PPRCODE.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YaoYinYing/PPRCODE_Guideline/blob/main/PPRCODE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-da9fTk4awf"
      },
      "source": [
        "# PPRCODE on Colab \n",
        "<img src=\"https://raw.githubusercontent.com/YaoYinYing/PPRCODE_Guideline/main/image/5i9f.png\" height=\"300\" align=\"left\" style=\"height:240px\" hspace=\"20\">\n",
        "\n",
        "\n",
        "This is a reimplement work created by Yinying Yao.\n",
        "\n",
        "Original webserver (MAY not work): [PPRCODE - Yin Lab](http://yinlab.hzau.edu.cn/pprcode/)\n",
        "\n",
        "**If you find this tool useful to your research, please cite the following**:\n",
        "\n",
        "- [PPRCODE (whether webserver or notebook)](https://doi.org/10.1093/nar/gkz075) and its required packages([BioPython](https://doi.org/10.1093/bioinformatics/btp163), [Dna Features Viewer](https://doi.org/10.1093/bioinformatics/btaa213), [Matplotlib](https://doi.org/10.1109/MCSE.2007.55)).\n",
        "- Use default algorithm [PS_scan](https://doi.org/10.1093/nar/gkl124) with [Prosite profile](https://doi.org/10.1093/nar/gks1067) operated by Swiss Institute of Bioinformatics.\n",
        "- Use optional algorithm [PPRfinder](https://doi.org/10.1016/j.molp.2019.11.002) from Small's lab and its required [HMMER](http://hmmer.org/publications.html) package developed by Eddy's lab.\n",
        "\n"
      ]
    },
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      "cell_type": "code",
      "metadata": {
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        "cellView": "form",
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      },
      "source": [
        "#@title Install packages\n",
        "#@markdown First, click the _Play Button_ ▶️ of the cell to start a new colab session and install required packages.\n",
        "import os\n",
        "import glob\n",
        "import pathlib\n",
        "import sys\n",
        "import time\n",
        "import traceback\n",
        "import pickle\n",
        "import re\n",
        "from IPython.utils import io\n",
        "import subprocess\n",
        "import tqdm.notebook\n",
        "import tempfile\n",
        "import pandas as pd\n",
        "\n",
        "try:\n",
        "  from google.colab import files\n",
        "  IN_COLAB = True\n",
        "except:\n",
        "  IN_COLAB = False\n",
        "\n",
        "BENCHMARK_DATA='https://raw.githubusercontent.com/YaoYinYing/PPRCODE_Guideline/main/ATPPR.fasta'\n",
        "\n",
        "# PS_scan setting\n",
        "PROSITE_URL='https://ftp.expasy.org/databases/prosite/'\n",
        "PS_SCAN_URL='https://ftp.expasy.org/databases/prosite/ps_scan/ps_scan_linux_x86_elf.tar.gz'\n",
        "PS_MOTIF='PS51375'\n",
        "\n",
        "# PPRfinder setting\n",
        "# Yinying build a copy\n",
        "PPRFINDER_BUILD='https://raw.githubusercontent.com/YaoYinYing/PPRfinder/master/out/production/PPRfinder/PPRfinder.jar'\n",
        "HMM_PROFILE_URL='https://raw.githubusercontent.com/YaoYinYing/PPRfinder/master/data/'\n",
        "JDK_VER='openjdk-17-jdk-headless'\n",
        "#HMMER_VERSION='3.3.2'\n",
        "#HMMER_SRC_URL=f'http://eddylab.org/software/hmmer/hmmer-{HMMER_VERSION}.tar.gz'\n",
        "\n",
        "TQDM_BAR_FORMAT = '{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'\n",
        "\n",
        "\n",
        "TMP_DIR = \"tmp\"\n",
        "JOBS_DIR='jobs'\n",
        "RES_DIR='results'\n",
        "\n",
        "# PS_SCAN zone\n",
        "RES_DIR_SCAN=f\"{RES_DIR}/scan\"\n",
        "\n",
        "# PPRfinder zone\n",
        "RES_DIR_FIND=f'{RES_DIR}/find'\n",
        "\n",
        "# the common result zone\n",
        "RES_DIR_PICKLE=f'{RES_DIR}/pickle'\n",
        "RES_DIR_REPORT=f'{RES_DIR}/report'\n",
        "RES_DIR_FEATURE=f'{RES_DIR}/feature'\n",
        "RES_DIR_FIGURE=f'{RES_DIR}/figure'\n",
        "\n",
        "# conda path\n",
        "CONDA_PATH='/opt/miniconda'\n",
        "\n",
        "# if not already installed\n",
        "total = 11\n",
        "with tqdm.notebook.tqdm(total=total, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "  for dir in [TMP_DIR,JOBS_DIR,RES_DIR,RES_DIR_SCAN,RES_DIR_FIND,RES_DIR_PICKLE,RES_DIR_REPORT,RES_DIR_FEATURE,RES_DIR_FIGURE]:\n",
        "    os.makedirs(dir, exist_ok=True)\n",
        "  pbar.update(2)\n",
        "  os.system(f\"wget -qnc {PS_SCAN_URL} -P {TMP_DIR}\")\n",
        "  pbar.update(1)\n",
        "\n",
        "  # binaries setup\n",
        "  os.system(f'mkdir {TMP_DIR}/bin && cd {TMP_DIR}/bin && tar zxf ../ps_scan_linux_x86_elf.tar.gz')\n",
        "  os.system(f\"wget -qnc {PPRFINDER_BUILD} -P {TMP_DIR}/bin\")\n",
        "  \n",
        "  # HMMER compilation\n",
        "  # /usr/local/bin/hmmsearch\n",
        "  #os.system(f'wget -qnc {HMMER_SRC_URL} -P {TMP_DIR}')\n",
        "  #os.system(f'cd {TMP_DIR}/bin && tar xvzf ../$(basename \"{HMMER_SRC_URL}\") && cd hmmer-{HMMER_VERSION} && ./configure --prefix=/usr/local/ && make -j 2 && make install')\n",
        "  \n",
        "  # use precomiled binary\n",
        "  # check userguide: http://eddylab.org/software/hmmer/Userguide.pdf\n",
        "  os.system(f' apt install -y hmmer')\n",
        "  pbar.update(1)\n",
        "\n",
        "  # setup conda\n",
        "  os.system(f'if [ ! -f {TMP_DIR}/CONDA_READY ]; then  wget -qnc https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh; bash Miniconda3-latest-Linux-x86_64.sh -bfp {CONDA_PATH} 2>&1 1>/dev/null; rm Miniconda3-latest-Linux-x86_64.sh; touch {TMP_DIR}/CONDA_READY; fi')\n",
        "  pbar.update(1)\n",
        "\n",
        "  # conda env\n",
        "  os.system(f'conda install -y  python -c plotly plotly-orca')\n",
        "  pbar.update(1)\n",
        "\n",
        "  # profiles \n",
        "  os.system(f'mkdir {TMP_DIR}/profiles')\n",
        "  \n",
        "\n",
        "  for p in ['profile.txt','prosite.dat','prorule.dat','evaluator.dat']:\n",
        "    os.system(f\"wget -qnc {PROSITE_URL}{p} -P {TMP_DIR}/profiles/\")\n",
        "  pbar.update(1)\n",
        "\n",
        "  for p in ['morf.hmm','dyw_kp.hmm','dyw.hmm','all_PPR.hmm']:\n",
        "    os.system(f\"wget -qnc {HMM_PROFILE_URL}{p} -P {TMP_DIR}/profiles/\")\n",
        "  pbar.update(1)\n",
        "\n",
        "  # packages  \n",
        "  try:\n",
        "    from Bio import SeqIO, Seq\n",
        "  except:\n",
        "    os.system(f\"pip3 install biopython\")\n",
        "    os.system(f\"pip3 install dna_features_viewer\")\n",
        "    os.system(f\"pip3 install xlsxwriter\")\n",
        "    import xlsxwriter as xw\n",
        "    from dna_features_viewer import GraphicFeature, GraphicRecord\n",
        "    from Bio import SeqIO,Seq\n",
        "  pbar.update(1)\n",
        "\n",
        "  # JDK for PPRfinder https://github.com/ian-small/PPRfinder\n",
        "  os.system(f\"apt install -y {JDK_VER}\")\n",
        "  pbar.update(1)\n",
        "\n",
        "  # add PATHs\n",
        "  pths=[f\"{pathlib.Path(TMP_DIR).resolve()}/bin/ps_scan/\",f\"{pathlib.Path(TMP_DIR).resolve()}/bin/\"]\n",
        "  for p in pths:\n",
        "    if p  not in os.environ['PATH']:\n",
        "      os.environ['PATH'] += f\":{p}\"\n",
        "  pbar.update(1)\n",
        "\n",
        "# updated at 2018/12/20 for pulication, experimentally validated.\n",
        "code2rna_multi = {\"ND\": \"U>C>G\", \"NN\": \"C>U\", \"TD\": \"G>A>U\", \"SD\": \"G>>C\", \"TN\": \"A>G\", \"SN\": \"A\", \"NS\": \"C>U>A\",\n",
        "                  \"NT\": \"C>U\", \"GN\": \"A>C\", \"TS\": \"A>G\", \"GD\": \"G\", \"TT\": \"A>G\", \"RD\": \"-\", \"SS\": \"A\", \"AD\": \"G\",\n",
        "                  \"KD\": \"-\", \"ST\": \"A\", \"NE\": \"U>C\", \"DS\": \"-\", \"CD\": \"G\", \"DD\": \"-\", \"NG\": \"C>U>A\", \"AN\": \"A>>U\",\n",
        "                  \"GS\": \"-\", \"ES\": \"-\", \"LD\": \"-\", \"SG\": \"A>G\", \"GT\": \"C\", \"HD\": \"-\", \"CN\": \"A>>G\", \"VD\": \"G\",\n",
        "                  \"ID\": \"-\", \"SE\": \"-\", \"NC\": \"U>C>>A\", \"AS\": \"-\", \"RS\": \"-\", \"MD\": \"-\", \"NK\": \"C>U\", \"NL\": \"C\",\n",
        "                  \"DT\": \"-\", \"RN\": \"-\", \"TG\": \"A>>G\", \"GG\": \"-\", \"LN\": \"-\", \"LS\": \"-\", \"VN\": \"A\", \"TE\": \"G\", \"SP\": \"-\",\n",
        "                  \"KN\": \"C>U\", \"NR\": \"-\", \"DN\": \"C\", \"CS\": \"A\", \"QD\": \"-\", \"NV\": \"C>U\", \"ED\": \"-\", \"IN\": \"-\",\n",
        "                  \"VS\": \"-\", \"HN\": \"-\", \"AT\": \"-\", \"NH\": \"U>C\", \"AG\": \"-\", \"NI\": \"C\", }\n",
        "\n",
        "def ps_scan(f,s):\n",
        "  \n",
        "    if debug: print(s.id)\n",
        "    sequence_=s\n",
        "    #print(sequence_)\n",
        "    ps_out_file=f\"{RES_DIR_SCAN}/{s.id}_PPR_PSSCAN_pff.log\"\n",
        "    cmd=f\"perl {pathlib.Path(TMP_DIR).resolve()}/bin/ps_scan/ps_scan.pl -e {s.id} -p {PS_MOTIF} -o pff -d {pathlib.Path(TMP_DIR).resolve()}/profiles/prosite.dat {f} | sort -n -k 2 > {ps_out_file}\"\n",
        "    os.system(cmd)\n",
        "    ps_out_file_scan=f\"{RES_DIR_SCAN}/{s.id}_PPR_PSSCAN_scan.log\"\n",
        "    cmd=f\"perl {pathlib.Path(TMP_DIR).resolve()}/bin/ps_scan/ps_scan.pl -e {s.id} -p {PS_MOTIF} -o scan -d {pathlib.Path(TMP_DIR).resolve()}/profiles/prosite.dat {f} | sort -n -k 2 > {ps_out_file_scan}\"\n",
        "    os.system(cmd)\n",
        "    try:\n",
        "        \n",
        "        result_response_pff=open(ps_out_file,'r').read()\n",
        "        matchpos = re.findall(r'(\\d+)\\t(\\d+)\\t%s' % PS_MOTIF, result_response_pff)\n",
        "        #print(matchpos)\n",
        "        refinedpos = [[], []]\n",
        "\n",
        "        # print(matchpos)\n",
        "        match_motif = []\n",
        "\n",
        "        # get original match motifs\n",
        "        for pos in matchpos:\n",
        "            mypos=sequence_[int(pos[0])-1:int(pos[1])]\n",
        "            mypos.id=f'{s.id}_{int(pos[0])-1}_{pos[1]}'\n",
        "            #print(mypos)\n",
        "            match_motif.append(mypos)\n",
        "\n",
        "        #print(match_motif)\n",
        "        # find the motif sequence deletion by ProSite and refine the positions\n",
        "        \n",
        "        result_response_scan=open(ps_out_file_scan,'r').read()\n",
        "        for origin_motif, origin_pos in zip([str(x.seq) for x in match_motif], matchpos):\n",
        "            try:\n",
        "                if debug: print(origin_motif)\n",
        "                if fix_gap: \n",
        "                  motif_delete_find = re.findall(r'' + origin_motif + '(-+)', result_response_scan)[0]\n",
        "                else:\n",
        "                  motif_delete_find=''\n",
        "                if debug: print(\"orginal Motif= %s\\t motif deletion=%s\\t deletion length=%s\" % (origin_motif, motif_delete_find,len(motif_delete_find)))\n",
        "                refinedpos[0].append(int(origin_pos[0]))\n",
        "\n",
        "                # V1.3.1 solve the motif boundary overlap\n",
        "                if (len(refinedpos[1]) > 0 and refinedpos[1][-1] > refinedpos[0][-1]):\n",
        "                    if debug: print(\"==>refine pos 0 = %s\\t refine pos 1 = %s\" % (refinedpos[0][-1], refinedpos[1][-1]))\n",
        "                    refinedpos[1][-1] = refinedpos[0][-1]\n",
        "                    if debug: print(\"-->refine pos 0 = %s\\t refine pos 1 = %s\" % (refinedpos[0][-1], refinedpos[1][-1]))\n",
        "\n",
        "\n",
        "                refinedpos[1].append(int(origin_pos[1]) + 1 + len(motif_delete_find))\n",
        "\n",
        "            except Exception as e:\n",
        "                #traceback.print_exc()\n",
        "                #if debug: print(\"orginal Motif= %s\\t motif deletion=%s\" % (origin_motif, \"deletion not found.\"))\n",
        "                refinedpos[0].append(int(origin_pos[0]))\n",
        "                refinedpos[1].append(int(origin_pos[1]) + 1)\n",
        "\n",
        "\n",
        "\n",
        "        # the second round of refinement of position: the conserved \"P\" and the 36 aa length motif  # the conserved \"P\" label is based on weblogo results\n",
        "        # Eureka!\n",
        "        refindpos_end_2nd = []\n",
        "        try:\n",
        "            pos_index = 0\n",
        "            for refinedpos_start_slide, refinedpos_end_slide in zip(refinedpos[0], refinedpos[1]):\n",
        "                if debug: print(\"refined pos[%s]: %s -> %s \" % (\n",
        "                    pos_index, refinedpos_start_slide, refinedpos_end_slide))\n",
        "                if debug: print(sequence[refinedpos_start_slide:refinedpos_end_slide])\n",
        "                if debug: print(\"Scanning the %s/%s motif...\" % (pos_index + 1, refinedpos[0].__len__()))\n",
        "                if pos_index < (refinedpos[0].__len__() - 1):\n",
        "                    try:\n",
        "                      if (refinedpos[0][pos_index + 1] - refinedpos[1][\n",
        "                          pos_index] == 1 and\n",
        "                              sequence[refinedpos_start_slide:refinedpos_end_slide][-1] == \"P\"):\n",
        "                          if debug: print(\"Find P!\")\n",
        "                          if debug: print(len(sequence[refinedpos_start_slide:refinedpos_end_slide]))\n",
        "                          if debug: print(sequence[refinedpos_start_slide:refinedpos_end_slide][-1])\n",
        "                          refindpos_end_2nd.append(refinedpos_end_slide + 1)\n",
        "                          pass\n",
        "                      else:\n",
        "                          # print(\"No P find!\")\n",
        "                          refindpos_end_2nd.append(refinedpos_end_slide)\n",
        "                    except:\n",
        "                        refindpos_end_2nd.append(refinedpos_end_slide)\n",
        "                elif pos_index == (refinedpos[0].__len__() - 1):\n",
        "                    if sequence[refinedpos_start_slide:refinedpos_end_slide][-1] == \"P\":\n",
        "                        if debug: print(\"Find P in the last motif...\")\n",
        "                        if debug: print(len(sequence[refinedpos_start_slide:refinedpos_end_slide]))\n",
        "                        if debug: print(sequence[refinedpos_start_slide:refinedpos_end_slide][-1])\n",
        "                        refindpos_end_2nd.append(refinedpos_end_slide + 1)\n",
        "\n",
        "                    else:\n",
        "                        if debug: print(\"No P find in the last motif...\")\n",
        "                        refindpos_end_2nd.append(refinedpos_end_slide)\n",
        "                pos_index += 1\n",
        "\n",
        "\n",
        "\n",
        "        except Exception as e:\n",
        "            print(\"Oops! Something wrong hanpens with the following sequence!\")\n",
        "            print(f\"--->{s.id}\")\n",
        "            traceback.print_exc() if debug else print(e)\n",
        "        finally:\n",
        "            if debug: print(refindpos_end_2nd)\n",
        "\n",
        "        # print(match_motif)\n",
        "        # print(refinedpos)\n",
        "\n",
        "        # extract motifs scores according to the match positions\n",
        "        motif_score = []\n",
        "        \n",
        "        for line in open(ps_out_file,'r'):\n",
        "          motif_score.append(float(line.split(\"\\t\")[-2]))\n",
        "        # print(motif_score)\n",
        "        parsed_ppr=[]\n",
        "        for pos_start,pos_end,score in zip(refinedpos[0], refindpos_end_2nd,motif_score):\n",
        "            mypos=sequence_[pos_start:pos_end]\n",
        "            mypos.id=f'{s.id}'\n",
        "            mypos.annotations={\n",
        "                \"PS_SCORE\"      :   score,\n",
        "                \"PPR_STARTS\"    :   pos_start+1,\n",
        "                \"PPR_ENDS\"      :   pos_end,\n",
        "                \"FIFTH_AA\"      :   sequence_[pos_start+4],\n",
        "                \"LAST_AA\"       :   sequence_[pos_end-1],\n",
        "                \"PPR_CODE\"      :   sequence_[pos_start+4]+sequence_[pos_end-1],\n",
        "                \"RNA_BASE\"      :   code2rna_multi[sequence_[pos_start+4]+sequence_[pos_end-1]] if sequence_[pos_start+4]+sequence_[pos_end-1] in code2rna_multi.keys() else '?',\n",
        "                               }\n",
        "            parsed_ppr.append(mypos)\n",
        "            \n",
        "        return parsed_ppr\n",
        "    except Exception as e:\n",
        "        print(\"Oops! Something wrong hanpens with the following sequence!\")\n",
        "        print(f\"--->{s.id}\")\n",
        "        traceback.print_exc() if debug else print(e)\n",
        "\n",
        "def pprfinder(f,s):\n",
        "  if debug: print(s.id)\n",
        "  sequence_=s\n",
        "  with  tempfile.NamedTemporaryFile(suffix=f'_{s.id}.fasta',delete=True) as tmp_fasta_file:\n",
        "    SeqIO.write(s, tmp_fasta_file.name, \"fasta\")\n",
        "\n",
        "    #run scanning\n",
        "    hmmsearch_out_file=pathlib.Path(f\"{RES_DIR_FIND}/{s.id}_PPR_HMMSEARCH.domt\").resolve()\n",
        "\n",
        "    # recommended setting from Small's Lab. see https://github.com/ian-small/OneKP\n",
        "    cmd_1=f\"hmmsearch --domtblout {hmmsearch_out_file} --noali -E 0.1 --cpu 2 {TMP_DIR}/profiles/all_PPR.hmm {tmp_fasta_file.name}\"\n",
        "    os.system(cmd_1)\n",
        "    cmd_2=f\"java -jar {pathlib.Path(TMP_DIR).resolve()}/bin/PPRfinder.jar {tmp_fasta_file.name} {pathlib.Path(hmmsearch_out_file).resolve()}\"\n",
        "    os.system(cmd_2)\n",
        "\n",
        "    # expected output: f'{}{s.id}_PPR_HMMSEARCH.domt_motifs.txt'}\n",
        "    ppr_df=pd.read_table(f'{RES_DIR_FIND}/{s.id}_PPR_HMMSEARCH.domt_motifs.txt',names=['id','start','end','score', 'motif_seq','second','fifth_aa','last_aa','motif_type'])\n",
        "    #print(ppr_df)\n",
        "    parsed_ppr=[]\n",
        "    for pos_start,pos_end,score,motif_type,fifth,last,motif_seq in zip(ppr_df.start, ppr_df.end,ppr_df.score,ppr_df.motif_type,ppr_df.fifth_aa,ppr_df.last_aa,ppr_df.motif_seq):\n",
        "      mypos=sequence_[pos_start:pos_end]\n",
        "      mypos.id=f'{s.id}'\n",
        "      mypos.annotations={\n",
        "        \"PPR_TYPE\"      :   motif_type,\n",
        "        \"PS_SCORE\"      :   score,\n",
        "        \"PPR_STARTS\"    :   pos_start,\n",
        "        \"PPR_ENDS\"      :   pos_end,\n",
        "        \"FIFTH_AA\"      :   fifth,\n",
        "        \"LAST_AA\"       :   last,\n",
        "        \"PPR_CODE\"      :   fifth+last,\n",
        "        \"RNA_BASE\"      :   code2rna_multi[fifth+last] if fifth+last in code2rna_multi.keys() else '?',\n",
        "                               }\n",
        "      parsed_ppr.append(mypos)\n",
        "            \n",
        "  return parsed_ppr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11 [elapsed: 00:00 remaining: ?]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4af0e85f26ec46098e090a72758d4008"
            }
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdTziSdN7yT_",
        "cellView": "form"
      },
      "source": [
        "#@title Paste/Upload the amino acid sequences to predict ⬇️\n",
        "import glob\n",
        "\n",
        "# define sequence\n",
        "#@markdown For one single sequence: \n",
        "jobname = \"ZmPPR10\" #@param {type:\"string\"}\n",
        "if jobname == '': jobname='undefined'\n",
        "sequence = 'MEATGRGLFPNKPTLPAGPRKRGPLLPAAPPPPSPSSLPLDSLLLHLTAPAPAPAPAPRRSHQTPTPPHSFLSPDAQVLVLAISSHPLPTLAAFLASRRDELLRADITSLLKALELSGHWEWALALLRWAGKEGAADASALEMVVRALGREGQHDAVCALLDETPLPPGSRLDVRAYTTVLHALSRAGRYERALELFAELRRQGVAPTLVTYNVVLDVYGRMGRSWPRIVALLDEMRAAGVEPDGFTASTVIAACCRDGLVDEAVAFFEDLKARGHAPCVVTYNALLQVFGKAGNYTEALRVLGEMEQNGCQPDAVTYNELAGTYARAGFFEEAARCLDTMASKGLLPNAFTYNTVMTAYGNVGKVDEALALFDQMKKTGFVPNVNTYNLVLGMLGKKSRFTVMLEMLGEMSRSGCTPNRVTWNTMLAVCGKRGMEDYVTRVLEGMRSCGVELSRDTYNTLIAAYGRCGSRTNAFKMYNEMTSAGFTPCITTYNALLNVLSRQGDWSTAQSIVSKMRTKGFKPNEQSYSLLLQCYAKGGNVAGIAAIENEVYGSGAVFPSWVILRTLVIANFKCRRLDGMETAFQEVKARGYNPDLVIFNSMLSIYAKNGMYSKATEVFDSIKRSGLSPDLITYNSLMDMYAKCSESWEAEKILNQLKCSQTMKPDVVSYNTVINGFCKQGLVKEAQRVLSEMVADGMAPCAVTYHTLVGGYSSLEMFSEAREVIGYMVQHGLKPMELTYRRVVESYCRAKRFEEARGFLSEVSETDLDFDKKALEAYIEDAQFGR' #@param {type:\"string\"}\n",
        "sequence=sequence.upper()\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown OR upload your multiple sequences in FASTA format to apply batch processing: \n",
        "\n",
        "#@markdown Click the icon like a folder 📁 on left panel, upload your own file into a subfolder named **jobs**.\n",
        "\n",
        "#@markdown After uploading is over, you must keep the `only_process_my_fasta` checked and click the _Play Button_ ▶️ on each cell to continue the processing.\n",
        "\n",
        "find_ppr_by=\"PS_Scan (Default)\" #@param ['PS_Scan (Default)','PPRfinder (Small\\'s Lab)']\n",
        "only_process_my_fasta = False #@param {type:\"boolean\"}\n",
        "run_benchmark = False #@param {type:\"boolean\"}\n",
        "#@markdown - `find_ppr_by` - Choose a proper algorithm to process your sequence. PPRCODE use PS_Scan by default. However, you may use PPRfinder from Small's Lab.\n",
        "#@markdown - `only_process_my_fasta` - Analyse uploaded files. **This will ignore any input in boxes above.**\n",
        "#@markdown - `run_benchmark` - Fetch a benchmark dataset. **This could make your job running messy.**\n",
        "#@markdown ---\n",
        "\n",
        "if find_ppr_by.startswith('PS_Scan'):\n",
        "  program='PS_Scan'\n",
        "elif find_ppr_by.startswith('PPRfinder'):\n",
        "  program='PPRfinder'\n",
        "\n",
        "\n",
        "#@markdown Output settings\n",
        "\n",
        "generate_excel_report=True #@param {type:\"boolean\"}\n",
        "download_full_report=True #@param {type:\"boolean\"}\n",
        "\n",
        "plot_ppr=True #@param {type:\"boolean\"}\n",
        "plot_rna=True #@param {type:\"boolean\"}\n",
        "plot_score = False #@param {type:\"boolean\"}\n",
        "plot_edge = True #@param {type:\"boolean\"}\n",
        "plot_type = True #@param {type:\"boolean\"}\n",
        "plot_color_scheme='RdBu' #@param ['Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean'] {type:\"string\"}\n",
        "plot_color_bar = True #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown - `generate_excel_report` - Generate a human-friendly report file in xlsx format\n",
        "#@markdown - `download_full_report` - Once the processing is finished, the full result will be zipped and downloaded automatically.\n",
        "#@markdown - `plot_ppr` - Plot PPR motifs, colored by normalized score\n",
        "#@markdown - `plot_rna` - Plot PPR motifs w/ RNA base, colored by normalized score. **DISABLED when benchmark is running.**\n",
        "#@markdown - `plot_score` - Plot and color PPR motifs by normalized score. **DISABLED when benchmark is running.**\n",
        "#@markdown - `plot_edge` - Plot edges of each PPR motifs, colored by normalized score. **DISABLED when benchmark is running.**\n",
        "#@markdown - `plot_type` - Plot motif type of each PPR motifs(PPRfinder only), colored by normalized score. **DISABLED when benchmark is running.**\n",
        "#@markdown - `plot_color_scheme` - Color scheme used in plots.\n",
        "#@markdown - `plot_color_bar` - Plot color bar\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "debug = False #@param {type:\"boolean\"}\n",
        "#@markdown - `debug` - Some debug messages\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown Experimental (manually correction to scan results)\n",
        "\n",
        "fix_gap = False #@param {type:\"boolean\"}\n",
        "#@markdown - `fix_gap` - Fix gap in sequence scanning results. Turn it off so that the results will not be weird.\n",
        "if run_benchmark:\n",
        "  \n",
        "  plot_rna = False\n",
        "  plot_score = False\n",
        "  plot_edge = False\n",
        "  plot_type = False\n",
        "  os.system(f\"wget -qnc {BENCHMARK_DATA} -P {JOBS_DIR}/\")\n",
        "\n",
        "\n",
        "\n",
        "if not only_process_my_fasta:\n",
        "  open(f\"./{JOBS_DIR}/{jobname}.fasta\",'w').write(f'>{jobname}\\n{sequence}')\n",
        "else:\n",
        "  if len(glob.glob(f'{JOBS_DIR}/*.fasta'))==0:\n",
        "    raise FileNotFoundError(\"NO FASTA file is found!\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "15951fba894f4b03b970266ca4003612",
            "be3e358b896e4140b7d9a9d7e39d059c",
            "0990b9e146e34300b5c1fff619632be5",
            "ea4e6988aa8e4aa3be9a56063c980604",
            "a34f41874e8f42cfaf5c8b909276af6a",
            "d121be791eb2463ea3d08a0fe74e3456",
            "1ac14af781774cedb2f470e9d8ac31f8",
            "5fe5910bd812413fb2547845db046790",
            "56be84d1a0954749b7c92cd121ea34ab",
            "344949e3f1874a1984d277171b920980",
            "60e0fea5f573411099b70aec6dca0444"
          ]
        },
        "id": "LQ5Kdj0pCzAV",
        "cellView": "form",
        "outputId": "b50417b6-a2ac-4ee3-ea4c-1572044e6508"
      },
      "source": [
        "#@title Process the FASTAs\n",
        "#@markdown Click the _Play Button_ to process your data.\n",
        "fastas=glob.glob(f'{JOBS_DIR}/*.fasta')\n",
        "if debug: print(fastas)\n",
        "\n",
        "job_list=[s for f in fastas for s in SeqIO.parse(f,'fasta') ]\n",
        "if debug: print(len(job_list))\n",
        "total=len(job_list)\n",
        "\n",
        "with tqdm.notebook.tqdm(total=total, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "  for f in fastas:\n",
        "    for s in SeqIO.parse(f,'fasta'):\n",
        "      if program=='PS_Scan':\n",
        "        ppr=ps_scan(f,s)\n",
        "      elif program=='PPRfinder':\n",
        "        ppr=pprfinder(f,s)\n",
        "      else:\n",
        "        raise ValueError('What do you want me to do haiyaaaah?')\n",
        "\n",
        "      pickle.dump(ppr,open(f'{RES_DIR_PICKLE}/{s.id}.pkl','wb'))\n",
        "      pbar.update(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "version_minor": 0,
              "model_id": "15951fba894f4b03b970266ca4003612"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677,
          "referenced_widgets": [
            "faa498b63d294b03b615514578d28687",
            "6f76ad925d08442d8ac259c3fc11f266",
            "b780bfc62d654d869bbe1e623d2e80b2",
            "b0a2e35d0ef940c684e2c67a24563a88",
            "c4189c893a244f8d9e48eda621d2f860",
            "e02746eaa00a4f5c9e3c37ffba45941e",
            "f5e9f91023b44b7899719b6955b4c050",
            "fdba460a053a4175a48b5bd0ab3570b1",
            "e184d1936c8c4eddb02e9b6467c47e0d",
            "0fcbcaeed5b74df5a403da7e4fa83935",
            "2f5572f283f54984a82f60e3700c2308"
          ]
        },
        "id": "G-1aryIPoR3u",
        "outputId": "0f9d91ff-d1a8-45f1-b4b4-a4f67a1b0e79",
        "cellView": "form"
      },
      "source": [
        "#@title Plot PPRs\n",
        "#@markdown Click the _Play Button_ to plot your results.\n",
        "fixed_plot_width = 20 #@param {type:\"slider\", min:0, max:200, step:10}\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "from matplotlib import cm\n",
        "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
        "from __future__ import division\n",
        "\n",
        "def renormalize(n, range1, range2):\n",
        "    delta1 = range1[1] - range1[0]\n",
        "    delta2 = range2[1] - range2[0]\n",
        "    return (delta2 * (n - range1[0]) / delta1) + range2[0]\n",
        "\n",
        "\n",
        "viridis = cm.get_cmap(plot_color_scheme)\n",
        "\n",
        "\n",
        "\n",
        "def draw_my_ppr(s,ppr,features):\n",
        "  draw_features={\n",
        "      'Motif'     : [GraphicFeature(start=a, end=b, color=viridis( renormalize(float(c),[min(scores),max(scores)],[0,1])),strand=+1,\n",
        "                    label=d) for p in ppr for a,b,c,d in zip([p.annotations[\"PPR_STARTS\"]],[p.annotations[\"PPR_ENDS\"]],[p.annotations[\"PS_SCORE\"]],[p.annotations[\"PPR_CODE\"]])],\n",
        "      'RNA'       : [GraphicFeature(start=a, end=b, color=viridis(renormalize(float(c),[min(scores),max(scores)],[0,1])),strand=+1,\n",
        "                    label=d) for p in ppr for a,b,c,d in zip([p.annotations[\"PPR_STARTS\"]],[p.annotations[\"PPR_ENDS\"]],[p.annotations[\"PS_SCORE\"]],[p.annotations[\"RNA_BASE\"]])],\n",
        "      'Score'     : [GraphicFeature(start=a, end=b, color=viridis(renormalize(float(c),[min(scores),max(scores)],[0,1])),strand=+1,\n",
        "                    label=str(d)) for p in ppr for a,b,c,d in zip([p.annotations[\"PPR_STARTS\"]],[p.annotations[\"PPR_ENDS\"]],[p.annotations[\"PS_SCORE\"]],[p.annotations[\"PS_SCORE\"]])],\n",
        "      'Edge'      : [GraphicFeature(start=a, end=b, color=viridis(renormalize(float(c),[min(scores),max(scores)],[0,1])),strand=+1,\n",
        "                    label=\"-\".join([str(a),str(b)])) for p in ppr for a,b,c in zip([p.annotations[\"PPR_STARTS\"]],[p.annotations[\"PPR_ENDS\"]],[p.annotations[\"PS_SCORE\"]])],\n",
        "      'Type'      : [GraphicFeature(start=a, end=b, color=viridis(renormalize(float(c),[min(scores),max(scores)],[0,1])),strand=+1,\n",
        "                    label=str(d)) for p in ppr for a,b,c,d in zip([p.annotations[\"PPR_STARTS\"]],[p.annotations[\"PPR_ENDS\"]],[p.annotations[\"PS_SCORE\"]],[p.annotations[\"PPR_TYPE\"]])] if program==\"PPRfinder\" else [],\n",
        "  }\n",
        "\n",
        "  title={'Motif'      : 'PPR Motif',\n",
        "         'RNA'        : 'RNA bases',\n",
        "         'Score'      : f'{program} Score',\n",
        "         'Edge'       : 'Motif Edge',\n",
        "         'Type'       : 'Type'}\n",
        "  for feature in features:\n",
        "    assert feature in draw_features,'Haiyaahh!'\n",
        "    \n",
        "    record = GraphicRecord(sequence_length=len(s.seq), features=draw_features[feature])\n",
        "    ax, _ =record.plot(figure_width=fixed_plot_width if fixed_plot_width != 0 else len(s.seq)/20)\n",
        "\n",
        "    ax.set_title(f\"{title[feature]}: {s.id}\", loc='left', weight='bold')\n",
        "    ax.figure.savefig(f'{RES_DIR_FIGURE}/{feature}-{s.id}.png')\n",
        "\n",
        "total=len(job_list)\n",
        "\n",
        "with tqdm.notebook.tqdm(total=total, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "\n",
        "  pkls_files=glob.glob(f'{RES_DIR_PICKLE}/*.pkl')\n",
        "  pkls={pathlib.Path(x).stem: x for x in pkls_files}\n",
        "  fastas=glob.glob(f'{JOBS_DIR}/*.fasta')\n",
        "  job_list={s.id:s for f in fastas for s in SeqIO.parse(f,'fasta') }\n",
        "  \n",
        "  scores=[]\n",
        "  for s in job_list.keys():\n",
        "    data = pickle.load(open(f'{RES_DIR_PICKLE}/{s}.pkl','rb'))\n",
        "    scores+=[sc for p in data for sc in  [p.annotations[\"PS_SCORE\"]]]\n",
        "  assert len(scores)!=0,f'Ohhhhh it seems like no PPR motif are detected by {program} !'\n",
        "  print(f'score minima: {min(scores)}')\n",
        "  print(f'score maxima: {max(scores)}')\n",
        "  \n",
        "\n",
        "  # color bar\n",
        "  if plot_color_bar:\n",
        "    fig, ax = plt.subplots(figsize=(6, 1))\n",
        "    fig.subplots_adjust(bottom=0.5)\n",
        "\n",
        "    norm = matplotlib.colors.Normalize(vmin=min(scores), vmax=max(scores))\n",
        "\n",
        "    fig.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=viridis),\n",
        "                cax=ax, orientation='horizontal', label=f'{program} Score')\n",
        "    fig.savefig(f'{RES_DIR_FIGURE}/Colorbar-{program}.png')\n",
        "\n",
        "  what_to_plot=[]\n",
        "  if plot_ppr: what_to_plot.append('Motif')\n",
        "  if plot_rna: what_to_plot.append('RNA')\n",
        "  if plot_score: what_to_plot.append('Score')\n",
        "  if plot_edge: what_to_plot.append('Edge')\n",
        "  if plot_type and program=='PPRfinder': what_to_plot.append('Type')\n",
        "\n",
        "  for s in job_list.keys():\n",
        "    data = pickle.load(open(f'{RES_DIR_PICKLE}/{s}.pkl','rb'))\n",
        "    #print(s)\n",
        "    try:\n",
        "      draw_my_ppr(job_list[s],data,what_to_plot)\n",
        "    except Exception as e:\n",
        "      traceback.print_exc() #if debug else print(e)\n",
        "    finally:\n",
        "      pbar.update(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "faa498b63d294b03b615514578d28687"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score minima: 5.031\n",
            "score maxima: 14.096\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x158.4 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x158.4 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x187.2 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jduRSMFAh5HO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "562d85a82c54415eb0119fa387990de1",
            "5ac007b1fe044aae81d7a058506ba790",
            "1fed37e4fb244bda96ea686e687e26aa",
            "151af6ca557045f2a2648e1c168cd9f4",
            "295fd9b94284418fa6430c82ecf363e1",
            "66881d1299a4483d806af14b32901dbb",
            "9588d1b1206843c7b59ec090ece79557",
            "cda9c57fbdc04b1785182d940c51e9c1",
            "6b0a4dfe67ea4de0a4681d782c42f4cb",
            "0e6973ea418242249e0a6282b41bb9fd",
            "4b82be7d13a54cdc9af2723a8526a186"
          ]
        },
        "cellView": "form",
        "outputId": "8492b02b-422f-46d7-9e4b-e3d7962a3f90"
      },
      "source": [
        "#@title Generate report and download it!\n",
        "#@markdown Click the _Play Button_ to generate result in `xlsx` format\n",
        "def generate_full_report():\n",
        "  \n",
        "  pkls_files=glob.glob(f'{RES_DIR_PICKLE}/*.pkl')\n",
        "  process_id = time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())\n",
        "  xlsx_filename = f\"{RES_DIR_REPORT}/PPRCODE_RESULTS_{process_id}.xlsx\" \n",
        "  test_book = xw.Workbook(xlsx_filename)\n",
        "\n",
        "  # set workbook property\n",
        "  test_book.set_properties({\n",
        "                'title': 'PPRCODE PREDICTION',\n",
        "                'subject': 'https://colab.research.google.com/github/YaoYinYing/PPRCODE_Guideline/blob/master/PPRCODE.ipynb',\n",
        "                'author': 'PPRCODE project, Colab release',\n",
        "                'manager': 'Yan et al.',\n",
        "                'comments': 'Created with pprcode project.'})\n",
        "\n",
        "  total=len(pkls_files)\n",
        "  with tqdm.notebook.tqdm(total=total, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
        "    for pf in pkls_files:\n",
        "      try:\n",
        "        data = pickle.load(open(pf,'rb'))\n",
        "        motif_starts  = [x.annotations[  \"PPR_STARTS\"   ] for x in data]\n",
        "        motif_ends    = [x.annotations[  \"PPR_ENDS\"     ] for x in data]\n",
        "        motif_sequence= [str(x.seq) for x in data]\n",
        "        fifth_code    = [x.annotations[  \"FIFTH_AA\"     ] for x in data]\n",
        "        last_code     = [x.annotations[  \"LAST_AA\"      ] for x in data]\n",
        "        ppr_code      = [x.annotations[  \"PPR_CODE\"     ] for x in data]\n",
        "        rna_base      = [x.annotations[  \"RNA_BASE\"     ] for x in data]\n",
        "        motif_length  = [len(str(x.seq)) for x in data]\n",
        "        motif_score   = [x.annotations[  \"PS_SCORE\"     ] for x in data]\n",
        "        motif_type    = [x.annotations[  \"PPR_TYPE\"     ] for x in data] if program=='PPRfinder' else ['' for x in data]\n",
        "        data_list=[[a,b,c,d,e,f,g,h,i,j] \n",
        "               for a,b,c,d,e,f,g,h,i,j in zip(\n",
        "                   motif_starts,\n",
        "                   motif_ends,\n",
        "                   motif_sequence,\n",
        "                   fifth_code,\n",
        "                   last_code,\n",
        "                   ppr_code,\n",
        "                   rna_base,\n",
        "                   motif_length,\n",
        "                   motif_score,\n",
        "                   motif_type)]\n",
        "        seq_id=pathlib.Path(pf).resolve().stem\n",
        "        try:\n",
        "          write_excel_tables(test_book,seq_id,data_list)\n",
        "        except Exception as e:\n",
        "          print(\"Oops! Something wrong hanpens when writing in to excel report!\")\n",
        "          print(seq_id)\n",
        "          traceback.print_exc() if debug else print(e)\n",
        "      except Exception as e:\n",
        "        print(\"Oops! Something wrong hanpens when loading pickle file!\")\n",
        "        print(seq_id)\n",
        "        traceback.print_exc() if debug else print(e)\n",
        "      finally:\n",
        "        pbar.update(1)\n",
        "    print(\"All jobs are accomplished.\")\n",
        "    test_book.close()\n",
        "\n",
        "def write_excel_tables(test_book, sequence_name, ppr_total_list_reversed):\n",
        "    # reversed ppr code result list for making xlsx file\n",
        "\n",
        "    code_count_result_name = \"PPR motifs and PPR codes: %s\" % sequence_name\n",
        "    format_title = test_book.add_format()\n",
        "    format_content = test_book.add_format()\n",
        "    format_title.set_font(\"Times New Roman\")\n",
        "    format_title.set_font_size(11)\n",
        "    format_title.set_bold()\n",
        "    format_title.set_bottom(2)\n",
        "\n",
        "    format_content.set_font(\"Times New Roman\")\n",
        "    format_content.set_font_size(11)\n",
        "    sheet_name=sequence_name\n",
        "    for c in \"[]:*?/\\\\+@!^'{}()|#%&~`\\\"\":\n",
        "      sheet_name=sheet_name.replace(c,\"_\")\n",
        "    worksheet = test_book.add_worksheet(sheet_name)\n",
        "    worksheet.set_column(0, 1, 10)\n",
        "    worksheet.set_column(2, 2, 50)\n",
        "    worksheet.set_column(3, 8, 10)\n",
        "\n",
        "    worksheet.write(0, 0, code_count_result_name, format_title)\n",
        "    for n in range(1, 9):\n",
        "        worksheet.write(0, n, \"\", format_title)\n",
        "    worksheet.write(1, 0, \"Motif Start\", format_title)\n",
        "    worksheet.write(1, 1, \"Motif End\", format_title)\n",
        "    worksheet.write(1, 2, \"Motif Sequence\", format_title)\n",
        "    worksheet.write(1, 3, \"Fifth amino acid\", format_title)\n",
        "    worksheet.write(1, 4, \"Last amino acid\", format_title)\n",
        "    worksheet.write(1, 5, \"PPR code\", format_title)\n",
        "    worksheet.write(1, 6, \"RNA base\", format_title)\n",
        "    worksheet.write(1, 7, \"Motif Length\", format_title)\n",
        "    worksheet.write(1, 8, f\"{program} Score\" , format_title)\n",
        "    if program=='PPRfinder':\n",
        "      worksheet.write(1, 9, \"Motif Type\" , format_title) \n",
        "    row = 2\n",
        "    col = 0\n",
        "\n",
        "    rna = \"\"\n",
        "    for ppr_list in ppr_total_list_reversed:\n",
        "        # print(ppr_list)\n",
        "        motif_start = ppr_list[0]\n",
        "        motif_end = ppr_list[1]\n",
        "        motif_sequence = ppr_list[2]\n",
        "        fifth_code = ppr_list[3]\n",
        "        last_code = ppr_list[4]\n",
        "        ppr_code = ppr_list[5]\n",
        "        rna_base = ppr_list[6]\n",
        "\n",
        "        rna = ''.join([rna, \" (\", rna_base, \")\"])\n",
        "\n",
        "        motif_length = ppr_list[7]\n",
        "        motif_score = ppr_list[8]\n",
        "        if program=='PPRfinder':\n",
        "          motif_type = ppr_list[9] \n",
        "\n",
        "        worksheet.write(row, col, motif_start, format_content)\n",
        "        worksheet.write(row, col + 1, motif_end, format_content)\n",
        "        worksheet.write(row, col + 2, motif_sequence, format_content)\n",
        "        worksheet.write(row, col + 3, fifth_code, format_content)\n",
        "        worksheet.write(row, col + 4, last_code, format_content)\n",
        "        worksheet.write(row, col + 5, ppr_code, format_content)\n",
        "        worksheet.write(row, col + 6, rna_base, format_content)\n",
        "        worksheet.write(row, col + 7, motif_length, format_content)\n",
        "        worksheet.write(row, col + 8, motif_score, format_content)\n",
        "        if program=='PPRfinder':\n",
        "          worksheet.write(row, col + 9, motif_type, format_content) \n",
        "        row += 1\n",
        "        pass\n",
        "    worksheet.write(row + 1, col, \"The RNA predicted by this PPR sequence:\", format_title)\n",
        "    worksheet.write(row + 2, col, rna, format_title)\n",
        "    worksheet.write(row + 4, col, \"Notes:\", format_title)\n",
        "    worksheet.write(row + 5, col,\n",
        "                    \"'-' represents no correlated RNA bases for this PPR code identified by biochemical assays (EMSA, ITC);\",\n",
        "                    format_content)\n",
        "    worksheet.write(row + 6, col,\n",
        "                    \"'?' represents unknown bases, as no biochemical assays have been performed to identify the correlated RNA bases for the PPR code.\",\n",
        "                    format_content)\n",
        "    worksheet.write(row + 8, col, \"If you find this tool useful to your research, please cite the following:\",\n",
        "                    format_title)\n",
        "    worksheet.write(row + 9, col,\n",
        "                    \"Junjie Yan, Yinying Yao, Sixing Hong, Yan Yang, Cuicui Shen, Qunxia Zhang, Delin Zhang, Tingting Zou, Ping Yin; Delineation of pentatricopeptide repeat codes for target RNA prediction, Nucleic Acids Research, gkz075, https://doi.org/10.1093/nar/gkz075\",\n",
        "                    format_content)\n",
        "    worksheet.write(row + 11, col, \"Copyright: Yin lab\", format_title)\n",
        "    pass\n",
        "\n",
        "\n",
        "if generate_excel_report: \n",
        "  generate_full_report()\n",
        "\n",
        "zipped=f'PPRCODE_{time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())}.zip'\n",
        "os.system(f\"zip -FSr {zipped} {RES_DIR}\")\n",
        "\n",
        "if download_full_report:\n",
        "  if IN_COLAB:\n",
        "    files.download(f'{zipped}')\n",
        "  else:\n",
        "    print(\"this notebook appears to be running locally, to download click folder icon to the left, navigate to file, right click and download\")\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "562d85a82c54415eb0119fa387990de1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [elapsed: 00:00 remaining: ?]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All jobs are accomplished.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_f0814603-aa3e-4981-badd-c292105474ad\", \"PPRCODE_20220122_114538.zip\", 11648)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJzqlRNKqP87"
      },
      "source": [
        "# Acknowledgements\n",
        "\n",
        "\n",
        "\n",
        "1.   [ColabFold](https://github.com/sokrypton/ColabFold) for fancy Colab codes.\n",
        "2.   [DnaFeaturesViewer](https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer) for impressive plotting of sequences\n",
        "3.   [Plotly](https://plotly.com/python) and [Plotly-Orca](https://github.com/plotly/orca) \n",
        "4.   [Matplotlib](https://matplotlib.org/index.html)\n",
        "5.   [Prosite](https://ftp.expasy.org/databases/prosite) and [PS_Scan](https://ftp.expasy.org/databases/prosite/ps_scan/) program\n",
        "6.   [Biopython](https://biopython.org/)\n",
        "7.   [PPRfinder](https://github.com/ian-small/PPRfinder) \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RghQ3tpxGAZn"
      },
      "source": [
        "# FAQ\n",
        "*   I'm new here. How do I process my PPR protein using this Notebook?\n",
        "  *   First of all, you need a Google account to get you logged in.\n",
        "  *   Click the `Play Buttom` ▶ beside the first cell marking `Install packages` to start a new Colab session. If you are warned by a message like _**\"This notebook was not authored by Google\"**_ , please don't worry about it. Click `Run anyway` , then re-click that `Play Buttom` ▶ to go ahead.\n",
        "  *   If you have only one sequence, just paste its **amino-acid** in the input box marking `sequence:` in the second cell. Don't forget naming your jobs running in `jobname:` box.\n",
        "  *    Now find `Runtime` in `top menu bar` and hit `Run all` to execute the following cells to perform PPR processing, plotting, report generation and file downloading.\n",
        "  *    After the processing is over, just feel free to close the browser tab directly.\n",
        "*   I have handreds of thousands of sequences to process. How do I run a batch processing?\n",
        "   *   First of all, you need a Google account to get you logged in.\n",
        "   *   Click the `Play Buttom` ▶ beside the first cell marking `Install packages` to start a new Colab session. If you are warned by a message like _**\"This notebook was not authored by Google\"**_ , please don't worry about it. Click `Run anyway` , then re-click that `Play Buttom` ▶ to go ahead.\n",
        "   *    Now you will find a icon like a folder 📁 on left side panel. Click it to check all your working directory.\n",
        "   *    You will find a subfolder named `jobs`. Right-click it and upload your sequences file. Note that we only support [FASTA](https://www.ncbi.nlm.nih.gov/genbank/fastaformat/) format and the suffix should be `.fasta`. If anything werd hanppens, please check your file first.\n",
        "   *    After uploading is over, please make sure the option `only_process_my_fasta:` is checked because we want to ignore the example data in these boxes.\n",
        "   *    Still find `Runtime` in `top menu bar` and hit `Run all` to execute the following cells to perform PPR processing, plotting, report generation and file downloading.\n",
        "   *    After the processing is over, just feel free to close the browser tab directly.\n",
        "*   How do I re-initialize my working space?\n",
        "  *   Still find `Runtime` in `top menu bar` and you will find `Factory rest runtime`. \n",
        "*   Does Colab install anything in my computer?\n",
        "  * No, everything hanpens in Colab backend server. **Await is all you need.**\n",
        "*  What is Colab? Do I need to pay for it?\n",
        "   *  Please see [Colab FAQ document](https://research.google.com/colaboratory/faq.html)\n",
        "   *  PPRCODE only uses the CPU resource of Colab. So it is definitely free-of-charge. **Please do not set `runtime` to `GPU` or `TPU` because neither of them will help at all.**\n",
        "*  How long will the job running take?\n",
        "   *   PPRCODE colab release has got much faster than orignal server because we use a copy of `PS_scan` program for local processing instead of webpage submission. For accurate timing of the running, please check the `progress bar` on each cell. \n",
        "*  I received some error messages, what should I do?\n",
        "   * It doesn't matter if the processing is correct.\n",
        "   * Meanwhile, please don't name your sequences with uncommon characters.\n",
        "*  How should I share my feedback or report a bug?\n",
        "   *  Please share your any feedback or report bugs as a issue on [GitHub](https://github.com/YaoYinYing/PPRCODE_Guideline/issues)\n",
        "   *  Please do not send/reply anything to the email address of [original server](http://yinlab.hzau.edu.cn/pprcode/) because **no one** will dig around that inbox.\n",
        "*  Why does the final report look a bit different from that of orginal server?\n",
        "   *  In the program running on our original server, one of the options is enabled to make a _manual correction_ of `PS_scan` result. However, this is now disabled by default in Colab version as a experimental option `fix_gap:` so that you won't receive any odd result.\n",
        "*  Can I run this jupyter notebook on my local machine?\n",
        "   * Absolutely yes! If your are familiar with Python programming and basic Unix shell scripts, just have fun with it! But be aware that this notebook is written for linux only."
      ]
    }
  ]
}