make_amplicon_fastas_for_laura.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "import pandas as pd\n",
    "\n",
    "import itertools\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mv: /Users/olgabot/Downloads/antisense_library.xlsx: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "! mv /Users/olgabot/Downloads/antisense_library.xlsx ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ACCACTTCCAGCACCGGTTCCANNANNGGTGCAGTGCCTGGCCACG...\n",
       "1    ACCACTTCCAGCACCGGTTCCANNCNNGGTGCAGTGCCTGGCCACG...\n",
       "2    ACCACTTCCAGCACCGGTTCCANNGCCANNGCAGTGCCTGGCCACG...\n",
       "3    ACCACTTCCAGCACCGGTTCCANNGCCCNNGCAGTGCCTGGCCACG...\n",
       "4    ACCACTTCCAGCACCGGTTCCANNGCCGGTANNGTGCCTGGCCACG...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template_sequences = pd.read_excel(\"antisense_library.xlsx\", header=None, squeeze=True)\n",
    "template_sequences.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ACCACTTCCAGCACCGGTTCCANNANNGGTGCAGTGCCTGGCCACGCTCTTCTCGTACTGCTCCACCACGGTGTAGCCACTAGTCCCACCCGATCC'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template_sequence = template_sequences[0]\n",
    "template_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"SDF\".find(\"N\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template_sequence.index('N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template_sequence[21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<_sre.SRE_Match object; span=(22, 23), match='N'>,\n",
       " <_sre.SRE_Match object; span=(23, 24), match='N'>,\n",
       " <_sre.SRE_Match object; span=(25, 26), match='N'>,\n",
       " <_sre.SRE_Match object; span=(26, 27), match='N'>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(re.finditer(\"N\", template_sequence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(itertools.product(\"ACGT\", repeat=4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156672"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "records = []\n",
    "\n",
    "\n",
    "for template_sequence in template_sequences:\n",
    "    \n",
    "    for bases in itertools.product(\"ACGT\", repeat=4):\n",
    "        base_id = '{0}{1}-{2}{3}'.format(*bases)\n",
    "        \n",
    "        template_copy = str(template_sequence)\n",
    "        \n",
    "        for base, match in zip(bases, re.finditer(\"N\", template_sequence)):\n",
    "            template_copy = template_copy[:match.start()] + base + template_copy[match.end():]\n",
    "        seq_id = f\"{template_sequence}_{base_id}\"\n",
    "        record = SeqRecord(Seq(template_copy), seq_id)\n",
    "        records.append(record)\n",
    "\n",
    "len(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156672"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SeqIO.write(records, \"laura_antisense_library_genome.fasta\", 'fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 olgabot  staff    33M May 30 18:47 laura_antisense_library_genome.fasta\r\n"
     ]
    }
   ],
   "source": [
    "ls -lha laura_antisense_library_genome.fasta"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}