Mashkova_HW#3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from cigar import Cigar\n",
    "import pandas as pd\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[M::mm_idx_gen::0.161*1.06] collected minimizers\n",
      "[M::mm_idx_gen::0.183*1.30] sorted minimizers\n",
      "[M::main::0.283*1.19] loaded/built the index for 1 target sequence(s)\n",
      "[M::mm_idx_stat] kmer size: 15; skip: 10; is_hpc: 0; #seq: 1\n",
      "[M::mm_idx_stat::0.294*1.18] distinct minimizers: 838533 (98.18% are singletons); average occurrences: 1.034; average spacing: 5.352\n",
      "[M::main] Version: 2.17-r941\n",
      "[M::main] CMD: minimap2 -d reference.mmi reference.fasta\n",
      "[M::main] Real time: 0.307 sec; CPU: 0.360 sec; Peak RSS: 0.065 GB\n"
     ]
    }
   ],
   "source": [
    "! minimap2 -d reference.mmi reference.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WARNING]\u001b[1;31m Indexing parameters (-k, -w or -H) overridden by parameters used in the prebuilt index.\u001b[0m\n",
      "[M::main::0.085*1.05] loaded/built the index for 1 target sequence(s)\n",
      "[M::mm_mapopt_update::0.097*1.04] mid_occ = 11\n",
      "[M::mm_idx_stat] kmer size: 15; skip: 10; is_hpc: 0; #seq: 1\n",
      "[M::mm_idx_stat::0.106*1.03] distinct minimizers: 838533 (98.18% are singletons); average occurrences: 1.034; average spacing: 5.352\n",
      "[M::worker_pipeline::34.673*2.85] mapped 41447 sequences\n",
      "[M::main] Version: 2.17-r941\n",
      "[M::main] CMD: minimap2 -ax map-pb reference.mmi pacbio_40x.fq\n",
      "[M::main] Real time: 34.697 sec; CPU: 98.814 sec; Peak RSS: 1.985 GB\n"
     ]
    }
   ],
   "source": [
    "! minimap2 -ax map-pb reference.mmi pacbio_40x.fq > alignment_pacbio.sam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[M::main::0.091*1.00] loaded/built the index for 1 target sequence(s)\n",
      "[M::mm_mapopt_update::0.104*1.00] mid_occ = 11\n",
      "[M::mm_idx_stat] kmer size: 15; skip: 10; is_hpc: 0; #seq: 1\n",
      "[M::mm_idx_stat::0.115*1.00] distinct minimizers: 838533 (98.18% are singletons); average occurrences: 1.034; average spacing: 5.352\n",
      "[M::worker_pipeline::13.584*2.78] mapped 30810 sequences\n",
      "[M::main] Version: 2.17-r941\n",
      "[M::main] CMD: minimap2 -ax map-ont reference.mmi SRR6334890.fastq\n",
      "[M::main] Real time: 13.600 sec; CPU: 37.802 sec; Peak RSS: 0.921 GB\n"
     ]
    }
   ],
   "source": [
    "! minimap2 -ax map-ont reference.mmi SRR6334890.fastq > alignment_ont.sam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools view -S -b alignment_pacbio.sam > alignment_pacbio.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools view -S -b alignment_ont.sam > alignment_ont.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools sort alignment_pacbio.sam -o alignment_pacbio.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools sort alignment_ont.sam -o alignment_ont.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools index alignment_pacbio.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "! samtools index alignment_ont.bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49743 + 0 in total (QC-passed reads + QC-failed reads)\r\n",
      "1528 + 0 secondary\r\n",
      "6768 + 0 supplementary\r\n",
      "0 + 0 duplicates\r\n",
      "48434 + 0 mapped (97.37% : N/A)\r\n",
      "0 + 0 paired in sequencing\r\n",
      "0 + 0 read1\r\n",
      "0 + 0 read2\r\n",
      "0 + 0 properly paired (N/A : N/A)\r\n",
      "0 + 0 with itself and mate mapped\r\n",
      "0 + 0 singletons (N/A : N/A)\r\n",
      "0 + 0 with mate mapped to a different chr\r\n",
      "0 + 0 with mate mapped to a different chr (mapQ>=5)\r\n"
     ]
    }
   ],
   "source": [
    "! samtools flagstat alignment_pacbio.bam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что процент приложившихся ридов Pacbio равен 97.37."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32103 + 0 in total (QC-passed reads + QC-failed reads)\r\n",
      "1072 + 0 secondary\r\n",
      "221 + 0 supplementary\r\n",
      "0 + 0 duplicates\r\n",
      "29565 + 0 mapped (92.09% : N/A)\r\n",
      "0 + 0 paired in sequencing\r\n",
      "0 + 0 read1\r\n",
      "0 + 0 read2\r\n",
      "0 + 0 properly paired (N/A : N/A)\r\n",
      "0 + 0 with itself and mate mapped\r\n",
      "0 + 0 singletons (N/A : N/A)\r\n",
      "0 + 0 with mate mapped to a different chr\r\n",
      "0 + 0 with mate mapped to a different chr (mapQ>=5)\r\n"
     ]
    }
   ],
   "source": [
    "! samtools flagstat alignment_ont.bam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что процент приложившихся ридов Oxford Nanopores равен 92.09."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Покрытие генома"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coverage(sam_file_name, ref_file_name):    \n",
    "    sam_file = open(sam_file_name, 'r')\n",
    "    ref_file = open(ref_file_name, 'r')\n",
    "    sam_file.readline()\n",
    "    sam_file.readline()\n",
    "    ref_length = 0\n",
    "    ref_file.readline()\n",
    "    for line in ref_file:\n",
    "        ref_length += len(line)\n",
    "    per_base_coverage = np.zeros(ref_length)\n",
    "    for line in sam_file:\n",
    "        line = line.strip().split()\n",
    "        start = int(line[3])\n",
    "        read_length = len(line[9])\n",
    "        stop = min(start + read_length - 1, ref_length)\n",
    "        per_base_coverage[start:stop] = per_base_coverage[start:stop] + 1\n",
    "    ref_file.close()\n",
    "    sam_file.close()\n",
    "    return per_base_coverage, ref_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Pacbio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d648580>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# покрытие с выбросами\n",
    "per_base_coverage, ref_length = get_coverage('alignment_pacbio.sam', 'reference.fasta')\n",
    "n = len(per_base_coverage)\n",
    "M = []\n",
    "N = []\n",
    "for i in range(0, n, 5000):\n",
    "    M.append(i)\n",
    "    N.append(sum(per_base_coverage[i:(i+5000)]) / 5000)\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.title('Coverage with outliers')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4705957\n"
     ]
    }
   ],
   "source": [
    "print(ref_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1157b4110>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# покрытие без выбросов\n",
    "N = np.asarray(N)\n",
    "indices = np.argwhere(N < 200).flatten()\n",
    "N_ = [N[i] for i in range(len(N)) if i in indices]\n",
    "M_ = [M[i] for i in range(len(M)) if i in indices]\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Coverage without outliers')\n",
    "plt.scatter(M_, N_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее покрытие: 48\n",
      "Процент покрытия: 98.73390258347027 %\n"
     ]
    }
   ],
   "source": [
    "print(f'Среднее покрытие: {int(sum(per_base_coverage) / len(per_base_coverage))}')\n",
    "count_covered_bases = 0\n",
    "for base in per_base_coverage:\n",
    "    if base != 0:\n",
    "        count_covered_bases += 1\n",
    "print(f'Процент покрытия: {(count_covered_bases / n) * 100} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравним с данными из bbmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/olga/Downloads/bbmap//calcmem.sh: line 75: [: -v: unary operator expected\n",
      "Max memory cannot be determined.  Attempting to use 3200 MB.\n",
      "If this fails, please add the -Xmx flag (e.g. -Xmx24g) to your command, \n",
      "or run this program qsubbed or from a qlogin session on Genepool, or set ulimit to an appropriate value.\n",
      "java -ea -Xmx3200m -cp /Users/olga/Downloads/bbmap/current/ jgi.CoveragePileup in=alignment_pacbio.sam out=bbmap_pacbio.txt hist=hist_pacbio.txt\n",
      "Executing jgi.CoveragePileup [in=alignment_pacbio.sam, out=bbmap_pacbio.txt, hist=hist_pacbio.txt]\n",
      "\n",
      "Reads:                               \t49743\n",
      "Mapped reads:                        \t48434\n",
      "Mapped bases:                        \t193109287\n",
      "Ref scaffolds:                       \t1\n",
      "Ref bases:                           \t4639675\n",
      "\n",
      "Percent mapped:                      \t97.368\n",
      "Percent proper pairs:                \t0.000\n",
      "Average coverage:                    \t41.621\n",
      "Average coverage with deletions:     \t40.062\n",
      "Standard deviation:                    \t9.255\n",
      "Percent scaffolds with any coverage: \t100.00\n",
      "Percent of reference bases covered:  \t100.00\n",
      "\n",
      "Time: \t1.295 seconds.\n"
     ]
    }
   ],
   "source": [
    "! /Users/olga/Downloads/bbmap/pileup.sh in=alignment_pacbio.sam out=bbmap_pacbio.txt hist=hist_pacbio.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Oxford Nanopores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11593d050>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# покрытие с выбросами\n",
    "per_base_coverage = get_coverage('alignment_ont.sam', 'reference.fasta')\n",
    "n = len(per_base_coverage)\n",
    "M = []\n",
    "N = []\n",
    "for i in range(0, n, 5000):\n",
    "    M.append(i)\n",
    "    N.append(sum(per_base_coverage[i:(i+5000)]) / 5000)\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.title('Coverage with outliers')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x115a8ce50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# покрытие без выбросов\n",
    "N = np.asarray(N)\n",
    "indices = np.argwhere(N < 200).flatten()\n",
    "N_ = [N[i] for i in range(len(N)) if i in indices]\n",
    "M_ = [M[i] for i in range(len(M)) if i in indices]\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Coverage without outliers')\n",
    "plt.scatter(M_, N_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее покрытие: 28\n",
      "Процент покрытия: 98.79550535629629 %\n"
     ]
    }
   ],
   "source": [
    "print(f'Среднее покрытие: {int(sum(per_base_coverage) / len(per_base_coverage))}')\n",
    "count_covered_bases = 0\n",
    "for base in per_base_coverage:\n",
    "    if base != 0:\n",
    "        count_covered_bases += 1\n",
    "print(f'Процент покрытия: {(count_covered_bases / n) * 100} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравним с данными из bbmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/olga/Downloads/bbmap//calcmem.sh: line 75: [: -v: unary operator expected\n",
      "Max memory cannot be determined.  Attempting to use 3200 MB.\n",
      "If this fails, please add the -Xmx flag (e.g. -Xmx24g) to your command, \n",
      "or run this program qsubbed or from a qlogin session on Genepool, or set ulimit to an appropriate value.\n",
      "java -ea -Xmx3200m -cp /Users/olga/Downloads/bbmap/current/ jgi.CoveragePileup in=alignment_ont.sam out=bbmap_ont.txt hist=hist_ont.txt\n",
      "Executing jgi.CoveragePileup [in=alignment_ont.sam, out=bbmap_ont.txt, hist=hist_ont.txt]\n",
      "\n",
      "Reads:                               \t32103\n",
      "Mapped reads:                        \t29565\n",
      "Mapped bases:                        \t125216746\n",
      "Ref scaffolds:                       \t1\n",
      "Ref bases:                           \t4639675\n",
      "\n",
      "Percent mapped:                      \t92.094\n",
      "Percent proper pairs:                \t0.000\n",
      "Average coverage:                    \t26.988\n",
      "Average coverage with deletions:     \t28.341\n",
      "Standard deviation:                    \t6.312\n",
      "Percent scaffolds with any coverage: \t100.00\n",
      "Percent of reference bases covered:  \t100.00\n",
      "\n",
      "Time: \t0.624 seconds.\n"
     ]
    }
   ],
   "source": [
    "! /Users/olga/Downloads/bbmap/pileup.sh in=alignment_ont.sam out=bbmap_ont.txt hist=hist_ont.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При сравнении результатов покрытия генома выяснилось, что у ридов Oxford Nanopores и Pacbio почти совпадает процент покрытия генома. Среднее покрытие различается (у Oxford Nanopores оно равно 28, тогда как у Pacbio оно равно 48). Также различаются графики покрытий - как с выбросами, так и без них: значения выбросов по количеству ридов у Oxford Nanopores выше (около 250 и 1200 по сравнению со значениями 100 и примерно 300 для Pacbio), дисперсия распределения покрытия выше у ридов Pacbio. Сравнивая данные результаты с результатами bbmap, выяснилось, что процент покрытия почти совпадает для ридов обоих типов, среднее покрытие ридов Oxford Nanopores также практически сопадает, а среднее покрытие ридов Pacbio несколько различается."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Таблица частот замен / вставок / удалений + средний процент ошибок в ридах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_errors(sam_file_name, ref_file_name):\n",
    "    errors = {('A', 'A'): 0, ('A', 'C'): 0, ('A', 'G'): 0, ('A', 'T'): 0, ('A', '-'): 0, \n",
    "              ('C', 'A'): 0, ('C', 'C'): 0, ('C', 'G'): 0, ('C', 'T'): 0, ('C', '-'): 0,\n",
    "              ('G', 'A'): 0, ('G', 'C'): 0, ('G', 'G'): 0, ('G', 'T'): 0, ('G', '-'): 0, \n",
    "              ('T', 'A'): 0, ('T', 'C'): 0, ('T', 'G'): 0, ('T', 'T'): 0, ('T', '-'): 0,\n",
    "              ('-', 'A'): 0, ('-', 'C'): 0, ('-', 'G'): 0, ('-', 'T'): 0, ('-', '-'): 0}\n",
    "    sam_file = open(sam_file_name, 'r')\n",
    "    ref_file = open(ref_file_name, 'r')\n",
    "    sam_file.readline()\n",
    "    sam_file.readline()\n",
    "    ref_file.readline()\n",
    "    ref_seq = ''\n",
    "    ref_length = 0\n",
    "    for line in ref_file:\n",
    "        ref_seq += line.strip()\n",
    "    ref_file.close()\n",
    "    ref_length = len(ref_seq)\n",
    "    total_align_info = {}\n",
    "    percents = []\n",
    "    total_len = 0\n",
    "    for line in sam_file:\n",
    "        line = line.strip().split()\n",
    "        if line[5] == '*' or line[2] == '*':\n",
    "                continue\n",
    "        start = int(line[3]) - 1\n",
    "        if start == -1:\n",
    "            continue\n",
    "        read_length = len(line[9])\n",
    "        p = [0] * read_length\n",
    "        total_len += read_length\n",
    "        read_seq = line[9]\n",
    "        stop = min(start + read_length - 1, ref_length - 1)\n",
    "        align_info = list(Cigar(line[5]).items())\n",
    "        for (num, letter) in align_info:\n",
    "            if letter not in total_align_info.keys():\n",
    "                total_align_info[letter] = num\n",
    "            else:\n",
    "                total_align_info[letter] += num\n",
    "        align_info_string = ''.join([letter*num for (num, letter) in align_info])\n",
    "        for i in range(start, stop):\n",
    "            if (read_seq[i - start + 1], ref_seq[i]) == ('A', 'C') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('A', 'C')] += 1\n",
    "                p[i - start + 1] = 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('A', 'G') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('A', 'G')] += 1\n",
    "                p[i - start + 1] = 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('A', 'T') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('A', 'T')] += 1\n",
    "                p[i - start + 1] = 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('C', 'A') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('C', 'A')] += 1\n",
    "                p[i - start + 1] = 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('C', 'G') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('C', 'G')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('C', 'T') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('C', 'T')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('G', 'A') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('G', 'A')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('G', 'C') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('G', 'C')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('G', 'T') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('G', 'T')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('T', 'A') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('T', 'A')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('T', 'C') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('T', 'C')] += 1\n",
    "            elif (read_seq[i - start + 1], ref_seq[i]) == ('T', 'G') and align_info_string[i - start + 1] == 'M':\n",
    "                errors[('T', 'G')] += 1\n",
    "            elif read_seq[i - start + 1] == 'A' and align_info_string[i - start + 1] == 'I':\n",
    "                errors[('A', '-')] += 1\n",
    "            elif read_seq[i - start + 1] == 'C' and align_info_string[i - start + 1] == 'I':\n",
    "                errors[('C', '-')] += 1\n",
    "            elif read_seq[i - start + 1] == 'G' and align_info_string[i - start + 1] == 'I':\n",
    "                errors[('G', '-')] += 1\n",
    "            elif read_seq[i - start + 1] == 'T' and align_info_string[i - start + 1] == 'I':\n",
    "                errors[('T', '-')] += 1\n",
    "            elif read_seq[i - start + 1] == 'A' and align_info_string[i - start + 1] == 'D':\n",
    "                errors[('-', 'A')] += 1\n",
    "            elif read_seq[i - start + 1] == 'C' and align_info_string[i - start + 1] == 'D':\n",
    "                errors[('-', 'C')] += 1\n",
    "            elif read_seq[i - start + 1] == 'G' and align_info_string[i - start + 1] == 'D':\n",
    "                errors[('-', 'G')] += 1\n",
    "            elif read_seq[i - start + 1] == 'T' and align_info_string[i - start + 1] == 'D':\n",
    "                errors[('-', 'T')] += 1\n",
    "    sam_file.close()\n",
    "    #total_len = np.sum(list(total_align_info.values()))\n",
    "    return errors, total_len, total_align_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEXES = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
    "CIGAR_ABBR = {\n",
    "    'M':'Matches',\n",
    "    'N':'Alignment gaps',\n",
    "    'D':'Deletions',\n",
    "    'I':'Insertions',\n",
    "    'S':'Soft clipped alignment',\n",
    "    'H':'Hard clipped alignment'\n",
    "}\n",
    "\n",
    "def get_errors(sam_f, fasta_f):\n",
    "    reference = []\n",
    "    for line in open(fasta_f):\n",
    "        if line.startswith('>'):\n",
    "            continue\n",
    "        reference.append(line.strip())\n",
    "    reference = list(''.join(reference))\n",
    "    reference_len = len(reference)\n",
    "    \n",
    "    substitution_matrix = np.zeros([4, 4])\n",
    "    total_cigars = Counter()\n",
    "    \n",
    "    for num, line in enumerate(open(sam_f)):\n",
    "        if num and (num % 1e6 == 0):\n",
    "            print(f\"Processed {num} SAM-lines\")\n",
    "        if line.startswith('@'):\n",
    "            continue\n",
    "        parts = line.strip().split()\n",
    "        if parts[2] == '*' or parts[5] == '*':\n",
    "            continue\n",
    "        start_pos = int(parts[3])\n",
    "        if start_pos == 0:\n",
    "            continue\n",
    "        read = list(parts[9])\n",
    "        read_len = len(read)\n",
    "        end_pos = min(start_pos + read_len - 1, reference_len - 1)\n",
    "        \n",
    "        reference_region = reference[start_pos-1:end_pos]\n",
    "        for pos, r in enumerate(reference_region):\n",
    "            if r != read[pos] and read[pos] != 'N' and read[pos] != '*':\n",
    "                substitution_matrix[INDEXES[read[pos]], INDEXES[r]] += 1\n",
    "        try:\n",
    "            cigars = list(Cigar(parts[5]).items())\n",
    "            for (cnt, c) in cigars:\n",
    "                total_cigars[c] += cnt\n",
    "        except Exception as ex:\n",
    "            print(num, parts)\n",
    "            raise ex\n",
    "    return total_cigars, substitution_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) PacBio\n",
      "Всего ошибок: 36.81%\n",
      "2) Oxford Nanopores\n",
      "Всего ошибок: 9.67%\n"
     ]
    }
   ],
   "source": [
    "total_cigars, _ = get_errors('alignment_pacbio.sam', 'reference.fasta')\n",
    "total_len = np.sum(list(total_cigars.values()))\n",
    "print(\"1) PacBio\")\n",
    "#for k, v in sorted(total_cigars.items()):\n",
    "    #print(f\"\\t[{k}] {CIGAR_ABBR[k]}: {np.round(v/total_len*100, 3)}%\")\n",
    "print(f\"Всего ошибок: {np.round(100 - total_cigars['M']/total_len*100, 3)}%\")\n",
    "\n",
    "total_cigars, _ = get_errors('alignment_ont.sam', 'reference.fasta')\n",
    "print('2) Oxford Nanopores')\n",
    "total_len = np.sum(list(total_cigars.values()))\n",
    "#for k, v in sorted(total_cigars.items()):\n",
    "    #print(f\"\\t[{k}] {CIGAR_ABBR[k]}: {np.round(v/total_len*100, 3)}%\")\n",
    "print(f\"Всего ошибок: {np.round(100 - total_cigars['M']/total_len*100, 3)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Pacbio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица замен / инделов:\n",
      "          A         C         G         T        -\n",
      "A         0  10401334  10340406  10255098  3766276\n",
      "C  10392489         0  10756393  10355762  3848421\n",
      "G  10342468  10757965         0  10348600  3839961\n",
      "T  10257416  10363020  10346171         0  3757195\n",
      "-   1642205   1665093   1659864   1641680        0\n",
      "------------------------------\n",
      "Матрица частот замен / инделов:\n",
      "          A         C         G         T         -\n",
      "A  0.000000  0.046565  0.046293  0.045911  0.016861\n",
      "C  0.046526  0.000000  0.048155  0.046361  0.017229\n",
      "G  0.046302  0.048162  0.000000  0.046329  0.017191\n",
      "T  0.045921  0.046394  0.046318  0.000000  0.016820\n",
      "-  0.007352  0.007454  0.007431  0.007350  0.000000\n"
     ]
    }
   ],
   "source": [
    "errors, total_len, total_align_info = get_errors('alignment_pacbio.sam', 'reference.fasta')\n",
    "print('Матрица замен / инделов:')\n",
    "errors_df = pd.DataFrame(np.array(list(errors.values())).reshape(5, 5), columns=['A', 'C', 'G', 'T', '-'], index=['A', 'C', 'G', 'T', '-'])\n",
    "print(errors_df)\n",
    "print('------------------------------')\n",
    "print('Матрица частот замен / инделов:')\n",
    "errors_freq_arr = np.array(list(errors.values())) / total_len\n",
    "errors_freq_df = pd.DataFrame(errors_freq_arr.reshape(5, 5), columns=['A', 'C', 'G', 'T', '-'], index=['A', 'C', 'G', 'T', '-'])\n",
    "print(errors_freq_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Oxford Nanopores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица замен / инделов:\n",
      "         A        C        G        T       -\n",
      "A        0  7274281  7242573  7100176  359483\n",
      "C  7289567        0  7668432  7274002  374540\n",
      "G  7271676  7664075        0  7281951  376215\n",
      "T  7119098  7265368  7262873        0  359415\n",
      "-  1459478  1512796  1512472  1458982       0\n",
      "------------------------------\n",
      "Матрица частот замен / инделов:\n",
      "          A         C         G         T         -\n",
      "A  0.000000  0.056996  0.056748  0.055632  0.002817\n",
      "C  0.057116  0.000000  0.060085  0.056994  0.002935\n",
      "G  0.056976  0.060051  0.000000  0.057056  0.002948\n",
      "T  0.055780  0.056927  0.056907  0.000000  0.002816\n",
      "-  0.011435  0.011853  0.011851  0.011432  0.000000\n"
     ]
    }
   ],
   "source": [
    "errors, total_len, total_align_info = get_errors('alignment_ont.sam', 'reference.fasta')\n",
    "print('Матрица замен / инделов:')\n",
    "errors_df = pd.DataFrame(np.array(list(errors.values())).reshape(5, 5), columns=['A', 'C', 'G', 'T', '-'], index=['A', 'C', 'G', 'T', '-'])\n",
    "print(errors_df)\n",
    "print('------------------------------')\n",
    "print('Матрица частот замен / инделов:')\n",
    "errors_freq_arr = np.array(list(errors.values())) / total_len\n",
    "errors_freq_df = pd.DataFrame(errors_freq_arr.reshape(5, 5), columns=['A', 'C', 'G', 'T', '-'], index=['A', 'C', 'G', 'T', '-'])\n",
    "print(errors_freq_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Средний процент ошибок в ридах Oxford Nanopores несколько выше, чем у ридов Pacbio. Части матриц частот замен у ридов обоих типов почти симметричны, причём у ридов Oxford Nanopores ошибки замен происходят чаще (приблизительно 0.035 и 0.052 соответственно). Что касается вставок / удалений, то у ридов обоих типов удаления происходят чаще, вставки / удаления распределены почти равномерно по нуклеотидам. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Распределение длин инделов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_indel_lengths(sam_file_name):\n",
    "    sam_file = open(sam_file_name, 'r')\n",
    "    sam_file.readline()\n",
    "    sam_file.readline()\n",
    "    insertion_lengths = {}\n",
    "    deletion_lengths = {}\n",
    "    indel_lengths = {}\n",
    "    for line in sam_file:\n",
    "        line = line.strip().split()\n",
    "        if line[5] == '*':\n",
    "            continue\n",
    "        align_info = list(Cigar(line[5]).items())\n",
    "        for (num, letter) in align_info:\n",
    "            if letter == 'D':\n",
    "                if num not in deletion_lengths.keys():\n",
    "                    deletion_lengths[num] = 1\n",
    "                else:\n",
    "                    deletion_lengths[num] += 1\n",
    "                if num not in indel_lengths.keys():\n",
    "                    indel_lengths[num] = 1\n",
    "                else:\n",
    "                    indel_lengths[num] += 1\n",
    "            elif letter == 'I':\n",
    "                if num not in insertion_lengths.keys():\n",
    "                    insertion_lengths[num] = 1\n",
    "                else:\n",
    "                    insertion_lengths[num] += 1\n",
    "                if num not in indel_lengths.keys():\n",
    "                    indel_lengths[num] = 1\n",
    "                else:\n",
    "                    indel_lengths[num] += 1\n",
    "    sam_file.close()\n",
    "    return deletion_lengths, insertion_lengths, indel_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Pacbio (графики построены в логарифмической шкале)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина делеций: 76.0\n",
      "Минимальная длина делеций: 1\n",
      "Максимальная длина делеций: 1900\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d902e20>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deletion_lengths, insertion_lengths, indel_lengths = get_indel_lengths('alignment_pacbio.sam')\n",
    "print(f'Медианная длина делеций: {np.median(list(deletion_lengths.keys()))}')\n",
    "print(f'Минимальная длина делеций: {min(list(deletion_lengths.keys()))}')\n",
    "print(f'Максимальная длина делеций: {max(list(deletion_lengths.keys()))}')\n",
    "\n",
    "n = len(list(deletion_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in deletion_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(deletion_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Deletion lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина вставок: 220.5\n",
      "Минимальная длина вставок: 1\n",
      "Максимальная длина вставок: 2798\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d7674f0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Медианная длина вставок: {np.median(list(insertion_lengths.keys()))}')\n",
    "print(f'Минимальная длина вставок: {min(list(insertion_lengths.keys()))}')\n",
    "print(f'Максимальная длина вставок: {max(list(insertion_lengths.keys()))}')\n",
    "\n",
    "n = len(list(insertion_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in insertion_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(insertion_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Insertion lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина инделов: 228.0\n",
      "Минимальная длина инделов: 1\n",
      "Максимальная длина инделов: 2798\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11e228ee0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Медианная длина инделов: {np.median(list(indel_lengths.keys()))}')\n",
    "print(f'Минимальная длина инделов: {min(list(indel_lengths.keys()))}')\n",
    "print(f'Максимальная длина инделов: {max(list(indel_lengths.keys()))}')\n",
    "\n",
    "n = len(list(indel_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in indel_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(indel_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Indel lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Риды Oxford Nanopores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина делеций: 77.0\n",
      "Минимальная длина делеций: 1\n",
      "Максимальная длина делеций: 798\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d752f40>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deletion_lengths, insertion_lengths, indel_lengths = get_indel_lengths('alignment_ont.sam')\n",
    "print(f'Медианная длина делеций: {np.median(list(deletion_lengths.keys()))}')\n",
    "print(f'Минимальная длина делеций: {min(list(deletion_lengths.keys()))}')\n",
    "print(f'Максимальная длина делеций: {max(list(deletion_lengths.keys()))}')\n",
    "\n",
    "n = len(list(deletion_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in deletion_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(deletion_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Deletion lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина вставок: 139.0\n",
      "Минимальная длина вставок: 1\n",
      "Максимальная длина вставок: 1196\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d7d4490>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Медианная длина вставок: {np.median(list(insertion_lengths.keys()))}')\n",
    "print(f'Минимальная длина вставок: {min(list(insertion_lengths.keys()))}')\n",
    "print(f'Максимальная длина вставок: {max(list(insertion_lengths.keys()))}')\n",
    "\n",
    "n = len(list(insertion_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in insertion_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(insertion_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Insertion lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианная длина инделов: 126.0\n",
      "Минимальная длина инделов: 1\n",
      "Максимальная длина инделов: 1196\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11db85ee0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Медианная длина инделов: {np.median(list(indel_lengths.keys()))}')\n",
    "print(f'Минимальная длина инделов: {min(list(indel_lengths.keys()))}')\n",
    "print(f'Максимальная длина инделов: {max(list(indel_lengths.keys()))}')\n",
    "\n",
    "n = len(list(indel_lengths.keys()))\n",
    "M = []\n",
    "N = []\n",
    "for elem in indel_lengths.keys():\n",
    "    M.append(elem)\n",
    "    N.append(np.log(indel_lengths[elem]))\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Indel lengths')\n",
    "plt.scatter(M, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У ридов Pacbio распределение длин делеций варьируется в большем диапазоне (до 1900 bp), чем у ридов Oxford Nanopore (до 798 bp), но медианная длина делеций у ридов обоих типов практически одинакова. То же верно и для распределения длин вставок: у ридов Pacbio распределение длин вставок варьируется от 1 до 2798 bp, тогда как у ридов Oxford Nanopores - от 1 до 1196 bp. Медианная длина вставок у Pacbio выше. Скетчи графиков распределений длин делеций / вставок / инделов в целом практически одинаковы. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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