parser_test_ver.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import re\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data and select columns\n",
    "MSF_raw = pd.read_csv('data_old/peptide.tsv', sep='\\t')\n",
    "MM_raw = pd.read_csv('data_old/AllQuantifiedPeptides.tsv', sep='\\t')\n",
    "MQ_raw = pd.read_csv('data_old/evidence.txt', sep='\\t')\n",
    "AP_raw = pd.read_csv(\"data_old/results_peptides.csv\")\n",
    "MM_all_raw = pd.read_csv(\"data_old/AllPeptides.psmtsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define modifications dictionary\n",
    "mods_dict = {\n",
    "    \"cC\": \"C_carbamidomethylation\",\n",
    "    \"C(57.0214)\": \"C_carbamidomethylation\",\n",
    "    \"[Common Fixed:Carbamidomethyl on C]\": \"C_carbamidomethylation\",\n",
    "    \"(Oxidation (M))\": \"M_oxidation\",\n",
    "    \"M(15.9949)\": \"M_oxidation\",\n",
    "    \"oxM\": \"M_oxidation\",\n",
    "    \"[Common Variable:Oxidation on M]\": \"M_oxidation\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function for removing duplicates and sorting\n",
    "def _sort_and_remove_duplicates(protein):\n",
    "    proteins = protein.split(\", \")\n",
    "    unique_proteins = sorted(set(proteins))\n",
    "    return \", \".join(unique_proteins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSF parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define MSF parser\n",
    "def MSF_parser(df, mapper=mods_dict):\n",
    "    \n",
    "    # select and rename cols\n",
    "    df = df[['Peptide', 'Probability', 'Assigned Modifications', 'Protein']]\n",
    "    df = df.rename(columns={'Peptide': 'peptide', \"Probability\": \"MSF_probability\", 'Assigned Modifications': 'mods_type', 'Protein': 'proteinID'})\n",
    "\n",
    "    # add mods col \n",
    "    df['mods'] = df['mods_type'].apply(lambda x: 'modified' if isinstance(x, str) else 'unmodified')\n",
    "    \n",
    "    # remove '-p1', remove duplicates and sort proteinID col\n",
    "    df['proteinID'] = df['proteinID'].str.replace('-p1', '').apply(_sort_and_remove_duplicates)\n",
    "\n",
    "    # use mapper to get wished output\n",
    "    for key, val in mapper.items():\n",
    "        df['mods_type'] = df['mods_type'].str.replace(key, val)\n",
    "    df['mods_type'] = df['mods_type'].astype(str)\n",
    "\n",
    "    # order mods_type\n",
    "    def _mods_type_sorter(val: str):\n",
    "        return ', '.join(sorted(val.split(', '), key=lambda el: None if el == 'nan' else int(re.search('^\\d+', el).group(0))))\n",
    "    \n",
    "    df['mods_type'] = df['mods_type'].apply(_mods_type_sorter)\n",
    "\n",
    "    # select max values for those with matching keys\n",
    "    df = df.groupby(['peptide', 'proteinID', 'mods', 'mods_type'], as_index=False)['MSF_probability'].max()\n",
    "\n",
    "    return df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>peptide</th>\n",
       "      <th>proteinID</th>\n",
       "      <th>mods</th>\n",
       "      <th>mods_type</th>\n",
       "      <th>MSF_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AEMLYSGESGPDDKYYVGIK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1</td>\n",
       "      <td>modified</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FLADKFDWDVAEAR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GGIVGGGGGGGGFSR</td>\n",
       "      <td>TcCLB.503575.27:mRNA</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.9910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GLKPDIPPLDTFLDK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GVIIGEENRPGTPIYNVR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                peptide              proteinID        mods     mods_type   \n",
       "0  AEMLYSGESGPDDKYYVGIK  TCSYLVIO_009936-t26_1    modified  3M_oxidation  \\\n",
       "1        FLADKFDWDVAEAR  TCSYLVIO_009936-t26_1  unmodified           nan   \n",
       "2       GGIVGGGGGGGGFSR   TcCLB.503575.27:mRNA  unmodified           nan   \n",
       "3       GLKPDIPPLDTFLDK  TCSYLVIO_009936-t26_1  unmodified           nan   \n",
       "4    GVIIGEENRPGTPIYNVR  TCSYLVIO_009936-t26_1  unmodified           nan   \n",
       "\n",
       "   MSF_probability  \n",
       "0           1.0000  \n",
       "1           0.9999  \n",
       "2           0.9910  \n",
       "3           0.9969  \n",
       "4           1.0000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSF = MSF_parser(MSF_raw)\n",
    "MSF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MM parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define MM parser\n",
    "def MM_parser(df, df_all, mapper=mods_dict):\n",
    "\n",
    "    # add new col from df_all, select and rename cols\n",
    "    df['MM_score'] = df['Sequence'].map(df_all.set_index('Full Sequence')['Score'])\n",
    "    df = df[['MM_score', 'Base Sequence', 'Sequence', 'Protein Groups']]\n",
    "    df = df.rename(columns={'Base Sequence': 'peptide', 'Sequence': 'mods_type', 'Protein Groups': 'proteinID'})\n",
    "\n",
    "    # add mods col\n",
    "    df['mods'] = df.apply(lambda row: 'modified' if row['mods_type'] != row['peptide'] else 'unmodified', axis=1)\n",
    "\n",
    "    # remove '-p1', remove duplicates and sort proteinID col\n",
    "    df['proteinID'] = df['proteinID'].str.replace('-p1', '').str.replace(' | ', ',').str.replace('|', ',').apply(_sort_and_remove_duplicates)\n",
    "    \n",
    "    # order mods type\n",
    "    def _mods_type_extract(val):\n",
    "        res = []\n",
    "        while re.search('(\\[[^\\[\\]]+\\])', val):\n",
    "            m = re.search('(\\[[^\\[\\]]+\\])', val)\n",
    "            res.append(f'{m.start()}{mapper[m.group(1)]}')\n",
    "            val = val[:m.start()] + val[m.end():]\n",
    "\n",
    "        if not res:\n",
    "            return 'nan'\n",
    "            \n",
    "        return ', '.join(res)\n",
    "    \n",
    "    df['mods_type'] = df['mods_type'].apply(_mods_type_extract)\n",
    "    \n",
    "    # select max values for those with matching keys\n",
    "    df = df.groupby(['peptide', 'proteinID', 'mods_type', 'mods'], as_index=False)['MM_score'].max()\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>peptide</th>\n",
       "      <th>proteinID</th>\n",
       "      <th>mods_type</th>\n",
       "      <th>mods</th>\n",
       "      <th>MM_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AEMLYSGESGPDDK</td>\n",
       "      <td>TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>modified</td>\n",
       "      <td>10.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AEMLYSGESGPDDK</td>\n",
       "      <td>TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...</td>\n",
       "      <td>nan</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>11.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AEMLYSGESGPDDKYYVGIK</td>\n",
       "      <td>TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>modified</td>\n",
       "      <td>13.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AYLPVAESFGFTADLR</td>\n",
       "      <td>TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...</td>\n",
       "      <td>nan</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>11.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DLQEDFMNGAPLK</td>\n",
       "      <td>TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...</td>\n",
       "      <td>7M_oxidation</td>\n",
       "      <td>modified</td>\n",
       "      <td>14.344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                peptide                                          proteinID   \n",
       "0        AEMLYSGESGPDDK  TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...  \\\n",
       "1        AEMLYSGESGPDDK  TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...   \n",
       "2  AEMLYSGESGPDDKYYVGIK  TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...   \n",
       "3      AYLPVAESFGFTADLR  TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...   \n",
       "4         DLQEDFMNGAPLK  TcCLB.508169.20:mRNA,TcCLB.508169.20:mRNA,TcCL...   \n",
       "\n",
       "      mods_type        mods  MM_score  \n",
       "0  3M_oxidation    modified    10.509  \n",
       "1           nan  unmodified    11.598  \n",
       "2  3M_oxidation    modified    13.495  \n",
       "3           nan  unmodified    11.561  \n",
       "4  7M_oxidation    modified    14.344  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MM = MM_parser(MM_raw, MM_all_raw)\n",
    "MM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MQ parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MQ_parser(df, mapper=mods_dict):\n",
    "\n",
    "    # select and rename cols\n",
    "    df = df[[\"Sequence\", \"Modified sequence\", \"Score\", \"Proteins\"]]\n",
    "    df = df.rename(\n",
    "        columns={\"Sequence\": \"peptide\", \"Score\": \"MQ_score\", \"Modified sequence\": \"mods_type\", \"Proteins\": \"proteinID\"}\n",
    "    )\n",
    "\n",
    "    # remove contaminants\n",
    "    df = df.drop(df[df[\"proteinID\"].str.contains(\"CON__\")].index)\n",
    "\n",
    "    # add mods col\n",
    "    df[\"mods_type\"] = df[\"mods_type\"].str.replace(\"_\", \"\")\n",
    "    df[\"mods\"] = df.apply(lambda row: \"unmodified\" if row[\"mods_type\"] == row[\"peptide\"] else \"modified\", axis=1)\n",
    "\n",
    "    # remove '-p1', remove duplicates and sort proteinID col\n",
    "    df[\"proteinID\"] = df[\"proteinID\"].str.replace(\"-p1\", \"\").str.replace(\";\", \", \").apply(_sort_and_remove_duplicates)\n",
    "\n",
    "    # order mods type\n",
    "    def _mods_type_extract(val):\n",
    "        res = []\n",
    "        while re.search(\"(\\(\\w+\\s+\\([A-Z]\\)\\))\", val):\n",
    "            m = re.search(\"(\\(\\w+\\s+\\([A-Z]\\)\\))\", val)\n",
    "            res.append(f\"{m.start()}{mapper[m.group(0)]}\")\n",
    "            val = val[: m.start()] + val[m.end() :]\n",
    "\n",
    "        if not res:\n",
    "            return \"nan\"\n",
    "\n",
    "        return \", \".join(res)\n",
    "\n",
    "    df[\"mods_type\"] = df[\"mods_type\"].apply(_mods_type_extract)\n",
    "\n",
    "    # select max values for those with matching keys\n",
    "    df = df.groupby([\"peptide\", \"proteinID\", \"mods\", \"mods_type\"], as_index=False)[\"MQ_score\"].max()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>peptide</th>\n",
       "      <th>proteinID</th>\n",
       "      <th>mods</th>\n",
       "      <th>mods_type</th>\n",
       "      <th>MQ_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AEMLYSGESGPDDK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>22.4860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AEMLYSGESGPDDKYYVGIK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.7873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALLSLSPR</td>\n",
       "      <td>TCSYLVIO_010625-t26_1, TcCLB.510407.40:mRNA</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>44.4070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALMDYPEQIR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TCSYLVIO_009940-t26_1, ...</td>\n",
       "      <td>modified</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>49.5540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALMDYPEQIR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TCSYLVIO_009940-t26_1, ...</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>40.2390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                peptide                                          proteinID   \n",
       "0        AEMLYSGESGPDDK  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...  \\\n",
       "1  AEMLYSGESGPDDKYYVGIK  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...   \n",
       "2              ALLSLSPR        TCSYLVIO_010625-t26_1, TcCLB.510407.40:mRNA   \n",
       "3            ALMDYPEQIR  TCSYLVIO_009936-t26_1, TCSYLVIO_009940-t26_1, ...   \n",
       "4            ALMDYPEQIR  TCSYLVIO_009936-t26_1, TCSYLVIO_009940-t26_1, ...   \n",
       "\n",
       "         mods     mods_type  MQ_score  \n",
       "0  unmodified           nan   22.4860  \n",
       "1  unmodified           nan    4.7873  \n",
       "2  unmodified           nan   44.4070  \n",
       "3    modified  3M_oxidation   49.5540  \n",
       "4  unmodified           nan   40.2390  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MQ = MQ_parser(MQ_raw)\n",
    "MQ.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AA parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define AP parser\n",
    "def AP_parser(df, mapper=mods_dict):\n",
    "\n",
    "    # select and rename cols\n",
    "    df = df[['sequence', 'sequence_naked', 'score', 'protein']]\n",
    "    df = df.rename(columns={'sequence_naked': 'peptide', 'score': 'AP_score', 'sequence': 'mods_type', 'protein': 'proteinID'})\n",
    "\n",
    "    # add mods col\n",
    "    df['mods'] = df.apply(lambda row: 'modified' if row['mods_type'] != row['peptide'] else 'unmodified', axis=1)\n",
    "\n",
    "    # remove '-p1', remove duplicates and sort proteinID col\n",
    "    df['proteinID'] = df['proteinID'].str.replace('-p1', '').str.replace(',', ', ').apply(_sort_and_remove_duplicates)\n",
    "\n",
    "    # order mods type\n",
    "    def _mods_type_extract(val):\n",
    "        res = []\n",
    "        while re.search('([a-z]+[A-Z])', val):\n",
    "            m = re.search('([a-z]+[A-Z])', val)\n",
    "            res.append(f'{m.start()+1}{mapper[m.group(1)]}')\n",
    "            val = val[:m.start()] + val[m.end()-1:]\n",
    "\n",
    "        if not res:\n",
    "            return 'nan'\n",
    "            \n",
    "        return ', '.join(res)\n",
    "    \n",
    "    df['mods_type'] = df.mods_type.apply(_mods_type_extract)\n",
    "\n",
    "    # select max values for those with matching keys\n",
    "    df = df.groupby(['peptide', 'proteinID', 'mods', 'mods_type'], as_index=False)['AP_score'].max()\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>peptide</th>\n",
       "      <th>proteinID</th>\n",
       "      <th>mods</th>\n",
       "      <th>mods_type</th>\n",
       "      <th>AP_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AEMLYSGESGPDDK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>modified</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>0.989660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AEMLYSGESGPDDKYYVGIK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>modified</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AEMLYSGESGPDDKYYVGIK</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.914360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALMDYPEQIR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>modified</td>\n",
       "      <td>3M_oxidation</td>\n",
       "      <td>0.943901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALMDYPEQIR</td>\n",
       "      <td>TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...</td>\n",
       "      <td>unmodified</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.919905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                peptide                                          proteinID   \n",
       "0        AEMLYSGESGPDDK  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...  \\\n",
       "1  AEMLYSGESGPDDKYYVGIK  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...   \n",
       "2  AEMLYSGESGPDDKYYVGIK  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...   \n",
       "3            ALMDYPEQIR  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...   \n",
       "4            ALMDYPEQIR  TCSYLVIO_009936-t26_1, TcCLB.508169.20:mRNA, T...   \n",
       "\n",
       "         mods     mods_type  AP_score  \n",
       "0    modified  3M_oxidation  0.989660  \n",
       "1    modified  3M_oxidation  1.000000  \n",
       "2  unmodified           nan  0.914360  \n",
       "3    modified  3M_oxidation  0.943901  \n",
       "4  unmodified           nan  0.919905  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AP = AP_parser(AP_raw)\n",
    "AP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge dataframes\n",
    "merged = MSF.merge(MM, how='outer', left_on=['peptide', 'mods', 'proteinID', 'mods_type'], right_on=['peptide', 'mods', 'proteinID', 'mods_type']).merge(MQ, how='outer', left_on=['peptide', 'mods', 'proteinID', 'mods_type'], right_on=['peptide', 'mods', 'proteinID', 'mods_type']).merge(AP, how='outer', left_on=['peptide', 'mods', 'proteinID', 'mods_type'], right_on=['peptide', 'mods', 'proteinID', 'mods_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_date = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n",
    "merged.to_csv(f'{current_date}_merged.csv', index=False)"
   ]
  }
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