tests/example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "157edf0d-1056-43b8-bcef-b676d9794192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(-9)\n",
       "\u001b[48;5;11matgccctaa\u001b[0m\n",
       "tacgggatt"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydna.dseqrecord import Dseqrecord\n",
    "\n",
    "dsr = Dseqrecord(\"atgccctaa\")\n",
    "dsr.add_feature()  # a small gene: atgccctaa\n",
    "dsr.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a0c0235-b358-446f-88a5-0a8a28408399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5atg...taa3\n",
       "       |||\n",
       "      3att5\n",
       "5atg3\n",
       " |||\n",
       "3tac...att5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydna.design import primer_design\n",
    "\n",
    "amplicon = primer_design(dsr, limit=3, target_tm=0)\n",
    "amplicon.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54cfbab9-1f54-434a-a37a-819af88a3263",
   "metadata": {},
   "outputs": [],
   "source": [
    "forward_primer = \"ccccGGATCC\" + amplicon.forward_primer\n",
    "reverse_primer = \"ttttGGATCC\" + amplicon.reverse_primer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "332f14b9-a444-4aae-896b-2b3d53bacee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          5atg...taa3\n",
       "                 |||\n",
       "                3attCCTAGGtttt5\n",
       "5ccccGGATCCatg3\n",
       "           |||\n",
       "          3tac...att5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pydna.amplify import pcr\n",
    "\n",
    "pcr_product = pcr(forward_primer, reverse_primer, dsr, limit=3)\n",
    "pcr_product.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12d6cf52-c50e-434b-bdb2-9eda540b4eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(-29)\n",
       "ccccGGATCC\u001b[48;5;11matgccctaa\u001b[0mGGATCCaaaa\n",
       "ggggCCTAGGtacgggattCCTAGGtttt"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dseqrecord(pcr_product).figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39f030ff",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dseqrecord(-9)\n",
      "\u001b[48;5;11m\u001b[0mccccG    \n",
      "ggggCCTAG\n",
      "\n",
      "Dseqrecord(-19)\n",
      "GATCC\u001b[48;5;11matgccctaa\u001b[0mG    \n",
      "    GtacgggattCCTAG\n",
      "\n",
      "Dseqrecord(-9)\n",
      "\u001b[48;5;11m\u001b[0mGATCCaaaa\n",
      "    Gtttt\n"
     ]
    }
   ],
   "source": [
    "from Bio.Restriction import BamHI  # cuts GGATCC\n",
    "\n",
    "a, b, c = pcr_product.cut(BamHI)\n",
    "\n",
    "print(a.figure())\n",
    "print()\n",
    "print(b.figure())\n",
    "print()\n",
    "print(c.figure())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff017051-8946-474a-8bcc-1e9e6b6e0a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = Dseqrecord(\"aatgtttttccctCCCGGGcaaaatAGATCTtgctatgcatcatcgatct\", circular=True, name=\"vect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f51cd402-a788-4dbe-852c-4d9df340dac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(o50)\n",
       "\u001b[48;5;11m\u001b[0maatgtttttccctCCCGGGcaaaatAGATCTtgctatgcatcatcgatct\n",
       "ttacaaaaagggaGGGCCCgttttaTCTAGAacgatacgtagtagctaga"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13cf920d-de6d-47d8-b693-4048e7af5d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(o65)\n",
       "aatgtttttccctCCCGGGcaaaatAGATCC\u001b[48;5;11matgccctaa\u001b[0mGGATCTtgctatgcatcatcgatct\n",
       "ttacaaaaagggaGGGCCCgttttaTCTAGGtacgggattCCTAGAacgatacgtagtagctaga"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Bio.Restriction import BglII  # cuts AGATCT\n",
    "\n",
    "linear_vector_bgl = vector.linearize(BglII)\n",
    "\n",
    "rec_vector = (linear_vector_bgl + b).looped().synced(vector)\n",
    "\n",
    "rec_vector.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fcc9ba91-c373-47cc-ae54-4a481171a27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(-21)\n",
       "cctCCC\u001b[48;5;11matgaaataa\u001b[0mGGGcaa\n",
       "ggaGGGtactttattCCCgtt"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene2 = Dseqrecord(\"cctCCCatgaaataaGGGcaa\", name=\"gene2\")\n",
    "gene2.add_feature(6, 15)\n",
    "gene2.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef2659b0-581c-40b4-8fbb-1a3b9aa6f145",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydna.assembly import Assembly\n",
    "from Bio.Restriction import SmaI  # cuts CCCGGG\n",
    "\n",
    "linear_vector_sma = vector.linearize(SmaI)\n",
    "\n",
    "asm = Assembly((linear_vector_sma, gene2), limit=6)\n",
    "\n",
    "candidate, *rest = asm.assemble_circular()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c630121-b93f-49eb-81c5-6d6b9c455340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " -|vect_lin| 6\n",
       "|           \\/\n",
       "|           /\\\n",
       "|            6|gene2| 6\n",
       "|                    \\/\n",
       "|                    /\\\n",
       "|                     6-\n",
       "|                       |\n",
       " -----------------------"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5e96478-d655-4bf7-9188-454b4cd8c9d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "candidate = candidate.synced(vector, limit=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fecfd6c-a1da-4e8d-b090-f0fa54c02533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dseqrecord(o59)\n",
       "aatgtttttccctCCC\u001b[48;5;11matgaaataa\u001b[0mGGGcaaaatAGATCTtgctatgcatcatcgatct\n",
       "ttacaaaaagggaGGGtactttattCCCgttttaTCTAGAacgatacgtagtagctaga"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dseqrecord(candidate).figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c42ba40-f7c3-47c0-81f0-86cd249c5a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydna.gel import gel\n",
    "from pydna.ladders import GeneRuler_1kb_plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3724afb9-545e-479a-8024-ff1d830a4716",
   "metadata": {},
   "outputs": [],
   "source": [
    "band = Dseqrecord(\"GATC\" * 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "02efdba4-c514-447d-b093-21b9b9011d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = gel(\n",
    "    [\n",
    "        GeneRuler_1kb_plus,\n",
    "        [\n",
    "            band,\n",
    "        ],\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9348612-69b8-4fa6-8bd3-2c6d48c2b7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=600x180>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.rotate(90, expand=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e458bc3-e6ca-45b0-9237-c9ac288b07e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import ImageOps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e3a744e-203f-4058-ae97-2d300e1da104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=180x600>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageOps.invert(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7909cef-0bed-497c-8823-675ad2d26e23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}