CoLab_TermProj_2024_2.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SmTogjeMD47"
      },
      "source": [
        "# 생물정보학 및 실습 1 - Term Project용 Mission 2 환경 셋업"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7NwoWdnMuHS"
      },
      "source": [
        "```\n",
        "생물정보학 및 실습 1\n",
        "서울대학교 생물정보학전공/생명과학부 2024년 1학기\n",
        "\n",
        "이 노트북은 Creative Commons BY-SA 조건으로 재사용 가능합니다.\n",
        "장혜식 <hyeshik@snu.ac.kr>, 서울대학교 생명과학부\n",
        "```\n",
        "\n",
        "이 노트북은 Google Colab을 이용해서 Term Project를 할 수 있도록 환경을 준비해 줍니다. 터미널 사용이 원활하지 않은 상태이고, 데이터를 오랫동안 보관하기 어렵기 때문에 되도록 개인용 컴퓨터에서 [Anaconda](https://www.anaconda.com/)나 [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) 등을 설치하고 사용하시는 것이 편하지만, 개인용 컴퓨터 사양이 부족한 경우에는 Google CoLab을 사용할 수도 있습니다. 개인용 컴퓨터를 쓸 때는 Windows에서는 [WSL2](https://docs.microsoft.com/en-us/windows/wsl/install-win10)를 쓰시면 리눅스 커맨드라인 환경을 쓸 수 있습니다. Google Colab은 창을 닫은 후 90분 후, 또는 창을 처음 열고 12시간 후에 환경을 닫아버리기 때문에 매번 실습할 때마다 새로 셋업이 필요합니다. `Runtime->Run All`을 실행해서 전체를 한 번 실행하시면 준비됩니다. 모든 것이 잘 안 되고 뭔가 꼬인 느낌이 강하게 들 때는 `Runtime->Factory reset runtime`을 누르고 새로 시작하시면 될 수도 있습니다.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4ofnSipMuHa"
      },
      "source": [
        "## Google Drive 마운트\n",
        "\n",
        "데이터 파일들을 불러오고 저장하기 위해서 Google Drive를 연결합니다. 화면에 나오는 주소를 눌러서 인증하고 인증 코드를 그 아래에 입력하세요. 그러면 `/contents/drive/MyDrive`에서 자신의 Drive 최상단 폴더가 보입니다. Google CoLab에서 접속하지 않고, 본인 컴퓨터 또는 연구실 워크스테이션 등 다른 컴퓨터를 쓰시는 경우에는 저장공간이 충분하므로 이 부분은 생략하고 지나갑니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kWbS8JWMuHa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7205b4e-8efa-450b-b689-838397741d9d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQZGShwEMuHa"
      },
      "source": [
        "## 실습용 Bioconda 환경 설치하기\n",
        "Google CoLab에서 접속하지 않고, 본인 컴퓨터 또는 연구실 워크스테이션 등 다른 컴퓨터를 쓰시는 경우에는 따로 실습용 [Bioconda](https://bioconda.github.io/) 환경을 구축해서 이 노트북을 그 환경 안에서 띄우시면 됩니다.\n",
        "\n",
        "CoLab을 쓰시는 분들은 다음에 따라서 실습용 프로그램들을 쉽게 설치할 수 있도록 [Bioconda](https://bioconda.github.io/) 환경을 만듭니다. 실행하고 진행상황을 관찰하다가 에러로 보이는 것이 있으면 잘 읽어보고 해결합니다.\n",
        "\n",
        "파이썬 모듈들은 Colab쪽의 파이썬 인터프리터를 바꿀 수 없기 때문에 Conda에 설치하더라도 사용하기 어렵습니다. 파이썬 모듈은 `pip` 명령어로 설치하시면 Colab에서 불러와서 사용할 수 있습니다.\n",
        "\n",
        "환경 초기화에 관련된 스크립트는 [GitHub 프로젝트](https://github.com/hyeshik/colab-biolab)에 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_i5ZwVHUMuHb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31e50635-a5ea-4a21-c60e-8500041fa96e"
      },
      "source": [
        "!git clone https://github.com/hyeshik/colab-biolab.git\n",
        "!cd colab-biolab && bash tools/setup.sh\n",
        "exec(open('colab-biolab/tools/activate_conda.py').read())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'colab-biolab'...\n",
            "remote: Enumerating objects: 76, done.\u001b[K\n",
            "remote: Counting objects: 100% (76/76), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 76 (delta 26), reused 59 (delta 15), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (76/76), 318.16 KiB | 7.76 MiB/s, done.\n",
            "Resolving deltas: 100% (26/26), done.\n",
            "./\n",
            "./root/\n",
            "./root/.profile\n",
            "./root/.vimrc\n",
            "./root/.bin.priority/\n",
            "./root/.bin.priority/pip\n",
            "./root/.bin.priority/pip3\n",
            "./root/.bin.priority/pip2\n",
            "./root/.condarc\n",
            "./root/.bashrc.biolab\n",
            "./root/.tmux.conf\n",
            "--2024-05-14 13:46:50--  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.191.158, 104.16.32.241, 2606:4700::6810:bf9e, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.191.158|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 143351488 (137M) [application/octet-stream]\n",
            "Saving to: ‘miniconda3.sh’\n",
            "\n",
            "miniconda3.sh       100%[===================>] 136.71M   141MB/s    in 1.0s    \n",
            "\n",
            "2024-05-14 13:46:51 (141 MB/s) - ‘miniconda3.sh’ saved [143351488/143351488]\n",
            "\n",
            "PREFIX=/root/conda\n",
            "Unpacking payload ...\n",
            "\n",
            "Installing base environment...\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /root/conda\n",
            "Activated conda environment `lab'!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Sabp7OAMuHb"
      },
      "source": [
        "설정이 끝났으면, 이제 콘솔을 열어서 터미널로 접속할 수 있는 통로를 열어줍니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6RiPiSRMuHb"
      },
      "source": [
        "## 시퀀싱 데이터 다운로드\n",
        "***첫 실습 시간에 데이터 복사를 마쳤으면 이 부분은 생략하고 지나갑니다.***\n",
        "\n",
        "이제 실습 수업에서 필요한 시퀀싱 데이터를 다운로드 받아서 설치합시다. 드라이브 공간만 충분하면 데이터는 계속 저장돼 있기 때문에 학기 전체를 통해서 딱 한 번만 하시면 됩니다. Colab에서는 작업용 데이터를 Google Drive 폴더 아래의 `binfo-datapack1`에 저장합니다. 실습용 데이터는 `/content/drive/MyDrive/binfo1-datapack1`에 풀리게 됩니다.\n",
        "\n",
        "### (Option 1) Google CoLab에서 접속한 경우\n",
        "1. [Google Drive 공유폴더](https://drive.google.com/drive/folders/1QBJednh-C7A17PFOlpZHBKyDmBvd6klh?usp=sharing)에 접속합니다.\n",
        "1. 중간 제목줄의 \"binfo1-datapack1 ▾\"에서 ▾을 누릅니다.\n",
        "1. Add shortcut to Drive를 누릅니다.\n",
        "1. My Drive를 선택해서 ADD SHORTCUT 버튼을 누릅니다.\n",
        "1. 다음 cell을 실행해서 링크가 제대로 들어왔는지 확인합니다. (바로 확인되지 않으면 3–5분 정도 기다렸다가 하면 됩니다.)\n",
        "1. 제대로 확인되면 다음으로 넘어갑니다.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -al /content/drive/MyDrive/binfo1-datapack1/"
      ],
      "metadata": {
        "id": "D24wKYriG8tI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d63a0127-65c7-4bc8-e41d-133874331c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 7162852\n",
            "-r-------- 1 root root 1370036258 Apr 27  2023 CLIP-35L33G.bam\n",
            "-r-------- 1 root root    3118336 Apr 27  2023 CLIP-35L33G.bam.bai\n",
            "-r-------- 1 root root       7113 May 11  2023 CLIP-let7g.bam\n",
            "-r-------- 1 root root      14561 May 11  2023 CLIP-let7g-gene.pileup\n",
            "-r-------- 1 root root    2685065 May 11  2023 CLIP-let7g.pileup\n",
            "-r-------- 1 root root  883334756 Apr 27  2023 gencode.gtf\n",
            "-r-------- 1 root root   24065406 Apr 27  2023 read-counts.txt\n",
            "-r-------- 1 root root        751 Apr 27  2023 read-counts.txt.summary\n",
            "-r-------- 1 root root 1003658801 Apr 27  2023 RNA-control.bam\n",
            "-r-------- 1 root root    2276104 Apr 27  2023 RNA-control.bam.bai\n",
            "-r-------- 1 root root 1260991122 Apr 27  2023 RNA-siLin28a.bam\n",
            "-r-------- 1 root root    2710744 Apr 27  2023 RNA-siLin28a.bam.bai\n",
            "-r-------- 1 root root  981684502 Apr 27  2023 RNA-siLuc.bam\n",
            "-r-------- 1 root root    2606104 Apr 27  2023 RNA-siLuc.bam.bai\n",
            "-r-------- 1 root root  737352902 Apr 27  2023 RPF-siLin28a.bam\n",
            "-r-------- 1 root root    2482768 Apr 27  2023 RPF-siLin28a.bam.bai\n",
            "-r-------- 1 root root 1055123479 Apr 27  2023 RPF-siLuc.bam\n",
            "-r-------- 1 root root    2605912 Apr 27  2023 RPF-siLuc.bam.bai\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### (Option 2) CoLab외의 다른 환경에서 접속한 경우\n",
        "본인 컴퓨터나 연구실 워크스테이션 등에서 작업하는 경우에는 다음 명령어로 다운로드 받으시면 됩니다. 첫 번째에는 wget이 접속오류를 내는 경우가 많으니, 접속에러가 발생하면 다시 한 번 실행해 보세요. `/content/drive/Mydrive` 부분은 원하는 저장 경로로 바꿔서 지정하시면 됩니다."
      ],
      "metadata": {
        "id": "OZ0KkKrmG-M4"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnLWxOljMuHb"
      },
      "source": [
        "!wget -O - --no-check-certificate https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar | tar -C /content/drive/MyDrive -xf -"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfQRBt3eMuHc"
      },
      "source": [
        "그리고, GENCODE Annotation 데이터도 미리 받아서 저장해 둡니다. 에러가 나면 다시 한 번 실행해 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3vbErniMuHc"
      },
      "source": [
        "!wget --no-check-certificate -O /content/drive/MyDrive/binfo1-datapack1/gencode.gtf.gz http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M27/gencode.vM27.annotation.gtf.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEsuYjeoMuHc"
      },
      "source": [
        "다루기 쉽도록 Annotation 압축을 풉니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47_i6FbYMuHc"
      },
      "source": [
        "!gunzip /content/drive/MyDrive/binfo1-datapack1/gencode.gtf.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRmsio_KMuHc"
      },
      "source": [
        "### 파일 체크섬 확인\n",
        "\n",
        "***첫 실습 시간에 데이터 복사를 마쳤으면 이 부분은 생략하고 지나갑니다.***\n",
        "\n",
        "받은 데이터의 MD5 체크섬을 확인합니다. 한참 걸립니다. 각 파일들의 원래 체크섬은 다음과 같습니다. 한 번 비교해 보세요.\n",
        "\n",
        "```\n",
        "140aaf30bcb9276cc716f8699f04ddd6  CLIP-35L33G.bam\n",
        "f1b3336ed7e2f97d562dcc71641251bd  CLIP-35L33G.bam.bai\n",
        "328883a73d507eafbf5b60bd6b906201  RNA-control.bam\n",
        "02073818e2f398a73c3b76e5169de1ca  RNA-control.bam.bai\n",
        "b09550d09d6c2a4ce27f0226f426fdb1  RNA-siLin28a.bam\n",
        "fef112c727244060ea62d3f2564a07f6  RNA-siLin28a.bam.bai\n",
        "28bbd0c47d725669340c784f1b772c01  RNA-siLuc.bam\n",
        "43590fdc4d81905c0432e0d1cb8cfd5b  RNA-siLuc.bam.bai\n",
        "5c08a9297307bc83259e658c4474f0cc  RPF-siLin28a.bam\n",
        "a1bb3e29be412dfd7fd8d16b1b1acc4c  RPF-siLin28a.bam.bai\n",
        "f2eebf50943024d0116c9cd3e744c707  RPF-siLuc.bam\n",
        "dc24f69e8f571fc8be30f28ce5b84fcd  RPF-siLuc.bam.bai\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8lC_YJRBCPf"
      },
      "source": [
        "!md5sum drive/MyDrive/binfo1-datapack1/*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVXzIs5FE3Q4"
      },
      "source": [
        "!ls -al drive/MyDrive/binfo*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 파일 복사하기\n",
        "**첫 시간에 파일을 복사해 두었으면 이 부분은 생략하고 다음으로 넘어갑니다.**\n",
        "\n",
        "공유한 폴더를 나의 작업 폴더로 복사합니다."
      ],
      "metadata": {
        "id": "mI3qWMY-KHdC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!mkdir -p /content/drive/MyDrive/binfo1-work\n",
        "%cd /content/drive/MyDrive/binfo1-work\n",
        "#!cp ../binfo1-datapack1/* ."
      ],
      "metadata": {
        "id": "vkK90WhtKWF7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7baf8d4-3d32-4be3-bc43-1a1ab4ae5719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/binfo1-work\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 프로그램 설치\n",
        "\n",
        "**첫 실습 시간에 데이터 복사를 마쳤으면 여기부터 시작합니다.**\n",
        "\n",
        "데이터 준비가 끝났으면, 이제 이번 시간 실습에 필요한 프로그램들 몇 가지를 추가로 설치합니다."
      ],
      "metadata": {
        "id": "WnmAcefjKi2G"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neteyWsWEKBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef6895ba-fe7a-41f6-9d3a-716a9952582c"
      },
      "source": [
        "!conda install -y bedtools bioawk samtools"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - conda-forge\n",
            " - bioconda\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /root/conda\n",
            "\n",
            "  added / updated specs:\n",
            "    - bedtools\n",
            "    - bioawk\n",
            "    - samtools\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n",
            "    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n",
            "    bedtools-2.31.1            |       hf5e1c6e_1         1.5 MB  bioconda\n",
            "    bioawk-1.0                 |      he4a0461_10         198 KB  bioconda\n",
            "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
            "    conda-24.5.0               |  py312h7900ff3_0         1.2 MB  conda-forge\n",
            "    frozendict-2.4.4           |  py312h9a8786e_0          30 KB  conda-forge\n",
            "    htslib-1.20                |       h81da01d_0         2.9 MB  bioconda\n",
            "    libcurl-8.7.1              |       h251f7ec_0         424 KB\n",
            "    libdeflate-1.20            |       hd590300_0          70 KB  conda-forge\n",
            "    libexpat-2.5.0             |       hcb278e6_1          76 KB  conda-forge\n",
            "    libgcc-ng-13.2.0           |       h77fa898_7         758 KB  conda-forge\n",
            "    libgomp-13.2.0             |       h77fa898_7         412 KB  conda-forge\n",
            "    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n",
            "    libsqlite-3.45.3           |       h2797004_0         840 KB  conda-forge\n",
            "    libstdcxx-ng-13.2.0        |       hc0a3c3a_7         3.7 MB  conda-forge\n",
            "    libuuid-2.38.1             |       h0b41bf4_0          33 KB  conda-forge\n",
            "    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge\n",
            "    libzlib-1.2.13             |       hd590300_5          60 KB  conda-forge\n",
            "    ncurses-6.5                |       h59595ed_0         867 KB  conda-forge\n",
            "    openssl-3.3.0              |       hd590300_0         2.8 MB  conda-forge\n",
            "    python-3.12.2              |hab00c5b_0_cpython        30.8 MB  conda-forge\n",
            "    python_abi-3.12            |          4_cp312           6 KB  conda-forge\n",
            "    samtools-1.20              |       h50ea8bc_0         459 KB  bioconda\n",
            "    tk-8.6.13                  |noxft_h4845f30_101         3.2 MB  conda-forge\n",
            "    zlib-1.2.13                |       hd590300_5          91 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        50.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  bedtools           bioconda/linux-64::bedtools-2.31.1-hf5e1c6e_1 \n",
            "  bioawk             bioconda/linux-64::bioawk-1.0-he4a0461_10 \n",
            "  frozendict         conda-forge/linux-64::frozendict-2.4.4-py312h9a8786e_0 \n",
            "  htslib             bioconda/linux-64::htslib-1.20-h81da01d_0 \n",
            "  libdeflate         conda-forge/linux-64::libdeflate-1.20-hd590300_0 \n",
            "  libexpat           conda-forge/linux-64::libexpat-2.5.0-hcb278e6_1 \n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n",
            "  libsqlite          conda-forge/linux-64::libsqlite-3.45.3-h2797004_0 \n",
            "  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.12-4_cp312 \n",
            "  samtools           bioconda/linux-64::samtools-1.20-h50ea8bc_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda              pkgs/main::conda-24.3.0-py312h06a4308~ --> conda-forge::conda-24.5.0-py312h7900ff3_0 \n",
            "  libcurl                                  8.5.0-h251f7ec_0 --> 8.7.1-h251f7ec_0 \n",
            "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-13.2.0-h77fa898_7 \n",
            "  libgomp              pkgs/main::libgomp-11.2.0-h1234567_1 --> conda-forge::libgomp-13.2.0-h77fa898_7 \n",
            "  libstdcxx-ng       pkgs/main::libstdcxx-ng-11.2.0-h12345~ --> conda-forge::libstdcxx-ng-13.2.0-hc0a3c3a_7 \n",
            "  libuuid              pkgs/main::libuuid-1.41.5-h5eee18b_0 --> conda-forge::libuuid-2.38.1-h0b41bf4_0 \n",
            "  ncurses                 pkgs/main::ncurses-6.4-h6a678d5_0 --> conda-forge::ncurses-6.5-h59595ed_0 \n",
            "  openssl              pkgs/main::openssl-3.0.13-h7f8727e_0 --> conda-forge::openssl-3.3.0-hd590300_0 \n",
            "  tk                        pkgs/main::tk-8.6.12-h1ccaba5_0 --> conda-forge::tk-8.6.13-noxft_h4845f30_101 \n",
            "  zlib                    pkgs/main::zlib-1.2.13-h5eee18b_0 --> conda-forge::zlib-1.2.13-hd590300_5 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  _libgcc_mutex           pkgs/main::_libgcc_mutex-0.1-main --> conda-forge::_libgcc_mutex-0.1-conda_forge \n",
            "  _openmp_mutex          pkgs/main::_openmp_mutex-5.1-1_gnu --> conda-forge::_openmp_mutex-4.5-2_gnu \n",
            "  certifi            pkgs/main/linux-64::certifi-2024.2.2-~ --> conda-forge/noarch::certifi-2024.2.2-pyhd8ed1ab_0 \n",
            "  python                pkgs/main::python-3.12.2-h996f2a0_0 --> conda-forge::python-3.12.2-hab00c5b_0_cpython \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "tk-8.6.13            | 3.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "htslib-1.20          | 2.9 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bedtools-2.31.1      | 1.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.5.0         | 1.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ncurses-6.5          | 867 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.45.3     | 840 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "samtools-1.20        | 459 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurl-8.7.1        | 424 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 412 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bioawk-1.0           | 198 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2024.2.2     | 157 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libxcrypt-4.4.36     | 98 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 91 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libexpat-2.5.0       | 76 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libdeflate-1.20      | 70 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "htslib-1.20          | 2.9 MB    | :   1% 0.005403421265351716/1 [00:00<01:12, 72.94s/it]\u001b[A\u001b[A\u001b[A\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0.0005070463002532972/1 [00:00<13:16, 796.91s/it]\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | :   1% 0.005659047239435657/1 [00:00<01:11, 72.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "tk-8.6.13            | 3.2 MB    | :   0% 0.00493661255696584/1 [00:00<01:31, 91.97s/it]\u001b[A\u001b[A\n",
            "python-3.12.2        | 30.8 MB   | :  21% 0.21295944610638484/1 [00:00<00:01,  2.05s/it]\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | :  56% 0.5635369481335716/1 [00:00<00:00,  1.34it/s] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bedtools-2.31.1      | 1.5 MB    | :   1% 0.010232472737242666/1 [00:00<01:04, 65.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  31% 0.3148757524572976/1 [00:00<00:01,  1.57s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  44% 0.4416373275206219/1 [00:00<00:00,  1.32s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | :   2% 0.021118681732288744/1 [00:00<00:40, 40.98s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.45.3     | 840 KB    | :   2% 0.0190543089672946/1 [00:00<00:45, 46.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  55% 0.5542016061768539/1 [00:00<00:00,  1.16s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bioawk-1.0           | 198 KB    | :   8% 0.08074674972647433/1 [00:00<00:11, 12.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 412 KB    | :   4% 0.03879375662979239/1 [00:01<00:24, 25.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  67% 0.6657517922325793/1 [00:01<00:00,  1.08s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libxcrypt-4.4.36     | 98 KB     | :  16% 0.163198629386511/1 [00:01<00:05,  6.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libexpat-2.5.0       | 76 KB     | :  21% 0.2101051551679918/1 [00:01<00:04,  5.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 91 KB     | :  18% 0.1765041745219499/1 [00:01<00:05,  6.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libdeflate-1.20      | 70 KB     | :  23% 0.22914685314685315/1 [00:01<00:03,  4.84s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  77% 0.7691892374842519/1 [00:01<00:00,  1.18s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  92% 0.9152185719572015/1 [00:01<00:00,  1.00it/s]\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | : 100% 1.0/1 [00:01<00:00,  1.30s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | : 100% 1.0/1 [00:01<00:00,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "tk-8.6.13            | 3.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.14s/it]                \u001b[A\u001b[A\n",
            "\n",
            "tk-8.6.13            | 3.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.14s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.5.0         | 1.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.65s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.5.0         | 1.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "htslib-1.20          | 2.9 MB    | : 100% 1.0/1 [00:03<00:00,  2.92s/it]                 \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "htslib-1.20          | 2.9 MB    | : 100% 1.0/1 [00:03<00:00,  2.92s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bedtools-2.31.1      | 1.5 MB    | : 100% 1.0/1 [00:03<00:00,  3.35s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bedtools-2.31.1      | 1.5 MB    | : 100% 1.0/1 [00:03<00:00,  3.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | : 100% 1.0/1 [00:03<00:00,  4.41s/it]               \u001b[A\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | : 100% 1.0/1 [00:03<00:00,  4.41s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "samtools-1.20        | 459 KB    | : 100% 1.0/1 [00:04<00:00,  3.86s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "samtools-1.20        | 459 KB    | : 100% 1.0/1 [00:04<00:00,  3.86s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | : 100% 1.0/1 [00:04<00:00,  3.98s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | : 100% 1.0/1 [00:04<00:00,  3.98s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.45.3     | 840 KB    | : 100% 1.0/1 [00:04<00:00,  4.03s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.45.3     | 840 KB    | : 100% 1.0/1 [00:04<00:00,  4.03s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "bioawk-1.0           | 198 KB    | : 100% 1.0/1 [00:04<00:00,  4.15s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "bioawk-1.0           | 198 KB    | : 100% 1.0/1 [00:04<00:00,  4.15s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:04<00:00,  4.21s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:04<00:00,  4.21s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libgomp-13.2.0       | 412 KB    | : 100% 1.0/1 [00:04<00:00,  4.20s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libgomp-13.2.0       | 412 KB    | : 100% 1.0/1 [00:04<00:00,  4.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libxcrypt-4.4.36     | 98 KB     | : 100% 1.0/1 [00:04<00:00,  4.41s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libxcrypt-4.4.36     | 98 KB     | : 100% 1.0/1 [00:04<00:00,  4.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libexpat-2.5.0       | 76 KB     | : 100% 1.0/1 [00:04<00:00,  4.50s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libexpat-2.5.0       | 76 KB     | : 100% 1.0/1 [00:04<00:00,  4.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "zlib-1.2.13          | 91 KB     | : 100% 1.0/1 [00:04<00:00,  4.48s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "zlib-1.2.13          | 91 KB     | : 100% 1.0/1 [00:04<00:00,  4.48s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libdeflate-1.20      | 70 KB     | : 100% 1.0/1 [00:04<00:00,  4.58s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "libdeflate-1.20      | 70 KB     | : 100% 1.0/1 [00:04<00:00,  4.58s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ncurses-6.5          | 867 KB    | : 100% 1.0/1 [00:04<00:00,  4.52s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "ncurses-6.5          | 867 KB    | : 100% 1.0/1 [00:04<00:00,  4.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libcurl-8.7.1        | 424 KB    | : 100% 1.0/1 [00:04<00:00,  4.44s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:06<00:00,  1.00it/s]               \n",
            "\n",
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            "                                                                        \n",
            "                                                                        \u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oe_54MZrEi4y"
      },
      "source": [
        "## Mission 2\n",
        "\n",
        "이번 미션에서는 start codon 주변에서 ribosome-protected fragment의 분포를 한 번 살펴보겠습니다. 보통은 RPF의 5'-end 분포가 start codon 앞 15nt 정도에서부터 본격적으로 많이 나오고, 거기서부터 CDS를 걸쳐서 3nt 주기로 올록볼록한 패턴이 나타납니다.\n",
        "\n",
        "이 그림을 제대로 그리려면 genome coordinate를 start codon을 기준으로 변환해서 작업해야 하지만, 이번 미션에서는 난이도를 적절히 내리기 위해서 start codon이 포함된 exon에 존재하는 5' end만 추려서 작업하도록 합시다. (여유가 좀 있어서 난이도를 적당히 올리고 싶은 분들은 negative strand를 포함할 수도 있고, start codon과 다른 exon에 있는 경우를 포함할 수도 있습니다. 또한 APPRIS 등을 기반으로 해서 transcript 중에 가장 )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IANz6DmLUkA6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e306f81-135c-47d0-a78c-bae5651226ef"
      },
      "source": [
        "%cd /content/drive/MyDrive/binfo1-work/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/binfo1-work\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68uh7WVkUjhr"
      },
      "source": [
        "우선 annotation에서 start codon이 포함된 exon만 추립니다. Gene 한 개에 isoform이 여러 개 있는 경우 모두 쓰면 read가 너무 부풀려질 수 있기 때문에 우선 transcript support level이 1인 것만 추려서 사용합니다. 여전히 TSL 1만 쓰더라도 부풀려질 가능성은 있기 때문에 제대로 하자면 APPRIS나 RNA-seq 데이터 기준으로 하나만 선정하는 것이 좋습니다만, 일단 여기서는 간단히..\n",
        "\n",
        "그리고, - strand가 들어가면 좀 계산이 복잡해지니까 또 간단하게 하기 위해서 + strand만 고릅니다. 이번엔 일단 무조건 간단하게 해 봅시다~"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvFgM8hJUzDx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6db209cc-7ae6-4fe4-9905-3f797c6c227b"
      },
      "source": [
        "!grep '\tstart_codon\t.*\t+\t.*transcript_support_level \"1\"' ../binfo1-datapack1/gencode.gtf | \\\n",
        " sed -e 's/\t[^\t]*transcript_id \"\\([^\"]*\\)\".*$/\t\\1/g' > gencode-start.gtf\n",
        "!head gencode-start.gtf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chr1\tHAVANA\tstart_codon\t4878137\t4878139\t.\t+\t0\tENSMUST00000134384.8\n",
            "chr1\tHAVANA\tstart_codon\t4878137\t4878139\t.\t+\t0\tENSMUST00000027036.11\n",
            "chr1\tHAVANA\tstart_codon\t4878137\t4878139\t.\t+\t0\tENSMUST00000150971.8\n",
            "chr1\tHAVANA\tstart_codon\t4928137\t4928139\t.\t+\t0\tENSMUST00000081551.14\n",
            "chr1\tHAVANA\tstart_codon\t5154674\t5154676\t.\t+\t0\tENSMUST00000044369.13\n",
            "chr1\tHAVANA\tstart_codon\t5659272\t5659274\t.\t+\t0\tENSMUST00000160777.8\n",
            "chr1\tHAVANA\tstart_codon\t5659272\t5659274\t.\t+\t0\tENSMUST00000027038.11\n",
            "chr1\tHAVANA\tstart_codon\t6300227\t6300229\t.\t+\t0\tENSMUST00000027040.13\n",
            "chr1\tHAVANA\tstart_codon\t6429555\t6429557\t.\t+\t0\tENSMUST00000133144.4\n",
            "chr1\tHAVANA\tstart_codon\t6839122\t6839124\t.\t+\t0\tENSMUST00000140079.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWvIeTYOWsR6"
      },
      "source": [
        "start codon가 들어가 있는 exon들을 고르기 위해서 미리 + strand에 있는 exon를 추립니다.. start codon 쪽에서 TSL 기준으로 골랐기 때문에 여기서는 고르지 않아도 됩니다. 물론 좀 느려지기는 하지만..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCqK2wyYWitn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1082458-8e60-4fcf-fade-43bd5c419b7c"
      },
      "source": [
        "# 아래 공백 중 일부는 \"탭(Tab)\" 문자입니다. 입력하기 힘들 때는 텍스트 편집기에서 입력해서 복사&붙여넣기 하면 됩니다.\n",
        "!grep '\texon\t.*\t+\t' ../binfo1-datapack1/gencode.gtf | \\\n",
        " sed -e 's/\t[^\t]*transcript_id \"\\([^\"]*\\)\".*$/\t\\1/g' > gencode-plusexon.gtf\n",
        "!head gencode-plusexon.gtf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chr1\tHAVANA\texon\t3143476\t3144545\t.\t+\t.\tENSMUST00000193812.2\n",
            "chr1\tENSEMBL\texon\t3172239\t3172348\t.\t+\t.\tENSMUST00000082908.3\n",
            "chr1\tHAVANA\texon\t3322980\t3323459\t.\t+\t.\tENSMUST00000192857.2\n",
            "chr1\tHAVANA\texon\t3536810\t3536910\t.\t+\t.\tENSMUST00000161581.2\n",
            "chr1\tHAVANA\texon\t3583628\t3583776\t.\t+\t.\tENSMUST00000161581.2\n",
            "chr1\tHAVANA\texon\t3602018\t3602943\t.\t+\t.\tENSMUST00000192183.2\n",
            "chr1\tHAVANA\texon\t3750378\t3752011\t.\t+\t.\tENSMUST00000193244.2\n",
            "chr1\tHAVANA\texon\t3822233\t3824583\t.\t+\t.\tENSMUST00000194454.2\n",
            "chr1\tHAVANA\texon\t4566774\t4569601\t.\t+\t.\tENSMUST00000193450.2\n",
            "chr1\tHAVANA\texon\t4567697\t4567877\t.\t+\t.\tENSMUST00000194935.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suULe5k6Yelf"
      },
      "source": [
        "그 다음엔 exon 리스트와 start codon 리스트를 겹치고, transcript가 같은 것만 살려둡니다. 기왕 좀 다루기 쉽도록 출력은 bed로 하면서 좌표계를 바꿉니다. 결과는 bedtools에서 빠르게 다룰 수 있게 sort해 둡니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zsv2_w5OXU9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cec80071-3ef9-4d68-ba15-800997fa10ca"
      },
      "source": [
        "# 아래 공백 중 일부는 \"탭(Tab)\" 문자입니다. 입력하기 힘들 때는 텍스트 편집기에서 입력해서 복사&붙여넣기 하면 됩니다.\n",
        "!bedtools intersect -a gencode-start.gtf -b gencode-plusexon.gtf -wa -wb | \\\n",
        " awk -F'\t' -v OFS='\t' '$9 == $18 { print $10, $13-1, $14, $18, $4-1, $16; }' | \\\n",
        " sort -k1,1 -k2,3n -k4,4 > gencode-exons-containing-startcodon.bed\n",
        "!head gencode-exons-containing-startcodon.bed; tail gencode-exons-containing-startcodon.bed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chr1\t4878010\t4878205\tENSMUST00000134384.8\t4878136\t+\n",
            "chr1\t4878045\t4878205\tENSMUST00000027036.11\t4878136\t+\n",
            "chr1\t4878052\t4878205\tENSMUST00000150971.8\t4878136\t+\n",
            "chr1\t4928036\t4928199\tENSMUST00000081551.14\t4928136\t+\n",
            "chr1\t5154639\t5154786\tENSMUST00000044369.13\t5154673\t+\n",
            "chr1\t5659227\t5659528\tENSMUST00000027038.11\t5659271\t+\n",
            "chr1\t5659257\t5659528\tENSMUST00000160777.8\t5659271\t+\n",
            "chr1\t6300182\t6300297\tENSMUST00000027040.13\t6300226\t+\n",
            "chr1\t6429441\t6429738\tENSMUST00000133144.4\t6429554\t+\n",
            "chr1\t6839121\t6839176\tENSMUST00000043578.13\t6839121\t+\n",
            "chrY\t80939672\t80939804\tENSMUST00000185340.2\t80939686\t+\n",
            "chrY\t81470683\t81470815\tENSMUST00000187135.2\t81470697\t+\n",
            "chrY\t82237903\t82238035\tENSMUST00000185636.2\t82237917\t+\n",
            "chrY\t83043623\t83043755\tENSMUST00000187165.2\t83043637\t+\n",
            "chrY\t84109956\t84110088\tENSMUST00000185776.7\t84109970\t+\n",
            "chrY\t84759361\t84759493\tENSMUST00000186110.2\t84759375\t+\n",
            "chrY\t86074448\t86074580\tENSMUST00000188754.2\t86074462\t+\n",
            "chrY\t87129500\t87129632\tENSMUST00000189543.7\t87129514\t+\n",
            "chrY\t87563647\t87563779\tENSMUST00000186493.2\t87563661\t+\n",
            "chrY\t88065992\t88066124\tENSMUST00000187146.2\t88066006\t+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvPNCpbtaYnC"
      },
      "source": [
        "컬럼 4번에 transcript id, 5번에 start codon 위치를 넣었는데, start codon이 같은 것들이 여러개 눈에 띕니다. 아주 찝찝하지만, 우선 이번은 간단하게 할 것이기 때문에 눈을 질끈 감고 넘어가 봅시다.\n",
        "\n",
        "이제 alignment를 준비합니다. + strand alignment에서 read length가 짧은 것도 미리 필터링 합니다. RPF는 보통 25nt 이상이기 때문에 그 이하는 거의 확실히 rRNA, tRNA 등에서 오염된 것들입니다. SAM 파일 전체를 필터링해서 다시 인코딩하기 때문에 시간이 좀 걸립니다.\n",
        "\n",
        "오래걸리는 작업을 돌릴 떄에는 CPU 사용량, 디스크 사용량을 계속 관찰하는 것이 좋습니다. 직접 접속할 수 있는 컴퓨터에서는 `htop` 등의 명령을 쓰면 되고, Colab에서는 오른쪽 위의 메뉴에서 Resources를 띄우면 되는데, 여기서는 CPU 사용량은 보기 힘듭니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZaOeHo1alCB"
      },
      "source": [
        "!(samtools view -H ../binfo1-datapack1/RPF-siLuc.bam; \\\n",
        "  samtools view -F20 ../binfo1-datapack1/RPF-siLuc.bam | \\\n",
        "  bioawk -c sam '{ if (length($seq) >= 25) print $0; }') | \\\n",
        " samtools view -b -o filtered-RPF-siLuc.bam"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1JDNb8ndTdk"
      },
      "source": [
        "큰 일을 했으니 용량을 한 번 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OrnDxegFAip",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8c5779d-e8b5-4c58-9d10-df6e02a1cfd4"
      },
      "source": [
        "!ls -al *RPF-siLuc.bam"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw------- 1 root root  554373659 May 14 13:54 filtered-RPF-siLuc.bam\n",
            "-rw------- 1 root root 1055123479 May  8 10:03 RPF-siLuc.bam\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pr9zpKFmdi2e"
      },
      "source": [
        "자 이제 read의 5' end 위치를 셉니다. bedtools의 `genomecov`를 쓰면 아주 편리합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZjHau7zftoU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d43de776-9d51-4a3d-96b0-2fb58607c9a8"
      },
      "source": [
        "!bedtools genomecov -ibam filtered-RPF-siLuc.bam -bg -5 > fivepcounts-RPF-siLuc.bed\n",
        "!head fivepcounts-RPF-siLuc.bed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chr1\t3176534\t3176535\t1\n",
            "chr1\t3196256\t3196257\t1\n",
            "chr1\t3221526\t3221527\t1\n",
            "chr1\t3221542\t3221543\t2\n",
            "chr1\t3221545\t3221546\t3\n",
            "chr1\t3221546\t3221547\t2\n",
            "chr1\t3221548\t3221550\t1\n",
            "chr1\t3221571\t3221572\t1\n",
            "chr1\t3221897\t3221898\t1\n",
            "chr1\t3221994\t3221995\t1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_AHnHnZmYJu"
      },
      "source": [
        "그렇게 match 된 것들 중에 앞에서 찾은 start codon을 포함한 exon과 겹치는 것만 골라냅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1cOiX2Hftlx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d822190-5c9c-4008-de26-b93b45de1309"
      },
      "source": [
        "!bedtools intersect -a fivepcounts-RPF-siLuc.bed -b gencode-exons-containing-startcodon.bed \\\n",
        " -wa -wb -nonamecheck > fivepcounts-filtered-RPF-siLuc.txt\n",
        "!head fivepcounts-filtered-RPF-siLuc.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chr1\t4878048\t4878049\t1\tchr1\t4878010\t4878205\tENSMUST00000134384.8\t4878136\t+\n",
            "chr1\t4878048\t4878049\t1\tchr1\t4878045\t4878205\tENSMUST00000027036.11\t4878136\t+\n",
            "chr1\t4878077\t4878078\t1\tchr1\t4878010\t4878205\tENSMUST00000134384.8\t4878136\t+\n",
            "chr1\t4878077\t4878078\t1\tchr1\t4878045\t4878205\tENSMUST00000027036.11\t4878136\t+\n",
            "chr1\t4878077\t4878078\t1\tchr1\t4878052\t4878205\tENSMUST00000150971.8\t4878136\t+\n",
            "chr1\t4878101\t4878102\t4\tchr1\t4878010\t4878205\tENSMUST00000134384.8\t4878136\t+\n",
            "chr1\t4878101\t4878102\t4\tchr1\t4878045\t4878205\tENSMUST00000027036.11\t4878136\t+\n",
            "chr1\t4878101\t4878102\t4\tchr1\t4878052\t4878205\tENSMUST00000150971.8\t4878136\t+\n",
            "chr1\t4878103\t4878104\t1\tchr1\t4878010\t4878205\tENSMUST00000134384.8\t4878136\t+\n",
            "chr1\t4878103\t4878104\t1\tchr1\t4878045\t4878205\tENSMUST00000027036.11\t4878136\t+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OcjYZ9bnL_6"
      },
      "source": [
        "모든 준비가 끝났습니다. 이제 Figure S5A 도전해 보세요!\n",
        "\n",
        "* stop codon은 빼고 start codon 만입니다.\n",
        "* 논문에서는 처리가 어려운 부분까지 모두 포함됐고, read를 중첩해서 세지 않았기 때문에, 여기서 세는 것과는 수치는 다를 수 있습니다.\n",
        "* `pandas`로 한꺼번에 데이터를 모두 불러들이면 메모리가 부족할 수도 있습니다. 그런 경우에는 chunk를 쓰거나, 줄 단위로 처리해 보세요."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPPW73t4X3pp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd02fdff-459b-4a5f-b176-51034fd7979e"
      },
      "source": [
        "# !(samtools view -H ../binfo1-datapack1/RPF-siLin28a.bam; \\\n",
        "#   samtools view -F20 ../binfo1-datapack1/RPF-siLin28a.bam | \\\n",
        "#   bioawk -c sam '{ if (length($seq) >= 25) print $0; }') | \\\n",
        "#  samtools view -b -o filtered-RPF-siLin28a.bam\n",
        "# !bedtools genomecov -ibam filtered-RPF-siLin28a.bam -bg -5 > fivepcounts-RPF-siLin28a.bed\n",
        "# !head fivepcounts-RPF-siLin28a.bed\n",
        "# !bedtools intersect -a fivepcounts-RPF-siLin28a.bed -b gencode-exons-containing-startcodon.bed \\\n",
        "#  -wa -wb -nonamecheck > fivepcounts-filtered-RPF-siLin28a.txt\n",
        "!head fivepcounts-filtered-RPF-siLin28a.txt"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "head: cannot open 'fivepcounts-filtered-RPF-siLin28a.txt' for reading: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker"
      ],
      "metadata": {
        "id": "0FTap4R0oboh"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def data_generation(data_path):\n",
        "  data = pd.read_table(data_path, names = [\"chra\", \"5'start\", \"3'end\", \"occurence\", \"chrb\", \"exonstart\", \"exonend\", \"geneid\", \"start\", \"strand\"])\n",
        "  data.head()\n",
        "  relativeposition_occurence = {}\n",
        "  for index, row in siLuc_data.iterrows():\n",
        "      diff = row[\"5'start\"] - row[\"start\"]\n",
        "      if diff in relativeposition_occurence:\n",
        "          relativeposition_occurence[diff] += row[\"occurence\"]\n",
        "      else:\n",
        "          relativeposition_occurence[diff] = row[\"occurence\"]\n",
        "\n",
        "  plotdf = pd.DataFrame.from_dict(relativeposition_occurence, orient='index', columns=['occurence'])\n",
        "  plotdf.index.rename('position', inplace=True)\n",
        "  plotdf.head()\n",
        "  return plotdf\n"
      ],
      "metadata": {
        "id": "Q3Lj-tu_iJWY"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot(name, plotdf):\n",
        "  fig, ax = plt.subplots()\n",
        "\n",
        "  ax.bar(plotdf.index, plotdf[\"occurence\"] / 1000, color='black')\n",
        "\n",
        "  ax.axvline(0, color='red', linestyle='-')\n",
        "\n",
        "  ax.set_xlabel(\"Position\")\n",
        "  ax.set_ylabel(f\"{name} \\n Raw read count \\n (x1000)\")\n",
        "  ax.set_xlim([-50, 50])\n",
        "  ax.set_ylim([0, 120])\n",
        "\n",
        "  ax.set_xticks(range(-50, 51, 10))\n",
        "  ax.set_yticks(range(0, 121, 40))\n",
        "\n",
        "  ax.yaxis.grid(True)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "7WJaOsbljyLF"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "siLin28a=data_generation(\"fivepcounts-filtered-RPF-siLin28a.txt\")\n",
        "plot(\"siLin28a\", siLin28a)\n",
        "siLuc= data_generation(\"fivepcounts-filtered-RPF-siLuc.txt\")\n",
        "plot(\"siLuc\", siLin28a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "id": "4QQHisxvmNYc",
        "outputId": "279e2c2e-4eaa-4437-ad77-70c74336e4dd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J_fAmW85qK0r"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}