YPIC_fun_final_upload.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import PyPDF2\n",
    "import collections\n",
    "from collections import Counter\n",
    "import operator\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_pnovo_Sequences (df, score, seq_len=3):\n",
    "    filtered_df=df.loc[(df['Score']>score) & (df['sequence_length']>seq_len)]\n",
    "    sequences=filtered_df['Sq']\n",
    "    return sequences\n",
    "\n",
    "def extract_words(dictionary, sequences):\n",
    "    all_words=[]\n",
    "    for j in sequences:\n",
    "\n",
    "        words_extracted=[]\n",
    "        for i in dictionary:\n",
    "            if str(i) in j.lower():\n",
    "                words_extracted.append(i)\n",
    "        all_words.append(words_extracted)\n",
    "    return all_words\n",
    "\n",
    "def replace_variants (sequence_list):\n",
    "    sequence_list_o=[i.replace('K', 'O') for i in sequence_list]\n",
    "    sequence_list_o=[i for i in sequence_list_o if 'O' in i]\n",
    "    sequence_list_u=[i.replace('K', 'U') for i in sequence_list]\n",
    "    sequence_list_u=[i for i in sequence_list_u if 'U' in i]\n",
    "    sequence_list_i=[i.replace('L', 'I') for i in sequence_list]\n",
    "    sequence_list_i=[i for i in sequence_list_i if 'I' in i]\n",
    "    sequence_list_none = [i for i in sequence_list if 'K' not in i]\n",
    "    sequence_all=sequence_list_none+sequence_list_o+sequence_list_u+sequence_list_i\n",
    "    #sequence_all.append(sequence_list)\n",
    "    sequence_all_reverse=[i[::-1] for i in sequence_all]\n",
    "    \n",
    "    sequences=[sequence_all, sequence_all_reverse]\n",
    "    return(sequences)\n",
    "\n",
    "def words_cut_dict (dictionary, letter):\n",
    "    words_letter=[i for i in dictionary if letter in str(i)]\n",
    "    words_letter_before=[i.split(letter)[0] + letter for i in words_letter]\n",
    "    words_letter_after=[i.split(letter)[1] for i in words_letter]\n",
    "    dict_before=dict(zip(words_letter_before, words_letter))\n",
    "    dict_after=dict(zip(words_letter_after, words_letter))\n",
    "    dict_before.update(dict_after)\n",
    "    return dict_before\n",
    "\n",
    "def return_word_dict (dictionary, sequences, str_len, dict_complete):   \n",
    "    words_extracted=extract_words(dictionary, sequences)\n",
    "    all_words=[]\n",
    "    #all_words_complete=[]\n",
    "    for lists in words_extracted:\n",
    "        for item in lists:\n",
    "            all_words.append(item)\n",
    "            #all_words_complete.append(dict_complete[item]) \n",
    "    from collections import Counter\n",
    "    count= dict(Counter([i for i in all_words if len(str(i))>str_len]))\n",
    "    x = count\n",
    "    sorted_x = sorted(x.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    c_words=[]\n",
    "    for i in np.arange(len(sorted_x)):\n",
    "        c_words.append(sorted_x[i][0])\n",
    "    c_words_complete=[]\n",
    "    for j in c_words:\n",
    "        c_words_complete.append(dict_complete[j])\n",
    "    fun=dict(zip(c_words_complete, sorted_x))\n",
    "    return fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/niulili/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "## de novo result from pNovo\n",
    "file = 'selected_result.txt'\n",
    "df_Sq_hcd = pd.read_csv(file, sep='\\t')\n",
    "sequence_length = [len(str(i)) for i in list(df_Sq_hcd.Sq)]\n",
    "df_Sq_hcd['sequence_length']=sequence_length\n",
    "\n",
    "file='selected_result_ETD_DE.txt'\n",
    "df_pnovo_ETD_DE=pd.read_csv(file, sep='\\t')\n",
    "sequence_length=[len(str(i)) for i in list(df_pnovo_ETD_DE.Sq)]\n",
    "df_pnovo_ETD_DE['sequence_length']=sequence_length\n",
    "\n",
    "file='selected_lumos_cid.txt'\n",
    "df_pnovo_cid=pd.read_csv(file, sep='\\t')\n",
    "sequence_length=[len(str(i)) for i in list(df_pnovo_cid.Sq)]\n",
    "df_pnovo_cid['sequence_length']=sequence_length\n",
    "\n",
    "file='selected_lumos_HCD.txt'\n",
    "df_lumos_hcd=pd.read_csv(file, sep='\\t')\n",
    "sequence_length=[len(str(i)) for i in list(df_lumos_hcd.Sq)]\n",
    "df_lumos_hcd['sequence_length']=sequence_length\n",
    "\n",
    "## de novo result from PEAKS\n",
    "df_PEAKS=pd.read_csv('de novo peptides_peaks_qe.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary: Molecular Biology + Google 10k most commonly used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 19s, sys: 932 ms, total: 2min 19s\n",
      "Wall time: 2min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Molecular Biology (5th edition) by Robert F. Weaver downloaded from:\n",
    "# https://www.pdfdrive.com/molecular-biology-5th-ed-e14980853.html\n",
    "\n",
    "pdf_file = open('molecular_biology_r-_f-_weaver_5th_ed.pdf', 'rb')\n",
    "read_pdf = PyPDF2.PdfFileReader(pdf_file)\n",
    "number_of_pages = read_pdf.getNumPages()\n",
    "c = collections.Counter(range(number_of_pages))\n",
    "book=''\n",
    "for i in c:\n",
    "    page = read_pdf.getPage(i)\n",
    "    page_content = page.extractText()\n",
    "    book=book+page_content\n",
    "\n",
    "result=book\n",
    "punctuations=['\\n', '/', '.', ',',' ', '(', ')', ':', '?', '[', ']', ';', '“', '”', '_']\n",
    "for i in punctuations:\n",
    "    result=result.replace(i, ' ')\n",
    "result=result.replace('–', '')\n",
    "result=result.replace('-', '')\n",
    "result=result.split(' ')\n",
    "result=[j for j in result if len(j) > 1]\n",
    "\n",
    "result_dict=Counter([i for i in result])\n",
    "sorted_result = sorted(result_dict.items(), key=operator.itemgetter(1), reverse=True)\n",
    "#words_MB=[i[0] for i in sorted_result]\n",
    "#len(words_MB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32213, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MB=pd.DataFrame(sorted_result)\n",
    "df_MB.columns=('words', 'occurence')\n",
    "df_MB.to_csv('words_MB.csv')\n",
    "df_MB.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## google 10k most common words downloaded from https://github.com/first20hours/google-10000-english\n",
    "\n",
    "df_10k = pd.read_csv('10kwords.txt', sep='\\t')\n",
    "df_10k.columns=['words']\n",
    "words_10k=list(df_10k.words)\n",
    "words_10k=[i for i in words_10k if len(str(i))>1]\n",
    "\n",
    "words_MB=list(df_MB['words'])\n",
    "words_combined=list(set(words_10k+words_MB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path ='/Users/niulili/Dropbox/PythonCourse2018/Impractical Python projects/12dicts-6/American'\n",
    "file = path+'/2of4brif.txt'\n",
    "\n",
    "with open(file) as in_file:\n",
    "    word_list = in_file.read().strip().split('\\n')\n",
    "words_60k=word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4345"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(set(words_MB) & set(words_10k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9870\n",
      "32213\n",
      "37738\n"
     ]
    }
   ],
   "source": [
    "for i in [words_10k, words_MB, words_combined]:\n",
    "    print(len(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>words</th>\n",
       "      <th>occurence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>colleagues</td>\n",
       "      <td>948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         words  occurence\n",
       "68  colleagues        948"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MB[df_MB['words']=='colleagues']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prepare a dictionary that takes care of words cut in the middle e.g. WK_NDER_ED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37738\n",
      "22443\n",
      "56930\n"
     ]
    }
   ],
   "source": [
    "dictionary=words_combined\n",
    "dict_r=words_cut_dict(dictionary, 'r')\n",
    "dict_o=words_cut_dict(dictionary, 'o')\n",
    "dict_u=words_cut_dict(dictionary, 'u')\n",
    "dict_variant=dict_r.copy()\n",
    "dict_variant.update(dict_o)\n",
    "dict_variant.update(dict_u)\n",
    "\n",
    "dict_complete=dict(zip(dictionary, dictionary))\n",
    "\n",
    "dict_all=dict_variant.copy()\n",
    "dict_all.update(dict_complete)\n",
    "\n",
    "print(len(dict_complete))\n",
    "print(len(dict_variant))\n",
    "print(len(dict_all))\n",
    "\n",
    "words_all = list(dict_all.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peptide sequences extracted from pNovo result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24764\n"
     ]
    },
    {
     "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>#</th>\n",
       "      <th>Title</th>\n",
       "      <th>Charge</th>\n",
       "      <th>Sq</th>\n",
       "      <th>Mod_Sites</th>\n",
       "      <th>Score</th>\n",
       "      <th>Spectra Mass</th>\n",
       "      <th>Delta Mass</th>\n",
       "      <th>Delta Mass (PPM)</th>\n",
       "      <th>AA Score</th>\n",
       "      <th>sequence_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89405</td>\n",
       "      <td>E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw</td>\n",
       "      <td>2</td>\n",
       "      <td>NDAMENTALLLMRTATLGKNSLNLLFEAR</td>\n",
       "      <td>13,Arg-&gt;Orn[R];</td>\n",
       "      <td>114.797324</td>\n",
       "      <td>1995.023724</td>\n",
       "      <td>-0.011826</td>\n",
       "      <td>-5.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6898</td>\n",
       "      <td>E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw</td>\n",
       "      <td>2</td>\n",
       "      <td>TAVGPLSVALDQHESFLFYK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.795096</td>\n",
       "      <td>1995.023724</td>\n",
       "      <td>-0.006495</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33908</td>\n",
       "      <td>E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw</td>\n",
       "      <td>2</td>\n",
       "      <td>QDALNAVEQHGLVFLDELDK</td>\n",
       "      <td>5,Label_15N(2)[N];</td>\n",
       "      <td>98.992700</td>\n",
       "      <td>1995.023724</td>\n",
       "      <td>-0.006890</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26832</td>\n",
       "      <td>E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw</td>\n",
       "      <td>2</td>\n",
       "      <td>TYAATSDFTLLMFKHANPAFNLFWAGWDR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.770167</td>\n",
       "      <td>1995.023724</td>\n",
       "      <td>-0.004901</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24891</td>\n",
       "      <td>E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw</td>\n",
       "      <td>2</td>\n",
       "      <td>TLHVVAQVADVLQQQSLHVR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.187362</td>\n",
       "      <td>1995.023724</td>\n",
       "      <td>-0.000488</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                                           Title  Charge  \\\n",
       "0  89405  E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw       2   \n",
       "1   6898  E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw       2   \n",
       "2  33908  E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw       2   \n",
       "3  26832  E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw       2   \n",
       "4  24891  E:\\YPIC\\20181126_QE10_nLC14_LiNi_SA_Fun_01.raw       2   \n",
       "\n",
       "                              Sq           Mod_Sites       Score  \\\n",
       "0  NDAMENTALLLMRTATLGKNSLNLLFEAR     13,Arg->Orn[R];  114.797324   \n",
       "1           TAVGPLSVALDQHESFLFYK                 NaN  102.795096   \n",
       "2           QDALNAVEQHGLVFLDELDK  5,Label_15N(2)[N];   98.992700   \n",
       "3  TYAATSDFTLLMFKHANPAFNLFWAGWDR                 NaN   98.770167   \n",
       "4           TLHVVAQVADVLQQQSLHVR                 NaN   98.187362   \n",
       "\n",
       "   Spectra Mass  Delta Mass  Delta Mass (PPM) AA Score  sequence_length  \n",
       "0   1995.023724   -0.011826             -5.93      NaN               29  \n",
       "1   1995.023724   -0.006495             -3.26      NaN               20  \n",
       "2   1995.023724   -0.006890             -3.45      NaN               20  \n",
       "3   1995.023724   -0.004901             -2.46      NaN               29  \n",
       "4   1995.023724   -0.000488             -0.24      NaN               20  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Sq=df_Sq_hcd\n",
    "filtered_Sq = df_Sq.loc[(df_Sq['Score']>30) & (df_Sq['sequence_length']>3)]\n",
    "sequences_pnovo = filtered_Sq['Sq']\n",
    "print(len(filtered_Sq))\n",
    "filtered_Sq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24764"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_Sq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### replace 'K' to 'O' and 'U', 'L' to 'I'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58596"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequences_pnovo_all=replace_variants(sequences_pnovo)[0]\n",
    "len(sequences_pnovo_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peptide sequences extracted from pNovo result_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_pnovo_Sequences (df, score, seq_len=3):\n",
    "    filtered_df=df.loc[(df['Score']>score) & (df['sequence_length']>seq_len)]\n",
    "    sequences=filtered_df['Sq']\n",
    "    return sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sequences_etd_pnovo=filter_pnovo_Sequences(df_pnovo_ETD_DE, score=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### replace 'K' to 'O' and 'U', 'L' to 'I'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17914"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequences_etd_pnovo_all=replace_variants(sequences_etd_pnovo)[0]\n",
    "len(sequences_etd_pnovo_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peptide sequences extracted from pNovo result_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences_cid_pnovo=filter_pnovo_Sequences(df_pnovo_cid, score=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58176"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequences_cid_pnovo_all=replace_variants(sequences_cid_pnovo)[0]\n",
    "len(sequences_cid_pnovo_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Peptide sequences extracted from PEAKS result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_peaks_sequences (df):\n",
    "    \"\"\"To process de novo output from PEAKS and apply filter\"\"\"\n",
    "    df_filter = df[(df.Area >10e4) &(abs(df.ppm < 5)) & (df['ALC (%)'] > 60)]\n",
    "    peptides = df_filter['Peptide']\n",
    "    \n",
    "    sequences = []\n",
    "    for i in peptides:\n",
    "        new_i = re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", i)\n",
    "        sequences.append(new_i)\n",
    "    \n",
    "    sequences_all = replace_variants(sequences)[0]\n",
    "    return sequences_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27176"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequences_peaks = filter_peaks_sequences(df_PEAKS)\n",
    "len(sequences_peaks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dictionary search "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18min 22s, sys: 1.92 s, total: 18min 24s\n",
      "Wall time: 38min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "dictionary=words_all\n",
    "sequences=sequences_pnovo_all\n",
    "dict_complete=dict_all\n",
    "afun=return_word_dict(dictionary, sequences, 2, dict_complete)\n",
    "\n",
    "possible=pd.DataFrame(afun).transpose().reset_index()\n",
    "possible.columns=['word','fragment', 'occurence']\n",
    "evidence_letters=[len(str(i)) for i in list(possible['fragment'])]\n",
    "possible['identified_letters']=evidence_letters\n",
    "aa=possible[(possible['identified_letters']>2)&(possible['occurence']>1)].sort_values('occurence', ascending=False)\n",
    "#aa.to_csv('Supple_1.csv')\n",
    "aa[aa['identified_letters']>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>word</th>\n",
       "      <th>fragment</th>\n",
       "      <th>occurence</th>\n",
       "      <th>identified_letters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>mental</td>\n",
       "      <td>mental</td>\n",
       "      <td>167</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>tournament</td>\n",
       "      <td>nament</td>\n",
       "      <td>112</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>theta</td>\n",
       "      <td>theta</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>ultracentrifuged</td>\n",
       "      <td>acent</td>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>prescan</td>\n",
       "      <td>escan</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>dered</td>\n",
       "      <td>dered</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>fundamental</td>\n",
       "      <td>ndamental</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>allel</td>\n",
       "      <td>allel</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>collectors</td>\n",
       "      <td>llect</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>somewhat</td>\n",
       "      <td>mewhat</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>respec</td>\n",
       "      <td>respec</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>ontario</td>\n",
       "      <td>ntari</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>names</td>\n",
       "      <td>names</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>mentary</td>\n",
       "      <td>mentar</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>postscan</td>\n",
       "      <td>stscan</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>wondered</td>\n",
       "      <td>ndered</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>allen</td>\n",
       "      <td>allen</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>foremost</td>\n",
       "      <td>emost</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>opment</td>\n",
       "      <td>pment</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>unloading</td>\n",
       "      <td>ading</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  word   fragment occurence  identified_letters\n",
       "251             mental     mental       167                   6\n",
       "406         tournament     nament       112                   6\n",
       "467              theta      theta        99                   5\n",
       "509   ultracentrifuged      acent        87                   5\n",
       "575            prescan      escan        73                   5\n",
       "707              dered      dered        57                   5\n",
       "887        fundamental  ndamental        39                   9\n",
       "1066             allel      allel        28                   5\n",
       "1385        collectors      llect        14                   5\n",
       "1408          somewhat     mewhat        14                   6\n",
       "1186            respec     respec        14                   6\n",
       "1449           ontario      ntari        13                   5\n",
       "1479             names      names        12                   5\n",
       "1474           mentary     mentar        12                   6\n",
       "1459          postscan     stscan        12                   6\n",
       "1528          wondered     ndered        11                   6\n",
       "1576             allen      allen        10                   5\n",
       "1690          foremost      emost         9                   5\n",
       "1683            opment      pment         9                   5\n",
       "1723         unloading      ading         9                   5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa[aa['identified_letters']>4][:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "peptides= [23, 351, 3253]\n",
    "proteins= [1, 35, 555]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iPV5N0tWS7izH75R0RPtNS5KkGa04on4EHA68Cv8OT/xoO41KkqR7WhHtQrb/2CXttaY5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c4c62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots(figsize=(3,4))\n",
    "plt.bar(x=[1,2,3], height=proteins, color='lightblue')\n",
    "plt.xticks(range(1, 4), ('the sentence', 'Contaminants', 'Co-purified proteins'), rotation=30)\n",
    "plt.title('Protein abundance')\n",
    "plt.ylabel('Raw intensity [log10]')\n",
    "plt.savefig('protein abundance.pdf', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}