BLASTCLUST Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2b3a4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "import os\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffb650b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read blast clust fasta file\n",
    "\n",
    "path = r\"C:\\Users\\Christie and Victori\\Documents\\christie\\work uni\\part II\\project\\blastclust_files\\blastclust_t2t_col_unique_cen180.Fasta\"\n",
    "text_file = open(path, \"r\")\n",
    "\n",
    "#get accession name\n",
    "accession = os.path.basename(path)\n",
    "accession = accession.replace(\"blastclust__\",\"\") #clean file name to get what accession it is\n",
    "accession = accession.replace(\"blastclust_\",\"\")\n",
    "accession = accession.replace(\"nondef\",\"\")\n",
    "accession = accession.replace(\"_unique_cen180.Fasta\",\"\")\n",
    "\n",
    "#split text file by new line and create list\n",
    "lines = text_file.read().split('\\n')\n",
    "\n",
    "#separate repeats within the list by whitespace\n",
    "separated = []\n",
    "for i in lines:\n",
    "    sep = i.split()\n",
    "    separated.append(sep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42fabfd9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10575\n",
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     ]
    }
   ],
   "source": [
    "noclusters = len(separated) #count number of clusters\n",
    "#count number of repeats in each cluster\n",
    "norep = []\n",
    "for i in separated:\n",
    "    rep = len(i)\n",
    "    norep.append(rep)\n",
    "print(noclusters)#shows number of clusters\n",
    "print(norep) #shows number of sequences in each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0579953e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\christie and victori\\pycharmprojects\\satellite_research_project\\venv\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of repeats in each cluster for t2t_col')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pyplot.figure(figsize=(10,7), dpi= 80) #change size of plot\n",
    "\n",
    "x = norep\n",
    "kwargs = dict(hist_kws={'alpha':.3}, kde_kws={'linewidth':3}) # alpha changes opacity of bars, kde plots line on graph\n",
    "sns.distplot(x, color=\"dodgerblue\", label=\"Compact\", **kwargs) \n",
    "plt.pyplot.xlim(0,20000)\n",
    "plt.pyplot.title(\"Number of repeats in each cluster for \"+accession)\n",
    "#plt.pyplot.savefig(r\"C:\\Users\\Christie and Victori\\Documents\\christie\\work uni\\part II\\project\\cleaning_sequences\\histograms\\clustergraph_\"+accession+\"\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4275306",
   "metadata": {},
   "outputs": [],
   "source": [
    "clustfast = []\n",
    "for i in separated:\n",
    "    if len(i) > 5: #if cluster has more than 5 sequences\n",
    "        choice = random.choices(i, k=2) #randomly pick 2 items from list\n",
    "    elif len(i) > 1: #else if cluster has more than 1 sequence\n",
    "        choice = random.choices(i, k=1)\n",
    "    else:\n",
    "        choice = i\n",
    "    clustfast.append(choice)\n",
    "\n",
    "clustfast = [item for sublist in clustfast for item in sublist] #flatten list (make list of lists into one big list)\n",
    "#clustfast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77b9c0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for general\n",
    "path2 = r\"C:\\Users\\Christie and Victori\\Documents\\christie\\work uni\\part II\\project\\cleaning_sequences\\drive-download-20211014T102922Z-001\\all.repeats.from.\"+accession+\".csv\"\n",
    "\n",
    "#for t2t\n",
    "#path2 = r\"C:\\Users\\Christie and Victori\\Documents\\christie\\work uni\\part II\\project\\cleaning_sequences\\drive-download-20211014T102922Z-001\\all.repeats.from.t2t-col.csv\"\n",
    "\n",
    "df = pd.read_csv(path2)\n",
    "df[\"names\"] = df[\"fasta.name\"].str.cat(df[\"start\"].astype(\"str\"), sep =\"_\") \n",
    "# concatenate strings in fasta.name and start column to make names file; make sure variables are strings\n",
    "df[\"names\"] = accession+\"_\"+ df[\"names\"]\n",
    "\n",
    "#subsetting repeats into cen180 and cen160\n",
    "cen180 = df[df[\"length\"].between(175, 180)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c51059fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "clustdf = cen180[cen180.names.isin(clustfast)] #filter dataset to only those we want from clustering!\n",
    "clustdf[\"names\"] = \"cen180_\" + clustdf[\"names\"]\n",
    "wantdf = clustdf.drop_duplicates(subset = [\"sequence\"])\n",
    "#clustdf\n",
    "#wantdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4700d695",
   "metadata": {},
   "outputs": [],
   "source": [
    "uniq_fasta = open(r\"nondefblastclust_\"+accession+\"_uniquecen180.Fasta\",\"w+\") #open fasta file, \"w+\" means you intend to write in it\n",
    "empty_uniq_fasta = [] #empty list to compile all rows\n",
    "for j in range(len(wantdf)): #do for all rows\n",
    "    string_uniq = wantdf.iloc[j,4]\n",
    "    n=60\n",
    "    split_uniq = [string_uniq[i:i+n] for i in range(0, len(string_uniq), n)] #splits sequence string into length 60\n",
    "    join_uniq = \"\\n\".join(split_uniq) #joins string with new lines\n",
    "    write_fasta_uniq = \">\"+wantdf.iloc[j,8]+\"\\n\"+join_uniq #create fasta format\n",
    "    empty_uniq_fasta.append(write_fasta_uniq) #append row's fasta to list\n",
    "joined_fasta_uniq = \"\\n\".join(empty_uniq_fasta) #join strings in list into one big string with new line breaks\n",
    "uniq_fasta.write(joined_fasta_uniq) #enter list into fasta file\n",
    "uniq_fasta.close() #close fasta file"
   ]
  }
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