Orug_contamination_removal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline to remove Camponotus planatus (Cpla) contamination from Odontomachus ruginodis (Orug) transcriptome\n",
    "This takes single gene phylogenies as input, along with the transcriptomes and other stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "1. For ~3500 candidate genes that have >90% blast similarity to a gene in Cpla, find their peptide sequences (MAGOT), find their best hit in 11 other ants and bees (BLASTP), align (MAFFT), and build ML phylogenies (RAxML)\n",
    "2. Read in trees and analyse\n",
    "3. Construct dataframe with sequence and phylogenetic features\n",
    "4. Plots showing that these features indicate contamination\n",
    "5. Identify and remove contaminants from the transcriptome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "from ete3 import Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. For ~3500 candidate genes that have >90% blast similarity to a gene in Cpla, find their peptide sequences (MAGOT), find their best hit in 11 other ants and bees (BLASTP), align (MAFFT), and build ML phylogenies (RAxML)\n",
    "\n",
    "The best BLAST result for each of these resultant Orug candidate genes from a range of Hymenoptera was determined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3522\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Orug|SGF62645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orug|SGF62647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orug|SGF62652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Orug|SGF62674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orug|SGF62679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "0  Orug|SGF62645\n",
       "1  Orug|SGF62647\n",
       "2  Orug|SGF62652\n",
       "3  Orug|SGF62674\n",
       "4  Orug|SGF62679"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feed in list of candidate Orug sequences from which to identify contaminated genes. \n",
    "#This list contains 3522 genes that have >90% identity to their best blastp hit in Cpla\n",
    "s_Orug_candidates = pd.read_table('Orug_candidates.txt', header = None)\n",
    "print(len(s_Orug_candidates))\n",
    "s_Orug_candidates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create dictionary of blast results\n",
    "blast_cols = ['qseqid', 'sseqid', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue', 'bitscore']\n",
    "sp_list = ['Aech', 'Amel', 'Bimp', 'Cpla', 'Cflo', 'Dqua', 'Hsal', 'Lnig', 'Mrot', 'Obir', 'Sinv']\n",
    "fp_list = [\"contam_Orug_vs_{}.bx\".format(x) for x in sp_list ]\n",
    "           \n",
    "sp_blast = { sp:pd.read_csv(os.path.join(\"contamination_assessment\",fp), \n",
    "                            header=None, \n",
    "                            names=blast_cols,\n",
    "                            sep='\\t',\n",
    "                           ) for sp,fp in zip(sp_list, fp_list)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>qseqid</th>\n",
       "      <th>sseqid</th>\n",
       "      <th>pident</th>\n",
       "      <th>length</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>gapopen</th>\n",
       "      <th>qstart</th>\n",
       "      <th>qend</th>\n",
       "      <th>sstart</th>\n",
       "      <th>send</th>\n",
       "      <th>evalue</th>\n",
       "      <th>bitscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Orug|SGF62645</td>\n",
       "      <td>Amel|LOC411540</td>\n",
       "      <td>98.08</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>517</td>\n",
       "      <td>620</td>\n",
       "      <td>8.000000e-67</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orug|SGF62647</td>\n",
       "      <td>Amel|LOC411273</td>\n",
       "      <td>32.43</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>83</td>\n",
       "      <td>199</td>\n",
       "      <td>235</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orug|SGF62652</td>\n",
       "      <td>Amel|LOC551967</td>\n",
       "      <td>87.42</td>\n",
       "      <td>310</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>309</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Orug|SGF62674</td>\n",
       "      <td>Amel|Iscu</td>\n",
       "      <td>86.13</td>\n",
       "      <td>137</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>150</td>\n",
       "      <td>14</td>\n",
       "      <td>150</td>\n",
       "      <td>1.000000e-84</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orug|SGF62679</td>\n",
       "      <td>Amel|LKR</td>\n",
       "      <td>80.50</td>\n",
       "      <td>200</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>719</td>\n",
       "      <td>918</td>\n",
       "      <td>2.000000e-109</td>\n",
       "      <td>336.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          qseqid          sseqid  pident  length  mismatch  gapopen  qstart  \\\n",
       "0  Orug|SGF62645  Amel|LOC411540   98.08     104         2        0       1   \n",
       "1  Orug|SGF62647  Amel|LOC411273   32.43      37        25        0      47   \n",
       "2  Orug|SGF62652  Amel|LOC551967   87.42     310        38        1       1   \n",
       "3  Orug|SGF62674       Amel|Iscu   86.13     137        19        0      14   \n",
       "4  Orug|SGF62679        Amel|LKR   80.50     200        39        0       1   \n",
       "\n",
       "   qend  sstart  send         evalue  bitscore  \n",
       "0   104     517   620   8.000000e-67     215.0  \n",
       "1    83     199   235   3.000000e+00      26.9  \n",
       "2   310       1   309   0.000000e+00     571.0  \n",
       "3   150      14   150   1.000000e-84     248.0  \n",
       "4   200     719   918  2.000000e-109     336.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_blast['Amel'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all genes with an evalue match < 0.01\n",
    "df_concat1 = pd.concat([sp_blast[sp][sp_blast[sp]['evalue'] < 0.01] for sp in sp_list], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orug_hits = df_concat1.pivot_table(values='sseqid', \n",
    "                                       index='qseqid', \n",
    "                                       aggfunc=lambda x: list(x)\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vchandra/anaconda/envs/ilp/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_orug_hits.sseqid.apply(lambda s: len(s)+1), kde=False, bins=13)\n",
    "plt.title(\"Number of good matches for {} Orug genes\".format(len(df_orug_hits)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure shows that most of the candidate contaminant genes have a good match from all of the reference genomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#save lists of Orug orthologue groups; one group per file\n",
    "for ogene in df_orug_hits.index:\n",
    "    handle = open('Orug_gene_lists/{}.out'.format(ogene), 'w')\n",
    "    handle.write('\\n'.join(df_orug_hits.loc[ogene][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Used mafft and raxml on the server to construct phylogenies for each of these orthologue groups (scripts attached below)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAGOT Script\n",
    "This script takes each species' gene name, searches the corresponding peptidome and returns the peptide sequence for each gene. Install from https://github.com/biorover/MAGOT\n",
    "\n",
    "```\n",
    "#!/usr/bin/bash\n",
    "\n",
    "for SPECIES in Aech Bimp Amel Cflo Cpla Dqua Hsal Lnig Obir Orug Mrot Sinv; \n",
    "do \n",
    "    for i in $(cat ${SPECIES}_candidate_seqs.txt); \n",
    "    do \n",
    "\n",
    "        python genome_tools.py get_seq_from_fasta '${SPECIES}.pep' $i True >> ${i}_seqs.fasta \n",
    "\n",
    "    done\n",
    "done\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAFFT Script\n",
    "\n",
    "As input, this takes lists of sequences (one sequence from each of roughly 12 species' peptidomes), and performs a multiple sequence alignment. \n",
    "\n",
    "```\n",
    "#!/usr/bin/bash\n",
    "\n",
    "for i in $(ls 'Orug_candidates/Orug_sequences/'); do\n",
    "\n",
    "   mafft --localpair --thread 30 --maxiterate 1000 Orug_candidates/Orug_sequences/$i > $i.aligned\n",
    "\n",
    "done\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RAxML Script\n",
    "\n",
    "This takes multiple sequence alignments as input and generates maximum likelihood phylogenies for each alignment. \n",
    "\n",
    "```\n",
    "#!/usr/bin/bash\n",
    "\n",
    "for i in $(ls 'Orug_candidates/alignments/'); do\n",
    "\n",
    "    raxml -s Orug_candidates/alignments/$i -m PROTGAMMAJTT -p 6666 -T 30 -n $i.out \n",
    "\n",
    "done\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Read in trees and analyse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3469\n"
     ]
    }
   ],
   "source": [
    "#list all phylogenies for Orug candidate genes in treelist\n",
    "treelist = []\n",
    "for file in os.listdir('contamination_assessment/Orug_trees/'):\n",
    "    if 'bestTree' in file:\n",
    "        treelist.append('contamination_assessment/Orug_trees/' + file)\n",
    "\n",
    "print(len(treelist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "         /-Cbir|LOC105285907\n",
      "        |\n",
      "      /-|      /-Cpla|SGF56824\n",
      "     |  |   /-|\n",
      "     |  |  |   \\-Cflo|XP_011260747.1\n",
      "     |   \\-|\n",
      "   /-|     |   /-Orug|SGF66875\n",
      "  |  |      \\-|\n",
      "  |  |         \\-KMQ93665.1\n",
      "  |  |\n",
      "  |  |   /-Sinv|XP_011159147.1\n",
      "  |   \\-|\n",
      "  |     |   /-Dqua|LOC106746720\n",
      "  |      \\-|\n",
      "--|         \\-Hsal|LOC105187517\n",
      "  |\n",
      "  |   /-Mrot|XP_003706718.1\n",
      "  |--|\n",
      "  |  |   /-Bimp|XP_003487350.1\n",
      "  |   \\-|\n",
      "  |      \\-Amel|LOC408372\n",
      "  |\n",
      "   \\-Aech|XP_011062839.1\n",
      "\n",
      "   /-Sinv|XP_011169572.1\n",
      "  |\n",
      "  |      /-Cbir|LOC105275518\n",
      "  |     |\n",
      "  |   /-|   /-Dqua|LOC106742533\n",
      "  |  |  |  |\n",
      "  |  |   \\-|      /-Orug|SGF64046\n",
      "  |  |     |   /-|\n",
      "  |  |      \\-|   \\-Cpla|SGF48456\n",
      "  |--|        |\n",
      "  |  |         \\-Hsal|LOC105191572\n",
      "--|  |\n",
      "  |  |   /-KMQ96987.1\n",
      "  |  |  |\n",
      "  |  |  |         /-Bimp|XP_003488371.1\n",
      "  |   \\-|      /-|\n",
      "  |     |   /-|   \\-Mrot|XP_003706904.1\n",
      "  |     |  |  |\n",
      "  |      \\-|   \\-Amel|LOC550651\n",
      "  |        |\n",
      "  |         \\-Cflo|XP_011258337.1\n",
      "  |\n",
      "   \\-Aech|XP_011066661.1\n"
     ]
    }
   ],
   "source": [
    "#print examples of these phylogenies\n",
    "\n",
    "#note that genes beginning with 'KM' are from Lasius niger. All other species are \n",
    "#named following the four letter nomenclature: \n",
    "#first letter from the first letter of the genus, and the next three letters from the start of the species name\n",
    "\n",
    "for i in range(0, 2):\n",
    "    x = Tree(treelist[i])\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for each tree in the list, ask whether Cpla is sister to Orug. \n",
    "# store names of possible contaminant sequences\n",
    "# store distances between Orug and Cpla in separate files; one for good trees and one for possible contaminants\n",
    "# plot distributions of these distances\n",
    "\n",
    "contaminants = {}\n",
    "good_genes = {}\n",
    "s_clade = {}\n",
    "bee_tree_errors = []\n",
    "no_bees = []\n",
    "weird_bees = []\n",
    "s_clade_errors = []\n",
    "bee_seq_errors = []\n",
    "scl1 = 0\n",
    "nobiroi = []\n",
    "fail = []\n",
    "sis_clade_fail = []\n",
    "\n",
    "\n",
    "for tree in treelist:\n",
    "    Cpla = ''\n",
    "    Orug = ''\n",
    "    Cflo = ''\n",
    "    Lnig = ''\n",
    "    Hsal = ''\n",
    "    Dqua = ''\n",
    "    Mrot = ''\n",
    "    Amel = ''\n",
    "    Bimp = ''\n",
    "    x = ''\n",
    "    sis_clade = ''\n",
    "    bee_ancestor = []\n",
    "    bad = False\n",
    "\n",
    "    intree = Tree(tree)\n",
    "    for leaf in intree: \n",
    "        if 'Mrot' in leaf.name:\n",
    "            Mrot = leaf.name\n",
    "        elif 'Amel' in leaf.name:\n",
    "            Amel = leaf.name\n",
    "        elif 'Bimp' in leaf.name:\n",
    "            Bimp = leaf.name\n",
    "        elif 'Cbir' in leaf.name:\n",
    "            Obir = leaf.name\n",
    "    \n",
    "    #find bees\n",
    "    nbees = []\n",
    "    for i in [Mrot, Amel, Bimp]:\n",
    "        if i != '':\n",
    "            nbees.append(i)\n",
    "    \n",
    "    #find ancestor of all bees\n",
    "    try:\n",
    "        if len(nbees) > 1:\n",
    "            bee_ancestor = intree.get_common_ancestor(nbees)\n",
    "        elif len(nbees) == 1:\n",
    "            bee_ancestor = [nbees[0]]\n",
    "        else:\n",
    "            no_bees.append(tree)\n",
    "    except:\n",
    "        bee_seq_errors.append(tree)\n",
    "        continue\n",
    "    \n",
    "    #reroot tree on ancestral bee (allow bee clade to contain up to one non-bee and if not, try rooting on any one bee. \n",
    "    # If this also fails, try rooting on biroi. Catch exceptions in nobiroi and go to next tree)\n",
    "    if len(bee_ancestor) <= 4 and len(bee_ancestor) > 1:\n",
    "        intree.set_outgroup(bee_ancestor)\n",
    "    elif len(bee_ancestor) > 4 or len(bee_ancestor) == 1:\n",
    "        intree.set_outgroup(nbees[0])\n",
    "        weird_bees.append(tree)\n",
    "    elif len(bee_ancestor) == 0:\n",
    "        try:\n",
    "            intree.set_outgroup(Obir)\n",
    "        except:\n",
    "            nobiroi.append(tree)\n",
    "            bad = True\n",
    "    \n",
    "    if not bad:\n",
    "        for leaf in intree:\n",
    "            if 'Cpla' in leaf.name:\n",
    "                Cpla = leaf.name\n",
    "            elif 'Orug' in leaf.name:\n",
    "                Orug = leaf.name\n",
    "            elif 'Hsal' in leaf.name:\n",
    "                Hsal = leaf.name\n",
    "            elif 'KM' in leaf.name:\n",
    "                Lnig = leaf.name\n",
    "            elif 'Dqua' in leaf.name:\n",
    "                Dqua = leaf.name\n",
    "            elif 'Cflo' in leaf.name:\n",
    "                Cflo = leaf.name\n",
    "                \n",
    "                \n",
    "\n",
    "        if len(Cpla) > 0: \n",
    "            x = str(tree)\n",
    "            x = x.split('.')\n",
    "            x = x[1]\n",
    "            cont_check = intree.get_common_ancestor(Cpla, Orug)\n",
    "            if len(cont_check) == 2:\n",
    "                contaminants[x] = intree.get_distance(Cpla, Orug)\n",
    "            elif len(cont_check) > 2:\n",
    "                good_genes[x] = intree.get_distance(Cpla, Orug)\n",
    "            sis_clade = cont_check.get_sisters()\n",
    "            \n",
    "            if sis_clade != '':\n",
    "                if Lnig != '' and Cflo != '' and Dqua != '' and Hsal != '':\n",
    "                    Hmrca = intree.get_common_ancestor(cont_check, Hsal)\n",
    "                    Dmrca = intree.get_common_ancestor(cont_check, Dqua)\n",
    "                    Cmrca = intree.get_common_ancestor(cont_check, Cflo)\n",
    "                    Lmrca = intree.get_common_ancestor(cont_check, Lnig)\n",
    "                    Pav = min(len(Hmrca), len(Dmrca))\n",
    "                    Fav = min(len(Cmrca), len(Lmrca))\n",
    "                \n",
    "                    #check if Orug is in ponerine or formicine clade\n",
    "                    if len(sis_clade) == 1:\n",
    "                        if Pav > Fav:\n",
    "                            s_clade[x] = 'F'\n",
    "                        elif Pav < Fav:\n",
    "                            s_clade[x] = 'P'\n",
    "                        else:\n",
    "                            if len(cont_check) == 2:\n",
    "                                s_clade[x] = 'both'\n",
    "                                sis_clade_fail.append(tree)\n",
    "                        scl1 += 1\n",
    "                else: \n",
    "                    s_clade_errors.append(x)    \n",
    "                \n",
    "            \n",
    "    else: \n",
    "        fail.append(tree)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3184\n",
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Trees with no bees and no O. biroi sequences:        5\n",
       "failures:                                            5\n",
       "Trees with no bees:                                 43\n",
       "Possible contaminants:                             522\n",
       "Good genes:                                       2940\n",
       "Successful classification of clade:                805\n",
       "Bee sequence errors:                                 0\n",
       "Bee tree errors:                                     0\n",
       "Weird bees:                                        330\n",
       "Clade classification errors:                       253\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.Series({'Trees with no bees and no O. biroi sequences: ' : str(len(nobiroi)),\n",
    "                        'failures: ' : str(len(fail)),\n",
    "                        'Trees with no bees: ' : str(len(no_bees)),\n",
    "                        'Possible contaminants: ' : str(len(contaminants)),\n",
    "                        'Good genes: ' : str(len(good_genes)),\n",
    "                        'Successful classification of clade: ' : str(len(s_clade)),\n",
    "                        'Bee sequence errors: ' : str(len(bee_seq_errors)),\n",
    "                        'Bee tree errors: ' : str(len(bee_tree_errors)),\n",
    "                        'Weird bees: ' : str(len(weird_bees)),\n",
    "                        'Clade classification errors: ' : str(len(s_clade_errors)),\n",
    "                       })\n",
    "\n",
    "print(scl1)\n",
    "print(len(sis_clade_fail))\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#define true species tree\n",
    "truetree = Tree(\"(((Dqua,(Orug, Hsal)),(Obir,((Lnig,(Cflo, Cpla)),(Aech, Sinv)))),(Mrot, (Amel, Bimp)));\")\n",
    "\n",
    "#make dictionary with Robinson-Foulds distances between each tree and the true species tree\n",
    "\n",
    "rfs = {}\n",
    "bee_tree_errors2 = []\n",
    "bee_seq_errors2 = []\n",
    "\n",
    "for tree in treelist:\n",
    "    Cpla = []\n",
    "    Orug = []\n",
    "    Cflo = []\n",
    "    Lnig = []\n",
    "    Hsal = []\n",
    "    Dqua = []\n",
    "    Obir = []\n",
    "    Amel = []\n",
    "    Mrot = []\n",
    "    Bimp = []\n",
    "    Sinv = []\n",
    "    Aech = []\n",
    "\n",
    "    intree = Tree(tree)\n",
    "    for leaf in intree: \n",
    "        if 'Mrot' in leaf.name:\n",
    "            Mrot = leaf.name\n",
    "        elif 'Amel' in leaf.name:\n",
    "            Amel = leaf.name\n",
    "        elif 'Bimp' in leaf.name:\n",
    "            Bimp = leaf.name\n",
    "        elif 'Orug' in leaf.name:\n",
    "            Orug = leaf.name\n",
    "   \n",
    "    nbees = []\n",
    "    for i in [Mrot, Amel, Bimp]:\n",
    "        if i != '':\n",
    "            nbees.append(i)\n",
    "    \n",
    "    try:\n",
    "        bee_ancestor = intree.get_common_ancestor(nbees)\n",
    "    except:\n",
    "        bee_seq_errors2.append(tree)\n",
    "        continue\n",
    "        \n",
    "    if len(bee_ancestor) <= 4 and len(bee_ancestor) > 0:\n",
    "        intree.set_outgroup(bee_ancestor)\n",
    "    else:\n",
    "        bee_tree_errors2.append(tree)\n",
    "    \n",
    "    if len(bee_ancestor) <= 4 and len(bee_ancestor) > 0:\n",
    "        for leaf in intree:\n",
    "            if 'Cpla' in leaf.name:\n",
    "                leaf.name = 'Cpla'\n",
    "            elif 'Orug' in leaf.name:\n",
    "                leaf.name = 'Orug'\n",
    "            elif 'Hsal' in leaf.name:\n",
    "                leaf.name = 'Hsal'\n",
    "            elif 'KM' in leaf.name:\n",
    "                leaf.name = 'Lnig'\n",
    "            elif 'Dqua' in leaf.name:\n",
    "                leaf.name = 'Dqua'\n",
    "            elif 'Cflo' in leaf.name:\n",
    "                leaf.name = 'Cflo'\n",
    "            elif 'Cbir' in leaf.name:\n",
    "                leaf.name = 'Obir'\n",
    "            elif 'Aech' in leaf.name:\n",
    "                leaf.name = 'Aech'\n",
    "            elif 'Sinv' in leaf.name:\n",
    "                leaf.name = 'Sinv'\n",
    "            elif 'Amel' in leaf.name:\n",
    "                leaf.name = 'Amel'\n",
    "            elif 'Bimp' in leaf.name:\n",
    "                leaf.name = 'Bimp'\n",
    "            elif 'Mrot' in leaf.name:\n",
    "                leaf.name = 'Mrot'\n",
    "        \n",
    "        a = truetree.robinson_foulds(intree)\n",
    "        normrf = float(a[0])/float(a[1])\n",
    "        rfs[Orug] = float(normrf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2993\n"
     ]
    }
   ],
   "source": [
    "print(len(rfs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot tree distance for good sequences and potential contaminants (i.e. dodgy sequences)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "lgd = [np.log2(x) for x in good_genes.values()]    \n",
    "lbd = [np.log2(x) for x in contaminants.values()]    \n",
    "\n",
    "aa = sns.violinplot(data=[lgd, lbd], scale='area', inner='point')\n",
    "aa.set(ylabel = 'log10 Orug-Cpla genetic distance')\n",
    "aa.set_xticklabels(['Good sequences', 'Dodgy sequences'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Construct dataframe with sequence and phylogenetic features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read in outputs for blastp of Orug against Cpla and two formicines\n",
    "Orug_vs_Cpla = pd.read_csv(\"contamination_assessment/Orug_vs_Cpla.txt\", header = None, sep='\\t', names=blast_cols)\n",
    "Orug_vs_Lnig = pd.read_table(\"contamination_assessment/Orug_vs_Lnig.bx\", header = None, sep='\\t', names=blast_cols)\n",
    "Orug_vs_Cflo = pd.read_table(\"contamination_assessment/Orug_vs_Cflo.bx\", header = None, sep='\\t', names=blast_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>qseqid</th>\n",
       "      <th>sseqid</th>\n",
       "      <th>pident</th>\n",
       "      <th>length</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>gapopen</th>\n",
       "      <th>qstart</th>\n",
       "      <th>qend</th>\n",
       "      <th>sstart</th>\n",
       "      <th>send</th>\n",
       "      <th>evalue</th>\n",
       "      <th>bitscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Orug|SGF62645</td>\n",
       "      <td>Cpla|SGF49990</td>\n",
       "      <td>100.00</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>517</td>\n",
       "      <td>620</td>\n",
       "      <td>5.000000e-68</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orug|SGF62647</td>\n",
       "      <td>Cpla|SGF48983</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>119</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orug|SGF62648</td>\n",
       "      <td>Cpla|SGF59801</td>\n",
       "      <td>86.18</td>\n",
       "      <td>340</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "      <td>23</td>\n",
       "      <td>360</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Orug|SGF62649</td>\n",
       "      <td>Cpla|SGF51865</td>\n",
       "      <td>29.41</td>\n",
       "      <td>68</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>115</td>\n",
       "      <td>96</td>\n",
       "      <td>157</td>\n",
       "      <td>4.700000e+00</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orug|SGF65438</td>\n",
       "      <td>Cpla|SGF57541</td>\n",
       "      <td>27.63</td>\n",
       "      <td>76</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>2964</td>\n",
       "      <td>3039</td>\n",
       "      <td>2.100000e+00</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          qseqid         sseqid  pident  length  mismatch  gapopen  qstart  \\\n",
       "0  Orug|SGF62645  Cpla|SGF49990  100.00     104         0        0       1   \n",
       "1  Orug|SGF62647  Cpla|SGF48983  100.00      15         0        0     105   \n",
       "2  Orug|SGF62648  Cpla|SGF59801   86.18     340        13        3       1   \n",
       "3  Orug|SGF62649  Cpla|SGF51865   29.41      68        42        1      48   \n",
       "4  Orug|SGF65438  Cpla|SGF57541   27.63      76        55        0      45   \n",
       "\n",
       "   qend  sstart  send        evalue  bitscore  \n",
       "0   104     517   620  5.000000e-68     219.0  \n",
       "1   119      22    36  2.000000e-03      35.0  \n",
       "2   308      23   360  0.000000e+00     545.0  \n",
       "3   115      96   157  4.700000e+00      25.8  \n",
       "4   120    2964  3039  2.100000e+00      27.3  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Orug_vs_Cpla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Orug|SGF82787</th>\n",
       "      <td>2400.887711</td>\n",
       "      <td>-2.557067</td>\n",
       "      <td>0.168923</td>\n",
       "      <td>-15.137451</td>\n",
       "      <td>9.169985e-52</td>\n",
       "      <td>1.387602e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF63944</th>\n",
       "      <td>600.445549</td>\n",
       "      <td>-3.010519</td>\n",
       "      <td>0.215700</td>\n",
       "      <td>-13.956944</td>\n",
       "      <td>2.854124e-44</td>\n",
       "      <td>2.159430e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF61220</th>\n",
       "      <td>2092.490625</td>\n",
       "      <td>-2.957865</td>\n",
       "      <td>0.213546</td>\n",
       "      <td>-13.851192</td>\n",
       "      <td>1.251180e-43</td>\n",
       "      <td>6.310950e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF64259</th>\n",
       "      <td>761.007066</td>\n",
       "      <td>-3.751297</td>\n",
       "      <td>0.274955</td>\n",
       "      <td>-13.643294</td>\n",
       "      <td>2.213327e-42</td>\n",
       "      <td>8.373017e-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF68061</th>\n",
       "      <td>194.643827</td>\n",
       "      <td>-2.377130</td>\n",
       "      <td>0.175826</td>\n",
       "      <td>-13.519819</td>\n",
       "      <td>1.194690e-41</td>\n",
       "      <td>3.031041e-38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE       stat        pvalue  \\\n",
       "Orug|SGF82787  2400.887711       -2.557067  0.168923 -15.137451  9.169985e-52   \n",
       "Orug|SGF63944   600.445549       -3.010519  0.215700 -13.956944  2.854124e-44   \n",
       "Orug|SGF61220  2092.490625       -2.957865  0.213546 -13.851192  1.251180e-43   \n",
       "Orug|SGF64259   761.007066       -3.751297  0.274955 -13.643294  2.213327e-42   \n",
       "Orug|SGF68061   194.643827       -2.377130  0.175826 -13.519819  1.194690e-41   \n",
       "\n",
       "                       padj  \n",
       "Orug|SGF82787  1.387602e-47  \n",
       "Orug|SGF63944  2.159430e-40  \n",
       "Orug|SGF61220  6.310950e-40  \n",
       "Orug|SGF64259  8.373017e-39  \n",
       "Orug|SGF68061  3.031041e-38  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames = ['baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj']\n",
    "Cpla_deseq = pd.read_csv(\"contamination_assessment/Camp_planatus.genes.QW.pvalues.nohed.out\", \n",
    "                         sep = ' ', header=None,\n",
    "                         names=colnames,\n",
    "                        )\n",
    "\n",
    "Orug_deseq = pd.read_csv(\"contamination_assessment/Odontomachus.genes.QW.pvalues.nohed.out\", \n",
    "                         sep = ' ', header=None,\n",
    "                         names=colnames,\n",
    "                        )\n",
    "Orug_deseq.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes = pd.DataFrame({'Orug_genes':s_Orug_candidates})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join the Cpla blast values to the dataframe\n",
    "df_genes = pd.merge(left=df_genes, right=Orug_vs_Cpla[['qseqid', 'sseqid','pident']], \n",
    "                     left_on='Orug_genes', right_on='qseqid', \n",
    "                     how='left'\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find lengths of each peptide sequence in the Orug and Cpla transcriptomes; save in dictionaries\n",
    "Cpla_records = list(SeqIO.parse('contamination_assessment/Cpla_trinity.pep', 'fasta'))\n",
    "Orug_records = list(SeqIO.parse('contamination_assessment/Orug_trinity.pep', 'fasta'))\n",
    "\n",
    "s_Cpla_txome = pd.Series({str(record.name):str(record.seq) for record in Cpla_records}, name='Cpla_length')\n",
    "s_Orug_txome = pd.Series({str(record.name):str(record.seq) for record in Orug_records}, name='Orug_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# append gene lengths\n",
    "df_genes = df_genes.join(s_Cpla_txome.apply(len), on=\"sseqid\")\n",
    "df_genes = df_genes.join(s_Orug_txome.apply(len), on=\"qseqid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join the Cflo, Lnig blast values to the dataframe\n",
    "df = Orug_vs_Cflo[['qseqid','pident']].rename({'pident':'pident_vs_Cflo'},axis=1)\n",
    "df_genes = pd.merge(left=df_genes, right=df, \n",
    "                     left_on='qseqid', right_on='qseqid', \n",
    "                     how='left'\n",
    "                   )\n",
    "\n",
    "df = Orug_vs_Lnig[['qseqid','pident']].rename({'pident':'pident_vs_Lnig'},axis=1)\n",
    "df_genes = pd.merge(left=df_genes, right=df, \n",
    "                     left_on='qseqid', right_on='qseqid', \n",
    "                     how='left'\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add mean expression of Cpla and Orug\n",
    "for df,sp,col in zip([Cpla_deseq, Orug_deseq], [\"Cpla\", \"Orug\"], ['sseqid', 'qseqid']):\n",
    "    df_genes = pd.merge(left=df_genes, right=df[['baseMean']], \n",
    "                         left_on=col, right_index=True, \n",
    "                         how='left'\n",
    "                       ).rename({\"baseMean\":'{}_expressionlevel'.format(sp)}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 overlap in contaminant and good gene lists.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vchandra/anaconda/envs/ilp/lib/python3.6/site-packages/ipykernel_launcher.py:14: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \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>class</th>\n",
       "      <th>contaminated</th>\n",
       "      <th>genetic_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Orug|SGF64046</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF73084</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orug|SGF83253</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.126817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              class  contaminated  genetic_distance\n",
       "Orug|SGF64046   NaN             1          0.000002\n",
       "Orug|SGF73084   NaN             1          0.000002\n",
       "Orug|SGF83253   NaN             1          0.126817"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label contaminants and class fields\n",
    "df_contaminants = pd.DataFrame({\"genetic_distance\":pd.Series(contaminants,),\n",
    "                               \"contaminated\":1,\n",
    "                              })\n",
    "\n",
    "df_goodgenes = pd.DataFrame({\"genetic_distance\":pd.Series(good_genes),\n",
    "                               \"contaminated\":0,\n",
    "                               \"class\":\"good\",\n",
    "                              })\n",
    "\n",
    "# check there is no overlap in goodgene and contaminants lists:\n",
    "print(len(df_contaminants.index.intersection(df_goodgenes.index)), \"overlap in contaminant and good gene lists.\")\n",
    "\n",
    "df_concat = pd.concat([df_contaminants, df_goodgenes], axis=0)\n",
    "df_concat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes = pd.merge(left=df_genes, right=df_concat, \n",
    "                     left_on='Orug_genes', right_index=True, \n",
    "                     how='left'\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add RF distance\n",
    "df_genes = df_genes.join(on='Orug_genes', other=pd.Series(rfs, name=\"rfdistance\"), how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normpident(s):\n",
    "    \"\"\"\n",
    "    This function calculates ... ### This also is giving slightly different results, but I \n",
    "    can't understand the logic at this point of what it's trying to achieve ###\n",
    "    \"\"\"\n",
    "    if s[[\"pident_vs_Lnig\", \"pident\", \"pident_vs_Cflo\"]].count()==3:\n",
    "        npd = s['pident'] / s[['pident_vs_Cflo','pident_vs_Lnig']].mean() \n",
    "    else: ### WHAT HAPPENS TO GENES WITHOUT A BLAST HIT FOR ONE OF LNIG/CFLO? ###\n",
    "        npd = np.nan\n",
    "    return npd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the normalized pident for Orug ... ### add details here ###\n",
    "df_genes['normpident'] = df_genes.apply(get_normpident, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add distance classification for genes ... ### add details here ###\n",
    "def get_dist_class(x):\n",
    "    if np.log10(x) >= -4:\n",
    "        return 0\n",
    "    elif np.log10(x) < -4:\n",
    "        return 1\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "df_genes['dist_classification'] = df_genes.genetic_distance.apply(get_dist_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set Orug expression of 0 to nan: ### WHY? ### \n",
    "df_genes.loc[df_genes.Orug_expressionlevel == 0,'Orug_expressionlevel'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the coefficient of variance for length and expression\n",
    "def get_coef_vars(s):\n",
    "    if s[['Cpla_length','Orug_length']].count() == 2:\n",
    "        len_cfv = (s['Orug_length'] - s['Cpla_length']) / (s['Cpla_length'] + s['Orug_length'])\n",
    "    else:\n",
    "        len_cfv = np.nan\n",
    "    if s[['Orug_expressionlevel', 'Cpla_expressionlevel']].count() == 2:\n",
    "        exp_cfv = (s['Orug_expressionlevel'] - s['Cpla_expressionlevel']) / (s['Cpla_expressionlevel'] + s['Orug_expressionlevel'])\n",
    "    else:\n",
    "        exp_cfv = np.nan\n",
    "    return pd.Series([len_cfv, exp_cfv])\n",
    "\n",
    "df_genes[['len_coeff','exp_coeff']] = df_genes.apply(get_coef_vars, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the sister taxa if given:\n",
    "df_genes = df_genes.join(pd.Series(s_clade, name='ctd_sis_taxon'), on='Orug_genes', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def refine_verdict(s):\n",
    "    if (s['contaminated'] == 1) and (s['ctd_sis_taxon'] == 'P'):\n",
    "        clss = 'guilty'\n",
    "    elif (s['contaminated'] == 1) and (s['ctd_sis_taxon'] == 'F' or s['ctd_sis_taxon'] == 'both'):\n",
    "        clss = 'bad'\n",
    "    else:\n",
    "        clss = s['class']\n",
    "    \n",
    "    return clss\n",
    "        \n",
    "df_genes['class'] = df_genes.apply(refine_verdict, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust column names for clarity\n",
    "df_genes = df_genes.rename({'pident':'pident_vs_Cpla',\n",
    "                             'sseqid':'Cpla_genes',\n",
    "                            },\n",
    "                            axis=1,\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Orug_genes</th>\n",
       "      <th>qseqid</th>\n",
       "      <th>Cpla_genes</th>\n",
       "      <th>pident_vs_Cpla</th>\n",
       "      <th>Cpla_length</th>\n",
       "      <th>Orug_length</th>\n",
       "      <th>pident_vs_Cflo</th>\n",
       "      <th>pident_vs_Lnig</th>\n",
       "      <th>Cpla_expressionlevel</th>\n",
       "      <th>Orug_expressionlevel</th>\n",
       "      <th>class</th>\n",
       "      <th>contaminated</th>\n",
       "      <th>genetic_distance</th>\n",
       "      <th>rfdistance</th>\n",
       "      <th>normpident</th>\n",
       "      <th>dist_classification</th>\n",
       "      <th>len_coeff</th>\n",
       "      <th>exp_coeff</th>\n",
       "      <th>ctd_sis_taxon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Orug|SGF62645</td>\n",
       "      <td>Orug|SGF62645</td>\n",
       "      <td>Cpla|SGF49990</td>\n",
       "      <td>100.00</td>\n",
       "      <td>795</td>\n",
       "      <td>104</td>\n",
       "      <td>99.04</td>\n",
       "      <td>87.07</td>\n",
       "      <td>1477.909318</td>\n",
       "      <td>0.243665</td>\n",
       "      <td>bad</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.074633</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.768632</td>\n",
       "      <td>-0.999670</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orug|SGF62647</td>\n",
       "      <td>Orug|SGF62647</td>\n",
       "      <td>Cpla|SGF48983</td>\n",
       "      <td>100.00</td>\n",
       "      <td>108</td>\n",
       "      <td>143</td>\n",
       "      <td>34.09</td>\n",
       "      <td>34.38</td>\n",
       "      <td>0.752753</td>\n",
       "      <td>62.445599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.920987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139442</td>\n",
       "      <td>0.976178</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orug|SGF62652</td>\n",
       "      <td>Orug|SGF62652</td>\n",
       "      <td>Cpla|SGF65473</td>\n",
       "      <td>96.36</td>\n",
       "      <td>220</td>\n",
       "      <td>313</td>\n",
       "      <td>95.81</td>\n",
       "      <td>40.08</td>\n",
       "      <td>0.745316</td>\n",
       "      <td>5.277055</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132791</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.418206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174484</td>\n",
       "      <td>0.752484</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Orug|SGF62674</td>\n",
       "      <td>Orug|SGF62674</td>\n",
       "      <td>Cpla|SGF48204</td>\n",
       "      <td>100.00</td>\n",
       "      <td>154</td>\n",
       "      <td>161</td>\n",
       "      <td>87.50</td>\n",
       "      <td>88.54</td>\n",
       "      <td>2.934701</td>\n",
       "      <td>6506.625398</td>\n",
       "      <td>guilty</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.136105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.999098</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orug|SGF62679</td>\n",
       "      <td>Orug|SGF62679</td>\n",
       "      <td>Cpla|SGF63546</td>\n",
       "      <td>90.55</td>\n",
       "      <td>939</td>\n",
       "      <td>201</td>\n",
       "      <td>89.50</td>\n",
       "      <td>90.00</td>\n",
       "      <td>10.304242</td>\n",
       "      <td>0.163538</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107483</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.008914</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.647368</td>\n",
       "      <td>-0.968754</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Orug_genes         qseqid     Cpla_genes  pident_vs_Cpla  Cpla_length  \\\n",
       "0  Orug|SGF62645  Orug|SGF62645  Cpla|SGF49990          100.00          795   \n",
       "1  Orug|SGF62647  Orug|SGF62647  Cpla|SGF48983          100.00          108   \n",
       "2  Orug|SGF62652  Orug|SGF62652  Cpla|SGF65473           96.36          220   \n",
       "3  Orug|SGF62674  Orug|SGF62674  Cpla|SGF48204          100.00          154   \n",
       "4  Orug|SGF62679  Orug|SGF62679  Cpla|SGF63546           90.55          939   \n",
       "\n",
       "   Orug_length  pident_vs_Cflo  pident_vs_Lnig  Cpla_expressionlevel  \\\n",
       "0          104           99.04           87.07           1477.909318   \n",
       "1          143           34.09           34.38              0.752753   \n",
       "2          313           95.81           40.08              0.745316   \n",
       "3          161           87.50           88.54              2.934701   \n",
       "4          201           89.50           90.00             10.304242   \n",
       "\n",
       "   Orug_expressionlevel   class  contaminated  genetic_distance  rfdistance  \\\n",
       "0              0.243665     bad           1.0          0.000002         0.7   \n",
       "1             62.445599     NaN           NaN               NaN         NaN   \n",
       "2              5.277055    good           0.0          0.132791         0.7   \n",
       "3           6506.625398  guilty           1.0          0.000002         0.7   \n",
       "4              0.163538    good           0.0          0.107483         0.2   \n",
       "\n",
       "   normpident  dist_classification  len_coeff  exp_coeff ctd_sis_taxon  \n",
       "0    1.074633                  1.0  -0.768632  -0.999670             F  \n",
       "1    2.920987                  NaN   0.139442   0.976178           NaN  \n",
       "2    1.418206                  0.0   0.174484   0.752484           NaN  \n",
       "3    1.136105                  1.0   0.022222   0.999098             P  \n",
       "4    1.008914                  0.0  -0.647368  -0.968754           NaN  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3522"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plots showing that these features indicate contamination\n",
    "The plots below show clearly that the phylogenetic classification identifies genes in Orug that have low expression, are very short, and have very low genetic distance to their best hit in Cpla. \n",
    "Moreover, the phylogenetic classification of the parent/sister clade of these likely contaminants adds confidence to this classification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.violinplot(data=pd.DataFrame(df_genes[[ 'Cpla_expressionlevel',\n",
    "                                           'Orug_expressionlevel',]].stack().apply(np.log2)\n",
    "                                ).reset_index(1\n",
    "                                ).join(df_genes[['contaminated']]\n",
    "                                ),\n",
    "               y=0,\n",
    "               x='level_1',\n",
    "               hue='contaminated',\n",
    "               scale='area' # I think area is more appropriate than count, since we want to compare \n",
    "                            # the distribution, not the number of genes here.\n",
    "              )\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"log2(mean expression)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.violinplot(data=pd.DataFrame(df_genes[[ 'pident_vs_Cpla',\n",
    "                                           'contaminated',]]\n",
    "                                ),\n",
    "                   y='pident_vs_Cpla',\n",
    "                   x='contaminated',\n",
    "                   scale='area' \n",
    "                  )\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"% identity cf C.planatus\")\n",
    "plt.xticks([0,1],['\"Good\" sequences', '\"Dodgy\" sequences'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.violinplot(data=pd.DataFrame(df_genes[[ 'len_coeff',\n",
    "                                           'contaminated',]]\n",
    "                                ),\n",
    "                   y='len_coeff',\n",
    "                   x='contaminated',\n",
    "                   scale='area' \n",
    "                  )\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Orug - Cpla length coefficient of variance\")\n",
    "plt.xticks([0,1],['\"Good\" sequences', '\"Dodgy\" sequences'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.violinplot(data=pd.DataFrame(df_genes[[ 'exp_coeff',\n",
    "                                           'contaminated',]]\n",
    "                                ),\n",
    "                   y='exp_coeff',\n",
    "                   x='contaminated',\n",
    "                   scale='area' \n",
    "                  )\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Orug - Cpla expression coefficient of variance\")\n",
    "plt.xticks([0,1],['\"Good\" sequences', '\"Dodgy\" sequences'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#which gene is contaminated?\n",
    "O_con_ldiff = []\n",
    "O_con_ediff = []\n",
    "C_con_ldiff = []\n",
    "C_con_ediff = []\n",
    "\n",
    "for ind, row in df_genes.iterrows():\n",
    "    if row['ctd_sis_taxon'] == 'P' or row['ctd_sis_taxon'] == 'both':\n",
    "        O_con_ldiff.append(row['len_coeff'])\n",
    "        O_con_ediff.append(row['exp_coeff'])\n",
    "    elif row['ctd_sis_taxon'] == 'F' or row['ctd_sis_taxon'] == 'both':\n",
    "        C_con_ldiff.append(row['len_coeff'])\n",
    "        C_con_ediff.append(row['exp_coeff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax9 = sns.violinplot(data = [O_con_ldiff, C_con_ldiff], scale = 'count', inner = 'point')\n",
    "ax9.set(ylabel = 'Orug - Cpla normalised length diff')\n",
    "ax9.set_xticklabels(['Ponerine', 'Formicine'])\n",
    "ax9.set_xlabel('Sister taxon to Orug-Cpla clade')\n",
    "ax9 = sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax10 = sns.violinplot(data = [O_con_ediff, C_con_ediff], scale = 'count', inner = 'point')\n",
    "ax10.set(ylabel = 'Orug - Cpla expression')\n",
    "ax10.set_xticklabels(['Ponerine', 'Formicine'])\n",
    "ax10.set_xlabel('Sister taxon to Orug-Cpla clade')\n",
    "ax10 = sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Identify and remove contaminants from the transcriptome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "703 332 91\n"
     ]
    }
   ],
   "source": [
    "#make list of contaminant genes that should be removed from the Orug transcriptome\n",
    "#include genes if contaminated says 1 and if rfdist >= 0.6\n",
    "genes_for_removal = []\n",
    "count = 0\n",
    "c2 = []\n",
    "\n",
    "for ind, row in df_genes.iterrows():\n",
    "    if row['class'] == 'bad':\n",
    "        genes_for_removal.append(row['Orug_genes'])\n",
    "    elif row['ctd_sis_taxon'] != 'P' \\\n",
    "        and (row['rfdistance'] >= 0.6 or \n",
    "              pd.isnull(row['rfdistance'])\n",
    "             ) \\\n",
    "        and (row['dist_classification'] == 1 or \n",
    "             (row['exp_coeff'] < -0.2 and \n",
    "              row['len_coeff'] < -0.2)\n",
    "             ):\n",
    "        genes_for_removal.append(row['Orug_genes'])\n",
    "        count += 1\n",
    "    elif row['ctd_sis_taxon'] == 'F' and \\\n",
    "         row['exp_coeff'] < -0.2      and  \\\n",
    "         row['len_coeff'] < -0.2:\n",
    "        genes_for_removal.append(row['Orug_genes'])\n",
    "        c2.append(row['Orug_genes'])\n",
    "print(len(genes_for_removal), count, len(c2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter Orug txome to remove contaminants:\n",
    "s_clean_Orug_txome = s_Orug_txome[~s_Orug_txome.index.isin(genes_for_removal)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26161\n",
      "25458\n"
     ]
    }
   ],
   "source": [
    "print(len(s_Orug_txome))\n",
    "print(len(s_clean_Orug_txome))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 's_clean_Orug_txome' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-91543edbd71a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#WRITE NEW ORUG TRANSCRIPTOME\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'clean_Orug_txome.fasta'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mgeneid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms_clean_Orug_txome\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'>{}\\n{}\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_clean_Orug_txome\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgeneid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 's_clean_Orug_txome' is not defined"
     ]
    }
   ],
   "source": [
    "#WRITE NEW ORUG TRANSCRIPTOME\n",
    "with open('clean_Orug_txome.fasta', 'a') as f:\n",
    "    for geneid in s_clean_Orug_txome.index:\n",
    "        f.write('>{}\\n{}\\n'.format(geneid, s_clean_Orug_txome.loc[geneid]))\n",
    "    f.close()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}