figures/Fig2/large_tree/large_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate tree for alphatectiviruses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from Bio import SeqIO\n",
    "from io import StringIO\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gather all genomes of new and previous alphatectiviruses\n",
    "\n",
    "records = []\n",
    "\n",
    "assemblies_dir = '../../../genomes/data/assemblies_oriented/'\n",
    "\n",
    "# these are redundant in our collection\n",
    "exclude = ['PRDcoral.fasta', 'PRDmauve.fasta', 'PRDsalmon.fasta']\n",
    "\n",
    "# append to list all of our assembled alphatectis, except PRD1\n",
    "for assembly in os.listdir(assemblies_dir):\n",
    "    if assembly.startswith('PRD'):\n",
    "           if assembly not in exclude:\n",
    "            path = os.path.join(assemblies_dir, assembly)\n",
    "            if assembly != 'PRD1.fasta':\n",
    "                record = list(SeqIO.parse(path, 'fasta'))[0]\n",
    "                record.id = assembly.split('.fasta')[0]\n",
    "                record.description = ''\n",
    "                records.append(record)\n",
    "\n",
    "                \n",
    "# ppend to list all previously isolated tectiviruses\n",
    "previous_dir = 'data/previous_alphatv'\n",
    "\n",
    "for assembly in os.listdir(previous_dir):\n",
    "    if assembly.endswith('.fasta'):\n",
    "        path = os.path.join(previous_dir, assembly)\n",
    "        record = list(SeqIO.parse(path, 'fasta'))[0]\n",
    "        record.id = assembly.split('.fasta')[0]\n",
    "        record.description = ''\n",
    "        records.append(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphatv_fasta = 'data/alphatv.fasta'\n",
    "#SeqIO.write(records, alphatv_fasta, 'fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Align with `clustalo`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustalo -i data/alphatv.fasta -o data/alphatv.msa.fasta --outfmt=fa\n"
     ]
    }
   ],
   "source": [
    "alphatv_afa = 'data/alphatv.msa.fasta'\n",
    "clustal_cmd = f'clustalo -i {alphatv_fasta} -o {alphatv_afa} --outfmt=fa'\n",
    "\n",
    "print(clustal_cmd)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#! $clustal_cmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually trim edges and fix alignment errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# manually curated and removed non-resolved ends and stored as:\n",
    "alphatv_afa_trim = 'data/alphatv.msa.trim.fasta'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build tree with `iqtree`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iqtree -st DNA -m MFP -bb 1000 -alrt 1000 -s data/alphatv.msa.trim.fasta\n"
     ]
    }
   ],
   "source": [
    "iqtree_cmd = f'iqtree -st DNA -m MFP -bb 1000 -alrt 1000 -s {alphatv_afa_trim}'\n",
    "\n",
    "print(iqtree_cmd)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#! $iqtree_cmd\n",
    "\n",
    "# moved all these results into data/iqtree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree drawing\n",
    "- Drawn with [https://itol.embl.de/](https://itol.embl.de/)\n",
    "- Saved as `data/svg/alphatv.tree.svg` and `data/svg/alphatv.tree.labeled.svg`\n",
    "- Colored according to assignments (see below)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pairwise %id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate with `esl-alipid` and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esl-alipid data/alphatv.msa.trim.fasta > data/pid.tbl\n"
     ]
    }
   ],
   "source": [
    "alphatv_pid = 'data/pid.tbl'\n",
    "\n",
    "esl_cmd = f'esl-alipid {alphatv_afa_trim} > {alphatv_pid}'\n",
    "\n",
    "print(esl_cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#! $esl_cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read table\n",
    "alphatv_pid = 'data/pid.tbl'\n",
    "\n",
    "df1 = pd.read_csv(alphatv_pid, delim_whitespace=True, comment='#', header=None)\n",
    "df1.columns = ['seq1', 'seq2', 'p_id', 'nid', 'denomid', 'p_match', 'n_match', 'denommatch']\n",
    "df1 = df1.sort_values('p_id', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fill other values to make symetrical matrix\n",
    "df2 = df1.rename(columns={'seq1': 'a', 'seq2': 'b'})\n",
    "df2 = df2.rename(columns={'a': 'seq2', 'b': 'seq1'})\n",
    "\n",
    "df_sym = pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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>seq1</th>\n",
       "      <th>seq2</th>\n",
       "      <th>p_id</th>\n",
       "      <th>nid</th>\n",
       "      <th>denomid</th>\n",
       "      <th>p_match</th>\n",
       "      <th>n_match</th>\n",
       "      <th>denommatch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>PRDvermilion</td>\n",
       "      <td>PRDpistachio</td>\n",
       "      <td>82.50</td>\n",
       "      <td>12147</td>\n",
       "      <td>14723</td>\n",
       "      <td>98.71</td>\n",
       "      <td>14641</td>\n",
       "      <td>14832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>PRDpistachio</td>\n",
       "      <td>PRDvermilion</td>\n",
       "      <td>82.50</td>\n",
       "      <td>12147</td>\n",
       "      <td>14723</td>\n",
       "      <td>98.71</td>\n",
       "      <td>14641</td>\n",
       "      <td>14832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>PRDpistachio</td>\n",
       "      <td>PRDcarrot</td>\n",
       "      <td>82.52</td>\n",
       "      <td>12160</td>\n",
       "      <td>14736</td>\n",
       "      <td>98.63</td>\n",
       "      <td>14641</td>\n",
       "      <td>14845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>PRDcarrot</td>\n",
       "      <td>PRDpistachio</td>\n",
       "      <td>82.52</td>\n",
       "      <td>12160</td>\n",
       "      <td>14736</td>\n",
       "      <td>98.63</td>\n",
       "      <td>14641</td>\n",
       "      <td>14845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>PRDpistachio</td>\n",
       "      <td>PRDshaddybee</td>\n",
       "      <td>82.57</td>\n",
       "      <td>12127</td>\n",
       "      <td>14687</td>\n",
       "      <td>98.52</td>\n",
       "      <td>14609</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>PRDjet</td>\n",
       "      <td>PRDmaroon</td>\n",
       "      <td>98.54</td>\n",
       "      <td>14500</td>\n",
       "      <td>14715</td>\n",
       "      <td>99.95</td>\n",
       "      <td>14714</td>\n",
       "      <td>14721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PRDturquoise</td>\n",
       "      <td>PRDmahogany</td>\n",
       "      <td>99.00</td>\n",
       "      <td>14505</td>\n",
       "      <td>14651</td>\n",
       "      <td>99.55</td>\n",
       "      <td>14648</td>\n",
       "      <td>14714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PRDmahogany</td>\n",
       "      <td>PRDturquoise</td>\n",
       "      <td>99.00</td>\n",
       "      <td>14505</td>\n",
       "      <td>14651</td>\n",
       "      <td>99.55</td>\n",
       "      <td>14648</td>\n",
       "      <td>14714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>PR3</td>\n",
       "      <td>L17</td>\n",
       "      <td>99.82</td>\n",
       "      <td>14687</td>\n",
       "      <td>14713</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>L17</td>\n",
       "      <td>PR3</td>\n",
       "      <td>99.82</td>\n",
       "      <td>14687</td>\n",
       "      <td>14713</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3306 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              seq1          seq2   p_id    nid  denomid  p_match  n_match  \\\n",
       "190   PRDvermilion  PRDpistachio  82.50  12147    14723    98.71    14641   \n",
       "190   PRDpistachio  PRDvermilion  82.50  12147    14723    98.71    14641   \n",
       "918   PRDpistachio     PRDcarrot  82.52  12160    14736    98.63    14641   \n",
       "918      PRDcarrot  PRDpistachio  82.52  12160    14736    98.63    14641   \n",
       "1174  PRDpistachio  PRDshaddybee  82.57  12127    14687    98.52    14609   \n",
       "...            ...           ...    ...    ...      ...      ...      ...   \n",
       "1536        PRDjet     PRDmaroon  98.54  14500    14715    99.95    14714   \n",
       "24    PRDturquoise   PRDmahogany  99.00  14505    14651    99.55    14648   \n",
       "24     PRDmahogany  PRDturquoise  99.00  14505    14651    99.55    14648   \n",
       "1651           PR3           L17  99.82  14687    14713   100.00    14713   \n",
       "1651           L17           PR3  99.82  14687    14713   100.00    14713   \n",
       "\n",
       "      denommatch  \n",
       "190        14832  \n",
       "190        14832  \n",
       "918        14845  \n",
       "918        14845  \n",
       "1174       14828  \n",
       "...          ...  \n",
       "1536       14721  \n",
       "24         14714  \n",
       "24         14714  \n",
       "1651       14713  \n",
       "1651       14713  \n",
       "\n",
       "[3306 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sym.sort_values('p_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>seq1</th>\n",
       "      <th>seq2</th>\n",
       "      <th>p_id</th>\n",
       "      <th>nid</th>\n",
       "      <th>denomid</th>\n",
       "      <th>p_match</th>\n",
       "      <th>n_match</th>\n",
       "      <th>denommatch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>PRD1</td>\n",
       "      <td>PRDcerulean</td>\n",
       "      <td>95.6</td>\n",
       "      <td>14060</td>\n",
       "      <td>14707</td>\n",
       "      <td>99.65</td>\n",
       "      <td>14689</td>\n",
       "      <td>14741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     seq1         seq2  p_id    nid  denomid  p_match  n_match  denommatch\n",
       "829  PRD1  PRDcerulean  95.6  14060    14707    99.65    14689       14741"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sym[(df_sym['seq1'] == 'PRD1') & (df_sym['seq2'] == 'PRDcerulean')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make matrix\n",
    "m = df_sym.pivot_table(index='seq1', columns='seq2', values='p_id', fill_value=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(m, cmap=\"mako_r\", figsize=(15, 15))\n",
    "#plt.savefig('./data/pid.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group phages by >95% pid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "proposed_species = ['PRD1', 'PR4', 'PRDpistachio', \n",
    "                    'PRDavocado', 'PRDcobalt', 'PRDindigo', \n",
    "                    'PRDsepia', 'PRDcanary', 'PRDaquamarine', \n",
    "                    'PRDwine', 'PRDamber', 'PRDpeacock', \n",
    "                    'PRDforest', 'PRDobsidian']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure proposed species don't overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simplified = df_sym[['seq1', 'seq2', 'p_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap = df_simplified[(df_simplified['seq1'].isin(proposed_species) & (df_simplified['seq2'].isin(proposed_species)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>seq1</th>\n",
       "      <th>seq2</th>\n",
       "      <th>p_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [seq1, seq2, p_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap[overlap['p_id'] > 95]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fill = pd.DataFrame([proposed_species, proposed_species, [100] * len(proposed_species)]).T\n",
    "df_fill.columns = ['seq1', 'seq2', 'p_id']\n",
    "df_simplified = pd.concat([df_simplified, df_fill])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_assignment = {}\n",
    "\n",
    "\n",
    "for phage in list(df_simplified['seq1'].unique()):\n",
    "    # subset dataframe for that phage, compared to the proposed species list\n",
    "    df_best = df_simplified[(df_simplified['seq1'] == phage) & (df_simplified['seq2'].isin(proposed_species))]\n",
    "\n",
    "    # sort pid values\n",
    "    df_best = df_best.sort_values('p_id', ascending=False)\n",
    "\n",
    "    # select higest pid value (best match to a proposed species)\n",
    "    best = df_best['seq2'].to_list()[0]\n",
    "\n",
    "    # add to dictionary\n",
    "    if best not in dict_assignment.keys():\n",
    "        dict_assignment[best] = []\n",
    "\n",
    "    dict_assignment[best].append(phage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_assignments = []\n",
    "\n",
    "for species in dict_assignment.keys():\n",
    "    \n",
    "    df = df_simplified[(df_simplified['seq1'] == species) & (df_simplified['seq2'].isin(dict_assignment[species]))]\n",
    "    \n",
    "    df = df[['seq1', 'seq2', 'p_id']]\n",
    "    df.columns = ['species', 'isolate', 'p_id']\n",
    "    \n",
    "    list_assignments.append(df)\n",
    "\n",
    "df_assignments = pd.concat(list_assignments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_assignments = df_assignments.sort_values(by=['species', 'p_id'], ascending=[True, False])\n",
    "df_assignments = df_assignments.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRD1             40\n",
       "PRDcobalt         4\n",
       "PRDforest         4\n",
       "PRDaquamarine     3\n",
       "PRDwine           3\n",
       "PR4               2\n",
       "PRDamber          2\n",
       "PRDavocado        2\n",
       "PRDcanary         2\n",
       "PRDindigo         2\n",
       "PRDobsidian       2\n",
       "PRDpeacock        2\n",
       "PRDpistachio      2\n",
       "PRDsepia          2\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_assignments['species'].value_counts()"
   ]
  }
 ],
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