LDV_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bdc1bf4-2511-4847-8fdc-a0554811067e",
   "metadata": {},
   "source": [
    "#### Regenerating Joyce's LDV analysis script.\n",
    "\n",
    "***A part of the analysis is written in R and the LDV extraction/exploration part is written in Python(Python tree implementations are kind of wonky, and I want to use Joyce's tree manipulation code to avoid reinventing the wheel)***\n",
    "\n",
    "***Step 1*** - Reading in IQtree and Gubbins fasta alignment, breaking up this tree into subtrees to get nodes and associated sample tiplabels. Write this file to \"2022-XX-XX_prefix_isolates_by_node.txt\". We will read in this file later for Python LDV extraction steps. Generate a variant matrix 2022-XX-XX_prefix_Variant_Matrix.csv from the Gubbins alignment.\n",
    "    \n",
    "***Step 2*** - Read in 2022-XX-XX_prefix_isolates_by_node.txt and extract genomes appearing on each subtree nodes. Read in 2022-XX-XX_prefix_Variant_Matrix.csv and extract LDV's that are specific to samples in each nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b200bb01-1f3a-493b-9ad7-4d0753ded521",
   "metadata": {},
   "outputs": [],
   "source": [
    "cd /scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b0e3d6f-06f8-43aa-9d69-69e5f4812287",
   "metadata": {},
   "outputs": [],
   "source": [
    "# {R}\n",
    "# VREfm extract geneomes for each nodes commands\n",
    "\n",
    "library(readxl)   \n",
    "library(treeio)\n",
    "library(ape)\n",
    "library(ggtree)\n",
    "library(ggplot2)\n",
    "library(randomcoloR)\n",
    "library(phytools)\n",
    "library(cowplot)\n",
    "library(RColorBrewer)\n",
    "prefix = \"VREfm\"\n",
    "setwd(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "iqtree = read.iqtree(\"2021_06_12_16_42_32_Aus0004_genome_aln_w_alt_allele_unmapped_exclude_controls.treefile\")\n",
    "phylo = iqtree@phylo\n",
    "phylo$bootstrap = phylo$node.label\n",
    "# Not sure what is happening in the below line of code but seems like renaming node label. But why and How?\n",
    "phylo$node.label = c(min(phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2]) - 1, phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2])\n",
    "\n",
    "vrefm_st = subtrees(phylo)\n",
    "\n",
    "node_genome_mat = matrix(NA, nrow = length(phylo$tip.label), ncol = length(vrefm_st), dimnames = list(NULL, 1:length(vrefm_st)))\n",
    "for (n in 1:length(vrefm_st)){\n",
    "  #print(n)\n",
    "  node_genome_mat[1:length(vrefm_st[[n]]$tip.label),n] = vrefm_st[[n]]$tip.label\n",
    "}\n",
    "\n",
    "filename = paste(gsub(\":\", \"-\", Sys.Date()),\"_\", prefix,\"_isolates_by_node.txt\",sep=\"\")\n",
    "write.table(node_genome_mat, file = filename, quote = F, sep = \"\\t\", row.names = F, col.names = T)\n",
    "\n",
    "setwd(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "dna = read.dna(\"2021_06_12_16_42_32_Aus0004_genome_aln_w_alt_allele_unmapped_exclude_controls.filtered_polymorphic_sites.fasta\", format = \"fasta\")\n",
    "var = read.table(\"2021_06_12_16_42_32_Aus0004_genome_aln_w_alt_allele_unmapped_exclude_controls.summary_of_snp_distribution.vcf\")\n",
    "dna2 = dna\n",
    "colnames(dna2) = var$V2\n",
    "dna2 = as.character(dna2)\n",
    "dna2 = apply(dna2, 1:2, toupper)\n",
    "var_pos2 = dna2[,apply(dna2, 2, FUN = function(x){sum(x %in% c(\"N\", \"-\")) == 0})]\n",
    "filename = paste(gsub(\":\", \"-\", Sys.Date()),\"_\", prefix,\"_Variant_Matrix.csv\",sep=\"\")\n",
    "write.csv(var_pos2, file = filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "185e7c41-539d-4ea3-9b7f-be3bd7895175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    151.000000\n",
       "mean      12.807947\n",
       "std       24.989522\n",
       "min        2.000000\n",
       "25%        2.000000\n",
       "50%        4.000000\n",
       "75%       10.500000\n",
       "max      153.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# {Python}\n",
    "# Read isolates by Node file and create a dictionary containing Node # and the geneomes that appeat on these nodes.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "import os\n",
    "import readline\n",
    "import argparse\n",
    "from itertools import islice\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "os.chdir(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "Genomes_in_node = pd.read_csv(\"2022-04-12_VREfm_isolates_by_node.txt\", sep='\\t', header=0, dtype = str)\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "plt.rcParams['figure.figsize'] = [20, 50]\n",
    "Genomes_in_node.count().plot(kind=\"barh\")\n",
    "Genomes_in_node.count().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41592bc7-1409-45b7-b564-aa24663d9015",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node :  1\n",
      "# of Genomes in Node :  153\n",
      "# of Genomes Not in Node :  0\n",
      "Node - 1\n",
      "Node :  2\n",
      "# of Genomes in Node :  151\n",
      "# of Genomes Not in Node :  2\n",
      "Node - 2,2607470 - ['C'],2633822 - ['G']\n",
      "Node :  3\n",
      "# of Genomes in Node :  149\n",
      "# of Genomes Not in Node :  4\n",
      "Node - 3\n",
      "Node :  4\n",
      "# of Genomes in Node :  30\n",
      "# of Genomes Not in Node :  123\n",
      "Node - 4,674001 - ['A'],696537 - ['T'],724654 - ['G'],2359427 - ['G']\n",
      "Node :  5\n",
      "# of Genomes in Node :  28\n",
      "# of Genomes Not in Node :  125\n",
      "Node - 5\n",
      "Node :  6\n",
      "# of Genomes in Node :  18\n",
      "# of Genomes Not in Node :  135\n",
      "Node - 6,1580221 - ['T']\n",
      "Node :  7\n",
      "# of Genomes in Node :  17\n",
      "# of Genomes Not in Node :  136\n",
      "Node - 7\n",
      "Node :  8\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 8,531614 - ['T'],1227758 - ['A'],1897460 - ['T'],2688889 - ['G']\n",
      "Node :  9\n",
      "# of Genomes in Node :  15\n",
      "# of Genomes Not in Node :  138\n",
      "Node - 9\n",
      "Node :  10\n",
      "# of Genomes in Node :  11\n",
      "# of Genomes Not in Node :  142\n",
      "Node - 10\n",
      "Node :  11\n",
      "# of Genomes in Node :  9\n",
      "# of Genomes Not in Node :  144\n",
      "Node - 11\n",
      "Node :  12\n",
      "# of Genomes in Node :  6\n",
      "# of Genomes Not in Node :  147\n",
      "Node - 12,884696 - ['A']\n",
      "Node :  13\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  148\n",
      "Node - 13,62684 - ['A'],2237084 - ['A'],2944402 - ['T']\n",
      "Node :  14\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 14,1051927 - ['T'],1053172 - ['A'],1276303 - ['C'],2287323 - ['C'],2750252 - ['A']\n",
      "Node :  15\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 15,1157192 - ['A'],1171184 - ['A'],1190867 - ['A'],1223747 - ['A'],2338127 - ['T']\n",
      "Node :  16\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 16,422420 - ['T'],706792 - ['T'],734948 - ['G'],1214710 - ['T'],1559301 - ['T'],1560784 - ['T'],1567550 - ['C'],1902612 - ['T'],2259095 - ['A'],2303310 - ['A'],2507283 - ['T'],2549975 - ['C'],2641066 - ['A'],2694524 - ['T'],2716845 - ['C'],2927105 - ['A']\n",
      "Node :  17\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 17,56609 - ['A'],497685 - ['G'],674347 - ['A'],935446 - ['C'],2230586 - ['G'],2604051 - ['C'],2626449 - ['A']\n",
      "Node :  18\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 18,482305 - ['T']\n",
      "Node :  19\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 19,79831 - ['A'],962530 - ['T'],964363 - ['A'],973386 - ['T'],1572879 - ['T'],1573286 - ['G'],2200601 - ['T'],2437438 - ['T'],2451625 - ['C']\n",
      "Node :  20\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 20,1773594 - ['G'],1808945 - ['A']\n",
      "Node :  21\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 21,23731 - ['A'],61278 - ['T'],64073 - ['C'],415166 - ['T'],415672 - ['A'],494705 - ['T'],696050 - ['G'],735983 - ['T'],917259 - ['A'],1000857 - ['T'],1041997 - ['A'],1053095 - ['T'],1160548 - ['C'],1168849 - ['T'],1176460 - ['C'],1198520 - ['T'],1203432 - ['A'],1317966 - ['C'],1390025 - ['A'],1536436 - ['T'],1548031 - ['G'],1551207 - ['C'],1571542 - ['T'],2208216 - ['T'],2242268 - ['C'],2396496 - ['T'],2457429 - ['A'],2467150 - ['T'],2516814 - ['T'],2627689 - ['T'],2654030 - ['A'],2946613 - ['A']\n",
      "Node :  22\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 22,95722 - ['A']\n",
      "Node :  23\n",
      "# of Genomes in Node :  10\n",
      "# of Genomes Not in Node :  143\n",
      "Node - 23\n",
      "Node :  24\n",
      "# of Genomes in Node :  8\n",
      "# of Genomes Not in Node :  145\n",
      "Node - 24,384934 - ['T'],538206 - ['G'],734073 - ['C'],938406 - ['A'],1003122 - ['C'],2529422 - ['G'],2755141 - ['A']\n",
      "Node :  25\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 25,44713 - ['C'],46203 - ['G'],48774 - ['C'],54969 - ['T'],59671 - ['G'],62326 - ['T'],63608 - ['C'],64797 - ['T'],1576959 - ['T'],2542451 - ['T'],2944172 - ['C']\n",
      "Node :  26\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 26,57932 - ['A']\n",
      "Node :  27\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 27,1555130 - ['T'],2237858 - ['G']\n",
      "Node :  28\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 28\n",
      "Node :  29\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 29,35091 - ['A'],817093 - ['A'],1387121 - ['G'],1581959 - ['G'],2208017 - ['A'],2279722 - ['C'],2324521 - ['T']\n",
      "Node :  30\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 30\n",
      "Node :  31\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
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      "Node - 130,1558400 - ['C'],1896893 - ['T'],2202187 - ['A']\n",
      "Node :  131\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 131,2302746 - ['C']\n",
      "Node :  132\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 132,111861 - ['A'],612744 - ['A'],1447953 - ['A'],2255710 - ['A'],2314946 - ['T'],2651596 - ['C']\n",
      "Node :  133\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 133,618594 - ['A']\n",
      "Node :  134\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 134,380300 - ['G']\n",
      "Node :  135\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 135,14463 - ['A'],392808 - ['C'],980080 - ['A'],1054945 - ['T'],1563681 - ['C'],1898850 - ['A'],2477596 - ['C'],2535877 - ['T']\n",
      "Node :  136\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 136,2585876 - ['T']\n",
      "Node :  137\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 137,1895595 - ['T'],1899250 - ['T']\n",
      "Node :  138\n",
      "# of Genomes in Node :  14\n",
      "# of Genomes Not in Node :  139\n",
      "Node - 138\n",
      "Node :  139\n",
      "# of Genomes in Node :  10\n",
      "# of Genomes Not in Node :  143\n",
      "Node - 139\n",
      "Node :  140\n",
      "# of Genomes in Node :  9\n",
      "# of Genomes Not in Node :  144\n",
      "Node - 140\n",
      "Node :  141\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 141\n",
      "Node :  142\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 142,1190909 - ['A'],1191128 - ['A']\n",
      "Node :  143\n",
      "# of Genomes in Node :  6\n",
      "# of Genomes Not in Node :  147\n",
      "Node - 143\n",
      "Node :  144\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  148\n",
      "Node - 144\n",
      "Node :  145\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 145\n",
      "Node :  146\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 146,508798 - ['T'],1178868 - ['T'],1503014 - ['G'],2300340 - ['G']\n",
      "Node :  147\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 147\n",
      "Node :  148\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  149\n",
      "Node - 148\n",
      "Node :  149\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  150\n",
      "Node - 149,1117459 - ['C']\n",
      "Node :  150\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 150,48043 - ['A'],1197977 - ['T'],2914747 - ['C']\n",
      "Node :  151\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  151\n",
      "Node - 151,59565 - ['T'],87565 - ['G'],384001 - ['T'],402127 - ['C'],405992 - ['A'],420223 - ['T'],1021686 - ['C'],1119491 - ['C'],1179783 - ['T'],1211038 - ['A'],1216691 - ['G'],1372446 - ['C'],1587824 - ['A'],2206911 - ['T'],2287980 - ['T'],2306070 - ['T'],2359338 - ['A'],2437385 - ['C'],2464682 - ['C'],2483031 - ['T'],2671150 - ['A'],2671265 - ['T'],2698320 - ['C'],2699521 - ['T'],2728564 - ['A'],2894344 - ['A'],2938398 - ['A']\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "# Read in the Gubbins Variant matrix generated by R script\n",
    "Gubbins_Variants = pd.read_csv(\"2022-04-12_VREfm_Variant_Matrix.csv\", sep=',', header=0, dtype = str, index_col=0)\n",
    "\n",
    "# Get the names of all the Samples in the Matrix. \n",
    "All_samples = Gubbins_Variants.index\n",
    "\n",
    "Node_shared_LDV = {}\n",
    "Node_LDV_Base = {}\n",
    "\n",
    "for (columnName, columnData) in Genomes_in_node.iteritems():\n",
    "    # Drop column values that are nan. \n",
    "    columnData_DropNA = [x for x in columnData.tolist() if pd.isnull(x) == False or str(x) != 'nan']\n",
    "    # From the Gubbins variant matrix extract sample variant  information (rows) specific to this Node.\n",
    "    Node_in_question = Gubbins_Variants.loc[columnData_DropNA]\n",
    "    # For each column (Position), Make sure that the variant observed are the same across the samples i.e length of unique observation should be equal to 1. Node_in_question_shared_uniq will contain posistions where the variant was same in all the samples.\n",
    "    Node_in_question_shared_uniq = Node_in_question.columns[(Node_in_question.nunique() == 1)]\n",
    "    # Insert this information in a dictionary Node_shared_LDV - will be used to compare these positions with other sample variant information. Where we will check if the variant is different that what was called in other samples.\n",
    "    # Hope i am making sense here :)    \n",
    "    Node_shared_LDV[columnName] = Node_in_question_shared_uniq\n",
    "    # Extract Sample names that are not in this node.\n",
    "    remaining_samples = [i for i in All_samples if i not in columnData_DropNA]\n",
    "    # Extract rows that are not in the Node.\n",
    "    Node_not_in_question = Gubbins_Variants.loc[remaining_samples]\n",
    "    \n",
    "    \n",
    "    Node_LDV_Base_string = \"Node - %s\" % columnName\n",
    "    # Extract Positions of Node in question, check if they have the same Nucleotide in Node not in question dataframe, These will be your LDVs for your Node in question\n",
    "    # Basically its a simple loop where I am checking if the position for the given node in question have a different allele than the other samples for the same position.\n",
    "    # This would mean that this position is unique and specific for the samples located on the Node.\n",
    "    for pos in  Node_shared_LDV[columnName]:\n",
    "        if Node_in_question[pos].unique() not in Node_not_in_question[pos].unique():\n",
    "            Node_LDV_Base_string = Node_LDV_Base_string + \",%s - %s\" %  (pos, Node_in_question[pos].unique())\n",
    "            #case = {pos: str(Node_in_question[pos].unique())}\n",
    "            #Node_LDV_Base[columnName].append(pos)\n",
    "            #Node_LDV_Base[columnName] = [Node_LDV_Base[columnName],pos]\n",
    "            #Node_LDV_Base[columnName][pos].append(Node_in_question[pos].unique())\n",
    "            #Node_LDV_Base[columnName].update(pos)\n",
    "    print('Node : ', columnName)\n",
    "    #print('Genomes in Node : ', columnData_DropNA)\n",
    "    print('# of Genomes in Node : ', len(columnData_DropNA))\n",
    "    #print('Genomes Not in Node : ', remaining_samples)\n",
    "    print('# of Genomes Not in Node : ', len(remaining_samples))\n",
    "    print (Node_LDV_Base_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "705211a0-399f-4eb8-b671-702d67788746",
   "metadata": {},
   "outputs": [],
   "source": [
    "Node_shared_LDV = {}\n",
    "for (columnName, columnData) in Genomes_in_node.iteritems():\n",
    "    print('Node : ', columnName)\n",
    "    columnData_DropNA = [x for x in columnData.tolist() if pd.isnull(x) == False or str(x) != 'nan']\n",
    "    #print('Genomes in Node : ', columnData_DropNA)\n",
    "    #print('# of Genomes in Node : ', len(columnData_DropNA))\n",
    "    \n",
    "    Node_in_question = Gubbins_Variants_test.loc[columnData_DropNA]\n",
    "    Node_in_question_shared_uniq = Node_in_question.columns[(Node_in_question.nunique() == 1)]\n",
    "    #print (len(Node_in_question_shared_uniq))\n",
    "    #print (Node_in_question_shared_uniq)\n",
    "    Node_shared_LDV[columnName] = Node_in_question_shared_uniq\n",
    "    #print (Node_shared_LDV)\n",
    "    remaining_samples = [i for i in All_samples if i not in columnData_DropNA]\n",
    "    Node_not_in_question = Gubbins_Variants_test.loc[remaining_samples]\n",
    "    \n",
    "    #print (Node_not_in_question)\n",
    "    #print('Genomes Not in Node : ', remaining_samples)\n",
    "    \n",
    "    Node_LDV_Base = {}\n",
    "    Node_LDV_Base_string = \"Node - %s\" % columnName\n",
    "    # Extract Positions of Node in question, check if they have the same Nucleotide in Node not in question dataframe, These will be your LDVs for your Node in question\n",
    "    for pos in  Node_shared_LDV[columnName]:\n",
    "        if Node_in_question[pos].unique() not in Node_not_in_question[pos].unique():\n",
    "            #Node_LDV_Base[columnName][pos] = Node_shared_LDV[pos]\n",
    "            Node_LDV_Base_string = Node_LDV_Base_string + \",%s - %s\" %  (pos, Node_in_question[pos].unique())\n",
    "            #print (Node_shared_LDV[columnName][pos])\n",
    "            #print (Node_in_question[pos].unique())\n",
    "    print (Node_LDV_Base_string)\n",
    "#     if len(Node_LDV_Base[columnName]) > 1:\n",
    "#         print (Node_LDV_Base[columnName])\n",
    "#     for key, pos in  Node_shared_LDV[columnName].items:\n",
    "        \n",
    "#     print (Gubbins_Variants_test.loc[['1929-4036-0-GVRE', '6950-4109-0-RVRE']])\n",
    "#     if len(columnData_DropNA) == 2:\n",
    "#         print (Gubbins_Variants_test.loc[columnData_DropNA])\n",
    "    #print (Gubbins_Variants.loc[['CP003351.1', '1016-2017-0-RVRE', '5948-6048-14-RVRE']])\n",
    "    #print (Gubbins_Variants.loc[Gubbins_Variants['Unnamed: 0'].isin([columnData])])\n",
    "#     if len(columnData.tolist()) < \n",
    "#     print (Gubbins_Variants_test.loc[[columnData.tolist()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cca02a9e-65e9-4976-a5eb-f2f9de1bc13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# {R}\n",
    "# Cdiff extract geneomes for each nodes commands\n",
    "\n",
    "library(readxl)   \n",
    "library(treeio)\n",
    "library(ape)\n",
    "library(ggtree)\n",
    "library(ggplot2)\n",
    "library(randomcoloR)\n",
    "library(phytools)\n",
    "library(cowplot)\n",
    "library(RColorBrewer)\n",
    "\n",
    "prefix = \"cdiff\"\n",
    "setwd(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "\n",
    "iqtree = read.tree(\"/nfs/esnitkin/Project_Cdiff/Sequence_data/Project_U01_SB/consensus/2020_05_29_12_15_15_core_results/gubbins/iqtree_masked_wga/2020_05_29_12_15_15_cdiff_630_genome_aln_w_alt_allele_unmapped.treefile\")\n",
    "# phylo = iqtree@phylo\n",
    "# phylo$bootstrap = phylo$node.label\n",
    "# # Not sure what is happening in the below line of code but seems like renaming node label. But why and How?\n",
    "# phylo$node.label = c(min(phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2]) - 1, phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2])\n",
    "iqtree_converted <- as.treedata(iqtree)\n",
    "phylo = iqtree_converted@phylo\n",
    "phylo$bootstrap = phylo$node.label\n",
    "phylo$node.label = c(min(phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2]) - 1, phylo$edge[phylo$edge[,2] > length(phylo$tip.label),2])\n",
    "\n",
    "sbtrees = subtrees(phylo)\n",
    "\n",
    "node_genome_mat = matrix(NA, nrow = length(phylo$tip.label), ncol = length(sbtrees), dimnames = list(NULL, 1:length(sbtrees)))\n",
    "for (n in 1:length(sbtrees)){\n",
    "  #print(n)\n",
    "  node_genome_mat[1:length(sbtrees[[n]]$tip.label),n] = sbtrees[[n]]$tip.label\n",
    "}\n",
    "\n",
    "filename = paste(gsub(\":\", \"-\", Sys.Date()),\"_\", prefix,\"_isolates_by_node.txt\",sep=\"\")\n",
    "write.table(node_genome_mat, file = filename, quote = F, sep = \"\\t\", row.names = F, col.names = T)\n",
    "\n",
    "setwd(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "dna = read.dna(\"2020_05_29_12_15_15_cdiff_630_genome_aln_w_alt_allele_unmapped.filtered_polymorphic_sites.fasta\", format = \"fasta\")\n",
    "var = read.table(\"2020_05_29_12_15_15_cdiff_630_genome_aln_w_alt_allele_unmapped.summary_of_snp_distribution.vcf\")\n",
    "dna2 = dna\n",
    "colnames(dna2) = var$V2\n",
    "dna2 = as.character(dna2)\n",
    "dna2 = apply(dna2, 1:2, toupper)\n",
    "var_pos2 = dna2[,apply(dna2, 2, FUN = function(x){sum(x %in% c(\"N\", \"-\")) == 0})]\n",
    "filename = paste(gsub(\":\", \"-\", Sys.Date()),\"_\", prefix,\"_Variant_Matrix.csv\",sep=\"\")\n",
    "write.csv(var_pos2, file = filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19419180-50ba-4de0-b3ce-46d3f7e6b450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1380.000000\n",
       "mean       31.723913\n",
       "std       139.456715\n",
       "min         2.000000\n",
       "25%         2.000000\n",
       "50%         5.000000\n",
       "75%        12.000000\n",
       "max      1382.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# {Python}\n",
    "# Read isolates by Node file and create a dictionary containing Node # and the geneomes that appeat on these nodes.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "import os\n",
    "import readline\n",
    "import argparse\n",
    "from itertools import islice\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "os.chdir(\"/scratch/esnitkin_root/esnitkin/apirani/Project_VRE_metagenomics_analysis/2022_04_07_Generate_LDV_scripts\")\n",
    "Genomes_in_node = pd.read_csv(\"2022-04-12_cdiff_isolates_by_node.txt\", sep='\\t', header=0, dtype = str)\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "plt.rcParams['figure.figsize'] = [20, 50]\n",
    "Genomes_in_node.count().plot(kind=\"barh\")\n",
    "Genomes_in_node.count().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7152de9-1b8b-4a1c-919d-aa781154b5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node :  1\n",
      "# of Genomes in Node :  1382\n",
      "# of Genomes Not in Node :  0\n",
      "Node - 1\n",
      "Node :  2\n",
      "# of Genomes in Node :  1378\n",
      "# of Genomes Not in Node :  4\n",
      "Node - 2\n",
      "Node :  3\n",
      "# of Genomes in Node :  1377\n",
      "# of Genomes Not in Node :  5\n",
      "Node - 3\n",
      "Node :  4\n",
      "# of Genomes in Node :  1376\n",
      "# of Genomes Not in Node :  6\n",
      "Node - 4\n",
      "Node :  5\n",
      "# of Genomes in Node :  1372\n",
      "# of Genomes Not in Node :  10\n",
      "Node - 5\n",
      "Node :  6\n",
      "# of Genomes in Node :  1358\n",
      "# of Genomes Not in Node :  24\n",
      "Node - 6\n",
      "Node :  7\n",
      "# of Genomes in Node :  1357\n",
      "# of Genomes Not in Node :  25\n",
      "Node - 7\n",
      "Node :  8\n",
      "# of Genomes in Node :  1346\n",
      "# of Genomes Not in Node :  36\n",
      "Node - 8\n",
      "Node :  9\n",
      "# of Genomes in Node :  1345\n",
      "# of Genomes Not in Node :  37\n",
      "Node - 9\n",
      "Node :  10\n",
      "# of Genomes in Node :  1343\n",
      "# of Genomes Not in Node :  39\n",
      "Node - 10\n",
      "Node :  11\n",
      "# of Genomes in Node :  1315\n",
      "# of Genomes Not in Node :  67\n",
      "Node - 11\n",
      "Node :  12\n",
      "# of Genomes in Node :  1313\n",
      "# of Genomes Not in Node :  69\n",
      "Node - 12\n",
      "Node :  13\n",
      "# of Genomes in Node :  1290\n",
      "# of Genomes Not in Node :  92\n",
      "Node - 13\n",
      "Node :  14\n",
      "# of Genomes in Node :  454\n",
      "# of Genomes Not in Node :  928\n",
      "Node - 14\n",
      "Node :  15\n",
      "# of Genomes in Node :  338\n",
      "# of Genomes Not in Node :  1044\n",
      "Node - 15\n",
      "Node :  16\n",
      "# of Genomes in Node :  319\n",
      "# of Genomes Not in Node :  1063\n",
      "Node - 16\n",
      "Node :  17\n",
      "# of Genomes in Node :  311\n",
      "# of Genomes Not in Node :  1071\n",
      "Node - 17\n",
      "Node :  18\n",
      "# of Genomes in Node :  305\n",
      "# of Genomes Not in Node :  1077\n",
      "Node - 18\n",
      "Node :  19\n",
      "# of Genomes in Node :  299\n",
      "# of Genomes Not in Node :  1083\n",
      "Node - 19\n",
      "Node :  20\n",
      "# of Genomes in Node :  234\n",
      "# of Genomes Not in Node :  1148\n",
      "Node - 20\n",
      "Node :  21\n",
      "# of Genomes in Node :  211\n",
      "# of Genomes Not in Node :  1171\n",
      "Node - 21\n",
      "Node :  22\n",
      "# of Genomes in Node :  209\n",
      "# of Genomes Not in Node :  1173\n",
      "Node - 22\n",
      "Node :  23\n",
      "# of Genomes in Node :  205\n",
      "# of Genomes Not in Node :  1177\n",
      "Node - 23\n",
      "Node :  24\n",
      "# of Genomes in Node :  72\n",
      "# of Genomes Not in Node :  1310\n",
      "Node - 24\n",
      "Node :  25\n",
      "# of Genomes in Node :  57\n",
      "# of Genomes Not in Node :  1325\n",
      "Node - 25\n",
      "Node :  26\n",
      "# of Genomes in Node :  50\n",
      "# of Genomes Not in Node :  1332\n",
      "Node - 26\n",
      "Node :  27\n",
      "# of Genomes in Node :  42\n",
      "# of Genomes Not in Node :  1340\n",
      "Node - 27\n",
      "Node :  28\n",
      "# of Genomes in Node :  34\n",
      "# of Genomes Not in Node :  1348\n",
      "Node - 28\n",
      "Node :  29\n",
      "# of Genomes in Node :  17\n",
      "# of Genomes Not in Node :  1365\n",
      "Node - 29\n",
      "Node :  30\n",
      "# of Genomes in Node :  16\n",
      "# of Genomes Not in Node :  1366\n",
      "Node - 30\n",
      "Node :  31\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  1377\n",
      "Node - 31\n",
      "Node :  32\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 32\n",
      "Node :  33\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 33\n",
      "Node :  34\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 34\n",
      "Node :  35\n",
      "# of Genomes in Node :  11\n",
      "# of Genomes Not in Node :  1371\n",
      "Node - 35\n",
      "Node :  36\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 36\n",
      "Node :  37\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 37\n",
      "Node :  38\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 38\n",
      "Node :  39\n",
      "# of Genomes in Node :  7\n",
      "# of Genomes Not in Node :  1375\n",
      "Node - 39\n",
      "Node :  40\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 40\n",
      "Node :  41\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  1377\n",
      "Node - 41\n",
      "Node :  42\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 42\n",
      "Node :  43\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 43\n",
      "Node :  44\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 44\n",
      "Node :  45\n",
      "# of Genomes in Node :  17\n",
      "# of Genomes Not in Node :  1365\n",
      "Node - 45\n",
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      "Node :  193\n",
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      "Node :  711\n",
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      "Node :  746\n",
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      "Node :  750\n",
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      "Node :  775\n",
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      "Node :  776\n",
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      "Node :  783\n",
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      "Node :  785\n",
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      "Node :  833\n",
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      "Node :  836\n",
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      "Node :  838\n",
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      "Node :  839\n",
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      "Node :  840\n",
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      "Node :  841\n",
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      "Node :  842\n",
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      "Node :  864\n",
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      "Node :  866\n",
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      "Node :  895\n",
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      "Node :  906\n",
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      "Node - 916,130708 - ['A']\n",
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      "Node :  940\n",
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      "Node :  941\n",
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      "Node - 941\n",
      "Node :  942\n",
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      "Node :  943\n",
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      "Node - 944\n",
      "Node :  945\n",
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      "Node :  946\n",
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      "Node :  947\n",
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      "Node :  948\n",
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      "Node :  949\n",
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      "Node :  950\n",
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      "Node :  951\n",
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      "Node :  952\n",
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      "Node :  959\n",
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      "Node :  961\n",
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      "Node :  967\n",
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      "Node :  969\n",
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      "Node :  970\n",
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      "Node :  980\n",
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      "Node :  984\n",
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      "Node :  987\n",
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      "Node :  990\n",
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      "Node :  991\n",
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      "Node :  1001\n",
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      "Node :  1016\n",
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      "Node - 1024\n",
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      "Node :  1039\n",
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      "Node - 1039\n",
      "Node :  1040\n",
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      "Node :  1046\n",
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      "Node :  1047\n",
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      "Node :  1048\n",
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      "Node - 1049\n",
      "Node :  1050\n",
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      "Node - 1050\n",
      "Node :  1051\n",
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      "Node :  1052\n",
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      "Node - 1052\n",
      "Node :  1053\n",
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      "Node - 1053\n",
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      "Node - 1054\n",
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      "Node - 1055\n",
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      "Node - 1056\n",
      "Node :  1057\n",
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      "Node - 1059\n",
      "Node :  1060\n",
      "# of Genomes in Node :  67\n",
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      "Node - 1060\n",
      "Node :  1061\n",
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      "Node - 1061\n",
      "Node :  1062\n",
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      "Node - 1062\n",
      "Node :  1063\n",
      "# of Genomes in Node :  50\n",
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      "Node - 1063\n",
      "Node :  1064\n",
      "# of Genomes in Node :  49\n",
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      "Node - 1064\n",
      "Node :  1065\n",
      "# of Genomes in Node :  23\n",
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      "Node - 1065\n",
      "Node :  1066\n",
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      "Node - 1066\n",
      "Node :  1067\n",
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      "Node - 1067\n",
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      "Node - 1070\n",
      "Node :  1071\n",
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      "Node :  1073\n",
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      "Node - 1073\n",
      "Node :  1074\n",
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      "Node - 1074\n",
      "Node :  1075\n",
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      "Node - 1075\n",
      "Node :  1076\n",
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      "Node :  1078\n",
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      "Node - 1078\n",
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      "Node - 1079\n",
      "Node :  1080\n",
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      "Node - 1080\n",
      "Node :  1081\n",
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      "Node - 1081\n",
      "Node :  1082\n",
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      "Node - 1082\n",
      "Node :  1083\n",
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      "Node - 1083\n",
      "Node :  1084\n",
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      "Node - 1084\n",
      "Node :  1085\n",
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      "Node - 1085\n",
      "Node :  1086\n",
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      "Node - 1086\n",
      "Node :  1087\n",
      "# of Genomes in Node :  26\n",
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      "Node - 1087\n",
      "Node :  1088\n",
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      "Node - 1088\n",
      "Node :  1089\n",
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      "Node - 1089\n",
      "Node :  1090\n",
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      "Node - 1090\n",
      "Node :  1091\n",
      "# of Genomes in Node :  16\n",
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      "Node - 1091\n",
      "Node :  1092\n",
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      "Node - 1092\n",
      "Node :  1093\n",
      "# of Genomes in Node :  14\n",
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      "Node - 1093\n",
      "Node :  1094\n",
      "# of Genomes in Node :  12\n",
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      "Node - 1094\n",
      "Node :  1095\n",
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      "Node - 1095\n",
      "Node :  1096\n",
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      "Node - 1096\n",
      "Node :  1097\n",
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      "Node - 1097\n",
      "Node :  1098\n",
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      "Node :  1099\n",
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      "Node :  1100\n",
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      "Node :  1101\n",
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      "Node - 1102\n",
      "Node :  1103\n",
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      "Node - 1103\n",
      "Node :  1104\n",
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      "Node - 1104\n",
      "Node :  1105\n",
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      "Node - 1105\n",
      "Node :  1106\n",
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      "Node - 1106,112394 - ['T']\n",
      "Node :  1107\n",
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      "Node - 1107\n",
      "Node :  1108\n",
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      "Node - 1108\n",
      "Node :  1109\n",
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      "Node - 1109\n",
      "Node :  1110\n",
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      "Node - 1110\n",
      "Node :  1111\n",
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      "Node - 1111\n",
      "Node :  1112\n",
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      "Node - 1112\n",
      "Node :  1113\n",
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      "Node - 1113\n",
      "Node :  1114\n",
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      "Node - 1114\n",
      "Node :  1115\n",
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      "Node - 1115\n",
      "Node :  1116\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1116\n",
      "Node :  1117\n",
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      "Node - 1117\n",
      "Node :  1118\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1118\n",
      "Node :  1119\n",
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      "Node - 1119\n",
      "Node :  1120\n",
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      "Node - 1120\n",
      "Node :  1121\n",
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      "Node - 1121\n",
      "Node :  1122\n",
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      "Node - 1122\n",
      "Node :  1123\n",
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      "Node - 1123\n",
      "Node :  1124\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1124\n",
      "Node :  1125\n",
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      "Node - 1125\n",
      "Node :  1126\n",
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      "Node - 1126\n",
      "Node :  1127\n",
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      "Node - 1127\n",
      "Node :  1128\n",
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      "Node - 1128\n",
      "Node :  1129\n",
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      "Node - 1129\n",
      "Node :  1130\n",
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      "Node - 1130\n",
      "Node :  1131\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1131\n",
      "Node :  1132\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1132\n",
      "Node :  1133\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1133\n",
      "Node :  1134\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1134\n",
      "Node :  1135\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1135\n",
      "Node :  1136\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1136\n",
      "Node :  1137\n",
      "# of Genomes in Node :  20\n",
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      "Node - 1137\n",
      "Node :  1138\n",
      "# of Genomes in Node :  19\n",
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      "Node - 1138\n",
      "Node :  1139\n",
      "# of Genomes in Node :  18\n",
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      "Node - 1139\n",
      "Node :  1140\n",
      "# of Genomes in Node :  14\n",
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      "Node - 1140\n",
      "Node :  1141\n",
      "# of Genomes in Node :  13\n",
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      "Node - 1141\n",
      "Node :  1142\n",
      "# of Genomes in Node :  12\n",
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      "Node - 1142\n",
      "Node :  1143\n",
      "# of Genomes in Node :  11\n",
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      "Node - 1143\n",
      "Node :  1144\n",
      "# of Genomes in Node :  10\n",
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      "Node - 1144\n",
      "Node :  1145\n",
      "# of Genomes in Node :  9\n",
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      "Node - 1145\n",
      "Node :  1146\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1146\n",
      "Node :  1147\n",
      "# of Genomes in Node :  7\n",
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      "Node - 1147\n",
      "Node :  1148\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1148\n",
      "Node :  1149\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1149\n",
      "Node :  1150\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1150\n",
      "Node :  1151\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1151\n",
      "Node :  1152\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1152\n",
      "Node :  1153\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1153\n",
      "Node :  1154\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1154\n",
      "Node :  1155\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1155\n",
      "Node :  1156\n",
      "# of Genomes in Node :  82\n",
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      "Node - 1156\n",
      "Node :  1157\n",
      "# of Genomes in Node :  74\n",
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      "Node - 1157\n",
      "Node :  1158\n",
      "# of Genomes in Node :  52\n",
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      "Node - 1158\n",
      "Node :  1159\n",
      "# of Genomes in Node :  48\n",
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      "Node - 1159\n",
      "Node :  1160\n",
      "# of Genomes in Node :  32\n",
      "# of Genomes Not in Node :  1350\n",
      "Node - 1160\n",
      "Node :  1161\n",
      "# of Genomes in Node :  31\n",
      "# of Genomes Not in Node :  1351\n",
      "Node - 1161\n",
      "Node :  1162\n",
      "# of Genomes in Node :  29\n",
      "# of Genomes Not in Node :  1353\n",
      "Node - 1162\n",
      "Node :  1163\n",
      "# of Genomes in Node :  28\n",
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      "Node - 1163\n",
      "Node :  1164\n",
      "# of Genomes in Node :  17\n",
      "# of Genomes Not in Node :  1365\n",
      "Node - 1164\n",
      "Node :  1165\n",
      "# of Genomes in Node :  12\n",
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      "Node - 1165\n",
      "Node :  1166\n",
      "# of Genomes in Node :  9\n",
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      "Node - 1166\n",
      "Node :  1167\n",
      "# of Genomes in Node :  7\n",
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      "Node - 1167\n",
      "Node :  1168\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1168\n",
      "Node :  1169\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1169\n",
      "Node :  1170\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1170\n",
      "Node :  1171\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1171\n",
      "Node :  1172\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1172\n",
      "Node :  1173\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1173\n",
      "Node :  1174\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1174\n",
      "Node :  1175\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1175\n",
      "Node :  1176\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1176\n",
      "Node :  1177\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1177\n",
      "Node :  1178\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1178\n",
      "Node :  1179\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1179\n",
      "Node :  1180\n",
      "# of Genomes in Node :  11\n",
      "# of Genomes Not in Node :  1371\n",
      "Node - 1180\n",
      "Node :  1181\n",
      "# of Genomes in Node :  10\n",
      "# of Genomes Not in Node :  1372\n",
      "Node - 1181\n",
      "Node :  1182\n",
      "# of Genomes in Node :  9\n",
      "# of Genomes Not in Node :  1373\n",
      "Node - 1182\n",
      "Node :  1183\n",
      "# of Genomes in Node :  8\n",
      "# of Genomes Not in Node :  1374\n",
      "Node - 1183\n",
      "Node :  1184\n",
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      "Node - 1184\n",
      "Node :  1185\n",
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      "Node - 1185\n",
      "Node :  1186\n",
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      "Node - 1186\n",
      "Node :  1187\n",
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      "Node - 1187\n",
      "Node :  1188\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1188\n",
      "Node :  1189\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1189\n",
      "Node :  1190\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1190\n",
      "Node :  1191\n",
      "# of Genomes in Node :  16\n",
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      "Node - 1191\n",
      "Node :  1192\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1192\n",
      "Node :  1193\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1193\n",
      "Node :  1194\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1194\n",
      "Node :  1195\n",
      "# of Genomes in Node :  12\n",
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      "Node - 1195\n",
      "Node :  1196\n",
      "# of Genomes in Node :  10\n",
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      "Node - 1196\n",
      "Node :  1197\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1197\n",
      "Node :  1198\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1198\n",
      "Node :  1199\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1199\n",
      "Node :  1200\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1200\n",
      "Node :  1201\n",
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      "Node - 1201\n",
      "Node :  1202\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1202\n",
      "Node :  1203\n",
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      "Node - 1203\n",
      "Node :  1204\n",
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      "Node - 1204\n",
      "Node :  1205\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1205\n",
      "Node :  1206\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1206\n",
      "Node :  1207\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1207\n",
      "Node :  1208\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1208\n",
      "Node :  1209\n",
      "# of Genomes in Node :  22\n",
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      "Node - 1209\n",
      "Node :  1210\n",
      "# of Genomes in Node :  21\n",
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      "Node - 1210\n",
      "Node :  1211\n",
      "# of Genomes in Node :  14\n",
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      "Node - 1211\n",
      "Node :  1212\n",
      "# of Genomes in Node :  13\n",
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      "Node - 1212\n",
      "Node :  1213\n",
      "# of Genomes in Node :  9\n",
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      "Node - 1213\n",
      "Node :  1214\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1214\n",
      "Node :  1215\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1215\n",
      "Node :  1216\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1216\n",
      "Node :  1217\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1217\n",
      "Node :  1218\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1218\n",
      "Node :  1219\n",
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      "Node - 1219\n",
      "Node :  1220\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1220\n",
      "Node :  1221\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1221\n",
      "Node :  1222\n",
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      "Node - 1222\n",
      "Node :  1223\n",
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      "Node - 1223\n",
      "Node :  1224\n",
      "# of Genomes in Node :  7\n",
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      "Node - 1224\n",
      "Node :  1225\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1225\n",
      "Node :  1226\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1226\n",
      "Node :  1227\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1227\n",
      "Node :  1228\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1228\n",
      "Node :  1229\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1229\n",
      "Node :  1230\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1230\n",
      "Node :  1231\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1231\n",
      "Node :  1232\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1232\n",
      "Node :  1233\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1233\n",
      "Node :  1234\n",
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      "Node - 1234\n",
      "Node :  1235\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1235\n",
      "Node :  1236\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1236\n",
      "Node :  1237\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1237\n",
      "Node :  1238\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1238\n",
      "Node :  1239\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1239\n",
      "Node :  1240\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1240\n",
      "Node :  1241\n",
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      "Node - 1241\n",
      "Node :  1242\n",
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      "Node - 1242\n",
      "Node :  1243\n",
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      "Node - 1243\n",
      "Node :  1244\n",
      "# of Genomes in Node :  59\n",
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      "Node - 1244\n",
      "Node :  1245\n",
      "# of Genomes in Node :  21\n",
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      "Node - 1245\n",
      "Node :  1246\n",
      "# of Genomes in Node :  16\n",
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      "Node - 1246\n",
      "Node :  1247\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1247\n",
      "Node :  1248\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1248\n",
      "Node :  1249\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1249\n",
      "Node :  1250\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1250\n",
      "Node :  1251\n",
      "# of Genomes in Node :  11\n",
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      "Node - 1251\n",
      "Node :  1252\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1252\n",
      "Node :  1253\n",
      "# of Genomes in Node :  7\n",
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      "Node - 1253\n",
      "Node :  1254\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1254\n",
      "Node :  1255\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1255\n",
      "Node :  1256\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1256\n",
      "Node :  1257\n",
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      "Node - 1257\n",
      "Node :  1258\n",
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      "Node - 1258\n",
      "Node :  1259\n",
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      "Node - 1259,129987 - ['G']\n",
      "Node :  1260\n",
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      "Node - 1260\n",
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      "# of Genomes in Node :  5\n",
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      "Node - 1261\n",
      "Node :  1262\n",
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      "Node - 1262\n",
      "Node :  1263\n",
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      "Node - 1263\n",
      "Node :  1264\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1264\n",
      "Node :  1265\n",
      "# of Genomes in Node :  38\n",
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      "Node - 1265\n",
      "Node :  1266\n",
      "# of Genomes in Node :  28\n",
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      "Node - 1266\n",
      "Node :  1267\n",
      "# of Genomes in Node :  8\n",
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      "Node - 1267\n",
      "Node :  1268\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1268\n",
      "Node :  1269\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1269\n",
      "Node :  1270\n",
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      "Node - 1270\n",
      "Node :  1271\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1271\n",
      "Node :  1272\n",
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      "Node - 1272\n",
      "Node :  1273\n",
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      "Node - 1273\n",
      "Node :  1274\n",
      "# of Genomes in Node :  20\n",
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      "Node - 1274\n",
      "Node :  1275\n",
      "# of Genomes in Node :  16\n",
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      "Node - 1275\n",
      "Node :  1276\n",
      "# of Genomes in Node :  15\n",
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      "Node - 1276\n",
      "Node :  1277\n",
      "# of Genomes in Node :  14\n",
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      "Node - 1277\n",
      "Node :  1278\n",
      "# of Genomes in Node :  12\n",
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      "Node - 1278\n",
      "Node :  1279\n",
      "# of Genomes in Node :  11\n",
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      "Node - 1279\n",
      "Node :  1280\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1280\n",
      "Node :  1281\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1281\n",
      "Node :  1282\n",
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      "Node - 1282\n",
      "Node :  1283\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1283\n",
      "Node :  1284\n",
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      "Node - 1284\n",
      "Node :  1285\n",
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      "Node - 1285\n",
      "Node :  1286\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1286\n",
      "Node :  1287\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1287\n",
      "Node :  1288\n",
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      "Node - 1288\n",
      "Node :  1289\n",
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      "Node - 1289\n",
      "Node :  1290\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1290\n",
      "Node :  1291\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1291\n",
      "Node :  1292\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1292\n",
      "Node :  1293\n",
      "# of Genomes in Node :  10\n",
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      "Node - 1293\n",
      "Node :  1294\n",
      "# of Genomes in Node :  9\n",
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      "Node - 1294\n",
      "Node :  1295\n",
      "# of Genomes in Node :  6\n",
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      "Node - 1295\n",
      "Node :  1296\n",
      "# of Genomes in Node :  3\n",
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      "Node - 1296\n",
      "Node :  1297\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1297\n",
      "Node :  1298\n",
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      "Node - 1298\n",
      "Node :  1299\n",
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      "Node - 1299\n",
      "Node :  1300\n",
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      "Node - 1300\n",
      "Node :  1301\n",
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      "Node - 1301\n",
      "Node :  1302\n",
      "# of Genomes in Node :  23\n",
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      "Node - 1302\n",
      "Node :  1303\n",
      "# of Genomes in Node :  21\n",
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      "Node - 1303\n",
      "Node :  1304\n",
      "# of Genomes in Node :  20\n",
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      "Node - 1304\n",
      "Node :  1305\n",
      "# of Genomes in Node :  15\n",
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      "Node - 1305\n",
      "Node :  1306\n",
      "# of Genomes in Node :  11\n",
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      "Node - 1306\n",
      "Node :  1307\n",
      "# of Genomes in Node :  10\n",
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      "Node - 1307\n",
      "Node :  1308\n",
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      "Node - 1308\n",
      "Node :  1309\n",
      "# of Genomes in Node :  7\n",
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      "Node - 1309\n",
      "Node :  1310\n",
      "# of Genomes in Node :  5\n",
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      "Node - 1310\n",
      "Node :  1311\n",
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      "Node - 1311\n",
      "Node :  1312\n",
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      "Node - 1312\n",
      "Node :  1313\n",
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      "Node - 1313\n",
      "Node :  1314\n",
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      "Node - 1314\n",
      "Node :  1315\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1315\n",
      "Node :  1316\n",
      "# of Genomes in Node :  4\n",
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      "Node - 1316\n",
      "Node :  1317\n",
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      "Node - 1317\n",
      "Node :  1318\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1318\n",
      "Node :  1319\n",
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      "Node - 1319\n",
      "Node :  1320\n",
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      "Node - 1320\n",
      "Node :  1321\n",
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      "Node - 1321\n",
      "Node :  1322\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1322\n",
      "Node :  1323\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1323\n",
      "Node :  1324\n",
      "# of Genomes in Node :  2\n",
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      "Node - 1324\n",
      "Node :  1325\n",
      "# of Genomes in Node :  28\n",
      "# of Genomes Not in Node :  1354\n",
      "Node - 1325\n",
      "Node :  1326\n",
      "# of Genomes in Node :  27\n",
      "# of Genomes Not in Node :  1355\n",
      "Node - 1326\n",
      "Node :  1327\n",
      "# of Genomes in Node :  6\n",
      "# of Genomes Not in Node :  1376\n",
      "Node - 1327\n",
      "Node :  1328\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1328,129548 - ['T']\n",
      "Node :  1329\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1329,31787 - ['T']\n",
      "Node :  1330\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1330\n",
      "Node :  1331\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1331\n",
      "Node :  1332\n",
      "# of Genomes in Node :  21\n",
      "# of Genomes Not in Node :  1361\n",
      "Node - 1332\n",
      "Node :  1333\n",
      "# of Genomes in Node :  8\n",
      "# of Genomes Not in Node :  1374\n",
      "Node - 1333\n",
      "Node :  1334\n",
      "# of Genomes in Node :  7\n",
      "# of Genomes Not in Node :  1375\n",
      "Node - 1334\n",
      "Node :  1335\n",
      "# of Genomes in Node :  6\n",
      "# of Genomes Not in Node :  1376\n",
      "Node - 1335\n",
      "Node :  1336\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  1377\n",
      "Node - 1336\n",
      "Node :  1337\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1337\n",
      "Node :  1338\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1338\n",
      "Node :  1339\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1339\n",
      "Node :  1340\n",
      "# of Genomes in Node :  13\n",
      "# of Genomes Not in Node :  1369\n",
      "Node - 1340\n",
      "Node :  1341\n",
      "# of Genomes in Node :  12\n",
      "# of Genomes Not in Node :  1370\n",
      "Node - 1341\n",
      "Node :  1342\n",
      "# of Genomes in Node :  11\n",
      "# of Genomes Not in Node :  1371\n",
      "Node - 1342\n",
      "Node :  1343\n",
      "# of Genomes in Node :  8\n",
      "# of Genomes Not in Node :  1374\n",
      "Node - 1343\n",
      "Node :  1344\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1344\n",
      "Node :  1345\n",
      "# of Genomes in Node :  6\n",
      "# of Genomes Not in Node :  1376\n",
      "Node - 1345\n",
      "Node :  1346\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  1377\n",
      "Node - 1346\n",
      "Node :  1347\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1347\n",
      "Node :  1348\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1348\n",
      "Node :  1349\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1349\n",
      "Node :  1350\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1350\n",
      "Node :  1351\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1351\n",
      "Node :  1352\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1352\n",
      "Node :  1353\n",
      "# of Genomes in Node :  11\n",
      "# of Genomes Not in Node :  1371\n",
      "Node - 1353\n",
      "Node :  1354\n",
      "# of Genomes in Node :  7\n",
      "# of Genomes Not in Node :  1375\n",
      "Node - 1354\n",
      "Node :  1355\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1355,130566 - ['T']\n",
      "Node :  1356\n",
      "# of Genomes in Node :  5\n",
      "# of Genomes Not in Node :  1377\n",
      "Node - 1356\n",
      "Node :  1357\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1357\n",
      "Node :  1358\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1358\n",
      "Node :  1359\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1359\n",
      "Node :  1360\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1360\n",
      "Node :  1361\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1361\n",
      "Node :  1362\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1362\n",
      "Node :  1363\n",
      "# of Genomes in Node :  14\n",
      "# of Genomes Not in Node :  1368\n",
      "Node - 1363\n",
      "Node :  1364\n",
      "# of Genomes in Node :  10\n",
      "# of Genomes Not in Node :  1372\n",
      "Node - 1364\n",
      "Node :  1365\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1365\n",
      "Node :  1366\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1366,113715 - ['C']\n",
      "Node :  1367\n",
      "# of Genomes in Node :  7\n",
      "# of Genomes Not in Node :  1375\n",
      "Node - 1367\n",
      "Node :  1368\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1368\n",
      "Node :  1369\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1369,31791 - ['G']\n",
      "Node :  1370\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1370\n",
      "Node :  1371\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1371\n",
      "Node :  1372\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1372\n",
      "Node :  1373\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1373\n",
      "Node :  1374\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1374\n",
      "Node :  1375\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1375\n",
      "Node :  1376\n",
      "# of Genomes in Node :  4\n",
      "# of Genomes Not in Node :  1378\n",
      "Node - 1376\n",
      "Node :  1377\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1377\n",
      "Node :  1378\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1378\n",
      "Node :  1379\n",
      "# of Genomes in Node :  3\n",
      "# of Genomes Not in Node :  1379\n",
      "Node - 1379\n",
      "Node :  1380\n",
      "# of Genomes in Node :  2\n",
      "# of Genomes Not in Node :  1380\n",
      "Node - 1380\n"
     ]
    }
   ],
   "source": [
    "# Read in the Gubbins Variant matrix generated by R script\n",
    "Gubbins_Variants = pd.read_csv(\"2022-04-12_cdiff_Variant_Matrix.csv\", sep=',', header=0, dtype = str, index_col=0)\n",
    "\n",
    "# Get the names of all the Samples in the Matrix. \n",
    "All_samples = Gubbins_Variants.index\n",
    "\n",
    "\n",
    "Node_shared_LDV = {}\n",
    "Node_LDV_Base = {}\n",
    "\n",
    "for (columnName, columnData) in Genomes_in_node.iteritems():\n",
    "    # Drop column values that are nan. \n",
    "    columnData_DropNA = [x for x in columnData.tolist() if pd.isnull(x) == False or str(x) != 'nan']\n",
    "    # From the Gubbins variant matrix extract sample variant  information (rows) specific to this Node.\n",
    "    Node_in_question = Gubbins_Variants.loc[columnData_DropNA]\n",
    "    # For each column (Position), Make sure that the variant observed are the same across the samples i.e length of unique observation should be equal to 1. Node_in_question_shared_uniq will contain posistions where the variant was same in all the samples.\n",
    "    Node_in_question_shared_uniq = Node_in_question.columns[(Node_in_question.nunique() == 1)]\n",
    "    # Insert this information in a dictionary Node_shared_LDV - will be used to compare these positions with other sample variant information. Where we will check if the variant is different that what was called in other samples.\n",
    "    # Hope i am making sense here :)    \n",
    "    Node_shared_LDV[columnName] = Node_in_question_shared_uniq\n",
    "    # Extract Sample names that are not in this node.\n",
    "    remaining_samples = [i for i in All_samples if i not in columnData_DropNA]\n",
    "    # Extract rows that are not in the Node.\n",
    "    Node_not_in_question = Gubbins_Variants.loc[remaining_samples]\n",
    "    \n",
    "    \n",
    "    Node_LDV_Base_string = \"Node - %s\" % columnName\n",
    "    # Extract Positions of Node in question, check if they have the same Nucleotide in Node not in question dataframe, These will be your LDVs for your Node in question\n",
    "    # Basically its a simple loop where I am checking if the position for the given node in question have a different allele than the other samples for the same position.\n",
    "    # This would mean that this position is unique and specific for the samples located on the Node.\n",
    "    for pos in  Node_shared_LDV[columnName]:\n",
    "        if Node_in_question[pos].unique() not in Node_not_in_question[pos].unique():\n",
    "            Node_LDV_Base_string = Node_LDV_Base_string + \",%s - %s\" %  (pos, Node_in_question[pos].unique())\n",
    "            #case = {pos: str(Node_in_question[pos].unique())}\n",
    "            #Node_LDV_Base[columnName].append(pos)\n",
    "            #Node_LDV_Base[columnName] = [Node_LDV_Base[columnName],pos]\n",
    "            #Node_LDV_Base[columnName][pos].append(Node_in_question[pos].unique())\n",
    "            #Node_LDV_Base[columnName].update(pos)\n",
    "    print('Node : ', columnName)\n",
    "    #print('Genomes in Node : ', columnData_DropNA)\n",
    "    print('# of Genomes in Node : ', len(columnData_DropNA))\n",
    "    #print('Genomes Not in Node : ', remaining_samples)\n",
    "    print('# of Genomes Not in Node : ', len(remaining_samples))\n",
    "    print (Node_LDV_Base_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcc03e4-2eb4-45c5-aabc-baafe3c21c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "gi|126697566|ref|NC_009089.1|"
   ]
  }
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