CoLab_TermProj_2024_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SmTogjeMD47"
      },
      "source": [
        "# 생물정보학 및 실습 1 - Term Project용 환경 셋업\n",
        "\n",
        "```\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": "zGYjheLcRJS0"
      },
      "source": [
        "## Google Drive 마운트\n",
        "\n",
        "데이터 파일들을 불러오고 저장하기 위해서 Google Drive를 연결합니다. **이번엔 스크립트 파일을 편집해야 하기 때문에 Google Drive 마운트를 하지 않으면 vim등 콘솔에서 쓸 수 있는 에디터를 쓰셔야 합니다.** 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": "0HgOxANiRHgl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e32f22f-c26b-47d2-ba13-22aa5141800e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ra3RB22DWEER"
      },
      "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": "TTXmNi4hcap3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3797d91-b490-47bc-b0df-5ce189177ce1"
      },
      "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": 3,
      "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 | 3.70 MiB/s, done.\n",
            "Resolving deltas: 100% (26/26), done.\n",
            "./\n",
            "./root/\n",
            "./root/.profile\n",
            "./root/.condarc\n",
            "./root/.tmux.conf\n",
            "./root/.vimrc\n",
            "./root/.bin.priority/\n",
            "./root/.bin.priority/pip2\n",
            "./root/.bin.priority/pip3\n",
            "./root/.bin.priority/pip\n",
            "./root/.bashrc.biolab\n",
            "--2024-05-09 04:30:53--  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:bf9e, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|: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   198MB/s    in 0.7s    \n",
            "\n",
            "2024-05-09 04:30:54 (198 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": "eDfJ0fCzsTTb"
      },
      "source": [
        "Google CoLab에서 shell command를 실행할 때는 명령 앞에 !을 붙이면 실행할 수 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J2LqGiP_N26s"
      },
      "source": [
        "## 시퀀싱 데이터 다운로드\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": "3855c5ca-8bd5-4e85-89d1-a236fafbc00c"
      },
      "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": "kIN484e7NSxR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a811beee-e7b1-4832-d440-bef01491986d"
      },
      "source": [
        "!wget -O - --no-check-certificate https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar | tar -C /content/drive/MyDrive -xf -"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-07 06:26:04--  https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar\n",
            "Resolving hyeshik.qbio.io (hyeshik.qbio.io)... 147.47.203.129\n",
            "Connecting to hyeshik.qbio.io (hyeshik.qbio.io)|147.47.203.129|:443... connected.\n",
            "WARNING: cannot verify hyeshik.qbio.io's certificate, issued by ‘CN=R3,O=Let's Encrypt,C=US’:\n",
            "  Unable to locally verify the issuer's authority.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6424668160 (6.0G) [application/octet-stream]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "-                    16%[==>                 ]   1.01G  4.62MB/s    in 3m 3s   \n",
            "\n",
            "2024-05-07 06:29:08 (5.62 MB/s) - Connection closed at byte 1080049365. Retrying.\n",
            "\n",
            "--2024-05-07 06:29:09--  (try: 2)  https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar\n",
            "Connecting to hyeshik.qbio.io (hyeshik.qbio.io)|147.47.203.129|:443... connected.\n",
            "WARNING: cannot verify hyeshik.qbio.io's certificate, issued by ‘CN=R3,O=Let's Encrypt,C=US’:\n",
            "  Unable to locally verify the issuer's authority.\n",
            "HTTP request sent, awaiting response... 206 Partial Content\n",
            "Length: 6424668160 (6.0G), 5344618795 (5.0G) remaining [application/octet-stream]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "-                    33%[+++==>              ]   2.01G  3.78MB/s    in 3m 24s  \n",
            "\n",
            "2024-05-07 06:32:34 (5.05 MB/s) - Connection closed at byte 2162097532. Retrying.\n",
            "\n",
            "--2024-05-07 06:32:36--  (try: 3)  https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar\n",
            "Connecting to hyeshik.qbio.io (hyeshik.qbio.io)|147.47.203.129|:443... connected.\n",
            "WARNING: cannot verify hyeshik.qbio.io's certificate, issued by ‘CN=R3,O=Let's Encrypt,C=US’:\n",
            "  Unable to locally verify the issuer's authority.\n",
            "HTTP request sent, awaiting response... 206 Partial Content\n",
            "Length: 6424668160 (6.0G), 4262570628 (4.0G) remaining [application/octet-stream]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "-                    50%[++++++===>          ]   3.02G  4.89MB/s    in 3m 25s  \n",
            "\n",
            "2024-05-07 06:36:03 (5.03 MB/s) - Connection closed at byte 3244112931. Retrying.\n",
            "\n",
            "--2024-05-07 06:36:06--  (try: 4)  https://hyeshik.qbio.io/binfo/binfo1-datapack1.tar\n",
            "Connecting to hyeshik.qbio.io (hyeshik.qbio.io)|147.47.203.129|:443... connected.\n",
            "Unable to establish SSL connection.\n",
            "tar: Unexpected EOF in archive\n",
            "tar: Unexpected EOF in archive\n",
            "tar: Error is not recoverable: exiting now\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiQ5y4dTD27M"
      },
      "source": [
        "그리고, GENCODE Annotation 데이터도 미리 받아서 저장해 둡니다. 에러가 나면 다시 한 번 실행해 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Efdd3x8hDMh4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3effc155-0a1e-433b-ccdd-579eaa99b804"
      },
      "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": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-07 06:36:06--  http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M27/gencode.vM27.annotation.gtf.gz\n",
            "Resolving ftp.ebi.ac.uk (ftp.ebi.ac.uk)... 193.62.193.165\n",
            "Connecting to ftp.ebi.ac.uk (ftp.ebi.ac.uk)|193.62.193.165|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 28359457 (27M) [application/x-gzip]\n",
            "Saving to: ‘/content/drive/MyDrive/binfo1-datapack1/gencode.gtf.gz’\n",
            "\n",
            "/content/drive/MyDr 100%[===================>]  27.04M   714KB/s    in 40s     \n",
            "\n",
            "2024-05-07 06:36:46 (699 KB/s) - ‘/content/drive/MyDrive/binfo1-datapack1/gencode.gtf.gz’ saved [28359457/28359457]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnlM-S70C-tY"
      },
      "source": [
        "### 파일 체크섬 확인\n",
        "\n",
        "받은 데이터의 MD5 체크섬을 확인합니다. 한참 걸립니다. 각 파일들의 원래 체크섬은 다음과 같습니다. 한 번 비교해 보세요. (나중에 자동으로 비교하고 싶으시면 `md5sum -c` 옵션을 사용하면 됩니다. 여기서는 MD5를 처음 써 보는 의미에서 직접 비교해 봅시다.)\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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10db5d9f-0ca1-4242-a29a-21302ea03b21"
      },
      "source": [
        "!md5sum drive/MyDrive/binfo1-datapack1/*"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140aaf30bcb9276cc716f8699f04ddd6  drive/MyDrive/binfo1-datapack1/CLIP-35L33G.bam\n",
            "f1b3336ed7e2f97d562dcc71641251bd  drive/MyDrive/binfo1-datapack1/CLIP-35L33G.bam.bai\n",
            "5d0265965487c33475f48869fc9df2d2  drive/MyDrive/binfo1-datapack1/CLIP-let7g.bam\n",
            "4ea14471c5d4bf9ab1fd91b9a5b2193c  drive/MyDrive/binfo1-datapack1/CLIP-let7g-gene.pileup\n",
            "a53f66a5575d4ac2d3b4c44af576d44d  drive/MyDrive/binfo1-datapack1/CLIP-let7g.pileup\n",
            "d54d428f5872030c4ffc43058709b896  drive/MyDrive/binfo1-datapack1/gencode.gtf\n",
            "2698e6c8e30005ebc0593d3b9e9b5c58  drive/MyDrive/binfo1-datapack1/read-counts.txt\n",
            "7c9528c08ed2f1ae6e2de599db1aa8fb  drive/MyDrive/binfo1-datapack1/read-counts.txt.summary\n",
            "328883a73d507eafbf5b60bd6b906201  drive/MyDrive/binfo1-datapack1/RNA-control.bam\n",
            "02073818e2f398a73c3b76e5169de1ca  drive/MyDrive/binfo1-datapack1/RNA-control.bam.bai\n",
            "b09550d09d6c2a4ce27f0226f426fdb1  drive/MyDrive/binfo1-datapack1/RNA-siLin28a.bam\n",
            "fef112c727244060ea62d3f2564a07f6  drive/MyDrive/binfo1-datapack1/RNA-siLin28a.bam.bai\n",
            "28bbd0c47d725669340c784f1b772c01  drive/MyDrive/binfo1-datapack1/RNA-siLuc.bam\n",
            "43590fdc4d81905c0432e0d1cb8cfd5b  drive/MyDrive/binfo1-datapack1/RNA-siLuc.bam.bai\n",
            "5c08a9297307bc83259e658c4474f0cc  drive/MyDrive/binfo1-datapack1/RPF-siLin28a.bam\n",
            "a1bb3e29be412dfd7fd8d16b1b1acc4c  drive/MyDrive/binfo1-datapack1/RPF-siLin28a.bam.bai\n",
            "f2eebf50943024d0116c9cd3e744c707  drive/MyDrive/binfo1-datapack1/RPF-siLuc.bam\n",
            "dc24f69e8f571fc8be30f28ce5b84fcd  drive/MyDrive/binfo1-datapack1/RPF-siLuc.bam.bai\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVXzIs5FE3Q4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "068bd225-8149-4e18-c112-f7d30520f13d"
      },
      "source": [
        "!ls -al drive/MyDrive/binfo*"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lrw------- 1 root root 0 May  8 08:45 drive/MyDrive/binfo1-datapack1 -> /content/drive/.shortcut-targets-by-id/1QBJednh-C7A17PFOlpZHBKyDmBvd6klh/binfo1-datapack1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecEA1PPQUlIK"
      },
      "source": [
        "실습에 필요한 프로그램들 몇 가지를 추가로 설치합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neteyWsWEKBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50024b01-4cc2-4dd4-df38-19eb80483781"
      },
      "source": [
        "!conda install -y subread"
      ],
      "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\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",
            "    - subread\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",
            "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
            "    conda-24.4.0               |  py312h7900ff3_0         1.1 MB  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",
            "    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",
            "    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",
            "    subread-2.0.6              |       he4a0461_2        26.1 MB  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:        66.6 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\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",
            "  subread            bioconda/linux-64::subread-2.0.6-he4a0461_2 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda              pkgs/main::conda-24.3.0-py312h06a4308~ --> conda-forge::conda-24.4.0-py312h7900ff3_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",
            "  libuuid              pkgs/main::libuuid-1.41.5-h5eee18b_0 --> conda-forge::libuuid-2.38.1-h0b41bf4_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",
            "subread-2.0.6        | 26.1 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",
            "openssl-3.3.0        | 2.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "conda-24.4.0         | 1.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\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\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\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\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\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\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\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\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 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",
            "libuuid-2.38.1       | 33 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",
            "libnsl-2.0.1         | 33 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",
            "_openmp_mutex-4.5    | 23 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",
            "python_abi-3.12      | 6 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",
            "_libgcc_mutex-0.1    | 3 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",
            "subread-2.0.6        | 26.1 MB   | :   0% 0.0005977190582524722/1 [00:00<08:56, 536.56s/it]\u001b[A\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0.0005070463002532972/1 [00:00<13:50, 830.60s/it]\n",
            "subread-2.0.6        | 26.1 MB   | :  28% 0.2773416430291471/1 [00:00<00:00,  1.17s/it]    \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "conda-24.4.0         | 1.1 MB    | :   1% 0.013599355225882601/1 [00:00<00:30, 30.84s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | :   1% 0.005659047239435657/1 [00:00<01:17, 77.78s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "tk-8.6.13            | 3.2 MB    | :  72% 0.7158088207600467/1 [00:00<00:00,  1.86it/s] \u001b[A\u001b[A\n",
            "python-3.12.2        | 30.8 MB   | :   8% 0.07707103763850118/1 [00:00<00:04,  5.28s/it]   \n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | :  78% 0.7809485190421206/1 [00:00<00:00,  1.90it/s]  \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  18% 0.1794943902896672/1 [00:00<00:02,  2.52s/it] \n",
            "subread-2.0.6        | 26.1 MB   | :  60% 0.6030985297767444/1 [00:00<00:00,  1.08it/s] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | :   2% 0.021118681732288744/1 [00:00<00:31, 31.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 412 KB    | :   4% 0.03879375662979239/1 [00:00<00:17, 18.61s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  27% 0.26619930763298105/1 [00:00<00:01,  1.95s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libxcrypt-4.4.36     | 98 KB     | :  16% 0.163198629386511/1 [00:00<00:03,  4.77s/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",
            "\n",
            "zlib-1.2.13          | 91 KB     | :  18% 0.1765041745219499/1 [00:00<00:03,  4.46s/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",
            "\n",
            "libexpat-2.5.0       | 76 KB     | :  21% 0.2101051551679918/1 [00:00<00:02,  3.75s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 KB     | :  27% 0.26602584919140093/1 [00:00<00:02,  3.09s/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",
            "libuuid-2.38.1       | 33 KB     | :  49% 0.4876045355792982/1 [00:00<00:00,  1.70s/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",
            "subread-2.0.6        | 26.1 MB   | :  74% 0.7435625084660754/1 [00:00<00:00,  1.03s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  36% 0.3615240120806009/1 [00:00<00:01,  1.61s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "_openmp_mutex-4.5    | 23 KB     | :  69% 0.6936200838237162/1 [00:00<00:00,  1.25s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python_abi-3.12      | 6 KB      | : 100% 1.0/1 [00:00<00:00,  1.15it/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",
            "_libgcc_mutex-0.1    | 3 KB      | : 100% 1.0/1 [00:00<00:00,  1.14it/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",
            "libsqlite-3.45.3     | 840 KB    | : 100% 1.0/1 [00:00<00:00,  1.37it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.45.3     | 840 KB    | : 100% 1.0/1 [00:00<00:00,  1.37it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "python-3.12.2        | 30.8 MB   | :  89% 0.8903733032447899/1 [00:01<00:00,  1.08it/s]\n",
            "\n",
            "\n",
            "openssl-3.3.0        | 2.8 MB    | : 100% 1.0/1 [00:01<00:00,  1.90it/s]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "conda-24.4.0         | 1.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.79s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "conda-24.4.0         | 1.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.79s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 412 KB    | : 100% 1.0/1 [00:02<00:00,  1.83s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 412 KB    | : 100% 1.0/1 [00:02<00:00,  1.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:02<00:00,  1.92s/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",
            "certifi-2024.2.2     | 157 KB    | : 100% 1.0/1 [00:02<00:00,  1.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | : 100% 1.0/1 [00:02<00:00,  2.01s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 758 KB    | : 100% 1.0/1 [00:02<00:00,  2.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libxcrypt-4.4.36     | 98 KB     | : 100% 1.0/1 [00:02<00:00,  2.10s/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",
            "libxcrypt-4.4.36     | 98 KB     | : 100% 1.0/1 [00:02<00:00,  2.10s/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",
            "\n",
            "\n",
            "libexpat-2.5.0       | 76 KB     | : 100% 1.0/1 [00:02<00:00,  2.16s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libexpat-2.5.0       | 76 KB     | : 100% 1.0/1 [00:02<00:00,  2.16s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 91 KB     | : 100% 1.0/1 [00:02<00:00,  2.18s/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",
            "zlib-1.2.13          | 91 KB     | : 100% 1.0/1 [00:02<00:00,  2.18s/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",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 KB     | : 100% 1.0/1 [00:02<00:00,  2.27s/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",
            "libzlib-1.2.13       | 60 KB     | : 100% 1.0/1 [00:02<00:00,  2.27s/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",
            "libuuid-2.38.1       | 33 KB     | : 100% 1.0/1 [00:02<00:00,  2.47s/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",
            "\n",
            "libuuid-2.38.1       | 33 KB     | : 100% 1.0/1 [00:02<00:00,  2.47s/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",
            "\n",
            "\n",
            "libnsl-2.0.1         | 33 KB     | : 100% 1.0/1 [00:02<00:00,  2.51s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnsl-2.0.1         | 33 KB     | : 100% 1.0/1 [00:02<00:00,  2.51s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "_openmp_mutex-4.5    | 23 KB     | : 100% 1.0/1 [00:02<00:00,  2.76s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "_openmp_mutex-4.5    | 23 KB     | : 100% 1.0/1 [00:02<00:00,  2.76s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python_abi-3.12      | 6 KB      | : 100% 1.0/1 [00:02<00:00,  1.15it/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",
            "_libgcc_mutex-0.1    | 3 KB      | : 100% 1.0/1 [00:02<00:00,  1.14it/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",
            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:05<00:00,  1.08it/s]               \n",
            "                                                                        \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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "                                                                        \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",
            "Preparing transaction: / \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\bdone\n",
            "Executing transaction: - \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": [
        "## Transcript count table 마련하기\n",
        "\n",
        "제공된 alignment와 annotation 파일들을 이용해서 transcript별 read count를 구해봅니다. 실제 연구에서는 multi-mapping 등을 고려해야 하지만, 여기서는 단순화해서 모두 무시합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "먼저 작업 디렉토리를 만들고 데이터를 원본 디렉토리에서 복사해 옵니다. (이미 이 노트북을 실행한 적이 있으면 다시 할 필요는 없습니다.)"
      ],
      "metadata": {
        "id": "rrkL8Zl-s978"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/binfo1-work2"
      ],
      "metadata": {
        "id": "WJEqpSd_rgYK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7de5f117-cd0b-41b4-e8b0-497d7f0ff8de"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/binfo1-work2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/drive/MyDrive/binfo1-work2/\n",
        "!cp -f /content/drive/MyDrive/binfo1-datapack1/*.* /content/drive/MyDrive/binfo1-work2/\n",
        "!ls -al /content/drive/MyDrive/binfo1-work2/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hny7_ZxfpmeU",
        "outputId": "e6373f39-18dd-4077-bf5b-e2479fcb78c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 7162852\n",
            "-rw------- 1 root root 1370036258 May  8 10:02 CLIP-35L33G.bam\n",
            "-rw------- 1 root root    3118336 May  8 10:02 CLIP-35L33G.bam.bai\n",
            "-rw------- 1 root root       7113 May  8 10:02 CLIP-let7g.bam\n",
            "-rw------- 1 root root      14561 May  8 10:02 CLIP-let7g-gene.pileup\n",
            "-rw------- 1 root root    2685065 May  8 10:02 CLIP-let7g.pileup\n",
            "-rw------- 1 root root  883334756 May  8 10:02 gencode.gtf\n",
            "-rw------- 1 root root   24065406 May  8 10:02 read-counts.txt\n",
            "-rw------- 1 root root        751 May  8 10:02 read-counts.txt.summary\n",
            "-rw------- 1 root root 1003658801 May  8 10:02 RNA-control.bam\n",
            "-rw------- 1 root root    2276104 May  8 10:02 RNA-control.bam.bai\n",
            "-rw------- 1 root root 1260991122 May  8 10:02 RNA-siLin28a.bam\n",
            "-rw------- 1 root root    2710744 May  8 10:02 RNA-siLin28a.bam.bai\n",
            "-rw------- 1 root root  981684502 May  8 10:03 RNA-siLuc.bam\n",
            "-rw------- 1 root root    2606104 May  8 10:03 RNA-siLuc.bam.bai\n",
            "-rw------- 1 root root  737352902 May  8 10:03 RPF-siLin28a.bam\n",
            "-rw------- 1 root root    2482768 May  8 10:03 RPF-siLin28a.bam.bai\n",
            "-rw------- 1 root root 1055123479 May  8 10:03 RPF-siLuc.bam\n",
            "-rw------- 1 root root    2605912 May  8 10:03 RPF-siLuc.bam.bai\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!featureCounts -a gencode.gtf -o read-counts.txt *.bam"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vs4ADvD3pxVW",
        "outputId": "3a2a2124-18fd-48e2-9bde-9cf9f93b36c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/binfo1-work2\n",
            "\n",
            "       \u001b[44;37m ========== \u001b[0m\u001b[36m    _____ _    _ ____  _____  ______          _____  \n",
            "       \u001b[44;37m =====      \u001b[0m\u001b[36m   / ____| |  | |  _ \\|  __ \\|  ____|   /\\   |  __ \\ \n",
            "       \u001b[44;37m   =====    \u001b[0m\u001b[36m  | (___ | |  | | |_) | |__) | |__     /  \\  | |  | |\n",
            "       \u001b[44;37m     ====   \u001b[0m\u001b[36m   \\___ \\| |  | |  _ <|  _  /|  __|   / /\\ \\ | |  | |\n",
            "       \u001b[44;37m       ==== \u001b[0m\u001b[36m   ____) | |__| | |_) | | \\ \\| |____ / ____ \\| |__| |\n",
            "       \u001b[44;37m ========== \u001b[0m\u001b[36m  |_____/ \\____/|____/|_|  \\_\\______/_/    \\_\\_____/\u001b[0m\n",
            "\t  v2.0.6\n",
            "\n",
            "//==========================\u001b[36m featureCounts setting \u001b[0m===========================\\\\\n",
            "||  \u001b[0m                                                                          ||\n",
            "||             Input files : \u001b[36m7 BAM files  \u001b[0m \u001b[0m                                   ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "||                           \u001b[36mCLIP-35L33G.bam\u001b[0m \u001b[0m                                 ||\n",
            "||                           \u001b[36mCLIP-let7g.bam\u001b[0m \u001b[0m                                  ||\n",
            "||                           \u001b[36mRNA-control.bam\u001b[0m \u001b[0m                                 ||\n",
            "||                           \u001b[36mRNA-siLin28a.bam\u001b[0m \u001b[0m                                ||\n",
            "||                           \u001b[36mRNA-siLuc.bam\u001b[0m \u001b[0m                                   ||\n",
            "||                           \u001b[36mRPF-siLin28a.bam\u001b[0m \u001b[0m                                ||\n",
            "||                           \u001b[36mRPF-siLuc.bam\u001b[0m \u001b[0m                                   ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "||             Output file : \u001b[36mread-counts.txt\u001b[0m \u001b[0m                                 ||\n",
            "||                 Summary : \u001b[36mread-counts.txt.summary\u001b[0m \u001b[0m                         ||\n",
            "||              Paired-end : \u001b[36mno\u001b[0m \u001b[0m                                              ||\n",
            "||        Count read pairs : \u001b[36mno\u001b[0m \u001b[0m                                              ||\n",
            "||              Annotation : \u001b[36mgencode.gtf (GTF)\u001b[0m \u001b[0m                               ||\n",
            "||      Dir for temp files : \u001b[36m./\u001b[0m \u001b[0m                                              ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "||                 Threads : \u001b[36m1\u001b[0m \u001b[0m                                               ||\n",
            "||                   Level : \u001b[36mmeta-feature level\u001b[0m \u001b[0m                              ||\n",
            "||      Multimapping reads : \u001b[36mnot counted\u001b[0m \u001b[0m                                     ||\n",
            "|| Multi-overlapping reads : \u001b[36mnot counted\u001b[0m \u001b[0m                                     ||\n",
            "||   Min overlapping bases : \u001b[36m1\u001b[0m \u001b[0m                                               ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "\\\\============================================================================//\n",
            "\n",
            "//=================================\u001b[36m Running \u001b[0m==================================\\\\\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Load annotation file gencode.gtf \u001b[0m... \u001b[0m                                      ||\n",
            "||    Features : \u001b[36m841952\u001b[0m \u001b[0m                                                      ||\n",
            "||    Meta-features : \u001b[36m55359\u001b[0m \u001b[0m                                                  ||\n",
            "||    Chromosomes/contigs : \u001b[36m22\u001b[0m \u001b[0m                                               ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file CLIP-35L33G.bam... \u001b[0m                                       ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m38880853\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m13630945 (35.1%)\u001b[0m \u001b[0m                    ||\n",
            "||    Running time : \u001b[36m1.03 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file CLIP-let7g.bam... \u001b[0m                                        ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m163\u001b[0m \u001b[0m                                                 ||\n",
            "||    Successfully assigned alignments : \u001b[36m154 (94.5%)\u001b[0m \u001b[0m                         ||\n",
            "||    Running time : \u001b[36m0.00 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file RNA-control.bam... \u001b[0m                                       ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m24971058\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m5200836 (20.8%)\u001b[0m \u001b[0m                     ||\n",
            "||    Running time : \u001b[36m0.63 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file RNA-siLin28a.bam... \u001b[0m                                      ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m35108178\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m12338280 (35.1%)\u001b[0m \u001b[0m                    ||\n",
            "||    Running time : \u001b[36m0.88 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file RNA-siLuc.bam... \u001b[0m                                         ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m28117241\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m9748787 (34.7%)\u001b[0m \u001b[0m                     ||\n",
            "||    Running time : \u001b[36m0.68 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file RPF-siLin28a.bam... \u001b[0m                                      ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m30095551\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m10694162 (35.5%)\u001b[0m \u001b[0m                    ||\n",
            "||    Running time : \u001b[36m0.56 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Process BAM file RPF-siLuc.bam... \u001b[0m                                         ||\n",
            "||    Single-end reads are included. \u001b[0m                                         ||\n",
            "||    Total alignments : \u001b[36m42002435\u001b[0m \u001b[0m                                            ||\n",
            "||    Successfully assigned alignments : \u001b[36m12932285 (30.8%)\u001b[0m \u001b[0m                    ||\n",
            "||    Running time : \u001b[36m0.84 minutes\u001b[0m \u001b[0m                                            ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Write the final count table. \u001b[0m                                              ||\n",
            "|| Write the read assignment summary. \u001b[0m                                        ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "|| Summary of counting results can be found in file \"read-counts.txt.summary \u001b[0m ||\n",
            "|| \" \u001b[0m                                                                         ||\n",
            "||  \u001b[0m                                                                          ||\n",
            "\\\\============================================================================//\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "자! bam 파일에 담긴 aligned reads와 gtf 파일에 담긴 gene annotation으로 gene별 read count를 구합니다."
      ],
      "metadata": {
        "id": "gudJrFYlsTun"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2yJztZvGl9Q"
      },
      "source": [
        "결과를 pandas로 읽어와 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMyNT3ARFAWi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "b635536a-301f-42d0-b8e8-f8a9a86b3e8c"
      },
      "source": [
        "import pandas as pd\n",
        "cnts = pd.read_csv('read-counts.txt', sep='\\t', comment='#', index_col=0)\n",
        "cnts.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     Chr  \\\n",
              "Geneid                                                     \n",
              "ENSMUSG00000102693.2                                chr1   \n",
              "ENSMUSG00000064842.3                                chr1   \n",
              "ENSMUSG00000051951.6  chr1;chr1;chr1;chr1;chr1;chr1;chr1   \n",
              "ENSMUSG00000102851.2                                chr1   \n",
              "ENSMUSG00000103377.2                                chr1   \n",
              "\n",
              "                                                                  Start  \\\n",
              "Geneid                                                                    \n",
              "ENSMUSG00000102693.2                                            3143476   \n",
              "ENSMUSG00000064842.3                                            3172239   \n",
              "ENSMUSG00000051951.6  3276124;3276746;3283662;3283832;3284705;349192...   \n",
              "ENSMUSG00000102851.2                                            3322980   \n",
              "ENSMUSG00000103377.2                                            3435954   \n",
              "\n",
              "                                                                    End  \\\n",
              "Geneid                                                                    \n",
              "ENSMUSG00000102693.2                                            3144545   \n",
              "ENSMUSG00000064842.3                                            3172348   \n",
              "ENSMUSG00000051951.6  3277540;3277540;3285855;3286567;3287191;349212...   \n",
              "ENSMUSG00000102851.2                                            3323459   \n",
              "ENSMUSG00000103377.2                                            3438772   \n",
              "\n",
              "                             Strand  Length  CLIP-35L33G.bam  CLIP-let7g.bam  \\\n",
              "Geneid                                                                         \n",
              "ENSMUSG00000102693.2              +    1070                0               0   \n",
              "ENSMUSG00000064842.3              +     110                0               0   \n",
              "ENSMUSG00000051951.6  -;-;-;-;-;-;-    6094                4               0   \n",
              "ENSMUSG00000102851.2              +     480                3               0   \n",
              "ENSMUSG00000103377.2              -    2819                0               0   \n",
              "\n",
              "                      RNA-control.bam  RNA-siLin28a.bam  RNA-siLuc.bam  \\\n",
              "Geneid                                                                   \n",
              "ENSMUSG00000102693.2                0                 0              0   \n",
              "ENSMUSG00000064842.3                0                 0              0   \n",
              "ENSMUSG00000051951.6                1                 1              1   \n",
              "ENSMUSG00000102851.2                0                 0              0   \n",
              "ENSMUSG00000103377.2                0                 0              0   \n",
              "\n",
              "                      RPF-siLin28a.bam  RPF-siLuc.bam  \n",
              "Geneid                                                 \n",
              "ENSMUSG00000102693.2                 0              0  \n",
              "ENSMUSG00000064842.3                 0              0  \n",
              "ENSMUSG00000051951.6                 0              0  \n",
              "ENSMUSG00000102851.2                 0              0  \n",
              "ENSMUSG00000103377.2                 0              0  "
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fca081a0-cf78-46b8-9857-976aba0338eb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fca081a0-cf78-46b8-9857-976aba0338eb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ae0c62b7-9e46-4a86-9c32-6628ccd6700a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae0c62b7-9e46-4a86-9c32-6628ccd6700a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae0c62b7-9e46-4a86-9c32-6628ccd6700a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cnts",
              "summary": "{\n  \"name\": \"cnts\",\n  \"rows\": 55359,\n  \"fields\": [\n    {\n      \"column\": \"Geneid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 55359,\n        \"samples\": [\n          \"ENSMUSG00000034116.18\",\n          \"ENSMUSG00000089988.3\",\n          \"ENSMUSG00000039501.15\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chr\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3072,\n        \"samples\": [\n          \"chr18;chr18;chr18;chr18;chr18;chr18;chr18;chr18;chr18;chr18;chr18;chr18\",\n          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\"number\",\n        \"std\": 10490,\n        \"min\": 0,\n        \"max\": 2451339,\n        \"num_unique_values\": 2759,\n        \"samples\": [\n          1705,\n          32741,\n          4261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CLIP-let7g.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 154,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          154,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RNA-control.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 681,\n        \"min\": 0,\n        \"max\": 119734,\n        \"num_unique_values\": 1764,\n        \"samples\": [\n          1459,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RNA-siLin28a.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1468,\n        \"min\": 0,\n        \"max\": 214518,\n        \"num_unique_values\": 2982,\n        \"samples\": [\n          2396,\n          827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RNA-siLuc.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1354,\n        \"min\": 0,\n        \"max\": 237513,\n        \"num_unique_values\": 2609,\n        \"samples\": [\n          7368,\n          2116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RPF-siLin28a.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19989,\n        \"min\": 0,\n        \"max\": 4698218,\n        \"num_unique_values\": 1841,\n        \"samples\": [\n          1666,\n          610\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RPF-siLuc.bam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23527,\n        \"min\": 0,\n        \"max\": 5531458,\n        \"num_unique_values\": 2185,\n        \"samples\": [\n          134,\n          157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyrUZ7WBPAmv"
      },
      "source": [
        "이제 한 번 대충 그림을 그려 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlSZiJgVPAZS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "4b4461cc-ec63-460b-c965-6214e7565819"
      },
      "source": [
        "cnts['clip_enrichment'] = cnts['CLIP-35L33G.bam'] / cnts['RNA-control.bam']\n",
        "cnts['rden_change'] = (cnts['RPF-siLin28a.bam'] / cnts['RNA-siLin28a.bam']) / (cnts['RPF-siLuc.bam'] / cnts['RNA-siLuc.bam'])\n",
        "cnts.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     Chr  \\\n",
              "Geneid                                                     \n",
              "ENSMUSG00000102693.2                                chr1   \n",
              "ENSMUSG00000064842.3                                chr1   \n",
              "ENSMUSG00000051951.6  chr1;chr1;chr1;chr1;chr1;chr1;chr1   \n",
              "ENSMUSG00000102851.2                                chr1   \n",
              "ENSMUSG00000103377.2                                chr1   \n",
              "\n",
              "                                                                  Start  \\\n",
              "Geneid                                                                    \n",
              "ENSMUSG00000102693.2                                            3143476   \n",
              "ENSMUSG00000064842.3                                            3172239   \n",
              "ENSMUSG00000051951.6  3276124;3276746;3283662;3283832;3284705;349192...   \n",
              "ENSMUSG00000102851.2                                            3322980   \n",
              "ENSMUSG00000103377.2                                            3435954   \n",
              "\n",
              "                                                                    End  \\\n",
              "Geneid                                                                    \n",
              "ENSMUSG00000102693.2                                            3144545   \n",
              "ENSMUSG00000064842.3                                            3172348   \n",
              "ENSMUSG00000051951.6  3277540;3277540;3285855;3286567;3287191;349212...   \n",
              "ENSMUSG00000102851.2                                            3323459   \n",
              "ENSMUSG00000103377.2                                            3438772   \n",
              "\n",
              "                             Strand  Length  CLIP-35L33G.bam  CLIP-let7g.bam  \\\n",
              "Geneid                                                                         \n",
              "ENSMUSG00000102693.2              +    1070                0               0   \n",
              "ENSMUSG00000064842.3              +     110                0               0   \n",
              "ENSMUSG00000051951.6  -;-;-;-;-;-;-    6094                4               0   \n",
              "ENSMUSG00000102851.2              +     480                3               0   \n",
              "ENSMUSG00000103377.2              -    2819                0               0   \n",
              "\n",
              "                      RNA-control.bam  RNA-siLin28a.bam  RNA-siLuc.bam  \\\n",
              "Geneid                                                                   \n",
              "ENSMUSG00000102693.2                0                 0              0   \n",
              "ENSMUSG00000064842.3                0                 0              0   \n",
              "ENSMUSG00000051951.6                1                 1              1   \n",
              "ENSMUSG00000102851.2                0                 0              0   \n",
              "ENSMUSG00000103377.2                0                 0              0   \n",
              "\n",
              "                      RPF-siLin28a.bam  RPF-siLuc.bam  clip_enrichment  \\\n",
              "Geneid                                                                   \n",
              "ENSMUSG00000102693.2                 0              0              NaN   \n",
              "ENSMUSG00000064842.3                 0              0              NaN   \n",
              "ENSMUSG00000051951.6                 0              0              4.0   \n",
              "ENSMUSG00000102851.2                 0              0              inf   \n",
              "ENSMUSG00000103377.2                 0              0              NaN   \n",
              "\n",
              "                      rden_change  \n",
              "Geneid                             \n",
              "ENSMUSG00000102693.2          NaN  \n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cnts",
              "repr_error": "Out of range float values are not JSON compliant: inf"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU3ce_HEFALl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "03868540-c884-41a9-a54a-00b0894fcf5a"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "ax.scatter(np.log2(cnts['clip_enrichment']),\n",
        "           np.log2(cnts['rden_change']))\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/core/arraylike.py:396: RuntimeWarning: divide by zero encountered in log2\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7c87c84311e0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksVjIL10mV_v"
      },
      "source": [
        "이 그림에는 문제가 많이 있습니다. 논문의 그림 Figure 4D처럼 한 번 만들어 봅시다~"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZ-EV3s5X3u5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "3b03bf0e-493b-44fa-8e74-47285693c593"
      },
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "x=np.log2(cnts['clip_enrichment'])\n",
        "y=np.log2(cnts['rden_change'])\n",
        "ax.scatter(x,y, alpha=.2, s=4, color = \"gray\")\n",
        "ax.set_xlabel('LIN28A CLIP enrichment (log$_2$)')\n",
        "ax.set_ylabel('Ribosome density change upon Lin28a knockdown(log$_2$)')\n",
        "ax.grid(linestyle = '--', linewidth = 0.5)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sf6EJJ60Qbxg"
      },
      "source": [
        "## Protein localization 반영하기\n",
        "\n",
        "논문 그림(Figure 5B, S6A)에서는 protein localization을 반영해서 색으로 나타냈습니다. 이것을 한 번 구현해 봅시다. 각 gene product의 localization을 따로 구하려면 좀 귀찮기 때문에 미리 모아 두었습니다. UniProt에서 매핑된 데이터를 토대로 아주 명확한 것만 일부 정리한 것입니다. 이것도 마찬가지로 처음엔 에러가 나기도 하니까, 다시 실행해보면 거의 잘 됩니다~"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivb1q7_f-gl4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6041d3ce-f48a-45b6-f34e-09f64bfc1811"
      },
      "source": [
        "import ssl\n",
        "ssl._create_default_https_context = ssl._create_unverified_context\n",
        "mouselocal = pd.read_csv('https://hyeshik.qbio.io/binfo/mouselocalization-20210507.txt', sep='\\t')\n",
        "mouselocal.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              gene_id            Gene names       type\n",
              "0  ENSMUSG00000000001                 Gnai3  cytoplasm\n",
              "1  ENSMUSG00000000028  Cdc45 Cdc45l Cdc45l2    nucleus\n",
              "2  ENSMUSG00000000049            Apoh B2gp1  cytoplasm\n",
              "3  ENSMUSG00000000058                  Cav2  cytoplasm\n",
              "4  ENSMUSG00000000085                 Scmh1    nucleus"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c07f3693-5524-4dcc-9222-4f3f4742c8d5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gene_id</th>\n",
              "      <th>Gene names</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ENSMUSG00000000001</td>\n",
              "      <td>Gnai3</td>\n",
              "      <td>cytoplasm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENSMUSG00000000028</td>\n",
              "      <td>Cdc45 Cdc45l Cdc45l2</td>\n",
              "      <td>nucleus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENSMUSG00000000049</td>\n",
              "      <td>Apoh B2gp1</td>\n",
              "      <td>cytoplasm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENSMUSG00000000058</td>\n",
              "      <td>Cav2</td>\n",
              "      <td>cytoplasm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENSMUSG00000000085</td>\n",
              "      <td>Scmh1</td>\n",
              "      <td>nucleus</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c07f3693-5524-4dcc-9222-4f3f4742c8d5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c07f3693-5524-4dcc-9222-4f3f4742c8d5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c07f3693-5524-4dcc-9222-4f3f4742c8d5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-915e36db-175d-4d6a-9ec6-063eabee7507\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-915e36db-175d-4d6a-9ec6-063eabee7507')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-915e36db-175d-4d6a-9ec6-063eabee7507 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mouselocal",
              "summary": "{\n  \"name\": \"mouselocal\",\n  \"rows\": 9523,\n  \"fields\": [\n    {\n      \"column\": \"gene_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9414,\n        \"samples\": [\n          \"ENSMUSG00000063065\",\n          \"ENSMUSG00000026586\",\n          \"ENSMUSG00000096068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gene names\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9304,\n        \"samples\": [\n          \"Lats1 Warts\",\n          \"Pla2g4f\",\n          \"Uhmk1 Kis Kist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"cytoplasm\",\n          \"nucleus\",\n          \"integral membrane\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gene_type = dict(zip(mouselocal.gene_id, mouselocal.type))\n",
        "cnts_reset = cnts.reset_index()\n",
        "rowsForFig5 = []\n",
        "for idx, cnt_data in cnts_reset.iterrows():\n",
        "    geneid = cnt_data['Geneid'].split('.')[0]  # Corrected line\n",
        "    if geneid in gene_type:\n",
        "        rowsForFig5.append([geneid, cnt_data['clip_enrichment'], cnt_data['rden_change'], gene_type[geneid]])  # Corrected line\n",
        "dfForFig5 = pd.DataFrame(rowsForFig5, columns=[\"geneid\", \"clip_enrichment\", \"rden_change\", \"type\" ])\n",
        "dfForFig5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DhtwupyOSvfn",
        "outputId": "db917a6f-26e7-474d-b611-fee1cc219335"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  geneid  clip_enrichment  rden_change               type\n",
              "0     ENSMUSG00000051951         4.000000          NaN  integral membrane\n",
              "1     ENSMUSG00000025902         6.000000     0.416667            nucleus\n",
              "2     ENSMUSG00000025903         3.562718     0.760459          cytoplasm\n",
              "3     ENSMUSG00000002459         5.000000    12.000000            nucleus\n",
              "4     ENSMUSG00000025905        49.000000     1.142857  integral membrane\n",
              "...                  ...              ...          ...                ...\n",
              "9409  ENSMUSG00000064360         0.000000     1.051948  integral membrane\n",
              "9410  ENSMUSG00000065947         0.333333     0.500000  integral membrane\n",
              "9411  ENSMUSG00000064363         0.173488     1.107123  integral membrane\n",
              "9412  ENSMUSG00000064367         0.693997     1.263275  integral membrane\n",
              "9413  ENSMUSG00000064368         0.843117     1.263780  integral membrane\n",
              "\n",
              "[9414 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1594d24-251c-4605-88ce-f9b2eb146bd2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>geneid</th>\n",
              "      <th>clip_enrichment</th>\n",
              "      <th>rden_change</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ENSMUSG00000051951</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENSMUSG00000025902</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>nucleus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENSMUSG00000025903</td>\n",
              "      <td>3.562718</td>\n",
              "      <td>0.760459</td>\n",
              "      <td>cytoplasm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENSMUSG00000002459</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>nucleus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENSMUSG00000025905</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9409</th>\n",
              "      <td>ENSMUSG00000064360</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.051948</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9410</th>\n",
              "      <td>ENSMUSG00000065947</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9411</th>\n",
              "      <td>ENSMUSG00000064363</td>\n",
              "      <td>0.173488</td>\n",
              "      <td>1.107123</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9412</th>\n",
              "      <td>ENSMUSG00000064367</td>\n",
              "      <td>0.693997</td>\n",
              "      <td>1.263275</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9413</th>\n",
              "      <td>ENSMUSG00000064368</td>\n",
              "      <td>0.843117</td>\n",
              "      <td>1.263780</td>\n",
              "      <td>integral membrane</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9414 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1594d24-251c-4605-88ce-f9b2eb146bd2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1594d24-251c-4605-88ce-f9b2eb146bd2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a1594d24-251c-4605-88ce-f9b2eb146bd2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-23c5b7b6-121e-4706-bafc-53fd433bb85d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23c5b7b6-121e-4706-bafc-53fd433bb85d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23c5b7b6-121e-4706-bafc-53fd433bb85d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bfaff2a3-d4d1-4a6a-9b7f-36aad4e5ee5b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dfForFig5')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bfaff2a3-d4d1-4a6a-9b7f-36aad4e5ee5b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dfForFig5');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dfForFig5",
              "repr_error": "Out of range float values are not JSON compliant: inf"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9vgSzeiQxDU"
      },
      "source": [
        "이제 이 localization 데이터와 위에서 만든 scatter를 결합해서 논문 그림과 비슷하게 만들어 봅시다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPPW73t4X3pp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "7d62b96f-2e82-46fa-cbd7-727adb2c3f1f"
      },
      "source": [
        "color_mapping = {\n",
        "    'integral membrane': 'red',\n",
        "    'nucleus': 'blue',\n",
        "    'cytoplasm': 'green'\n",
        "}\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "x = np.log2(dfForFig5['clip_enrichment'])\n",
        "y = np.log2(dfForFig5['rden_change'])\n",
        "\n",
        "# Scatter plot with different colors for each type\n",
        "for typ, color in color_mapping.items():\n",
        "    ax.scatter(x[dfForFig5['type'] == typ], y[dfForFig5['type'] == typ], label=typ, color=color, alpha=1,s=4)\n",
        "\n",
        "ax.set_xlabel('LIN28A CLIP enrichment (log$_2$)')\n",
        "ax.set_ylabel('Ribosome density change upon Lin28a knockdown(log$_2$)')\n",
        "ax.yaxis.grid()\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/core/arraylike.py:396: RuntimeWarning: divide by zero encountered in log2\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ByHcMUyETd6K"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}