Evaluate_BleTIES_MILRAA_with_simulated_ONT_R9.5_reads.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate BleTIES MILRAA output with simulated ONT R9.5 reads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "import glob\n",
    "import datetime\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from Bio import SeqIO\n",
    "from Bio import Seq\n",
    "from Bio import pairwise2\n",
    "\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load previously annotated IES coordinates from published sequences\n",
    "with open(\"ref/ptetraurelia_mac_51.min100k_max100N.iescoords.json\", \"r\") as fh:\n",
    "    iescoords = json.load(fh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the following:\n",
    "\n",
    " * Number of IESs recovered vs. MAC+IES average read coverage\n",
    "  * Thereof: Number with correct coordinate; TA junction; correct length\n",
    " * Number of mismatches between assembled IES sequence and vs. MAC+IES average read coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fields to report\n",
    "# iescov, ID, scaffold, coord, TA, length, score (coord, coord_TA, coord_TA_len)\n",
    "out = []\n",
    "for iescov in ['00','05','10','20','40']:\n",
    "    with open(f\"bleties_ont/ptet_sim_ontR95.mix{iescov}.milraa_subreads.milraa_ies.gff3\",\"r\") as fh:\n",
    "        for line in fh:\n",
    "            if not re.match(r\"#\", line):\n",
    "                spl=line.rstrip().split(\"\\t\")\n",
    "                scaff=spl[0]\n",
    "                coord=spl[3]\n",
    "                outcoord = coord\n",
    "                attr=spl[-1].rstrip(\";\").split(\";\")\n",
    "                attrdict={a.split(\"=\")[0]:a.split(\"=\")[1] for a in attr}\n",
    "                score = \"nomatch\"\n",
    "                if \"ta_pointer_start\" in attrdict and attrdict[\"ta_pointer_start\"] in iescoords[scaff]:\n",
    "                    outcoord = attrdict[\"ta_pointer_start\"]\n",
    "                    # Correct coordinate and TA pointer present\n",
    "                    if int(attrdict[\"IES_length\"]) == iescoords[scaff][attrdict[\"ta_pointer_start\"]]:\n",
    "                        # Correct coordinate and TA pointer and length\n",
    "                        score = \"coord_TA_len\"\n",
    "                    else:\n",
    "                        # Correct coordinate and TA pointer but wrong length\n",
    "                        score = \"coord_TA\"\n",
    "                else:\n",
    "                    if coord in iescoords[scaff]:\n",
    "                        # Correct coordinate but no TA pointer\n",
    "                        if int(attrdict[\"IES_length\"]) == iescoords[scaff][coord]:\n",
    "                            # Correct coordinate and length but no TA pointer\n",
    "                            score = \"coord_len\"\n",
    "                        else:\n",
    "                            score = \"coord\"\n",
    "                    else:\n",
    "                        for coord_i in range(int(coord)-10, int(coord)+10):\n",
    "                            if str(coord_i) in iescoords[scaff]:\n",
    "                                score = \"coord_10\"\n",
    "                        for coord_i in range(int(coord)-5, int(coord)+5):\n",
    "                            if str(coord_i) in iescoords[scaff]:\n",
    "                                score = \"coord_5\"\n",
    "                out.append([iescov, attrdict[\"ID\"], scaff, outcoord, attrdict[\"IES_length\"], score])\n",
    "out = pd.DataFrame(out, columns=[\"iescov\",\"ID\",\"scaffold\",\"pos\",\"len\",\"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>iescov</th>\n",
       "      <th>ID</th>\n",
       "      <th>scaffold</th>\n",
       "      <th>pos</th>\n",
       "      <th>len</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_256802_78</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>256803</td>\n",
       "      <td>78</td>\n",
       "      <td>coord_TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_257911_45</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>257911</td>\n",
       "      <td>45</td>\n",
       "      <td>coord_TA_len</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_258619_43</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>258620</td>\n",
       "      <td>43</td>\n",
       "      <td>coord_TA_len</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_258810_74</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>258811</td>\n",
       "      <td>74</td>\n",
       "      <td>coord_TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_259946_97</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>259946</td>\n",
       "      <td>97</td>\n",
       "      <td>coord_TA_len</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iescov                                              ID        scaffold  \\\n",
       "0     05  BREAK_POINTS_SUBREADS_scaffold51_100_256802_78  scaffold51_100   \n",
       "1     05  BREAK_POINTS_SUBREADS_scaffold51_100_257911_45  scaffold51_100   \n",
       "2     05  BREAK_POINTS_SUBREADS_scaffold51_100_258619_43  scaffold51_100   \n",
       "3     05  BREAK_POINTS_SUBREADS_scaffold51_100_258810_74  scaffold51_100   \n",
       "4     05  BREAK_POINTS_SUBREADS_scaffold51_100_259946_97  scaffold51_100   \n",
       "\n",
       "      pos len         score  \n",
       "0  256803  78      coord_TA  \n",
       "1  257911  45  coord_TA_len  \n",
       "2  258620  43  coord_TA_len  \n",
       "3  258811  74      coord_TA  \n",
       "4  259946  97  coord_TA_len  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview table\n",
    "out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'BleTIES MILRAA on simulated Nanopore data')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 462.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(hue='score',x='iescov',kind='count',data=out)\n",
    "plt.xlabel(\"MAC+IES coverage\")\n",
    "plt.ylabel(\"Number of IESs predicted\")\n",
    "plt.title(\"BleTIES MILRAA on simulated Nanopore data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tabulate by score type and reformat table for barplot\n",
    "cts = out[['iescov','score']].groupby(['iescov','score']).size().reset_index()\n",
    "ctsspread = cts.pivot(index='iescov',columns='score',values=0).reset_index().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iescov\n",
       "05      308.0\n",
       "10     5515.0\n",
       "20    11276.0\n",
       "40    11609.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total IESs predicted per mixture ratio\n",
    "cts.pivot(index='iescov',columns='score',values=0).fillna(0).transpose().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>iescov</th>\n",
       "      <th>05</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coord</th>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_5</th>\n",
       "      <td>29.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_TA</th>\n",
       "      <td>69.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>1545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_TA_len</th>\n",
       "      <td>207.0</td>\n",
       "      <td>3726.0</td>\n",
       "      <td>8645.0</td>\n",
       "      <td>9188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_len</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nomatch</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "iescov           05      10      20      40\n",
       "score                                      \n",
       "coord           1.0    41.0    53.0    42.0\n",
       "coord_10        2.0    43.0    66.0    67.0\n",
       "coord_5        29.0   439.0   805.0   735.0\n",
       "coord_TA       69.0  1245.0  1679.0  1545.0\n",
       "coord_TA_len  207.0  3726.0  8645.0  9188.0\n",
       "coord_len       0.0     8.0     6.0     1.0\n",
       "nomatch         0.0    13.0    22.0    31.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total IESs predicted per mixture ratio\n",
    "cts.pivot(index='iescov',columns='score',values=0).fillna(0).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>score</th>\n",
       "      <th>iescov</th>\n",
       "      <th>coord</th>\n",
       "      <th>coord_10</th>\n",
       "      <th>coord_5</th>\n",
       "      <th>coord_TA</th>\n",
       "      <th>coord_TA_len</th>\n",
       "      <th>coord_len</th>\n",
       "      <th>nomatch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>3726.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>53.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>8645.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>42.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>9188.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "score iescov  coord  coord_10  coord_5  coord_TA  coord_TA_len  coord_len  \\\n",
       "0         05    1.0       2.0     29.0      69.0         207.0        0.0   \n",
       "1         10   41.0      43.0    439.0    1245.0        3726.0        8.0   \n",
       "2         20   53.0      66.0    805.0    1679.0        8645.0        6.0   \n",
       "3         40   42.0      67.0    735.0    1545.0        9188.0        1.0   \n",
       "\n",
       "score  nomatch  \n",
       "0          0.0  \n",
       "1         13.0  \n",
       "2         22.0  \n",
       "3         31.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctsspread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file\n",
    "ctsspread.to_csv(\"plots/ptet_sim_ontR95.mix.ies_results.tsv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12199"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of IESs in the input\n",
    "sum([len(iescoords[scaff]) for scaff in iescoords])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize key statistics in a single bargraph\n",
    "pal = plt.get_cmap('BrBG', 7) # palette\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.bar(ctsspread['iescov'], ctsspread['nomatch'], label='no match', color=pal(0))\n",
    "plt.bar(ctsspread['iescov'], ctsspread['coord_10'], label='within 10 bp', color=pal(1),\n",
    "        bottom = ctsspread['nomatch'])\n",
    "plt.bar(ctsspread['iescov'], ctsspread['coord_5'], label='within 5 bp', color=pal(2),\n",
    "        bottom = ctsspread['nomatch'] + ctsspread['coord_10'])\n",
    "plt.bar(ctsspread['iescov'],ctsspread['coord'], label='coord', color=pal(3),\n",
    "        bottom = ctsspread['nomatch'] + ctsspread['coord_10'] + ctsspread['coord_5'])\n",
    "plt.bar(ctsspread['iescov'],ctsspread['coord_len'], label='coord, length', color=pal(4),\n",
    "        bottom = ctsspread['nomatch'] + ctsspread['coord_10'] + ctsspread['coord_5'] + ctsspread['coord'])\n",
    "plt.bar(ctsspread['iescov'],ctsspread['coord_TA'], label='coord, TAs', color=pal(5),\n",
    "        bottom = ctsspread['nomatch'] + ctsspread['coord_10'] + ctsspread['coord_5'] + ctsspread['coord'] + ctsspread['coord_len'])\n",
    "plt.bar(ctsspread['iescov'],ctsspread['coord_TA_len'], label='coord, length, TAs', color=pal(6),\n",
    "        bottom = ctsspread['nomatch'] + ctsspread['coord_10'] + ctsspread['coord_5'] + ctsspread['coord'] + ctsspread['coord_len'] + ctsspread['coord_TA'])\n",
    "plt.legend()\n",
    "plt.xlabel(\"MAC+IES coverage\")\n",
    "plt.ylabel(\"Number of IESs predicted\")\n",
    "plt.title(\"BleTIES MILRAA on simulated Nanopore R9.5 data\")\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_results.png\")\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_results.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lenths for original IES annotations\n",
    "ieslens = []\n",
    "for scaff in iescoords:\n",
    "    ieslens.extend(iescoords[scaff].values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "out['len'] = pd.to_numeric(out['len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(out.query('len<300'), row='iescov', height=3, aspect=7/3)\n",
    "grid.map(sns.histplot, 'len', binwidth=1, linewidth=0)\n",
    "grid.set_titles(row_template=\"MAC+IES average coverage {row_name}x\")\n",
    "grid.set_xlabels(\"IES length (bp)\")\n",
    "grid.set_ylabels(\"No. MILRAA IESs\")\n",
    "grid.savefig(\"plots/ptet_sim_ontR95.mix.ieslengths.png\")\n",
    "grid.savefig(\"plots/ptet_sim_ontR95.mix.ieslengths.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare reconstructed IES seqs with original\n",
    "\n",
    "Extract IES sequences from MAC+IES assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "macies = SeqIO.to_dict(SeqIO.parse(\"ref/ptetraurelia_mac_51_with_ies.min100k_max100N.fa\",\"fasta\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "iesrecon = {}\n",
    "for cov in ['05','10','20','40']:\n",
    "    iesrecon[cov] = SeqIO.to_dict(\n",
    "        SeqIO.parse(\n",
    "            f\"bleties_ont/ptet_sim_ontR95.mix{cov}.milraa_subreads.milraa_ies.fasta\",\"fasta\"))\n",
    "    # Adjust IES sequences to be TA bound, instead of left-most coordinate\n",
    "    with open(f\"bleties_ont/ptet_sim_ontR95.mix{cov}.milraa_subreads.milraa_ies.gff3\",\"r\") as fh:\n",
    "        for line in fh:\n",
    "            if not re.match(r\"#\", line):\n",
    "                spl=line.rstrip().split(\"\\t\")\n",
    "                scaff=spl[0]\n",
    "                coord=spl[3]\n",
    "                attr=spl[-1].rstrip(\";\").split(\";\")\n",
    "                attrdict={a.split(\"=\")[0]:a.split(\"=\")[1] for a in attr}\n",
    "                if 'ta_pointer_start' in attrdict and attrdict['ta_pointer_start'] != coord:\n",
    "                    offset = int(attrdict['ta_pointer_start']) - int(coord)\n",
    "                    # Adjust sequence relative to TA pointer\n",
    "                    iesrecon[cov][attrdict['ID']].seq = iesrecon[cov][attrdict['ID']].seq[offset:] \\\n",
    "                        + iesrecon[cov][attrdict['ID']].seq[0:offset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IES annotations in published MAC+IES assembly\n",
    "with open(\"ref/ptetraurelia_mac_51_with_ies.min100k_max100N.iescoords.json\", \"r\") as fh:\n",
    "    iescoords_orig = json.load(fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract original IESs from the MAC+IES assembly\n",
    "orig_ies_seqs = defaultdict(dict)\n",
    "for scaff in iescoords_orig:\n",
    "    scaff_mac = scaff[:-9]\n",
    "    # dict of new coordinate vs. original coordinates\n",
    "    new_old = dict(zip(iescoords[scaff_mac],iescoords_orig[scaff]))\n",
    "    for pos in new_old:\n",
    "        name = 'ies_' + scaff_mac + \"_\" + str(pos)\n",
    "        orig_ies_seqs[scaff_mac][pos] = macies[scaff][new_old[pos][0]:new_old[pos][1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare original and reconstructed IES sequences\n",
    "iescomp = []\n",
    "for cov in iesrecon:\n",
    "    # Subset of IESs which have previously been found to have exactly matching coordinate\n",
    "    # (coordinates reported differently in annotation, so were adjusted above for TA repeats)\n",
    "    subset_coord_match = out.query(f\"iescov=='{cov}' & score in ['coord_TA_len', 'coord_TA', 'coord', 'coord_len']\")\n",
    "    counter = 0\n",
    "    for i, rec in subset_coord_match.iterrows():\n",
    "        scaff = rec['scaffold']\n",
    "        pos = rec['pos']\n",
    "        seqid = rec['ID']\n",
    "        if pos in orig_ies_seqs[scaff]:\n",
    "            origseq = orig_ies_seqs[scaff][pos].seq[:-2]\n",
    "            # Strip last two bases because in ParameciumDB both TA repeats are softmasked\n",
    "            origlen = len(origseq)\n",
    "            assemlen = len(iesrecon[cov][seqid].seq)\n",
    "            # define percent identity as number of matching bases \n",
    "            # in whichever is longer: original seq or consensus\n",
    "            alnscore = (pairwise2.align.globalxx(\n",
    "                str(origseq).upper(),\n",
    "                iesrecon[cov][seqid].seq,\n",
    "                score_only=True))\n",
    "            pid = alnscore / max([origlen, assemlen])\n",
    "            mismatch = int(origlen - alnscore)\n",
    "            indel = int(assemlen - origlen)\n",
    "            iescomp.append([cov, seqid, pid, mismatch, indel])\n",
    "iescomp_tbl = pd.DataFrame(iescomp, columns=['cov','seqid','pid','mm','indel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(iescomp_tbl, row='cov', height=3, aspect=7/3)\n",
    "grid.map(sns.histplot, 'pid', binwidth=0.01, linewidth=0)\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_seqid.png\")\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_seqid.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(iescomp_tbl.query('mm < 25'), row='cov', height=3, aspect=7/3)\n",
    "grid.map(sns.histplot, 'mm', binwidth=1, linewidth=0)\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_mm.png\")\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_mm.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(iescomp_tbl.query('indel > -25 & indel < 25'), row='cov', height=3, aspect=7/3)\n",
    "grid.map(sns.histplot, 'indel', binwidth=1, linewidth=0)\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_indel.png\")\n",
    "plt.savefig(\"plots/ptet_sim_ontR95.mix.ies_vs_orig_indel.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "05\n",
      "      pid    mm  indel\n",
      "1.00  1.0  51.0    2.0\n",
      "0.99  1.0   2.0    2.0\n",
      "0.98  1.0   2.0    2.0\n",
      "0.97  1.0   2.0    1.0\n",
      "0.96  1.0   1.0    1.0\n",
      "0.95  1.0   1.0    1.0\n",
      "\n",
      "10\n",
      "      pid    mm  indel\n",
      "1.00  1.0  77.0   59.0\n",
      "0.99  1.0   3.0    2.0\n",
      "0.98  1.0   2.0    2.0\n",
      "0.97  1.0   2.0    2.0\n",
      "0.96  1.0   1.0    1.0\n",
      "0.95  1.0   1.0    1.0\n",
      "\n",
      "20\n",
      "      pid     mm  indel\n",
      "1.00  1.0  153.0   81.0\n",
      "0.99  1.0    2.0    2.0\n",
      "0.98  1.0    2.0    1.0\n",
      "0.97  1.0    1.0    1.0\n",
      "0.96  1.0    1.0    1.0\n",
      "0.95  1.0    1.0    1.0\n",
      "\n",
      "40\n",
      "      pid    mm  indel\n",
      "1.00  1.0  45.0   80.0\n",
      "0.99  1.0   2.0    2.0\n",
      "0.98  1.0   1.0    1.0\n",
      "0.97  1.0   1.0    1.0\n",
      "0.96  1.0   1.0    1.0\n",
      "0.95  1.0   1.0    1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Report upper percentile stats for each metric\n",
    "for cov in ['05','10','20','40']:\n",
    "    print(cov)\n",
    "    print(iescomp_tbl.query(f\"cov == '{cov}'\").quantile([1, 0.99, 0.98, 0.97, 0.96, 0.95], axis=0))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Report summary statistics on sequence identity\n",
    "with open(\"plots/ptet_sim_ontR95.mix.seqid_summary.tsv\", \"w\") as fh:\n",
    "    # Print header\n",
    "    fh.write(\"\\t\".join(['cov','total','id 100','id 97','mm 0','mm 1','indel 0','indel 1', 'mm indel 1']))\n",
    "    fh.write(\"\\n\")\n",
    "    for cov in ['05','10','20','40']:\n",
    "        fh.write(\"\\t\".join([\n",
    "            cov,\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}'\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & pid == 1.0\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & pid > 0.97\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & mm == 0\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & mm <= 1\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & indel == 0\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & indel >= -1 & indel <= 1\").shape[0]),\n",
    "            str(iescomp_tbl.query(f\"cov == '{cov}' & indel >= -1 & indel <= 1 & mm <= 1\").shape[0])\n",
    "        ]))\n",
    "        fh.write(\"\\n\")"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cov</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>277</td>\n",
       "      <td>5020</td>\n",
       "      <td>10383</td>\n",
       "      <td>10776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id 100</th>\n",
       "      <td>193</td>\n",
       "      <td>3535</td>\n",
       "      <td>8425</td>\n",
       "      <td>8975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id 97</th>\n",
       "      <td>264</td>\n",
       "      <td>4730</td>\n",
       "      <td>10058</td>\n",
       "      <td>10503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm 0</th>\n",
       "      <td>225</td>\n",
       "      <td>4192</td>\n",
       "      <td>9323</td>\n",
       "      <td>9781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm 1</th>\n",
       "      <td>267</td>\n",
       "      <td>4835</td>\n",
       "      <td>10159</td>\n",
       "      <td>10601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indel 0</th>\n",
       "      <td>207</td>\n",
       "      <td>3734</td>\n",
       "      <td>8651</td>\n",
       "      <td>9189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indel 1</th>\n",
       "      <td>265</td>\n",
       "      <td>4766</td>\n",
       "      <td>10074</td>\n",
       "      <td>10486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm indel 1</th>\n",
       "      <td>260</td>\n",
       "      <td>4680</td>\n",
       "      <td>9976</td>\n",
       "      <td>10414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     1      2      3\n",
       "cov           5    10     20     40\n",
       "total       277  5020  10383  10776\n",
       "id 100      193  3535   8425   8975\n",
       "id 97       264  4730  10058  10503\n",
       "mm 0        225  4192   9323   9781\n",
       "mm 1        267  4835  10159  10601\n",
       "indel 0     207  3734   8651   9189\n",
       "indel 1     265  4766  10074  10486\n",
       "mm indel 1  260  4680   9976  10414"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pretty print\n",
    "pd.read_csv(\"plots/ptet_sim_ontR95.mix.seqid_summary.tsv\", sep=\"\\t\").transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfiles = glob.glob(\"bleties_ont/*.log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "timings = []\n",
    "for logfile in logfiles:\n",
    "    cov = re.search(r\"mix(\\d{2})\", logfile).group(1)\n",
    "    with open(logfile, \"r\") as fh:\n",
    "        for line in fh:\n",
    "            started = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Started BleTIES MILRAA\", line)\n",
    "            finished = re.search(r\"\\[(\\S+ \\d{2}:\\d{2}:\\d{2}\\,\\d{3})\\] .* Finished MILRAA\", line)\n",
    "            if started:\n",
    "                starttime = datetime.datetime.fromisoformat(started.group(1).replace(' ','T').replace(',', '.'))\n",
    "            elif re.search(r\"Finished\", line):\n",
    "                finishtime = datetime.datetime.fromisoformat(finished.group(1).replace(' ','T').replace(',', '.'))\n",
    "    duration = finishtime - starttime\n",
    "    timings.append({'cov' : cov, 'duration' : duration})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>cov</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00</td>\n",
       "      <td>00:02:56.811000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05</td>\n",
       "      <td>00:03:48.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>00:15:22.642000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>00:33:34.956000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>00:43:43.170000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cov        duration\n",
       "0  00 00:02:56.811000\n",
       "1  05 00:03:48.420000\n",
       "2  10 00:15:22.642000\n",
       "3  20 00:33:34.956000\n",
       "4  40 00:43:43.170000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(timings)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:bio-env]",
   "language": "python",
   "name": "conda-env-bio-env-py"
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   "codemirror_mode": {
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