4-Data-Engineering-Visualization.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "64a2db26",
   "metadata": {},
   "source": [
    "### Data Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98b71f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json as json\n",
    "import glob as glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb82f7df",
   "metadata": {},
   "outputs": [],
   "source": [
    "files= glob.glob(\"data/aac-data/data/*.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35a8b2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/8_4.json',\n",
       " 'data/8_8.json',\n",
       " 'data/8_9.json',\n",
       " 'data/9_11.json',\n",
       " 'data/8_10.json',\n",
       " 'data/9_7.json',\n",
       " 'data/9_10.json',\n",
       " 'data/6_16.json',\n",
       " 'data/9_8.json',\n",
       " 'data/9_4.json',\n",
       " 'data/9_13.json',\n",
       " 'data/9_12.json',\n",
       " 'data/9_5.json',\n",
       " 'data/9_9.json',\n",
       " 'data/9_2.json',\n",
       " 'data/8_14.json',\n",
       " 'data/6_11.json',\n",
       " 'data/8_15.json',\n",
       " 'data/8_7.json']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "252edd1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8_8.json'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[1].split(\"/\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92308db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,file in enumerate(files):\n",
    "    with open(file, 'r') as f:\n",
    "        data = json.load(f)\n",
    "        if i ==0:\n",
    "            df = pd.DataFrame(data)\n",
    "        else:\n",
    "            idf = pd.DataFrame(data)\n",
    "            df = pd.concat([df,idf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc961362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59623, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f20b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"alldata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c3fb45c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15455"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IDs= df['Uniprot_AC'].unique()\n",
    "len(IDs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97f64a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Q61598', 'Q9QXS1', 'P11983', ..., 'D3Z656', 'E9Q7S0', 'F7BQW7'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60509465",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd, requests\n",
    "import json\n",
    "\n",
    "\n",
    "def get_short_full_names(names, section_key, section_values):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get all names from an entry\n",
    "    \n",
    "    PARAMS:\n",
    "    - names (list)\n",
    "    '''\n",
    "    # Full name\n",
    "    if section_key == 'fullName':\n",
    "        name = section_values['value']\n",
    "        names.append(name)\n",
    "\n",
    "    # Short names\n",
    "    if section_key == 'shortNames':\n",
    "        for entry in section_values:\n",
    "            name = entry['value']\n",
    "            names.append(name)  \n",
    "            \n",
    "    # EC Numbers\n",
    "    if section_key == 'ecNumbers':\n",
    "         for entry in section_values:\n",
    "            name = entry['value']\n",
    "            names.append(name) \n",
    "            \n",
    "    return names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4efcf220",
   "metadata": {},
   "outputs": [],
   "source": [
    "id2names = dict()\n",
    "for key,value in id2syns.items():\n",
    "    #print(key,value)\n",
    "    id2names[key] = value[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87dc7c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "id2names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8394331",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pname'] = df['Uniprot_AC'].map(id2names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "746a8a92",
   "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>Organism</th>\n",
       "      <th>Uniprot_AC</th>\n",
       "      <th>position</th>\n",
       "      <th>standard_seq</th>\n",
       "      <th>PTM_Catalogy</th>\n",
       "      <th>PTM_Type</th>\n",
       "      <th>ref</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>Identification_Strategy</th>\n",
       "      <th>Identification_Technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q61598</td>\n",
       "      <td>317</td>\n",
       "      <td>EKVGQVIRVICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMI...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q61598</td>\n",
       "      <td>414</td>\n",
       "      <td>PKDLGTDSQIFISRAYDATTHFETTCDDIKDIYKRMTGSEFDFEEM...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q61598</td>\n",
       "      <td>302</td>\n",
       "      <td>CKQLICDPSYVKDRVEKVGQVIRVICILSHPIKNTNDANSCQIIIP...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q9QXS1</td>\n",
       "      <td>4078</td>\n",
       "      <td>LQLQEGLTSIEEVTKNLQKFLEGTSCIAGVFVDATKERLSVYQAMK...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q9QXS1</td>\n",
       "      <td>958</td>\n",
       "      <td>AKAIVQLKPRNPAHPVRGHVPLIAVCDYKQVEVTVHKGDQCQLVGP...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>D6REF3</td>\n",
       "      <td>98</td>\n",
       "      <td>KGGEDKLKMIREYRQMVETELKLICCDILDVLDKHLIPAANTGWQK...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>D3Z656</td>\n",
       "      <td>1091</td>\n",
       "      <td>LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3970</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>E9Q7S0</td>\n",
       "      <td>1051</td>\n",
       "      <td>LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3971</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>F7BQW7</td>\n",
       "      <td>1066</td>\n",
       "      <td>LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3972</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q8CHC4</td>\n",
       "      <td>1051</td>\n",
       "      <td>LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59623 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Organism Uniprot_AC  position  \\\n",
       "0     Mus musculus     Q61598       317   \n",
       "1     Mus musculus     Q61598       414   \n",
       "2     Mus musculus     Q61598       302   \n",
       "3     Mus musculus     Q9QXS1      4078   \n",
       "4     Mus musculus     Q9QXS1       958   \n",
       "...            ...        ...       ...   \n",
       "3968  Mus musculus     D6REF3        98   \n",
       "3969  Mus musculus     D3Z656      1091   \n",
       "3970  Mus musculus     E9Q7S0      1051   \n",
       "3971  Mus musculus     F7BQW7      1066   \n",
       "3972  Mus musculus     Q8CHC4      1051   \n",
       "\n",
       "                                           standard_seq   PTM_Catalogy  \\\n",
       "0     EKVGQVIRVICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMI...  Oxidation PTM   \n",
       "1     PKDLGTDSQIFISRAYDATTHFETTCDDIKDIYKRMTGSEFDFEEM...  Oxidation PTM   \n",
       "2     CKQLICDPSYVKDRVEKVGQVIRVICILSHPIKNTNDANSCQIIIP...  Oxidation PTM   \n",
       "3     LQLQEGLTSIEEVTKNLQKFLEGTSCIAGVFVDATKERLSVYQAMK...  Oxidation PTM   \n",
       "4     AKAIVQLKPRNPAHPVRGHVPLIAVCDYKQVEVTVHKGDQCQLVGP...  Oxidation PTM   \n",
       "...                                                 ...            ...   \n",
       "3968  KGGEDKLKMIREYRQMVETELKLICCDILDVLDKHLIPAANTGWQK...      Lipid PTM   \n",
       "3969  LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...      Lipid PTM   \n",
       "3970  LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...      Lipid PTM   \n",
       "3971  LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...      Lipid PTM   \n",
       "3972  LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...      Lipid PTM   \n",
       "\n",
       "              PTM_Type       ref             cell_line  \\\n",
       "0      S-nitrosylation  28710281  Ischemia heart cells   \n",
       "1      S-nitrosylation  28710281  Ischemia heart cells   \n",
       "2      S-nitrosylation  28710281  Ischemia heart cells   \n",
       "3      S-nitrosylation  28710281  Ischemia heart cells   \n",
       "4      S-nitrosylation  28710281  Ischemia heart cells   \n",
       "...                ...       ...                   ...   \n",
       "3968  S-palmitoylation  28680068       Forebrain cells   \n",
       "3969  S-palmitoylation  28680068       Forebrain cells   \n",
       "3970  S-palmitoylation  28680068       Forebrain cells   \n",
       "3971  S-palmitoylation  28680068       Forebrain cells   \n",
       "3972  S-palmitoylation  28680068       Forebrain cells   \n",
       "\n",
       "     Identification_Strategy Identification_Technique  \n",
       "0     Thiol isotope labeling                 SNOxICAT  \n",
       "1     Thiol isotope labeling                 SNOxICAT  \n",
       "2     Thiol isotope labeling                 SNOxICAT  \n",
       "3     Thiol isotope labeling                 SNOxICAT  \n",
       "4     Thiol isotope labeling                 SNOxICAT  \n",
       "...                      ...                      ...  \n",
       "3968          Thiol blocking                      ABE  \n",
       "3969          Thiol blocking                      ABE  \n",
       "3970          Thiol blocking                      ABE  \n",
       "3971          Thiol blocking                      ABE  \n",
       "3972          Thiol blocking                      ABE  \n",
       "\n",
       "[59623 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d763aa6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 59623 entries, 0 to 3972\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Organism                  59623 non-null  object\n",
      " 1   Uniprot_AC                59623 non-null  object\n",
      " 2   position                  59623 non-null  int64 \n",
      " 3   standard_seq              59623 non-null  object\n",
      " 4   PTM_Catalogy              59623 non-null  object\n",
      " 5   PTM_Type                  59623 non-null  object\n",
      " 6   ref                       59623 non-null  object\n",
      " 7   cell_line                 59623 non-null  object\n",
      " 8   Identification_Strategy   59623 non-null  object\n",
      " 9   Identification_Technique  59623 non-null  object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eeb55e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organism</th>\n",
       "      <th>Uniprot_AC</th>\n",
       "      <th>position</th>\n",
       "      <th>standard_seq</th>\n",
       "      <th>PTM_Catalogy</th>\n",
       "      <th>PTM_Type</th>\n",
       "      <th>ref</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>Identification_Strategy</th>\n",
       "      <th>Identification_Technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q61598</td>\n",
       "      <td>317</td>\n",
       "      <td>EKVGQVIRVICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMI...</td>\n",
       "      <td>Oxidation PTM</td>\n",
       "      <td>S-nitrosylation</td>\n",
       "      <td>28710281</td>\n",
       "      <td>Ischemia heart cells</td>\n",
       "      <td>Thiol isotope labeling</td>\n",
       "      <td>SNOxICAT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Organism Uniprot_AC  position  \\\n",
       "0  Mus musculus     Q61598       317   \n",
       "\n",
       "                                        standard_seq   PTM_Catalogy  \\\n",
       "0  EKVGQVIRVICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMI...  Oxidation PTM   \n",
       "\n",
       "          PTM_Type       ref             cell_line Identification_Strategy  \\\n",
       "0  S-nitrosylation  28710281  Ischemia heart cells  Thiol isotope labeling   \n",
       "\n",
       "  Identification_Technique  \n",
       "0                 SNOxICAT  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a50d5b14",
   "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>Organism</th>\n",
       "      <th>Uniprot_AC</th>\n",
       "      <th>position</th>\n",
       "      <th>standard_seq</th>\n",
       "      <th>PTM_Catalogy</th>\n",
       "      <th>PTM_Type</th>\n",
       "      <th>ref</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>Identification_Strategy</th>\n",
       "      <th>Identification_Technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3972</th>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>Q8CHC4</td>\n",
       "      <td>1051</td>\n",
       "      <td>LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...</td>\n",
       "      <td>Lipid PTM</td>\n",
       "      <td>S-palmitoylation</td>\n",
       "      <td>28680068</td>\n",
       "      <td>Forebrain cells</td>\n",
       "      <td>Thiol blocking</td>\n",
       "      <td>ABE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Organism Uniprot_AC  position  \\\n",
       "3972  Mus musculus     Q8CHC4      1051   \n",
       "\n",
       "                                           standard_seq PTM_Catalogy  \\\n",
       "3972  LPQHLQPSSSSGLGTSPSSSPRTSPCQSPTVPEYSAPSLPIRPSRA...    Lipid PTM   \n",
       "\n",
       "              PTM_Type       ref        cell_line Identification_Strategy  \\\n",
       "3972  S-palmitoylation  28680068  Forebrain cells          Thiol blocking   \n",
       "\n",
       "     Identification_Technique  \n",
       "3972                      ABE  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41d69fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_PTM_Type = df['PTM_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7bc423d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S-nitrosylation' 'S-glutathionylation' 'S-persulfidation'\n",
      " 'S-sulfinylation' 'S-succination' 'S-palmitoylation' 'S-sulfonylation'\n",
      " 'S-prenylation' 'S-disulfidation' 'S-sulfenylation' 'S-carbonylation'\n",
      " 'S-itaconation' 'S-oxidation(nec.)']\n"
     ]
    }
   ],
   "source": [
    "print (All_PTM_Type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4de1e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "organism_type = df['Organism'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "89bee7d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mus musculus' 'Homo sapiens' 'Rattus norvegicus']\n"
     ]
    }
   ],
   "source": [
    "print (organism_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e28f4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mus musculus         40109\n",
       "Homo sapiens         19336\n",
       "Rattus norvegicus      178\n",
       "Name: Organism, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Organism'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64189126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S-oxidation(nec.)      26924\n",
       "S-nitrosylation        10743\n",
       "S-glutathionylation     6556\n",
       "S-palmitoylation        5633\n",
       "S-sulfenylation         3438\n",
       "S-persulfidation        2271\n",
       "S-itaconation           1117\n",
       "S-succination           1024\n",
       "S-carbonylation          657\n",
       "S-disulfidation          607\n",
       "S-sulfinylation          473\n",
       "S-prenylation             91\n",
       "S-sulfonylation           89\n",
       "Name: PTM_Type, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PTM_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2da9e2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PTM_Type'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a148efa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Organism'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5f49aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Organism'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Organism'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "477badf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='PTM_Type'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PTM_Type'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95d0ba82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dataframe to dictionary\n",
    "df_dict = df.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f5ccb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Organism': 'Mus musculus',\n",
       " 'Uniprot_AC': 'Q61598',\n",
       " 'position': 317,\n",
       " 'standard_seq': 'EKVGQVIRVICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMISFAHN',\n",
       " 'PTM_Catalogy': 'Oxidation PTM',\n",
       " 'PTM_Type': 'S-nitrosylation',\n",
       " 'ref': '28710281',\n",
       " 'cell_line': 'Ischemia heart cells',\n",
       " 'Identification_Strategy': 'Thiol isotope labeling',\n",
       " 'Identification_Technique': 'SNOxICAT'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0c6cc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df_dict to json file\n",
    "with open('data/aac-data/data.json', 'w') as f:\n",
    "    json.dump(df_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b82a7ddf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb0dac2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3962734b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d23b7bf",
   "metadata": {},
   "outputs": [],
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