Chr20Research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d169af8-1d99-46f0-9a93-0e1354a72169",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import ucsc.api as api\n",
    "\n",
    "data = pd.read_csv(\"Randolph_chr20_donor_mutrate.tsv\",sep = \"\\t\")\n",
    "\n",
    "plt.scatter(data=data,x=\"Age\",y=\"MutRate\")\n",
    "plt.ylabel(\"Mutation Rate\")\n",
    "plt.xlabel(\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "129358ba-6339-47ca-9aeb-d95fd3abf4dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "file = pd.read_csv(\"Randolph_chr20_donor_mutrate.tsv\",sep = \"\\t\")\n",
    "\n",
    "sns.boxplot(data=file, x=\"Ethnicity\", y=\"MutRate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e53079c7-b5a4-428c-b05d-abe5eaa9650d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#For this part, I use a file I manually filled out the Germline mutations with \"Yes\" mutations using dbSNP and Gene names, using the USCS genome browser\n",
    "#I however did not write anything for non germline mutations or nucleotide positions without an ascribed poisition, leaving these cells empty\n",
    "\n",
    "#THIS PROGRAM SECTION FILLS ALL EMPTY CELLS, THE NON GERMLINE BECOME \"No\" AND THE ROWS WITH NO KNOWN GENE BECOME \"None\"\n",
    "import pandas as pd\n",
    "\n",
    "muts = pd.read_csv(\"MutationsU1.tsv\", sep = \"\\t\", encoding_errors = \"ignore\")\n",
    "\n",
    "mutsu = muts\n",
    "mutsu.fillna(0,inplace=True)\n",
    "mutsu.loc[mutsu[\"Germline\"] == 0, \"Germline\"] = \"No\"\n",
    "mutsu.loc[mutsu[\"Gene\"] ==0, \"Gene\"] = \"None\"\n",
    "\n",
    "mutsu.to_csv(\"MutationsUFinal.tsv\", sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b8d749b-7f97-4382-9dba-77ef6fd578d3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#REMOVES ALL GERMLINE \"Yes\" MUTATION ROWS FROM MATRIX IN NEW UPDATED MATRIX FILE\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "A = pd.read_csv(\"Randolph_chr20_mutationMatrix.tsv\", sep = \"\\t\")\n",
    "B = pd.read_csv(\"MutationsUFinal.tsv\", sep = \"\\t\", encoding_errors = \"ignore\")\n",
    "\n",
    "mutKeep = B.loc[B[\"Germline\"] == \"No\", \"Mutation\"]\n",
    "clearMut = A.loc[A[\"Mutation\"].isin(mutKeep),:]\n",
    "\n",
    "clearMut.to_csv(\"MutMatrixFiltered.tsv\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "03713213-9e4f-4d01-b600-ce0629854db9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Uses Filtered Matrix file and donor mutrate file\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "Matrix = pd.read_csv(\"MutMatrixFiltered.tsv\", sep = \"\\t\")\n",
    "DM = pd.read_csv(\"Randolph_chr20_donor_mutrate.tsv\",sep = \"\\t\")\n",
    "\n",
    "#Keep only the donor columns in matrix dataframe\n",
    "matrix_remove = Matrix[Matrix.columns[2:]]\n",
    "\n",
    "#get sum of mutations for each donor as mutSums\n",
    "mutSums = matrix_remove.sum().reset_index(name ='Total')\n",
    "mutSums.rename(columns = {'index':'Donor'}, inplace = True)\n",
    "\n",
    "mutSums.to_csv(\"newsums.tsv\",sep=\"\\t\")\n",
    "\n",
    "\n",
    "#mutSums is a dataframe containing the Sum for each donor of the number of mutations found in the matrix\n",
    "\n",
    "#merge the two dataframes, mutSums and DM. unused methods to merge in grey\n",
    "#mergeDM = DM.merge(mutSums, left_on='Donor', right_on='index')\n",
    "#merge = pd.merge(DM, mutSums, left_index=True, right_index=True)\n",
    "\n",
    "#merges DM and mutSums with unique identifier which is the donor ID\n",
    "merge = DM.merge(mutSums, on='Donor', how='outer')\n",
    "\n",
    "\n",
    "merge.to_csv(\"MergedTable.tsv\",sep = \"\\t\")\n",
    "\n",
    "#now need to update mutProps, which will be dividing the Sums column by the NCells column.\n",
    "#Then to get MutRate, the MutProp gets divided by 64,444,167)\n",
    "\n",
    "MutUpdated = merge\n",
    "\n",
    "#set Nmuts column equal to the new Total column (now without Germline mutations)\n",
    "MutUpdated[\"Nmuts\"] = MutUpdated[\"Total\"]\n",
    "MutUpdated = MutUpdated.drop(['Total'],axis=1)\n",
    "\n",
    "#now Nmuts is updated, now need to update MutProps and MutRate\n",
    "MutUpdated\n",
    "\n",
    "\n",
    "#i round here based on how the original mutrates were rounded\n",
    "MutUpdated[\"MutProp\"] = round(MutUpdated[\"Nmuts\"]/MutUpdated[\"NCells\"],16)\n",
    "\n",
    "#here i am using the unrounded version of the mutprop to calculate mutrate, for reliability\n",
    "MutUpdated[\"MutRate\"] = (MutUpdated[\"Nmuts\"]/MutUpdated[\"NCells\"])/64444167\n",
    "\n",
    "MutUpdated.to_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5758d11e-47ee-461c-a09e-615d4e35161a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now making new graphs to see if new relationship after germline mutations were removed\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "file = pd.read_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")\n",
    "\n",
    "sns.boxplot(data=file, x=\"Ethnicity\", y=\"MutRate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cb86d94d-b175-45bf-b2c9-1cfb0e57aac7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")\n",
    "\n",
    "plt.scatter(data=data,x=\"Age\",y=\"MutRate\")\n",
    "plt.ylabel(\"Mutation Rate\")\n",
    "plt.xlabel(\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76354847-ae8c-42bf-9c14-a05731da76cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#first I removed the extreme point by using plt.ylim()\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")\n",
    "\n",
    "plt.scatter(data=data,x=\"Age\",y=\"MutRate\")\n",
    "plt.ylabel(\"Mutation Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylim((0,0.000000002))\n",
    "plt.savefig('Figure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "79596909-80b4-4066-aaf2-1d2d43f6b2af",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This program is to instead of deleting the extreme value, it sets it equal to a y limit and colours it to show that it is an extreme datapoint\n",
    "\n",
    "#THIS PROGRAM ADDS TWO NEW COLUMNS TO THE DATA FROM \"DonorMutsU1.tsv\"\n",
    "#FIRST NEW COLUMN ADDED IS A DUPLICATE OF MutRate, WHERE THE VALUE HIGHER THAN THE LIMIT IS SET EQUAL TO LIMIT\n",
    "#THEN NEW COLOUMN IS TO TO CALL THIS ROW \"Outlier\"\n",
    "#THEN PLOT THE GRAPH AND COLOUR THE OUTLIER\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")\n",
    "\n",
    "#duplicates MutRate and sets it to maximum of y axis\n",
    "data['RateDup'] = data.loc[:, 'MutRate']\n",
    "data.loc[data[\"RateDup\"] > 0.000000002 , \"RateDup\"] = 0.000000002\n",
    "\n",
    "\n",
    "data['Outlier'] = ''\n",
    "\n",
    "data.loc[data[\"RateDup\"] == 0.000000002 , \"Outlier\"] = \"Outlier\"\n",
    "data.loc[data[\"RateDup\"] != 0.000000002 , \"Outlier\"] = \"Original\"\n",
    "#data.to_csv(\"test1.tsv\",sep=\"\\t\")\n",
    "\n",
    "\n",
    "#adds colour based on condition\n",
    "outlier = data[\"Outlier\"]\n",
    "colours = np.where(outlier == \"Outlier\", 'red', 'blue')\n",
    "plt.scatter(data=data,x=\"Age\",y=\"RateDup\", c = colours )\n",
    "plt.ylabel(\"Mutation Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "#plt.ylim((0,0.000000002))\n",
    "\n",
    "plt.savefig('newFig.png')\n",
    "\n",
    "\n",
    "#plt.scatter(data=data,x=\"Age\",y=\"MutRate\", c = )\n",
    "#plt.ylabel(\"Mutation Rate\")\n",
    "#plt.xlabel(\"Age\")\n",
    "#plt.ylim((0,0.000000002))\n",
    "#plt.savefig('Figure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcedce0a-3387-41dc-8c62-4fe03480a0fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4ff65d-2931-47a0-8fd6-6292eb04bf22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82727a65-4a49-4f44-8472-79a13d40d412",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "Matrix = pd.read_csv(\"MutMatrixFiltered.tsv\", sep = \"\\t\")\n",
    "DM = pd.read_csv(\"DonorMutsU1.tsv\",sep = \"\\t\")\n",
    "\n",
    "matrix_remove = Matrix[Matrix.columns[2:]]\n",
    "\n",
    "mutSums = matrix_remove.sum().reset_index(name ='Total')\n",
    "mutSums.rename(columns = {'index':'Donor'}, inplace = True)\n",
    "mutSums.to_csv(\"newsums.tsv\",sep=\"\\t\")\n",
    "\n",
    "merge = DM.merge(mutSums, on='Donor', how='outer')\n",
    "\n",
    "#merged table which is DM containing also the total for each donor\n",
    "merge.to_csv(\"MergedTable.tsv\",sep = \"\\t\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37784c82-03a1-454d-8546-328b189be3f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TO COMPARE AGE WITH TOTAL MUTATIONS (EXCEPT WASNT DEVIDED BY NUM CELLS\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "merge.loc[merge[\"Total\"] > 50 , \"Total\"] = 50\n",
    "\n",
    "\n",
    "colours = np.where(merge.Total == 50, 'red', 'blue')\n",
    "plt.scatter(data=merge,x=\"Age\",y=\"Total\", c = colours )\n",
    "plt.ylabel(\"Total mutations\")\n",
    "plt.xlabel(\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f774583f-e243-4579-8ffe-910028b5c4b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MutRate')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#COMPARE TOTAL MUTATIONS PER PERSON WITH MUTRATE\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#this merged table is a merge of the updater donor mutation file (where the mutrate and mutprop where filtered\n",
    "#to remove the germline mutations) and the updated matrix table sum (where germline mutations where removed)\n",
    "#to access this file, need to run one of previous cells of code to create it\n",
    "Matrix = pd.read_csv(\"MergedTable.tsv\", sep = \"\\t\")\n",
    "\n",
    "\n",
    "plt.scatter(data=Matrix,x=\"Total\",y=\"MutRate\", )\n",
    "plt.xlabel(\"Total mutations\")\n",
    "plt.ylabel(\"MutRate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3275f0cd-2968-46ea-b1af-5fc9810c5e6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1112bde5-8eaa-4aef-9101-617a456f15d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genome found\n"
     ]
    }
   ],
   "source": [
    "#FOLLOWING CELLS ARE ATTEMPTS TO USE THE USCS GENOME BROWSER REST API WRAPPER CREATED BY EYAD HAMZA\n",
    "from ucsc.api import Track, Genome\n",
    "v = Genome.find(\"hg38\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a78bddf-a8a6-4f9f-a8d9-86bc662377db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Genome' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m hg38 \u001b[38;5;241m=\u001b[39m \u001b[43mGenome\u001b[49m\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhg38\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Genome' is not defined"
     ]
    }
   ],
   "source": [
    "hg38 = Genome.find(\"hg38\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "832eeb9f-d37a-41c8-af68-1e96e220001f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "can't find track, Track does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m dbsnp \u001b[38;5;241m=\u001b[39m \u001b[43mhg38\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfindTrackBy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlongLabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdbSNP 155\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ucsc\\api.py:126\u001b[0m, in \u001b[0;36mGenome.findTrackBy\u001b[1;34m(self, attributeName, trackName)\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfindTrackBy\u001b[39m(\u001b[38;5;28mself\u001b[39m, attributeName, trackName):\n\u001b[1;32m--> 126\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mTrack\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfindBy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenomeName\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattributeName\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrackName\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ucsc\\api.py:220\u001b[0m, in \u001b[0;36mTrack.findBy\u001b[1;34m(genomeName, trackAttribute, value)\u001b[0m\n\u001b[0;32m    218\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrack found\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    219\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m track\n\u001b[1;32m--> 220\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find track, Track does not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mException\u001b[0m: can't find track, Track does not exist"
     ]
    }
   ],
   "source": [
    "dbsnp = hg38.findTrackBy(\"longLabel\", \"dbSNP 155\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0bdd81d4-cd30-4471-8506-3e99b90eedee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method findTrackBy in module ucsc.api:\n",
      "\n",
      "findTrackBy(attributeName, trackName) method of ucsc.api.Genome instance\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(hg38.findTrackBy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7641c48-a03b-441c-b9bc-8ae0a48192c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#className.get()  # Returns list of objects of the class\n",
    "\n",
    "#className.find()  # Find object by name\n",
    "\n",
    "#className.findBy()  # Find object by a specified attribute\n",
    "\n",
    "#className.exists()  # Check to see if an object exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2c04fe5-c866-42a3-8716-c189a5034ec4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Genome in module ucsc.api:\n",
      "\n",
      "class Genome(builtins.object)\n",
      " |  Genome(genomeName, **kwargs)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, genomeName, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  findTrack(self, trackName)\n",
      " |  \n",
      " |  findTrackBy(self, attributeName, trackName)\n",
      " |  \n",
      " |  isTrackExists(self, trackName)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  exists(genomeName)\n",
      " |  \n",
      " |  find(genomeName)\n",
      " |  \n",
      " |  findBy(genomeAttribute, value)\n",
      " |  \n",
      " |  get(hubUrl=None)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  tracks\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from ucsc.api import Hub, Genome, Track, TrackSchema, Chromosome, Sequence  \n",
    "\n",
    "help(Genome)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad03c9a7-9301-4ff5-832c-af06537ae52b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from ucsc.api import Sequence \n",
    "\n",
    "\n",
    "# Get DNA sequence from specified chromosome in UCSC database genome -\n",
    "\n",
    "sequence = Sequence.get(genome = 'hg38',chrom= 'chrM')\n",
    "\n",
    "#print(sequence.dna)\n",
    "\n",
    "# Get DNA sequence from specified chromosome and start,end coordinates in UCSC database genome -\n",
    "\n",
    "sequence = Sequence.get(genome= 'hg38',chrom= 'chrM',start=4321,end=5678)\n",
    "\n",
    "#print(sequence.dna)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37732552-b606-4d00-b86d-7fc9e7767606",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get in module ucsc.api:\n",
      "\n",
      "get(genome, chrom, hubUrl=None, start=None, end=None)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from ucsc.api import Sequence \n",
    "\n",
    "help(Sequence.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1b87f87b-dd23-41db-8e53-d7c8ac284c51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ends' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 10\u001b[0m\n\u001b[0;32m      6\u001b[0m starts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m34000000\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m      7\u001b[0m ende \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m34000010\u001b[39m\n\u001b[1;32m---> 10\u001b[0m sequence \u001b[38;5;241m=\u001b[39m Sequence\u001b[38;5;241m.\u001b[39mget(genome\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhg38\u001b[39m\u001b[38;5;124m'\u001b[39m,chrom\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m20\u001b[39m\u001b[38;5;124m'\u001b[39m,start\u001b[38;5;241m=\u001b[39mstarts,end\u001b[38;5;241m=\u001b[39m\u001b[43mends\u001b[49m)\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(sequence\u001b[38;5;241m.\u001b[39mdna\u001b[38;5;241m.\u001b[39mupper())\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ends' is not defined"
     ]
    }
   ],
   "source": [
    "from ucsc.api import Sequence \n",
    "\n",
    "\n",
    "\n",
    "# Get DNA sequence from specified chromosome and start,end coordinates in UCSC database genome -\n",
    "starts = 34000000-1\n",
    "ende = 34000010\n",
    "\n",
    "\n",
    "sequence = Sequence.get(genome= 'hg38',chrom= '20',start=starts,end=ends)\n",
    "\n",
    "print(sequence.dna.upper())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3ff1f4-ce3f-4ea1-96e1-a9d5fa4b6253",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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