b[0m\n",
            " - artic minion finished\n",
            "\u001b[34m\u001b[1mRunning alignment QC\u001b[0m\n",
            "Traceback (most recent call last):\n",
            "  File \"/opt/conda/bin/stats_from_bam\", line 10, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/opt/conda/lib/python3.6/site-packages/pomoxis/stats_from_bam.py\", line 243, in main\n",
            "    raise ValueError('No alignments processed. Check your bam and filtering options.')\n",
            "ValueError: No alignments processed. Check your bam and filtering options.\n",
            "[14:37:56 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Mapped/Unmapped/Short/Masked: 2/0/0/0\n",
            "[14:37:58 - root] Processing region MN908947.3:0-29900\n",
            "\u001b[32m\u001b[1m - Alignment summary complete for primer set .\u001b[0m\n",
            "Results for barcode05 can be found at /epi2melabs/ncov_tutorial/analysis/artic/barcode05/run0\n",
            "ARTIC finished for: barcode05\n",
            "=============================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9OTj_WT8vHU",
        "colab_type": "text"
      },
      "source": [
        "The ARTIC worflow produces the following files for each barcode (*\\<run_name>* is the value given at the top of this page):\n",
        "\n",
        "1. `<run_name>.rg.primertrimmed.bam` - BAM file for visualisation after primer-binding site trimming\n",
        "2. `<run_name>.trimmed.bam` - BAM file with the primers left on (used in variant calling)\n",
        "3. `<run_name>.merged.vcf` - all detected variants in VCF format\n",
        "4. `<run_name>.pass.vcf.gz` - detected variants in VCF format passing quality filter\n",
        "5. `<run_name>.fail.vcf` - detected variants in VCF format failing quality filter\n",
        "6. `<run_name>.primers.vcf` - detected variants falling in primer-binding regions\n",
        "7. `<run_name>.consensus.fasta` - consensus sequence\n",
        "\n",
        "These will be present in folders named as:\n",
        "\n",
        "    <output_folder>/analysis/artic/<barcode>/\n",
        "\n",
        "where `<output_folder>` is the value given at the top of this page and `<barcode>` is the identified barcode for each dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmzimehnR3f7",
        "colab_type": "text"
      },
      "source": [
        "#### Brief summary of results\n",
        "\n",
        "Running the below will produce a simple tabular summary for each barcoded dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdcHd0j785Lk",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "a4694d27-33db-4f4b-df86-3e39e48ce680"
      },
      "source": [
        "#@markdown **ARTIC Summary Table** *(click play)*\n",
        "from collections import Counter, defaultdict\n",
        "import gzip\n",
        "from pysam import FastxFile\n",
        "from Bio import SeqIO\n",
        "\n",
        "!cecho ok \"The tables below assume you are using up to 24 barcodes.\"\n",
        "\n",
        "results = defaultdict(dict)\n",
        "for barcode in ('barcode{:02}'.format(x) for x in range(1,25)):\n",
        "    results[barcode][\"Ns\"] = 'NA'\n",
        "    results[barcode]['variants'] = 'NA'\n",
        "    if not barcode in valid_barcodes:\n",
        "        continue\n",
        "    consensus_file = os.path.join(\n",
        "        artic_output, barcode, \"{}.consensus.fasta\".format(run_name))\n",
        "    vcf_file = os.path.join(\n",
        "        artic_output, barcode, \"{}.pass.vcf.gz\".format(run_name))\n",
        "    recs = 0\n",
        "    try:\n",
        "        for record in SeqIO.parse(consensus_file, \"fasta\"):\n",
        "            recs += 1\n",
        "            results[barcode][\"Ns\"] = record.seq.count(\"N\")\n",
        "    except FileNotFoundError as e:\n",
        "        !cecho error \"ARTIC pipeline failed to produce consensus.fasta file for $barcode\"\n",
        "        continue\n",
        "    else:\n",
        "        if recs != 1:\n",
        "            !cecho error \"ARTIC pipeline produced one than one consensus contig for $barcode\"\n",
        "            continue\n",
        "    variants = 0\n",
        "    with gzip.open(vcf_file) as vcf:\n",
        "        for line in (x.decode() for x in vcf):\n",
        "            if line.startswith('#'):\n",
        "                continue\n",
        "            variants += 1\n",
        "    results[barcode]['variants'] = variants\n",
        "\n",
        "part1 = ['barcode{:02}'.format(x) for x in range(1,13)]\n",
        "part2 = ['barcode{:02}'.format(x) for x in range(13,25)]\n",
        "df = pd.DataFrame.from_dict(results)\n",
        "display(df[part1])\n",
        "display(df[part2])\n",
        "    "
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mThe tables below assume you are using up to 24 barcodes.\u001b[0m\n",
            "\u001b[31m\u001b[1mARTIC pipeline failed to produce consensus.fasta file for barcode05\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          barcode01  barcode02  barcode03 barcode04 barcode05 barcode06  \\\n",
              "Ns             4000       4062       4600        NA        NA        NA   \n",
              "variants          3          4          0        NA        NA        NA   \n",
              "\n",
              "         barcode07 barcode08 barcode09 barcode10 barcode11 barcode12  \n",
              "Ns              NA        NA        NA        NA        NA        NA  \n",
              "variants        NA        NA        NA        NA        NA        NA  "
            ],
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            "text/plain": [
              "         barcode13 barcode14 barcode15 barcode16 barcode17 barcode18  \\\n",
              "Ns              NA        NA        NA        NA        NA        NA   \n",
              "variants        NA        NA        NA        NA        NA        NA   \n",
              "\n",
              "         barcode19 barcode20 barcode21 barcode22 barcode23 barcode24  \n",
              "Ns              NA        NA        NA        NA        NA        NA  \n",
              "variants        NA        NA        NA        NA        NA        NA  "
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              "      <th>barcode17</th>\n",
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              "      <th>barcode20</th>\n",
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              "      <th>barcode22</th>\n",
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              "      <td>NA</td>\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3ndPBUEQCsc",
        "colab_type": "text"
      },
      "source": [
        "### QC Summary of ARTIC pipeline results\n",
        "\n",
        "The results of the ARTIC pipeline include alignment of the reads to a reference genome. A summary of these alignments is produced by the section below. Things to look for here include even coverage of amplicons and that the negative control sample shows little to no data. For the case of the sample data provided with this tutorial, barcode05 is a negative control sample."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrtiaNeLQWAU",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@markdown **Collate ARTIC alignment statistics** *(press play)*\n",
        "from aplanat import lines\n",
        "from aplanat import util\n",
        "\n",
        "pinger.send_notebook_ping('stop', tutorial_name)\n",
        "\n",
        "bam_name = \".primertrimmed.nCoV-2019_{}.sorted.bam\"\n",
        "cover_suffix = \"_{}_0_{}.depth.txt\".format(ref_name, ref_len)\n",
        "stats_suffix = \".stats\"\n",
        "\n",
        "dfs_cover = list()\n",
        "dfs_stats = list()\n",
        "for barcode in valid_barcodes:\n",
        "    stats_stem = os.path.join(artic_output, barcode, run_name)\n",
        "    for i in (1,2):\n",
        "        df = pd.read_csv(stats_stem + bam_name.format(i) + cover_suffix, sep='\\t')\n",
        "        df['primer_set'] = i\n",
        "        df['barcode'] = barcode\n",
        "        dfs_cover.append(df)\n",
        "        df = pd.read_csv(stats_stem + bam_name.format(i) + stats_suffix, sep='\\t')\n",
        "        df['primer_set'] = i\n",
        "        df['barcode'] = barcode\n",
        "        dfs_stats.append(df)\n",
        "cover_summary = pd.concat(dfs_cover)\n",
        "stats_summary = pd.concat(dfs_stats)\n",
        "dfs_cover, dfs_stats = None, None"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYif9Xsgv74M",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "6a55d377-b32c-4185-b8e8-7619cc930a0b"
      },
      "source": [
        "#@markdown **Show coverage histograms** *(press play)*\n",
        "plots = list()\n",
        "df = cover_summary\n",
        "depth_lim = 100\n",
        "for barcode in valid_barcodes:\n",
        "    pset = df['primer_set']\n",
        "    bc = df['barcode'] == barcode\n",
        "    xs = [df.loc[(pset == i) & bc]['pos'] for i in (1,2)]\n",
        "    ys = [df.loc[(pset == i) & bc]['depth'] for i in (1,2)]\n",
        "    \n",
        "    depth = df[bc].groupby('pos')['depth'].sum()\n",
        "    depth_thresh = 100*(depth >= depth_lim).sum() / len(depth)\n",
        "\n",
        "    plot = lines.line(\n",
        "        xs, ys, colors=['blue', 'red'],\n",
        "        title=\"{}: {:.0f}X, {:.1f}% > {}X\".format(\n",
        "            barcode, depth.mean(), depth_thresh, depth_lim),\n",
        "        height=200, width=400,\n",
        "        x_axis_label='position', y_axis_label='depth')\n",
        "    plots.append(plot)\n",
        "aplanat.show(gridplot(plots, ncols=3))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
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              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
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              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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              "\n",
              "    for (var i = 0; i < css_urls.length; i++) {\n",
              "      var url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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        },
        {
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          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"d97ca7eb-cf26-47dd-8241-50a0dac5e27d\" data-root-id=\"1448\"></div>\n"
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          },
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Application\",\"version\":\"1.4.0\"}};\n",
              "  var render_items = [{\"docid\":\"9609891c-f19d-4995-b83f-aa87ee77762b\",\"roots\":{\"1448\":\"d97ca7eb-cf26-47dd-8241-50a0dac5e27d\"}}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "tags": [],
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1448"
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ir7BrT3Iv9AC",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "98d83510-f385-49b2-8f91-d4d71ee31595"
      },
      "source": [
        "#@markdown **Show basecall accuracies** *(press play)*\n",
        "plots = list()\n",
        "df = stats_summary\n",
        "for barcode in valid_barcodes:\n",
        "    pset = df['primer_set']\n",
        "    bc = df['barcode'] == barcode\n",
        "    try:\n",
        "        xs, ys = list(), list()\n",
        "        for i in (1, 2):\n",
        "            d = df.loc[(pset == i) & bc]\n",
        "            if len(d) == 0:\n",
        "                !cecho warning \"Warning: No basecalls for $barcode and primer set $i\"\n",
        "                # aplanat can't currently plot empty data\n",
        "                x, y = [99.9, 100], [0, 0.0001]\n",
        "            else:\n",
        "                x, y = util.kernel_density_estimate(d['acc'])\n",
        "            xs.append(x)\n",
        "            ys.append(y)\n",
        "    except:\n",
        "        pass\n",
        "    plot = lines.line(\n",
        "        list(xs), list(ys), colors=['blue', 'red'],\n",
        "        title=barcode,\n",
        "        height=200, width=400,\n",
        "        x_axis_label='read accuracy', y_axis_label='density')\n",
        "    plots.append(plot)\n",
        "aplanat.show(gridplot(plots, ncols=3))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[35m\u001b[1mWarning: No basecalls for barcode05 and primer set 1\u001b[0m\r\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error() {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < css_urls.length; i++) {\n",
              "      var url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"2564dac4-9ef6-4c71-b6ed-e116f0225f8b\" data-root-id=\"7167\"></div>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
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              "  var render_items = [{\"docid\":\"3407c669-d8dc-4439-8997-ab94e9893e90\",\"roots\":{\"7167\":\"2564dac4-9ef6-4c71-b6ed-e116f0225f8b\"}}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "\n",
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              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
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              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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              "    }, 10, root)\n",
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            "application/vnd.bokehjs_exec.v0+json": ""
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6ffD2g-pQFq",
        "colab_type": "text"
      },
      "source": [
        "## Summary\n",
        "\n",
        "In this tutorial we have demonstrated use of the Artic pipeline for analysing SARC-CoV-2 samples. The notebook can be used with any .fastq dataset output by MinKNOW.\n",
        "\n",
        "The analysis presented can be run on any dataset from an Oxford Nanopore Technologies' device. The code will run within **Google Colab** when connected to a local **EPI2ME Labs** notebook server.\n",
        "\n",
        "[Back to EPI2ME Labs](https://colab.research.google.com/github/epi2me-labs/resources/blob/master/welcome.ipynb)"
      ]
    }
  ]
}