Evaluate_BleTIES_MILRAA_with_simulated_PB_reads.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate BleTIES MILRAA output with simulated PacBio 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_pb/ptet_sim_clr.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_29749_28</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>29750</td>\n",
       "      <td>28</td>\n",
       "      <td>coord_TA_len</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_29829_55</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>29829</td>\n",
       "      <td>55</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_30723_127</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>30723</td>\n",
       "      <td>127</td>\n",
       "      <td>coord_TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_168431_49</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>168431</td>\n",
       "      <td>49</td>\n",
       "      <td>coord_TA_len</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>BREAK_POINTS_SUBREADS_scaffold51_100_168479_27</td>\n",
       "      <td>scaffold51_100</td>\n",
       "      <td>168479</td>\n",
       "      <td>27</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_29749_28  scaffold51_100   \n",
       "1     05   BREAK_POINTS_SUBREADS_scaffold51_100_29829_55  scaffold51_100   \n",
       "2     05  BREAK_POINTS_SUBREADS_scaffold51_100_30723_127  scaffold51_100   \n",
       "3     05  BREAK_POINTS_SUBREADS_scaffold51_100_168431_49  scaffold51_100   \n",
       "4     05  BREAK_POINTS_SUBREADS_scaffold51_100_168479_27  scaffold51_100   \n",
       "\n",
       "      pos  len         score  \n",
       "0   29750   28  coord_TA_len  \n",
       "1   29829   55  coord_TA_len  \n",
       "2   30723  127      coord_TA  \n",
       "3  168431   49  coord_TA_len  \n",
       "4  168479   27  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 PacBio CLR data')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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 PacBio CLR 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      269.0\n",
       "10     5512.0\n",
       "20    11312.0\n",
       "40    11642.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check: total number per mixture ratio should match number of records in GFF\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>4.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_TA</th>\n",
       "      <td>91.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>3492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_TA_len</th>\n",
       "      <td>133.0</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>6087.0</td>\n",
       "      <td>6886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coord_len</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nomatch</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "iescov           05      10      20      40\n",
       "score                                      \n",
       "coord           4.0    92.0   127.0   115.0\n",
       "coord_10        4.0    49.0    96.0    98.0\n",
       "coord_5        37.0   675.0  1152.0  1015.0\n",
       "coord_TA       91.0  2184.0  3810.0  3492.0\n",
       "coord_TA_len  133.0  2498.0  6087.0  6886.0\n",
       "coord_len       0.0     4.0    10.0     6.0\n",
       "nomatch         0.0    10.0    30.0    30.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check: total number per mixture ratio should match number of records in GFF\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>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>133.0</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>92.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>127.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>6087.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>3492.0</td>\n",
       "      <td>6886.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</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    4.0       4.0     37.0      91.0         133.0        0.0   \n",
       "1         10   92.0      49.0    675.0    2184.0        2498.0        4.0   \n",
       "2         20  127.0      96.0   1152.0    3810.0        6087.0       10.0   \n",
       "3         40  115.0      98.0   1015.0    3492.0        6886.0        6.0   \n",
       "\n",
       "score  nomatch  \n",
       "0          0.0  \n",
       "1         10.0  \n",
       "2         30.0  \n",
       "3         30.0  "
      ]
     },
     "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_clr.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": 42,
   "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 PacBio CLR data\")\n",
    "plt.savefig(\"plots/ptet_sim_clr.mix.ies_results.png\")\n",
    "plt.savefig(\"plots/ptet_sim_clr.mix.ies_results.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot length histogram for original IES annotations\n",
    "ieslens = []\n",
    "for scaff in iescoords:\n",
    "    ieslens.extend(iescoords[scaff].values())\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot([i for i in ieslens if i < 300],binwidth=1, linewidth=0)\n",
    "plt.savefig(\"plots/ptet_ies_orig_lengths.png\")\n",
    "plt.savefig(\"plots/ptet_ies_orig_lengths.pdf\")"
   ]
  },
  {
   "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": [
    "# Length histograms for predicted IESs at different coverage levels\n",
    "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_clr.mix.ieslengths.png\")\n",
    "grid.savefig(\"plots/ptet_sim_clr.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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iesrecon = {}\n",
    "for cov in ['05','10','20','40']:\n",
    "    iesrecon[cov] = SeqIO.to_dict(\n",
    "        SeqIO.parse(\n",
    "            f\"bleties_pb/ptet_sim_clr.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_pb/ptet_sim_clr.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": 18,
   "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": 19,
   "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": 27,
   "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": 28,
   "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_clr.mix.ies_vs_orig_seqid.png\")\n",
    "plt.savefig(\"plots/ptet_sim_clr.mix.ies_vs_orig_seqid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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_clr.mix.ies_vs_orig_mm.png\")\n",
    "plt.savefig(\"plots/ptet_sim_clr.mix.ies_vs_orig_mm.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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_clr.mix.ies_vs_orig_indel.png\")\n",
    "plt.savefig(\"plots/ptet_sim_clr.mix.ies_vs_orig_indel.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "05\n",
      "      pid     mm  indel\n",
      "1.00  1.0  15.00   7.00\n",
      "0.99  1.0   2.00   5.00\n",
      "0.98  1.0   1.46   4.46\n",
      "0.97  1.0   1.00   4.00\n",
      "0.96  1.0   1.00   3.00\n",
      "0.95  1.0   1.00   3.00\n",
      "\n",
      "10\n",
      "      pid    mm  indel\n",
      "1.00  1.0  44.0   60.0\n",
      "0.99  1.0   2.0    6.0\n",
      "0.98  1.0   2.0    5.0\n",
      "0.97  1.0   1.0    4.0\n",
      "0.96  1.0   1.0    4.0\n",
      "0.95  1.0   1.0    3.0\n",
      "\n",
      "20\n",
      "      pid    mm  indel\n",
      "1.00  1.0  64.0   80.0\n",
      "0.99  1.0   2.0    5.0\n",
      "0.98  1.0   1.0    4.0\n",
      "0.97  1.0   1.0    3.0\n",
      "0.96  1.0   1.0    3.0\n",
      "0.95  1.0   1.0    3.0\n",
      "\n",
      "40\n",
      "      pid     mm  indel\n",
      "1.00  1.0  859.0   82.0\n",
      "0.99  1.0    2.0    5.0\n",
      "0.98  1.0    1.0    4.0\n",
      "0.97  1.0    1.0    3.0\n",
      "0.96  1.0    1.0    3.0\n",
      "0.95  1.0    1.0    3.0\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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Report summary statistics on sequence identity\n",
    "with open(\"plots/ptet_sim_clr.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": 33,
   "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>228</td>\n",
       "      <td>4778</td>\n",
       "      <td>10034</td>\n",
       "      <td>10499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id 100</th>\n",
       "      <td>126</td>\n",
       "      <td>2367</td>\n",
       "      <td>5845</td>\n",
       "      <td>6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id 97</th>\n",
       "      <td>191</td>\n",
       "      <td>4011</td>\n",
       "      <td>8931</td>\n",
       "      <td>9545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm 0</th>\n",
       "      <td>206</td>\n",
       "      <td>4228</td>\n",
       "      <td>9140</td>\n",
       "      <td>9681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm 1</th>\n",
       "      <td>223</td>\n",
       "      <td>4678</td>\n",
       "      <td>9911</td>\n",
       "      <td>10357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indel 0</th>\n",
       "      <td>133</td>\n",
       "      <td>2502</td>\n",
       "      <td>6097</td>\n",
       "      <td>6892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indel 1</th>\n",
       "      <td>183</td>\n",
       "      <td>3837</td>\n",
       "      <td>8541</td>\n",
       "      <td>9099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mm indel 1</th>\n",
       "      <td>181</td>\n",
       "      <td>3791</td>\n",
       "      <td>8483</td>\n",
       "      <td>9041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     1      2      3\n",
       "cov           5    10     20     40\n",
       "total       228  4778  10034  10499\n",
       "id 100      126  2367   5845   6679\n",
       "id 97       191  4011   8931   9545\n",
       "mm 0        206  4228   9140   9681\n",
       "mm 1        223  4678   9911  10357\n",
       "indel 0     133  2502   6097   6892\n",
       "indel 1     183  3837   8541   9099\n",
       "mm indel 1  181  3791   8483   9041"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pretty print\n",
    "pd.read_csv(\"plots/ptet_sim_clr.mix.seqid_summary.tsv\", sep=\"\\t\").transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software timing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfiles = glob.glob(\"bleties_pb/*.log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 11,
   "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>00:03:20.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05</td>\n",
       "      <td>00:03:52.976000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>00:13:29.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>00:29:51.846000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>00:48:08.480000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cov        duration\n",
       "0  00 00:03:20.580000\n",
       "1  05 00:03:52.976000\n",
       "2  10 00:13:29.503000\n",
       "3  20 00:29:51.846000\n",
       "4  40 00:48:08.480000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(timings)"
   ]
  }
 ],
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   "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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