(D) eis and eis promoter analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook was created to 1. collect (SNP & INDEL) variants in *eis* and the *eis* promoter, 2. detect instances of co-occurence of LoF variants in *eis* and the *eis* promoter and 3. analyze/date mutations on the lineage 2.2.1.1.1.i3 cluster phylogeny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vcf\n",
    "\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "from itertools import compress\n",
    "from pylab import MaxNLocator\n",
    "import seaborn as sns; sns.set()\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import gridspec\n",
    "import ast\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import fastcluster\n",
    "from sklearn import cluster, datasets\n",
    "import scipy.cluster.hierarchy as hier\n",
    "from sklearn.cluster import KMeans\n",
    "import time\n",
    "import sys\n",
    "import pickle\n",
    "\n",
    "import Bio\n",
    "from Bio.Alphabet import IUPAC\n",
    "from Bio.Blast.Applications import NcbiblastnCommandline\n",
    "from Bio.Blast import NCBIXML\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
    "from Bio import pairwise2\n",
    "from Bio import SeqIO\n",
    "from Bio.Graphics import GenomeDiagram\n",
    "from Bio.SeqUtils import GC\n",
    "from Bio import Phylo\n",
    "\n",
    "from Bio.Align.Applications import MuscleCommandline\n",
    "from StringIO import StringIO\n",
    "from Bio import AlignIO\n",
    "from Bio.Align import AlignInfo\n",
    "from Bio.Seq import MutableSeq\n",
    "import itertools\n",
    "import gzip\n",
    "\n",
    "import networkx as nx\n",
    "import scipy\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *eis*\n",
    "\n",
    "promoter: 2,715,333 - 2,715,383\n",
    "\n",
    "coding: 2,714,124 - 2,715,332\n",
    "\n",
    "H37Rv region: 2,714,124 - 2,715,383"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "H37Rv_region_start = 2714124\n",
    "H37Rv_region_end = 2715383\n",
    "regulator_id = 'Rv2416c_Rv2417c'\n",
    "regulon_id = 'Rv2416c'\n",
    "gene_symbol = 'eis'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Function* to convert lineage-calls to new/simpler lineage call scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_lineage_calls(isolate_annotation_DF):\n",
    "\n",
    "    lineage_hierarchincal_to_lineage_map_df = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/lineage_hierachical_to_lineage_map.csv')\n",
    "    lineage_hierarchincal_to_lineage_map_series = pd.Series(list(lineage_hierarchincal_to_lineage_map_df.loc[:, 'lineage']), index = lineage_hierarchincal_to_lineage_map_df.lineage_hierarchical)\n",
    "\n",
    "    lineage_call_update_list = []\n",
    "\n",
    "    for hierarchical_lineage_call in isolate_annotation_DF.lineage_call:\n",
    "\n",
    "        # if lineage call has an updated shorter call\n",
    "        if hierarchical_lineage_call in lineage_hierarchincal_to_lineage_map_series.index:\n",
    "            lineage_call_update_list.append(lineage_hierarchincal_to_lineage_map_series[hierarchical_lineage_call])\n",
    "\n",
    "        # if lineage call doesn't have an updated shorter call\n",
    "        else:\n",
    "            lineage_call_update_list.append(hierarchical_lineage_call)\n",
    "\n",
    "    # replace the hierarchical calls with the shorter ones\n",
    "    isolate_annotation_DF.loc[:,'lineage_call'] = lineage_call_update_list\n",
    "    \n",
    "    return isolate_annotation_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [1] INDELs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [1.1] Load INDEL genotype matrix and Annotation Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31,428 isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "INDEL_isolate_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_isolate_annotation.pkl')\n",
    "INDEL_isolate_annotation_DF = INDEL_isolate_annotation_DF.loc[:, ['isolate_ID','lineage_call']] #drop columns\n",
    "INDEL_isolate_annotation_DF = convert_lineage_calls(INDEL_isolate_annotation_DF)\n",
    "\n",
    "#load INDEL annotation file (rows of Genotype Matrix) with gene annotation information\n",
    "INDEL_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_INDEL_functional_annotation.pkl')\n",
    "INDEL_annotation_DF.reset_index(inplace = True, drop = False)\n",
    "\n",
    "#load Genotypes Matrix\n",
    "INDEL_genotypes_array =  np.load('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAMN13051687</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAMN09100245</td>\n",
       "      <td>4.3.i4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SAMN08732238</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAMN07658260</td>\n",
       "      <td>3.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SAMN03648003</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     isolate_ID  lineage_call\n",
       "0  SAMN13051687  2.2.1.1.1.i3\n",
       "1  SAMN09100245      4.3.i4.1\n",
       "2  SAMN08732238     2.2.1.1.1\n",
       "3  SAMN07658260         3.1.1\n",
       "4  SAMN03648003     2.2.1.1.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_isolate_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACCGACGAAG_313_A</td>\n",
       "      <td>313</td>\n",
       "      <td>ACCGACGAAG</td>\n",
       "      <td>A</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>313</td>\n",
       "      <td>del</td>\n",
       "      <td>inframe</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TC_1549_T</td>\n",
       "      <td>1549</td>\n",
       "      <td>TC</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv0001_Rv0002</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_1552_TAA</td>\n",
       "      <td>1552</td>\n",
       "      <td>T</td>\n",
       "      <td>TAA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv0001_Rv0002</td>\n",
       "      <td>None</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAA_1552_T</td>\n",
       "      <td>1552</td>\n",
       "      <td>TAA</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv0001_Rv0002</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T_1552_TA</td>\n",
       "      <td>1552</td>\n",
       "      <td>T</td>\n",
       "      <td>TA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv0001_Rv0002</td>\n",
       "      <td>None</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                key   pos         ref  alt gene_category gene_name  \\\n",
       "0  ACCGACGAAG_313_A   313  ACCGACGAAG    A     Essential      dnaA   \n",
       "1         TC_1549_T  1549          TC    T          None      None   \n",
       "2        T_1552_TAA  1552           T  TAA          None      None   \n",
       "3        TAA_1552_T  1552         TAA    T          None      None   \n",
       "4         T_1552_TA  1552           T   TA          None      None   \n",
       "\n",
       "         gene_id gene_pos ins_del  INDEL_type codon_pos  \n",
       "0         Rv0001      313     del     inframe       105  \n",
       "1  Rv0001_Rv0002     None     del  frameshift      None  \n",
       "2  Rv0001_Rv0002     None     ins  frameshift      None  \n",
       "3  Rv0001_Rv0002     None     del  frameshift      None  \n",
       "4  Rv0001_Rv0002     None     ins  frameshift      None  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_genotypes_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 31428)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### additional 12 eis C-14T mutants with AG MICs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "isolate_annotation_DF_extra_strains = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/eis_promoter_mutant_strains_from_Milan/Genotypes/Genotypes_Filtered/genotypes_isolate_annotation.pkl')\n",
    "isolate_annotation_DF_extra_strains = isolate_annotation_DF_extra_strains.loc[:, ['isolate_ID','lineage_call']]\n",
    "isolate_annotation_DF_extra_strains = convert_lineage_calls(isolate_annotation_DF_extra_strains)\n",
    "\n",
    "#load Genotypes Matrix\n",
    "genotypes_array_extra_strains =  np.load('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/eis_promoter_mutant_strains_from_Milan/Genotypes_indels/Genotypes_Filtered/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168-19</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>622-19</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>655-19</td>\n",
       "      <td>4.3.i3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT1070</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IT123</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  isolate_ID  lineage_call\n",
       "0     168-19     2.2.1.1.1\n",
       "1     622-19  2.2.1.1.1.i3\n",
       "2     655-19      4.3.i3.1\n",
       "3     IT1070  2.2.1.1.1.i3\n",
       "4      IT123     2.2.1.1.1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_extra_strains.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 9, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genotypes_array_extra_strains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(genotypes_array_extra_strains)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge *isolate annotation files* and *genotypes matrices* together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge isolate annotation dataframes\n",
    "INDEL_isolate_annotation_DF = INDEL_isolate_annotation_DF.append(isolate_annotation_DF_extra_strains)\n",
    "INDEL_isolate_annotation_DF.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31440, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge genotypes matrices\n",
    "INDEL_genotypes_array = np.hstack((INDEL_genotypes_array, genotypes_array_extra_strains))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 31440)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [1.2] Subset to targeted chomosomal region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only indels that occur within chromosomal region\n",
    "indels_in_targeted_region = list(INDEL_annotation_DF[(INDEL_annotation_DF.pos >= H37Rv_region_start) & (INDEL_annotation_DF.pos <= H37Rv_region_end)].key)\n",
    "\n",
    "# construct boolean filter\n",
    "indels_to_keep_filter = [indel_i in indels_in_targeted_region for indel_i in INDEL_annotation_DF.key]\n",
    "\n",
    "# filter Genotypes Matrix\n",
    "INDEL_genotypes_array = INDEL_genotypes_array[indels_to_keep_filter , :]\n",
    "\n",
    "# filter INDEL annotation file\n",
    "INDEL_annotation_DF = INDEL_annotation_DF[indels_to_keep_filter]\n",
    "INDEL_annotation_DF.reset_index(drop = True , inplace = True) #re-index new filtered INDEL annotation DF (so new index matches indexing of genotypes matrix rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A_2714157_ACTG</td>\n",
       "      <td>2714157</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1176</td>\n",
       "      <td>ins</td>\n",
       "      <td>inframe</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T_2714272_TC</td>\n",
       "      <td>2714272</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1061</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714303_CG</td>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G_2714334_GA</td>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACTCG_2714384_A</td>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key      pos    ref   alt  gene_category gene_name  gene_id  \\\n",
       "0   A_2714157_ACTG  2714157      A  ACTG  Non-Essential       eis  Rv2416c   \n",
       "1     T_2714272_TC  2714272      T    TC  Non-Essential       eis  Rv2416c   \n",
       "2     C_2714303_CG  2714303      C    CG  Non-Essential       eis  Rv2416c   \n",
       "3     G_2714334_GA  2714334      G    GA  Non-Essential       eis  Rv2416c   \n",
       "4  ACTCG_2714384_A  2714384  ACTCG     A  Non-Essential       eis  Rv2416c   \n",
       "\n",
       "  gene_pos ins_del  INDEL_type codon_pos  \n",
       "0     1176     ins     inframe       392  \n",
       "1     1061     ins  frameshift       354  \n",
       "2     1030     ins  frameshift       344  \n",
       "3      999     ins  frameshift       333  \n",
       "4      949     del  frameshift       317  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 11)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_genotypes_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 31440)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [2] SNPs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31,428 isolates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [2.1] Load SNP genotype matrix and Annotation Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "SNP_isolate_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_isolate_annotation.pkl')\n",
    "SNP_isolate_annotation_DF = SNP_isolate_annotation_DF.loc[:, ['isolate_ID','lineage_call']] #drop columns\n",
    "SNP_isolate_annotation_DF = convert_lineage_calls(SNP_isolate_annotation_DF)\n",
    "\n",
    "#load INDEL annotation file (rows of Genotype Matrix) with gene annotation information\n",
    "SNP_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_SNP_annotation.pkl')\n",
    "SNP_annotation_DF = SNP_annotation_DF.loc[:, ['pos','ref','alt']] # drop columns\n",
    "SNP_annotation_DF.reset_index(inplace = True, drop = False)\n",
    "\n",
    "#load Genotypes Matrix\n",
    "SNP_genotypes_array =  np.load('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4.3.i4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4.4.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SAMN03647419</td>\n",
       "      <td>3.1.1.i1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4.3.i2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SAMN07659096</td>\n",
       "      <td>1.1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     isolate_ID lineage_call\n",
       "0  SAMEA3558733     4.3.i4.2\n",
       "1  SAMN03648641      4.4.1.1\n",
       "2  SAMN03647419     3.1.1.i1\n",
       "3  SAMEA3671418       4.3.i2\n",
       "4  SAMN07659096        1.1.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_isolate_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>G</td>\n",
       "      <td>[C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "      <td>[A, T]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  pos ref     alt\n",
       "0      0   48   C     [T]\n",
       "1      1   64   G     [C]\n",
       "2      2   67   G  [A, T]\n",
       "3      3   69   C     [T]\n",
       "4      4   71   C     [T]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       ...,\n",
       "       [1, 9, 9, ..., 1, 1, 1],\n",
       "       [1, 9, 1, ..., 1, 1, 1],\n",
       "       [1, 9, 1, ..., 1, 1, 1]], dtype=int8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_genotypes_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 31428)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### additional 12 eis C-14T mutants with AG MICs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "isolate_annotation_DF_extra_strains = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/eis_promoter_mutant_strains_from_Milan/Genotypes/Genotypes_Filtered/genotypes_isolate_annotation.pkl')\n",
    "isolate_annotation_DF_extra_strains = isolate_annotation_DF_extra_strains.loc[:, ['isolate_ID','lineage_call']]\n",
    "isolate_annotation_DF_extra_strains = convert_lineage_calls(isolate_annotation_DF_extra_strains)\n",
    "\n",
    "#load Genotypes Matrix\n",
    "genotypes_array_extra_strains =  np.load('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/eis_promoter_mutant_strains_from_Milan/Genotypes/Genotypes_Filtered/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168-19</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>622-19</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>655-19</td>\n",
       "      <td>4.3.i3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT1070</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IT123</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  isolate_ID  lineage_call\n",
       "0     168-19     2.2.1.1.1\n",
       "1     622-19  2.2.1.1.1.i3\n",
       "2     655-19      4.3.i3.1\n",
       "3     IT1070  2.2.1.1.1.i3\n",
       "4      IT123     2.2.1.1.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_extra_strains.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1]], dtype=int8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genotypes_array_extra_strains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 12)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(genotypes_array_extra_strains)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge *isolate annotation files* and *genotypes matrices* together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge isolate annotation dataframes\n",
    "SNP_isolate_annotation_DF = SNP_isolate_annotation_DF.append(isolate_annotation_DF_extra_strains)\n",
    "SNP_isolate_annotation_DF.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31440, 2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge genotypes matrices\n",
    "SNP_genotypes_array = np.hstack((SNP_genotypes_array, genotypes_array_extra_strains))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 31440)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [2.2] Subset to targeted chomosomal region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only SNPs that occur within chromosomal region\n",
    "SNPs_in_targeted_region = list(SNP_annotation_DF[(SNP_annotation_DF.pos >= H37Rv_region_start) & (SNP_annotation_DF.pos <= H37Rv_region_end)].pos)\n",
    "\n",
    "# construct boolean filter\n",
    "SNPs_to_keep_filter = [SNP_i in SNPs_in_targeted_region for SNP_i in SNP_annotation_DF.pos]\n",
    "\n",
    "# filter Genotypes Matrix\n",
    "SNP_genotypes_array = SNP_genotypes_array[SNPs_to_keep_filter , :]\n",
    "\n",
    "# filter SNP annotation file\n",
    "SNP_annotation_DF = SNP_annotation_DF[SNPs_to_keep_filter]\n",
    "SNP_annotation_DF.reset_index(drop = True , inplace = True) #re-index new filtered SNP annotation DF (so new index matches indexing of genotypes matrix rows)\n",
    "SNP_annotation_DF.loc[:,'array_index'] = SNP_annotation_DF.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>array_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489761</td>\n",
       "      <td>2714130</td>\n",
       "      <td>C</td>\n",
       "      <td>[G]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489762</td>\n",
       "      <td>2714139</td>\n",
       "      <td>G</td>\n",
       "      <td>[A]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489763</td>\n",
       "      <td>2714140</td>\n",
       "      <td>G</td>\n",
       "      <td>[T]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489764</td>\n",
       "      <td>2714145</td>\n",
       "      <td>G</td>\n",
       "      <td>[A]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489765</td>\n",
       "      <td>2714147</td>\n",
       "      <td>C</td>\n",
       "      <td>[A]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      pos ref  alt  array_index\n",
       "0  489761  2714130   C  [G]            0\n",
       "1  489762  2714139   G  [A]            1\n",
       "2  489763  2714140   G  [T]            2\n",
       "3  489764  2714145   G  [A]            3\n",
       "4  489765  2714147   C  [A]            4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 5)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Re-format SNP annotation DataFrame so that each alternate allele is its own row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_annotation_DF_mod = pd.DataFrame(columns = SNP_annotation_DF.columns)\n",
    "new_SNP_index = 0\n",
    "for SNP_i_row in SNP_annotation_DF.index:\n",
    "    \n",
    "    SNP_i_info = SNP_annotation_DF.loc[SNP_i_row, :]\n",
    "    \n",
    "    #iterate through alternate alleles, store a row for each one\n",
    "    for SNP_i_alt_allele in SNP_i_info.alt:\n",
    "        \n",
    "        SNP_annotation_DF_mod.loc[new_SNP_index, :] = list(SNP_i_info.loc[['index','pos','ref']]) + [SNP_i_alt_allele] + list(SNP_i_info.loc[['array_index']])\n",
    "        new_SNP_index += 1\n",
    "\n",
    "SNP_annotation_DF = SNP_annotation_DF_mod\n",
    "#create new column to merge func annotation on\n",
    "SNP_annotation_DF.loc[:, 'key'] = [str(SNP_i_pos) + '_' + SNP_i_alt for SNP_i_pos, SNP_i_alt in zip(SNP_annotation_DF.pos, SNP_annotation_DF.alt)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>array_index</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489761</td>\n",
       "      <td>2714130</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "      <td>2714130_G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489762</td>\n",
       "      <td>2714139</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>2714139_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489763</td>\n",
       "      <td>2714140</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>2714140_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489764</td>\n",
       "      <td>2714145</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>2714145_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489765</td>\n",
       "      <td>2714147</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2714147_A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      pos ref alt array_index        key\n",
       "0  489761  2714130   C   G           0  2714130_G\n",
       "1  489762  2714139   G   A           1  2714139_A\n",
       "2  489763  2714140   G   T           2  2714140_T\n",
       "3  489764  2714145   G   A           3  2714145_A\n",
       "4  489765  2714147   C   A           4  2714147_A"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284, 6)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge functional annotation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_func_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_SNP_functional_annotation.pkl')\n",
    "SNP_func_annotation_DF.loc[:, 'key'] = SNP_func_annotation_DF.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48_T</th>\n",
       "      <td>48</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>48</td>\n",
       "      <td>S</td>\n",
       "      <td>V16V</td>\n",
       "      <td>48_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64_C</th>\n",
       "      <td>64</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>64</td>\n",
       "      <td>N</td>\n",
       "      <td>G22R</td>\n",
       "      <td>64_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67_A</th>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>67</td>\n",
       "      <td>N</td>\n",
       "      <td>D23N</td>\n",
       "      <td>67_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67_T</th>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>67</td>\n",
       "      <td>N</td>\n",
       "      <td>D23Y</td>\n",
       "      <td>67_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69_T</th>\n",
       "      <td>69</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>69</td>\n",
       "      <td>S</td>\n",
       "      <td>D23D</td>\n",
       "      <td>69_T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pos ref alt gene_category gene_name gene_id gene_pos SNP_type AA_change  \\\n",
       "48_T   48   C   T     Essential      dnaA  Rv0001       48        S      V16V   \n",
       "64_C   64   G   C     Essential      dnaA  Rv0001       64        N      G22R   \n",
       "67_A   67   G   A     Essential      dnaA  Rv0001       67        N      D23N   \n",
       "67_T   67   G   T     Essential      dnaA  Rv0001       67        N      D23Y   \n",
       "69_T   69   C   T     Essential      dnaA  Rv0001       69        S      D23D   \n",
       "\n",
       "       key  \n",
       "48_T  48_T  \n",
       "64_C  64_C  \n",
       "67_A  67_A  \n",
       "67_T  67_T  \n",
       "69_T  69_T  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_func_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_annotation_DF = SNP_annotation_DF.merge(SNP_func_annotation_DF.loc[: , ['gene_category','gene_name','gene_id','gene_pos','SNP_type','AA_change','key']], how = 'left', on = 'key')\n",
    "SNP_annotation_DF = SNP_annotation_DF.loc[:, ['key','pos','ref','alt','gene_category','gene_name','gene_id','gene_pos','SNP_type','AA_change','array_index']] #re-arrange columns\n",
    "SNP_annotation_DF.rename(columns={'SNP_type':'mut_type', 'AA_change':'codon_pos'}, inplace = True) #change column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714130_G</td>\n",
       "      <td>2714130</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1203</td>\n",
       "      <td>N</td>\n",
       "      <td>E401D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2714139_A</td>\n",
       "      <td>2714139</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1194</td>\n",
       "      <td>S</td>\n",
       "      <td>T398T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2714140_T</td>\n",
       "      <td>2714140</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1193</td>\n",
       "      <td>N</td>\n",
       "      <td>T398N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2714145_A</td>\n",
       "      <td>2714145</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1188</td>\n",
       "      <td>S</td>\n",
       "      <td>V396V</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2714147_A</td>\n",
       "      <td>2714147</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1186</td>\n",
       "      <td>N</td>\n",
       "      <td>V396F</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key      pos ref alt  gene_category gene_name  gene_id gene_pos  \\\n",
       "0  2714130_G  2714130   C   G  Non-Essential       eis  Rv2416c     1203   \n",
       "1  2714139_A  2714139   G   A  Non-Essential       eis  Rv2416c     1194   \n",
       "2  2714140_T  2714140   G   T  Non-Essential       eis  Rv2416c     1193   \n",
       "3  2714145_A  2714145   G   A  Non-Essential       eis  Rv2416c     1188   \n",
       "4  2714147_A  2714147   C   A  Non-Essential       eis  Rv2416c     1186   \n",
       "\n",
       "  mut_type codon_pos array_index  \n",
       "0        N     E401D           0  \n",
       "1        S     T398T           1  \n",
       "2        N     T398N           2  \n",
       "3        S     V396V           3  \n",
       "4        N     V396F           4  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284, 11)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_genotypes_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 31440)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_genotypes_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [3] Pull info for isolates with mutant alleles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [3.1] Get genotypes for SNPs of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Function* to get the genotypes for a specifc SNP and get attributes for isolates that support SNP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_genotypes_for_SNP(SNP_i_pos, SNP_i_alt_allele):\n",
    "    \n",
    "    #get the number code that corresponds to the alt allele, 9 indicated bad quality call\n",
    "    base_code_dict = {'A':0, 'C':1, 'G':2, 'T':3}\n",
    "    SNP_i_alt_allele_code = base_code_dict[SNP_i_alt_allele]\n",
    "    SNP_i_genotypes = SNP_genotypes_array[SNP_annotation_DF[SNP_annotation_DF.pos == SNP_i_pos].array_index.values[0] , :] \n",
    "\n",
    "    #count the number of isolates that support this SNP call\n",
    "    num_isolates_SNP_i = np.sum(SNP_i_genotypes == SNP_i_alt_allele_code)\n",
    "\n",
    "    #create a boolean filter and extract information for the isolates that support this call\n",
    "    SNP_i_isolate_filter = SNP_i_genotypes == SNP_i_alt_allele_code\n",
    "    isolate_annotation_SNP_i = SNP_isolate_annotation_DF[SNP_i_isolate_filter]\n",
    "\n",
    "    #find number of sub-lineages that have SNP support in at least 1 isolate\n",
    "    num_sublineages_with_SNP_i = len(set(list(isolate_annotation_SNP_i.lineage_call.values)))\n",
    "    \n",
    "    #get list of sublineages w/ at least 1 isolates that supports SNP call\n",
    "    sublineages_with_SNP_count_dict = Counter(isolate_annotation_SNP_i.lineage_call.values)\n",
    "    sublineages_with_SNP_list = []\n",
    "    for sublineage_i in sublineages_with_SNP_count_dict.keys():\n",
    "\n",
    "        sublineage_i_with_SNP = sublineage_i + '({0})'.format(str(sublineages_with_SNP_count_dict[sublineage_i]))\n",
    "        sublineages_with_SNP_list.append(sublineage_i_with_SNP)\n",
    "\n",
    "    sublineages_with_SNP = ' - '.join(sublineages_with_SNP_list)\n",
    "\n",
    "    return [num_isolates_SNP_i , num_sublineages_with_SNP_i , sublineages_with_SNP]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714130_G</td>\n",
       "      <td>2714130</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1203</td>\n",
       "      <td>N</td>\n",
       "      <td>E401D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2714139_A</td>\n",
       "      <td>2714139</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1194</td>\n",
       "      <td>S</td>\n",
       "      <td>T398T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2714140_T</td>\n",
       "      <td>2714140</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1193</td>\n",
       "      <td>N</td>\n",
       "      <td>T398N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2714145_A</td>\n",
       "      <td>2714145</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1188</td>\n",
       "      <td>S</td>\n",
       "      <td>V396V</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2714147_A</td>\n",
       "      <td>2714147</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1186</td>\n",
       "      <td>N</td>\n",
       "      <td>V396F</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key      pos ref alt  gene_category gene_name  gene_id gene_pos  \\\n",
       "0  2714130_G  2714130   C   G  Non-Essential       eis  Rv2416c     1203   \n",
       "1  2714139_A  2714139   G   A  Non-Essential       eis  Rv2416c     1194   \n",
       "2  2714140_T  2714140   G   T  Non-Essential       eis  Rv2416c     1193   \n",
       "3  2714145_A  2714145   G   A  Non-Essential       eis  Rv2416c     1188   \n",
       "4  2714147_A  2714147   C   A  Non-Essential       eis  Rv2416c     1186   \n",
       "\n",
       "  mut_type codon_pos array_index  \n",
       "0        N     E401D           0  \n",
       "1        S     T398T           1  \n",
       "2        N     T398N           2  \n",
       "3        S     V396V           3  \n",
       "4        N     V396F           4  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284, 11)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_with_SNP = []\n",
    "num_sublineages_with_SNP = []\n",
    "sublineages_with_SNP = []\n",
    "\n",
    "for SNP_i_pos, SNP_i_alt_allele in zip(SNP_annotation_DF.pos, SNP_annotation_DF.alt):\n",
    "    \n",
    "    num_isolates_with_SNP_i , num_sublineages_with_SNP_i , sublineages_with_SNP_i = get_genotypes_for_SNP(SNP_i_pos, SNP_i_alt_allele)\n",
    "    \n",
    "    num_isolates_with_SNP.append(num_isolates_with_SNP_i)\n",
    "    num_sublineages_with_SNP.append(num_sublineages_with_SNP_i)\n",
    "    sublineages_with_SNP.append(sublineages_with_SNP_i)\n",
    "    \n",
    "SNP_annotation_DF.loc[: , 'num_isolates'] = num_isolates_with_SNP\n",
    "SNP_annotation_DF.loc[: , 'num_sublineages'] = num_sublineages_with_SNP\n",
    "SNP_annotation_DF.loc[: , 'sublineages'] = sublineages_with_SNP\n",
    "\n",
    "#drop SNPs present in 0 isolates\n",
    "SNP_annotation_DF = SNP_annotation_DF[SNP_annotation_DF.num_isolates > 0]\n",
    "SNP_annotation_DF.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714130_G</td>\n",
       "      <td>2714130</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1203</td>\n",
       "      <td>N</td>\n",
       "      <td>E401D</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1.1(25) - 3.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2714139_A</td>\n",
       "      <td>2714139</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1194</td>\n",
       "      <td>S</td>\n",
       "      <td>T398T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2714140_T</td>\n",
       "      <td>2714140</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1193</td>\n",
       "      <td>N</td>\n",
       "      <td>T398N</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2714145_A</td>\n",
       "      <td>2714145</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1188</td>\n",
       "      <td>S</td>\n",
       "      <td>V396V</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5(1) - 4.13(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2714147_A</td>\n",
       "      <td>2714147</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1186</td>\n",
       "      <td>N</td>\n",
       "      <td>V396F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.3(1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key      pos ref alt  gene_category gene_name  gene_id gene_pos  \\\n",
       "0  2714130_G  2714130   C   G  Non-Essential       eis  Rv2416c     1203   \n",
       "1  2714139_A  2714139   G   A  Non-Essential       eis  Rv2416c     1194   \n",
       "2  2714140_T  2714140   G   T  Non-Essential       eis  Rv2416c     1193   \n",
       "3  2714145_A  2714145   G   A  Non-Essential       eis  Rv2416c     1188   \n",
       "4  2714147_A  2714147   C   A  Non-Essential       eis  Rv2416c     1186   \n",
       "\n",
       "  mut_type codon_pos array_index  num_isolates  num_sublineages  \\\n",
       "0        N     E401D           0            27                2   \n",
       "1        S     T398T           1             1                1   \n",
       "2        N     T398N           2             2                1   \n",
       "3        S     V396V           3             2                2   \n",
       "4        N     V396F           4             1                1   \n",
       "\n",
       "          sublineages  \n",
       "0  3.1.1(25) - 3.1(2)  \n",
       "1     4.1.i1.1.1.1(1)  \n",
       "2          1.1.1.1(2)  \n",
       "3    4.5(1) - 4.13(1)  \n",
       "4            1.1.3(1)  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283, 14)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [3.2] Get genotypes for indels of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Function* to get the genotypes for a specifc indel and get attributes for isolates that support indel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_genotypes_for_indel(indel_i_key):\n",
    "    \n",
    "    #0 supports Ref, 1 supports Alt, 9 indicated bad quality call\n",
    "    indel_i_genotypes = INDEL_genotypes_array[INDEL_annotation_DF[INDEL_annotation_DF.key == indel_i_key].index[0] , :] \n",
    "\n",
    "    #count the number of isolates that support this indel call\n",
    "    num_isolates_indel_i = np.sum(indel_i_genotypes == 1)\n",
    "\n",
    "    #create a boolean filter and extract information for the isolates taht support this call\n",
    "    indel_i_isolate_filter = indel_i_genotypes == 1\n",
    "    isolate_annotation_indel_i = INDEL_isolate_annotation_DF[indel_i_isolate_filter]\n",
    "\n",
    "    #find number of sub-lineages that have indel support in at least 1 isolate\n",
    "    num_sublineages_with_indel_i = len(set(list(isolate_annotation_indel_i.lineage_call.values)))\n",
    "    \n",
    "    #get list of sublineages w/ at least 1 isolates that supports indel call\n",
    "    sublineage_with_indel_count_dict = Counter(isolate_annotation_indel_i.lineage_call.values)\n",
    "    sublineages_with_indel_list = []\n",
    "    for sublineage_i in sublineage_with_indel_count_dict.keys():\n",
    "\n",
    "        sublineage_i_with_indel = sublineage_i + '({0})'.format(str(sublineage_with_indel_count_dict[sublineage_i]))\n",
    "        sublineages_with_indel_list.append(sublineage_i_with_indel)\n",
    "\n",
    "    sublineages_with_indel = ' - '.join(sublineages_with_indel_list)\n",
    "    \n",
    "    return [num_isolates_indel_i , num_sublineages_with_indel_i , sublineages_with_indel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A_2714157_ACTG</td>\n",
       "      <td>2714157</td>\n",
       "      <td>A</td>\n",
       "      <td>ACTG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1176</td>\n",
       "      <td>ins</td>\n",
       "      <td>inframe</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T_2714272_TC</td>\n",
       "      <td>2714272</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1061</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714303_CG</td>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G_2714334_GA</td>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACTCG_2714384_A</td>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key      pos    ref   alt  gene_category gene_name  gene_id  \\\n",
       "0   A_2714157_ACTG  2714157      A  ACTG  Non-Essential       eis  Rv2416c   \n",
       "1     T_2714272_TC  2714272      T    TC  Non-Essential       eis  Rv2416c   \n",
       "2     C_2714303_CG  2714303      C    CG  Non-Essential       eis  Rv2416c   \n",
       "3     G_2714334_GA  2714334      G    GA  Non-Essential       eis  Rv2416c   \n",
       "4  ACTCG_2714384_A  2714384  ACTCG     A  Non-Essential       eis  Rv2416c   \n",
       "\n",
       "  gene_pos ins_del  INDEL_type codon_pos  \n",
       "0     1176     ins     inframe       392  \n",
       "1     1061     ins  frameshift       354  \n",
       "2     1030     ins  frameshift       344  \n",
       "3      999     ins  frameshift       333  \n",
       "4      949     del  frameshift       317  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 11)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_with_indel = []\n",
    "num_sublineages_with_indel = []\n",
    "sublineages_with_indel = []\n",
    "\n",
    "for indel_i_key in INDEL_annotation_DF.key:\n",
    "    \n",
    "    num_isolates_with_indel_i , num_sublineages_with_indel_i , sublineages_with_indel_i = get_genotypes_for_indel(indel_i_key)\n",
    "    \n",
    "    num_isolates_with_indel.append(num_isolates_with_indel_i)\n",
    "    num_sublineages_with_indel.append(num_sublineages_with_indel_i)\n",
    "    sublineages_with_indel.append(sublineages_with_indel_i)\n",
    "    \n",
    "INDEL_annotation_DF.loc[: , 'num_isolates'] = num_isolates_with_indel\n",
    "INDEL_annotation_DF.loc[: , 'num_sublineages'] = num_sublineages_with_indel\n",
    "INDEL_annotation_DF.loc[: , 'sublineages'] = sublineages_with_indel\n",
    "\n",
    "#drop indels present in 0 isolates\n",
    "INDEL_annotation_DF = INDEL_annotation_DF[INDEL_annotation_DF.num_isolates > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714303_CG</td>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>344</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G_2714334_GA</td>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACTCG_2714384_A</td>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4.1.1(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GT_2714433_G</td>\n",
       "      <td>2714433</td>\n",
       "      <td>GT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>900</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GGT_2714526_G</td>\n",
       "      <td>2714526</td>\n",
       "      <td>GGT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>807</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>269</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(112)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key      pos    ref alt  gene_category gene_name  gene_id  \\\n",
       "2     C_2714303_CG  2714303      C  CG  Non-Essential       eis  Rv2416c   \n",
       "3     G_2714334_GA  2714334      G  GA  Non-Essential       eis  Rv2416c   \n",
       "4  ACTCG_2714384_A  2714384  ACTCG   A  Non-Essential       eis  Rv2416c   \n",
       "5     GT_2714433_G  2714433     GT   G  Non-Essential       eis  Rv2416c   \n",
       "6    GGT_2714526_G  2714526    GGT   G  Non-Essential       eis  Rv2416c   \n",
       "\n",
       "  gene_pos ins_del  INDEL_type codon_pos  num_isolates  num_sublineages  \\\n",
       "2     1030     ins  frameshift       344             3                1   \n",
       "3      999     ins  frameshift       333             1                1   \n",
       "4      949     del  frameshift       317             4                1   \n",
       "5      900     del  frameshift       300             2                1   \n",
       "6      807     del  frameshift       269           112                1   \n",
       "\n",
       "       sublineages  \n",
       "2  4.1.i1.1.1.1(3)  \n",
       "3         3.1.1(1)  \n",
       "4       4.4.1.1(4)  \n",
       "5    4.1.i1.2.1(2)  \n",
       "6   2.2.1.1.1(112)  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 14)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] Check co-occurrence between *regulator* SNPs/INDELs & *regulon* SNPs/INDELs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SNPs in regulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_SNPs_DF = SNP_annotation_DF[SNP_annotation_DF.gene_id == regulator_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2715333_C</td>\n",
       "      <td>2715333</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.3.i3.1(1) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2715337_C</td>\n",
       "      <td>2715337</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2715338_A</td>\n",
       "      <td>2715338</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>251</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i4.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2715340_A</td>\n",
       "      <td>2715340</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4.3.i4.1(1) - 4.1.i1.1.1.1(7) - 2.2.1.1.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2715340_T</td>\n",
       "      <td>2715340</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>252</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4.11(2) - 2.2.1.1.1.i3(5) - 4.3.i3.1(3) - 2.2....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           key      pos ref alt gene_category gene_name          gene_id  \\\n",
       "263  2715333_C  2715333   A   C          None      None  Rv2416c_Rv2417c   \n",
       "264  2715337_C  2715337   T   C          None      None  Rv2416c_Rv2417c   \n",
       "265  2715338_A  2715338   C   A          None      None  Rv2416c_Rv2417c   \n",
       "266  2715340_A  2715340   G   A          None      None  Rv2416c_Rv2417c   \n",
       "267  2715340_T  2715340   G   T          None      None  Rv2416c_Rv2417c   \n",
       "\n",
       "    gene_pos mut_type codon_pos array_index  num_isolates  num_sublineages  \\\n",
       "263     None        I      None         249             2                2   \n",
       "264     None        I      None         250             1                1   \n",
       "265     None        I      None         251             2                1   \n",
       "266     None        I      None         252             9                3   \n",
       "267     None        I      None         252            11                4   \n",
       "\n",
       "                                           sublineages  \n",
       "263                           4.3.i3.1(1) - 1.1.1.1(1)  \n",
       "264                                           3.1.1(1)  \n",
       "265                                        4.3.i4.1(2)  \n",
       "266       4.3.i4.1(1) - 4.1.i1.1.1.1(7) - 2.2.1.1.2(1)  \n",
       "267  4.11(2) - 2.2.1.1.1.i3(5) - 4.3.i3.1(3) - 2.2....  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulator_SNPs_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 14)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(regulator_SNPs_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INDELs in regulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_INDELs_DF = INDEL_annotation_DF[INDEL_annotation_DF.gene_id == regulator_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GAATCCGAC_2715334_G</td>\n",
       "      <td>2715334</td>\n",
       "      <td>GAATCCGAC</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CG_2715339_C</td>\n",
       "      <td>2715339</td>\n",
       "      <td>CG</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2.2.1.1.1.i3(1) - 2.2.1.1.1(5) - 4.4.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AC_2715362_A</td>\n",
       "      <td>2715362</td>\n",
       "      <td>AC</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i3.2(1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    key      pos        ref alt gene_category gene_name  \\\n",
       "25  GAATCCGAC_2715334_G  2715334  GAATCCGAC   G          None      None   \n",
       "26         CG_2715339_C  2715339         CG   C          None      None   \n",
       "27         AC_2715362_A  2715362         AC   A          None      None   \n",
       "\n",
       "            gene_id gene_pos ins_del  INDEL_type codon_pos  num_isolates  \\\n",
       "25  Rv2416c_Rv2417c     None     del  frameshift      None             1   \n",
       "26  Rv2416c_Rv2417c     None     del  frameshift      None             7   \n",
       "27  Rv2416c_Rv2417c     None     del  frameshift      None             1   \n",
       "\n",
       "    num_sublineages                                sublineages  \n",
       "25                1                            2.2.1.1.1.i3(1)  \n",
       "26                3  2.2.1.1.1.i3(1) - 2.2.1.1.1(5) - 4.4.2(1)  \n",
       "27                1                                4.3.i3.2(1)  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulator_INDELs_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 14)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(regulator_INDELs_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop **INDEL_type** column and rename **ins_del** column to fit with SNPs DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/rv76/anaconda2/lib/python2.7/site-packages/pandas/core/frame.py:2746: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  **kwargs)\n"
     ]
    }
   ],
   "source": [
    "regulator_INDELs_DF.drop(['INDEL_type'], axis=1, inplace = True)\n",
    "regulator_INDELs_DF.rename(columns={'ins_del':'mut_type'}, inplace = True) #change column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SNPs in regulon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to include SNPs in a gene that either \n",
    "\n",
    "- cause a **premature STOP codon** (nonsense mutation)\n",
    "- **synonymous mutations at the first codon that abolish the start codon** (i.e. eis, whiB7 and mmpR which start with a **valine** as this is not possible with ahpC, mmpS5 or mmpL5, which start with a **methionine**)\n",
    "- **non-synonymous mutations at the first codon that abolish the start codon** (i.e. that change the start to an AA other than **valine** and **methionine**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulon_SNPs_DF = SNP_annotation_DF[SNP_annotation_DF.gene_id == regulon_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2714449_T</td>\n",
       "      <td>2714449</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>884</td>\n",
       "      <td>N</td>\n",
       "      <td>W295*</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2714543_A</td>\n",
       "      <td>2714543</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>790</td>\n",
       "      <td>N</td>\n",
       "      <td>R264*</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2714664_T</td>\n",
       "      <td>2714664</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>669</td>\n",
       "      <td>N</td>\n",
       "      <td>Y223*</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2714753_A</td>\n",
       "      <td>2714753</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>580</td>\n",
       "      <td>N</td>\n",
       "      <td>Q194*</td>\n",
       "      <td>118</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.10.i1(1) - 1.2.2.1(9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2714787_T</td>\n",
       "      <td>2714787</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>546</td>\n",
       "      <td>N</td>\n",
       "      <td>W182*</td>\n",
       "      <td>126</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10.i1(15)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2715294_T</td>\n",
       "      <td>2715294</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>39</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2715295_T</td>\n",
       "      <td>2715295</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2715305_A</td>\n",
       "      <td>2715305</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>28</td>\n",
       "      <td>N</td>\n",
       "      <td>E10*</td>\n",
       "      <td>237</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.1.3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2715330_T</td>\n",
       "      <td>2715330</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>V1V</td>\n",
       "      <td>248</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2.1.1.1.i3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           key      pos ref alt  gene_category gene_name  gene_id gene_pos  \\\n",
       "59   2714449_T  2714449   C   T  Non-Essential       eis  Rv2416c      884   \n",
       "82   2714543_A  2714543   G   A  Non-Essential       eis  Rv2416c      790   \n",
       "103  2714664_T  2714664   G   T  Non-Essential       eis  Rv2416c      669   \n",
       "123  2714753_A  2714753   G   A  Non-Essential       eis  Rv2416c      580   \n",
       "132  2714787_T  2714787   C   T  Non-Essential       eis  Rv2416c      546   \n",
       "244  2715294_T  2715294   C   T  Non-Essential       eis  Rv2416c       39   \n",
       "245  2715295_T  2715295   C   T  Non-Essential       eis  Rv2416c       38   \n",
       "249  2715305_A  2715305   C   A  Non-Essential       eis  Rv2416c       28   \n",
       "262  2715330_T  2715330   C   T  Non-Essential       eis  Rv2416c        3   \n",
       "\n",
       "    mut_type codon_pos array_index  num_isolates  num_sublineages  \\\n",
       "59         N     W295*          57             1                1   \n",
       "82         N     R264*          79             1                1   \n",
       "103        N     Y223*          99             2                1   \n",
       "123        N     Q194*         118            10                2   \n",
       "132        N     W182*         126            15                1   \n",
       "244        N      W13*         232             1                1   \n",
       "245        N      W13*         233             1                1   \n",
       "249        N      E10*         237             6                2   \n",
       "262        S       V1V         248             6                2   \n",
       "\n",
       "                      sublineages  \n",
       "59                       3.1.1(1)  \n",
       "82                       3.1.1(1)  \n",
       "103                      3.1.1(2)  \n",
       "123       4.10.i1(1) - 1.2.2.1(9)  \n",
       "132                   4.10.i1(15)  \n",
       "244                  2.2.1.1.1(1)  \n",
       "245                  2.2.1.1.1(1)  \n",
       "249         1.1.3(5) - 1.1.1.1(1)  \n",
       "262  2.2.1.1.1.i3(5) - 1.1.1.1(1)  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulon_SNPs_DF[[(AA_change[-1]=='*') | ((len(AA_change)==3) and (AA_change[1]=='1')) for AA_change in regulon_SNPs_DF.codon_pos]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulon_SNPs_DF = regulon_SNPs_DF[[(AA_change[-1]=='*') | ((len(AA_change)==3) and (AA_change[1]=='1')) for AA_change in regulon_SNPs_DF.codon_pos]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save SNPs that occur in the first codon seperately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulon_SNPs_DF[[((len(AA_change)==3) and (AA_change[1]=='1')) for AA_change in regulon_SNPs_DF.codon_pos]].to_excel('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/START_codon_SNVs/eis_START_codon_SNVs.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INDELs in regulon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### keep only *frameshift* indels for gene body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714303_CG</td>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>344</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G_2714334_GA</td>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACTCG_2714384_A</td>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4.1.1(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GT_2714433_G</td>\n",
       "      <td>2714433</td>\n",
       "      <td>GT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>900</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GGT_2714526_G</td>\n",
       "      <td>2714526</td>\n",
       "      <td>GGT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>807</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>269</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(112)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key      pos    ref alt  gene_category gene_name  gene_id  \\\n",
       "2     C_2714303_CG  2714303      C  CG  Non-Essential       eis  Rv2416c   \n",
       "3     G_2714334_GA  2714334      G  GA  Non-Essential       eis  Rv2416c   \n",
       "4  ACTCG_2714384_A  2714384  ACTCG   A  Non-Essential       eis  Rv2416c   \n",
       "5     GT_2714433_G  2714433     GT   G  Non-Essential       eis  Rv2416c   \n",
       "6    GGT_2714526_G  2714526    GGT   G  Non-Essential       eis  Rv2416c   \n",
       "\n",
       "  gene_pos ins_del  INDEL_type codon_pos  num_isolates  num_sublineages  \\\n",
       "2     1030     ins  frameshift       344             3                1   \n",
       "3      999     ins  frameshift       333             1                1   \n",
       "4      949     del  frameshift       317             4                1   \n",
       "5      900     del  frameshift       300             2                1   \n",
       "6      807     del  frameshift       269           112                1   \n",
       "\n",
       "       sublineages  \n",
       "2  4.1.i1.1.1.1(3)  \n",
       "3         3.1.1(1)  \n",
       "4       4.4.1.1(4)  \n",
       "5    4.1.i1.2.1(2)  \n",
       "6   2.2.1.1.1(112)  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF[(INDEL_annotation_DF.gene_id == regulon_id) & (INDEL_annotation_DF.INDEL_type == 'frameshift')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulon_INDELs_DF = INDEL_annotation_DF[(INDEL_annotation_DF.gene_id == regulon_id) & (INDEL_annotation_DF.INDEL_type == 'frameshift')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714303_CG</td>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>344</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G_2714334_GA</td>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACTCG_2714384_A</td>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4.1.1(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GT_2714433_G</td>\n",
       "      <td>2714433</td>\n",
       "      <td>GT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>900</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GGT_2714526_G</td>\n",
       "      <td>2714526</td>\n",
       "      <td>GGT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>807</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>269</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(112)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               key      pos    ref alt  gene_category gene_name  gene_id  \\\n",
       "2     C_2714303_CG  2714303      C  CG  Non-Essential       eis  Rv2416c   \n",
       "3     G_2714334_GA  2714334      G  GA  Non-Essential       eis  Rv2416c   \n",
       "4  ACTCG_2714384_A  2714384  ACTCG   A  Non-Essential       eis  Rv2416c   \n",
       "5     GT_2714433_G  2714433     GT   G  Non-Essential       eis  Rv2416c   \n",
       "6    GGT_2714526_G  2714526    GGT   G  Non-Essential       eis  Rv2416c   \n",
       "\n",
       "  gene_pos ins_del  INDEL_type codon_pos  num_isolates  num_sublineages  \\\n",
       "2     1030     ins  frameshift       344             3                1   \n",
       "3      999     ins  frameshift       333             1                1   \n",
       "4      949     del  frameshift       317             4                1   \n",
       "5      900     del  frameshift       300             2                1   \n",
       "6      807     del  frameshift       269           112                1   \n",
       "\n",
       "       sublineages  \n",
       "2  4.1.i1.1.1.1(3)  \n",
       "3         3.1.1(1)  \n",
       "4       4.4.1.1(4)  \n",
       "5    4.1.i1.2.1(2)  \n",
       "6   2.2.1.1.1(112)  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulon_INDELs_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 14)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(regulon_INDELs_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop **INDEL_type** column (since all indels are frameshifts) and rename **ins_del** column to fit with SNPs DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "regulon_INDELs_DF.drop(['INDEL_type'], axis=1, inplace = True)\n",
    "regulon_INDELs_DF.rename(columns={'ins_del':'mut_type'}, inplace = True) #change column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save DataFrame for *regulator* & *regulon* mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_SNPs_DF_for_export = regulator_SNPs_DF.drop(['array_index'], axis=1)\n",
    "regulon_SNPs_DF_for_export = regulon_SNPs_DF.drop(['array_index'], axis=1)\n",
    "regulator_regulon_mutations_df = regulator_SNPs_DF_for_export.append(regulon_SNPs_DF_for_export.append(regulator_INDELs_DF.append(regulon_INDELs_DF)))\n",
    "\n",
    "regulator_regulon_mutations_df.drop(['key'], axis=1, inplace = True) #drop col\n",
    "regulator_regulon_mutations_df.sort_values(by = 'pos', inplace = True) #sort by reference position\n",
    "regulator_regulon_mutations_df.reset_index(inplace = True, drop = True) #reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 12)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(regulator_regulon_mutations_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714303</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1030</td>\n",
       "      <td>ins</td>\n",
       "      <td>344</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2714334</td>\n",
       "      <td>G</td>\n",
       "      <td>GA</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>999</td>\n",
       "      <td>ins</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2714384</td>\n",
       "      <td>ACTCG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>949</td>\n",
       "      <td>del</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4.1.1(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2714433</td>\n",
       "      <td>GT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>900</td>\n",
       "      <td>del</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2714449</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>884</td>\n",
       "      <td>N</td>\n",
       "      <td>W295*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2714526</td>\n",
       "      <td>GGT</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>807</td>\n",
       "      <td>del</td>\n",
       "      <td>269</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(112)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2714543</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>790</td>\n",
       "      <td>N</td>\n",
       "      <td>R264*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2714557</td>\n",
       "      <td>AGGCCAATCAG</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>776</td>\n",
       "      <td>del</td>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2714664</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>669</td>\n",
       "      <td>N</td>\n",
       "      <td>Y223*</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2714740</td>\n",
       "      <td>TC</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>593</td>\n",
       "      <td>del</td>\n",
       "      <td>198</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.2(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2714753</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>580</td>\n",
       "      <td>N</td>\n",
       "      <td>Q194*</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.10.i1(1) - 1.2.2.1(9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2714760</td>\n",
       "      <td>TAGCA</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>573</td>\n",
       "      <td>del</td>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2714787</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>546</td>\n",
       "      <td>N</td>\n",
       "      <td>W182*</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>4.10.i1(15)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2714847</td>\n",
       "      <td>G</td>\n",
       "      <td>GCT</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>486</td>\n",
       "      <td>ins</td>\n",
       "      <td>162</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2714860</td>\n",
       "      <td>A</td>\n",
       "      <td>AT</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>473</td>\n",
       "      <td>ins</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2714885</td>\n",
       "      <td>GA</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>448</td>\n",
       "      <td>del</td>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2714966</td>\n",
       "      <td>CCTCG</td>\n",
       "      <td>C</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>367</td>\n",
       "      <td>del</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2715209</td>\n",
       "      <td>TG</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>124</td>\n",
       "      <td>del</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2715214</td>\n",
       "      <td>AC</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>119</td>\n",
       "      <td>del</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2715287</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>46</td>\n",
       "      <td>ins</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4.1.1(1) - 4.3.i2(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2715291</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>42</td>\n",
       "      <td>ins</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2715294</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>39</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2715295</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2715305</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>28</td>\n",
       "      <td>N</td>\n",
       "      <td>E10*</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.1.3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2715317</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>16</td>\n",
       "      <td>ins</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2715319</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>14</td>\n",
       "      <td>ins</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2715330</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>V1V</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2.1.1.1.i3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2715333</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.3.i3.1(1) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2715334</td>\n",
       "      <td>GAATCCGAC</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2715337</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2715338</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i4.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2715339</td>\n",
       "      <td>CG</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2.2.1.1.1.i3(1) - 2.2.1.1.1(5) - 4.4.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2715340</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4.3.i4.1(1) - 4.1.i1.1.1.1(7) - 2.2.1.1.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2715340</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4.11(2) - 2.2.1.1.1.i3(5) - 4.3.i3.1(3) - 2.2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2715341</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2715342</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>293</td>\n",
       "      <td>18</td>\n",
       "      <td>4.11(1) - 2.2.1.1.1.i3(58) - 4.3.i3.1(14) - 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2715342</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5(2) - 4.10.i1(1) - 4.3.i3.1(4) - 2.2.1.1.1(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2715344</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>332</td>\n",
       "      <td>17</td>\n",
       "      <td>4.10.i2(1) - 2.2.1.1.1.i3(70) - 4.3.i3.1(50) -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2715345</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>19</td>\n",
       "      <td>2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2715347</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2715347</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4.11(3) - 2.2.1.1.1.i3(2) - 4.3.i3.1(1) - 4.1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2715353</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.1.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2715355</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2715362</td>\n",
       "      <td>AC</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>del</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i3.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2715365</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i4.2(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2715369</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>285</td>\n",
       "      <td>8</td>\n",
       "      <td>4.11(4) - 2.2.1.1.1.i3(230) - 4.1.i1.1.1.1(1) ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2715375</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3.i4.2(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2715376</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2715382</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1.i1.2.1(1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pos          ref  alt  gene_category gene_name          gene_id  \\\n",
       "0   2714303            C   CG  Non-Essential       eis          Rv2416c   \n",
       "1   2714334            G   GA  Non-Essential       eis          Rv2416c   \n",
       "2   2714384        ACTCG    A  Non-Essential       eis          Rv2416c   \n",
       "3   2714433           GT    G  Non-Essential       eis          Rv2416c   \n",
       "4   2714449            C    T  Non-Essential       eis          Rv2416c   \n",
       "5   2714526          GGT    G  Non-Essential       eis          Rv2416c   \n",
       "6   2714543            G    A  Non-Essential       eis          Rv2416c   \n",
       "7   2714557  AGGCCAATCAG    A  Non-Essential       eis          Rv2416c   \n",
       "8   2714664            G    T  Non-Essential       eis          Rv2416c   \n",
       "9   2714740           TC    T  Non-Essential       eis          Rv2416c   \n",
       "10  2714753            G    A  Non-Essential       eis          Rv2416c   \n",
       "11  2714760        TAGCA    T  Non-Essential       eis          Rv2416c   \n",
       "12  2714787            C    T  Non-Essential       eis          Rv2416c   \n",
       "13  2714847            G  GCT  Non-Essential       eis          Rv2416c   \n",
       "14  2714860            A   AT  Non-Essential       eis          Rv2416c   \n",
       "15  2714885           GA    G  Non-Essential       eis          Rv2416c   \n",
       "16  2714966        CCTCG    C  Non-Essential       eis          Rv2416c   \n",
       "17  2715209           TG    T  Non-Essential       eis          Rv2416c   \n",
       "18  2715214           AC    A  Non-Essential       eis          Rv2416c   \n",
       "19  2715287            T   TC  Non-Essential       eis          Rv2416c   \n",
       "20  2715291            C   CG  Non-Essential       eis          Rv2416c   \n",
       "21  2715294            C    T  Non-Essential       eis          Rv2416c   \n",
       "22  2715295            C    T  Non-Essential       eis          Rv2416c   \n",
       "23  2715305            C    A  Non-Essential       eis          Rv2416c   \n",
       "24  2715317            A   AC  Non-Essential       eis          Rv2416c   \n",
       "25  2715319            A   AC  Non-Essential       eis          Rv2416c   \n",
       "26  2715330            C    T  Non-Essential       eis          Rv2416c   \n",
       "27  2715333            A    C           None      None  Rv2416c_Rv2417c   \n",
       "28  2715334    GAATCCGAC    G           None      None  Rv2416c_Rv2417c   \n",
       "29  2715337            T    C           None      None  Rv2416c_Rv2417c   \n",
       "30  2715338            C    A           None      None  Rv2416c_Rv2417c   \n",
       "31  2715339           CG    C           None      None  Rv2416c_Rv2417c   \n",
       "32  2715340            G    A           None      None  Rv2416c_Rv2417c   \n",
       "33  2715340            G    T           None      None  Rv2416c_Rv2417c   \n",
       "34  2715341            A    G           None      None  Rv2416c_Rv2417c   \n",
       "35  2715342            C    T           None      None  Rv2416c_Rv2417c   \n",
       "36  2715342            C    G           None      None  Rv2416c_Rv2417c   \n",
       "37  2715344            G    A           None      None  Rv2416c_Rv2417c   \n",
       "38  2715345            T    C           None      None  Rv2416c_Rv2417c   \n",
       "39  2715346            G    A           None      None  Rv2416c_Rv2417c   \n",
       "40  2715347            G    A           None      None  Rv2416c_Rv2417c   \n",
       "41  2715347            G    C           None      None  Rv2416c_Rv2417c   \n",
       "42  2715353            G    A           None      None  Rv2416c_Rv2417c   \n",
       "43  2715355            C    A           None      None  Rv2416c_Rv2417c   \n",
       "44  2715362           AC    A           None      None  Rv2416c_Rv2417c   \n",
       "45  2715365            T    C           None      None  Rv2416c_Rv2417c   \n",
       "46  2715369            C    A           None      None  Rv2416c_Rv2417c   \n",
       "47  2715375            T    C           None      None  Rv2416c_Rv2417c   \n",
       "48  2715376            A    G           None      None  Rv2416c_Rv2417c   \n",
       "49  2715382            A    G           None      None  Rv2416c_Rv2417c   \n",
       "\n",
       "   gene_pos mut_type codon_pos  num_isolates  num_sublineages  \\\n",
       "0      1030      ins       344             3                1   \n",
       "1       999      ins       333             1                1   \n",
       "2       949      del       317             4                1   \n",
       "3       900      del       300             2                1   \n",
       "4       884        N     W295*             1                1   \n",
       "5       807      del       269           112                1   \n",
       "6       790        N     R264*             1                1   \n",
       "7       776      del       259             1                1   \n",
       "8       669        N     Y223*             2                1   \n",
       "9       593      del       198             2                1   \n",
       "10      580        N     Q194*            10                2   \n",
       "11      573      del       191             1                1   \n",
       "12      546        N     W182*            15                1   \n",
       "13      486      ins       162             2                1   \n",
       "14      473      ins       158             1                1   \n",
       "15      448      del       150             7                1   \n",
       "16      367      del       123             1                1   \n",
       "17      124      del        42             1                1   \n",
       "18      119      del        40             2                1   \n",
       "19       46      ins        16             3                2   \n",
       "20       42      ins        14             1                1   \n",
       "21       39        N      W13*             1                1   \n",
       "22       38        N      W13*             1                1   \n",
       "23       28        N      E10*             6                2   \n",
       "24       16      ins         6             1                1   \n",
       "25       14      ins         5             1                1   \n",
       "26        3        S       V1V             6                2   \n",
       "27     None        I      None             2                2   \n",
       "28     None      del      None             1                1   \n",
       "29     None        I      None             1                1   \n",
       "30     None        I      None             2                1   \n",
       "31     None      del      None             7                3   \n",
       "32     None        I      None             9                3   \n",
       "33     None        I      None            11                4   \n",
       "34     None        I      None             1                1   \n",
       "35     None        I      None           293               18   \n",
       "36     None        I      None            11                5   \n",
       "37     None        I      None           332               17   \n",
       "38     None        I      None             3                1   \n",
       "39     None        I      None           181               19   \n",
       "40     None        I      None             1                1   \n",
       "41     None        I      None            16                6   \n",
       "42     None        I      None             1                1   \n",
       "43     None        I      None             5                1   \n",
       "44     None      del      None             1                1   \n",
       "45     None        I      None             2                1   \n",
       "46     None        I      None           285                8   \n",
       "47     None        I      None             1                1   \n",
       "48     None        I      None             4                1   \n",
       "49     None        I      None             1                1   \n",
       "\n",
       "                                          sublineages  \n",
       "0                                     4.1.i1.1.1.1(3)  \n",
       "1                                            3.1.1(1)  \n",
       "2                                          4.4.1.1(4)  \n",
       "3                                       4.1.i1.2.1(2)  \n",
       "4                                            3.1.1(1)  \n",
       "5                                      2.2.1.1.1(112)  \n",
       "6                                            3.1.1(1)  \n",
       "7                                        2.2.1.1.1(1)  \n",
       "8                                            3.1.1(2)  \n",
       "9                                            1.1.2(2)  \n",
       "10                            4.10.i1(1) - 1.2.2.1(9)  \n",
       "11                                       2.2.1.1.1(1)  \n",
       "12                                        4.10.i1(15)  \n",
       "13                                    4.1.i1.1.1.1(2)  \n",
       "14                                    2.2.1.1.1.i3(1)  \n",
       "15                                    2.2.1.1.1.i3(7)  \n",
       "16                                      4.1.i1.1.2(1)  \n",
       "17                                             4.5(1)  \n",
       "18                                       2.2.1.1.1(2)  \n",
       "19                             4.4.1.1(1) - 4.3.i2(2)  \n",
       "20                                         1.1.1.2(1)  \n",
       "21                                       2.2.1.1.1(1)  \n",
       "22                                       2.2.1.1.1(1)  \n",
       "23                              1.1.3(5) - 1.1.1.1(1)  \n",
       "24                                       2.2.1.1.1(1)  \n",
       "25                                       2.2.1.1.1(1)  \n",
       "26                       2.2.1.1.1.i3(5) - 1.1.1.1(1)  \n",
       "27                           4.3.i3.1(1) - 1.1.1.1(1)  \n",
       "28                                    2.2.1.1.1.i3(1)  \n",
       "29                                           3.1.1(1)  \n",
       "30                                        4.3.i4.1(2)  \n",
       "31          2.2.1.1.1.i3(1) - 2.2.1.1.1(5) - 4.4.2(1)  \n",
       "32       4.3.i4.1(1) - 4.1.i1.1.1.1(7) - 2.2.1.1.2(1)  \n",
       "33  4.11(2) - 2.2.1.1.1.i3(5) - 4.3.i3.1(3) - 2.2....  \n",
       "34                                       2.2.1.1.1(1)  \n",
       "35  4.11(1) - 2.2.1.1.1.i3(58) - 4.3.i3.1(14) - 4....  \n",
       "36  4.5(2) - 4.10.i1(1) - 4.3.i3.1(4) - 2.2.1.1.1(...  \n",
       "37  4.10.i2(1) - 2.2.1.1.1.i3(70) - 4.3.i3.1(50) -...  \n",
       "38                                    2.2.1.1.1.i3(3)  \n",
       "39  2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...  \n",
       "40                                           3.1.1(1)  \n",
       "41  4.11(3) - 2.2.1.1.1.i3(2) - 4.3.i3.1(1) - 4.1....  \n",
       "42                                      4.1.i1.1.2(1)  \n",
       "43                                    2.2.1.1.1.i3(5)  \n",
       "44                                        4.3.i3.2(1)  \n",
       "45                                        4.3.i4.2(2)  \n",
       "46  4.11(4) - 2.2.1.1.1.i3(230) - 4.1.i1.1.1.1(1) ...  \n",
       "47                                        4.3.i4.2(1)  \n",
       "48                                      4.1.i1.2.1(4)  \n",
       "49                                      4.1.i1.2.1(1)  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulator_regulon_mutations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_regulon_mutations_df.to_excel('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/mutations_detected_within_isolates/promoter_eis_gene_body_mutations_in_31440_isolates.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save DataFrame for *regulator* & *regulon* mutations for *Table 1*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**REGULATOR** Subset to the 4 most commonly occurring mutations in the regulator regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_mutations_df_subset = regulator_regulon_mutations_df[regulator_regulon_mutations_df.gene_id == regulator_id].sort_values(by = 'num_isolates', ascending = False).head(n=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**REGULON** Subset to mutations that occured in at least 2 Mtb sub-lineages in the regulon regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulon_mutations_df_subset = regulator_regulon_mutations_df[regulator_regulon_mutations_df.gene_id == regulon_id]\n",
    "regulon_mutations_df_subset = regulon_mutations_df_subset[regulon_mutations_df_subset.num_sublineages >= 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export as excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_regulon_subset_mutations_df = regulator_mutations_df_subset.append(regulon_mutations_df_subset)\n",
    "regulator_regulon_subset_mutations_df.sort_values(by = 'pos', ascending = True, inplace = True)\n",
    "regulator_regulon_subset_mutations_df.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714753</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>580</td>\n",
       "      <td>N</td>\n",
       "      <td>Q194*</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.10.i1(1) - 1.2.2.1(9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2715287</td>\n",
       "      <td>T</td>\n",
       "      <td>TC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>46</td>\n",
       "      <td>ins</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4.1.1(1) - 4.3.i2(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2715305</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>28</td>\n",
       "      <td>N</td>\n",
       "      <td>E10*</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.1.3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2715330</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>V1V</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2.1.1.1.i3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2715342</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>293</td>\n",
       "      <td>18</td>\n",
       "      <td>4.11(1) - 2.2.1.1.1.i3(58) - 4.3.i3.1(14) - 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2715344</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>332</td>\n",
       "      <td>17</td>\n",
       "      <td>4.10.i2(1) - 2.2.1.1.1.i3(70) - 4.3.i3.1(50) -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>19</td>\n",
       "      <td>2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2715369</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>285</td>\n",
       "      <td>8</td>\n",
       "      <td>4.11(4) - 2.2.1.1.1.i3(230) - 4.1.i1.1.1.1(1) ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pos ref alt  gene_category gene_name          gene_id gene_pos  \\\n",
       "0  2714753   G   A  Non-Essential       eis          Rv2416c      580   \n",
       "1  2715287   T  TC  Non-Essential       eis          Rv2416c       46   \n",
       "2  2715305   C   A  Non-Essential       eis          Rv2416c       28   \n",
       "3  2715330   C   T  Non-Essential       eis          Rv2416c        3   \n",
       "4  2715342   C   T           None      None  Rv2416c_Rv2417c     None   \n",
       "5  2715344   G   A           None      None  Rv2416c_Rv2417c     None   \n",
       "6  2715346   G   A           None      None  Rv2416c_Rv2417c     None   \n",
       "7  2715369   C   A           None      None  Rv2416c_Rv2417c     None   \n",
       "\n",
       "  mut_type codon_pos  num_isolates  num_sublineages  \\\n",
       "0        N     Q194*            10                2   \n",
       "1      ins        16             3                2   \n",
       "2        N      E10*             6                2   \n",
       "3        S       V1V             6                2   \n",
       "4        I      None           293               18   \n",
       "5        I      None           332               17   \n",
       "6        I      None           181               19   \n",
       "7        I      None           285                8   \n",
       "\n",
       "                                         sublineages  \n",
       "0                            4.10.i1(1) - 1.2.2.1(9)  \n",
       "1                             4.4.1.1(1) - 4.3.i2(2)  \n",
       "2                              1.1.3(5) - 1.1.1.1(1)  \n",
       "3                       2.2.1.1.1.i3(5) - 1.1.1.1(1)  \n",
       "4  4.11(1) - 2.2.1.1.1.i3(58) - 4.3.i3.1(14) - 4....  \n",
       "5  4.10.i2(1) - 2.2.1.1.1.i3(70) - 4.3.i3.1(50) -...  \n",
       "6  2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...  \n",
       "7  4.11(4) - 2.2.1.1.1.i3(230) - 4.1.i1.1.1.1(1) ...  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulator_regulon_subset_mutations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "regulator_regulon_subset_mutations_df.to_excel('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/mutations_detected_within_isolates/promoter_eis_gene_body_mutations_in_31440_isolates_for_Table_1.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct DataFrame (which displays each pair of co-occuring mutations as a seperate row with info for each pair of mutations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "variant_mutation_pair_df = pd.DataFrame(columns=['A_type', 'A_pos', 'A_ref', 'A_alt', 'A_mut_type', 'A_codon_pos', 'A_gene_id', 'A_gene_name', 'A_gene_pos' ,'A_num_isolates', 'B_type', 'B_pos', 'B_ref', 'B_alt', 'B_mut_type', 'B_codon_pos', 'B_gene_id', 'B_gene_name', 'B_gene_pos', 'B_num_isolates', 'num_isolates_co_occur', 'isolate_IDs', 'sublineages'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the ordering of the INDELs genotypes columns in terms of the SNPs genotypes columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_isolate_annotation_DF['isolate_order'] = SNP_isolate_annotation_DF.index\n",
    "SNP_isolate_annotation_DF.set_index('isolate_ID', drop = True, inplace = True)\n",
    "INDEL_isolate_ordering_for_SNP_genotypes_array = SNP_isolate_annotation_DF.loc[INDEL_isolate_annotation_DF.isolate_ID, :].isolate_order.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  188,  2733, 24409, ..., 31437, 31438, 31439])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_isolate_ordering_for_SNP_genotypes_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill in dataframe by quering the genotypes matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SNPs or INDELs in regulator x SNPs or INDELs in regulon\n",
    "regulator_mut_keys = list(regulator_SNPs_DF.key) + list(regulator_INDELs_DF.key)\n",
    "regulon_mut_keys = list(regulon_SNPs_DF.key) + list(regulon_INDELs_DF.key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#index for DataFrame\n",
    "variant_mutation_pair_df_index = 0\n",
    "\n",
    "for variant_i in regulator_mut_keys: #iterate through regulator SNPs & INDELs\n",
    "    \n",
    "    if variant_i.count('_') == 1: # if variant in regulator is a SNP\n",
    "\n",
    "        SNP_i_pos = int(variant_i.split('_')[0])\n",
    "        SNP_i_alt_allele = variant_i.split('_')[1]\n",
    "\n",
    "        #get the number code that corresponds to the alt allele, 9 indicated bad quality call\n",
    "        base_code_dict = {'A':0, 'C':1, 'G':2, 'T':3}\n",
    "        SNP_i_alt_allele_code = base_code_dict[SNP_i_alt_allele]\n",
    "\n",
    "        #genotypes for each SNP site\n",
    "        genotypes_for_SNP_i = SNP_genotypes_array[SNP_annotation_DF[SNP_annotation_DF.pos == SNP_i_pos].array_index.values[0] , :]\n",
    "\n",
    "        #re-order isolates according to the order of the columns in the INDEL genotypes matrix\n",
    "        genotypes_for_SNP_i = genotypes_for_SNP_i[INDEL_isolate_ordering_for_SNP_genotypes_array]\n",
    "\n",
    "        for variant_j in regulon_mut_keys: #iterate through mutations in regulon\n",
    "\n",
    "            if variant_j.count('_') == 1: #if variant in regulon is a SNP\n",
    "\n",
    "                SNP_j_pos = int(variant_j.split('_')[0])\n",
    "                SNP_j_alt_allele = variant_j.split('_')[1]\n",
    "\n",
    "                #get the number code that corresponds to the alt allele, 9 indicated bad quality call\n",
    "                SNP_j_alt_allele_code = base_code_dict[SNP_j_alt_allele]\n",
    "\n",
    "                #genotypes for each SNP site\n",
    "                genotypes_for_SNP_j = SNP_genotypes_array[SNP_annotation_DF[SNP_annotation_DF.pos == SNP_j_pos].array_index.values[0] , :]\n",
    "\n",
    "                #re-order isolates according to the order of the columns in the INDEL genotypes matrix\n",
    "                genotypes_for_SNP_j = genotypes_for_SNP_j[INDEL_isolate_ordering_for_SNP_genotypes_array]\n",
    "\n",
    "                #boolean filter of isolates with both variants\n",
    "                isolates_with_both_variants_filter = [(SNP_i_called and SNP_j_called) for SNP_i_called , SNP_j_called in zip(list(genotypes_for_SNP_i == SNP_i_alt_allele_code) , list(genotypes_for_SNP_j == SNP_j_alt_allele_code))]\n",
    "\n",
    "                #number of isolates that have both variants\n",
    "                num_isolates_with_both_variants = np.sum(isolates_with_both_variants_filter)\n",
    "\n",
    "                #check to see if the number of isolates carrying both variants was >= 1\n",
    "                if num_isolates_with_both_variants >= 1:\n",
    "\n",
    "                    # get info for mutation in regulator\n",
    "                    regulator_SNP_info = list(regulator_SNPs_DF[(regulator_SNPs_DF.pos == SNP_i_pos) & (regulator_SNPs_DF.alt == SNP_i_alt_allele)].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    # get info for mutation in regulon\n",
    "                    regulon_SNP_info = list(regulon_SNPs_DF[(regulon_SNPs_DF.pos == SNP_j_pos) & (regulon_SNPs_DF.alt == SNP_j_alt_allele)].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    strain_names = [' - '.join(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].isolate_ID))]\n",
    "\n",
    "                    # sublineage calls\n",
    "                    strain_lineage_calls_count_dict = Counter(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].lineage_call))\n",
    "                    strain_lineage_calls_list = []\n",
    "                    for sublineage_i in strain_lineage_calls_count_dict.keys():\n",
    "                        sublineage_i_with_mutations = sublineage_i + '({0})'.format(str(strain_lineage_calls_count_dict[sublineage_i]))\n",
    "                        strain_lineage_calls_list.append(sublineage_i_with_mutations)\n",
    "                    strain_lineage_calls = ' - '.join(strain_lineage_calls_list)\n",
    "\n",
    "                    co_occurring_SNP_i_and_SNP_or_indel_j_info = ['SNP'] + regulator_SNP_info + ['SNP'] + regulon_SNP_info + [num_isolates_with_both_variants] + strain_names + [strain_lineage_calls]\n",
    "                    variant_mutation_pair_df.loc[variant_mutation_pair_df_index, :] = co_occurring_SNP_i_and_SNP_or_indel_j_info\n",
    "                    variant_mutation_pair_df_index += 1\n",
    "\n",
    "            if variant_j.count('_') == 2: #if variant in regulon is an INDEL\n",
    "\n",
    "                #genotypes for each INDEL\n",
    "                genotypes_for_indel_j = INDEL_genotypes_array[INDEL_annotation_DF[INDEL_annotation_DF.key == variant_j].index[0] , :]\n",
    "\n",
    "                #boolean filter of isolates with both variants\n",
    "                isolates_with_both_variants_filter = [(SNP_i_called and indel_j_called) for SNP_i_called , indel_j_called in zip(list(genotypes_for_SNP_i == SNP_i_alt_allele_code) , list(genotypes_for_indel_j == 1))]\n",
    "\n",
    "                #number of isolates that have both variants\n",
    "                num_isolates_with_both_variants = np.sum(isolates_with_both_variants_filter)\n",
    "\n",
    "                #check to see if the number of isolates carrying both variants was >= 1\n",
    "                if num_isolates_with_both_variants >= 1:\n",
    "\n",
    "                    # get info for mutation in regulator\n",
    "                    regulator_SNP_info = list(regulator_SNPs_DF[(regulator_SNPs_DF.pos == SNP_i_pos) & (regulator_SNPs_DF.alt == SNP_i_alt_allele)].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    # get info for mutation in regulon\n",
    "                    regulon_INDEL_info = list(regulon_INDELs_DF[regulon_INDELs_DF.key == variant_j].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    strain_names = [' - '.join(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].isolate_ID))]\n",
    "\n",
    "                    # sublineage calls\n",
    "                    strain_lineage_calls_count_dict = Counter(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].lineage_call))\n",
    "                    strain_lineage_calls_list = []\n",
    "                    for sublineage_i in strain_lineage_calls_count_dict.keys():\n",
    "                        sublineage_i_with_mutations = sublineage_i + '({0})'.format(str(strain_lineage_calls_count_dict[sublineage_i]))\n",
    "                        strain_lineage_calls_list.append(sublineage_i_with_mutations)\n",
    "                    strain_lineage_calls = ' - '.join(strain_lineage_calls_list)\n",
    "\n",
    "                    co_occurring_SNP_i_and_SNP_or_indel_j_info = ['SNP'] + regulator_SNP_info + ['INDEL'] + regulon_INDEL_info + [num_isolates_with_both_variants] + strain_names + [strain_lineage_calls]\n",
    "                    variant_mutation_pair_df.loc[variant_mutation_pair_df_index, :] = co_occurring_SNP_i_and_SNP_or_indel_j_info\n",
    "                    variant_mutation_pair_df_index += 1\n",
    "    \n",
    "    elif variant_i.count('_') == 2: # if variant in regulator is an INDEL\n",
    "        \n",
    "        #genotypes for each INDEL\n",
    "        genotypes_for_indel_i = INDEL_genotypes_array[INDEL_annotation_DF[INDEL_annotation_DF.key == variant_i].index[0] , :]\n",
    "\n",
    "        for variant_j in regulon_mut_keys: #iterate through mutations in regulon\n",
    "\n",
    "            if variant_j.count('_') == 1: #if variant in regulon is a SNP\n",
    "\n",
    "                SNP_j_pos = int(variant_j.split('_')[0])\n",
    "                SNP_j_alt_allele = variant_j.split('_')[1]\n",
    "\n",
    "                #get the number code that corresponds to the alt allele, 9 indicated bad quality call\n",
    "                SNP_j_alt_allele_code = base_code_dict[SNP_j_alt_allele]\n",
    "\n",
    "                #genotypes for each SNP site\n",
    "                genotypes_for_SNP_j = SNP_genotypes_array[SNP_annotation_DF[SNP_annotation_DF.pos == SNP_j_pos].array_index.values[0] , :]\n",
    "\n",
    "                #re-order isolates according to the order of the columns in the INDEL genotypes matrix\n",
    "                genotypes_for_SNP_j = genotypes_for_SNP_j[INDEL_isolate_ordering_for_SNP_genotypes_array]\n",
    "\n",
    "                #boolean filter of isolates with both variants\n",
    "                isolates_with_both_variants_filter = [(indel_i_called and SNP_j_called) for indel_i_called , SNP_j_called in zip(list(genotypes_for_indel_i == 1) , list(genotypes_for_SNP_j == SNP_j_alt_allele_code))]\n",
    "\n",
    "                #number of isolates that have both variants\n",
    "                num_isolates_with_both_variants = np.sum(isolates_with_both_variants_filter)\n",
    "\n",
    "                #check to see if the number of isolates carrying both variants was >= 1\n",
    "                if num_isolates_with_both_variants >= 1:\n",
    "\n",
    "                    # get info for mutation in regulator\n",
    "                    regulator_INDEL_info = list(regulator_INDELs_DF[regulator_INDELs_DF.key == variant_i].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    # get info for mutation in regulon\n",
    "                    regulon_SNP_info = list(regulon_SNPs_DF[(regulon_SNPs_DF.pos == SNP_j_pos) & (regulon_SNPs_DF.alt == SNP_j_alt_allele)].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    strain_names = [' - '.join(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].isolate_ID))]\n",
    "\n",
    "                    # sublineage calls\n",
    "                    strain_lineage_calls_count_dict = Counter(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].lineage_call))\n",
    "                    strain_lineage_calls_list = []\n",
    "                    for sublineage_i in strain_lineage_calls_count_dict.keys():\n",
    "                        sublineage_i_with_mutations = sublineage_i + '({0})'.format(str(strain_lineage_calls_count_dict[sublineage_i]))\n",
    "                        strain_lineage_calls_list.append(sublineage_i_with_mutations)\n",
    "                    strain_lineage_calls = ' - '.join(strain_lineage_calls_list)\n",
    "\n",
    "                    co_occurring_SNP_i_and_SNP_or_indel_j_info = ['INDEL'] + regulator_INDEL_info + ['SNP'] + regulon_SNP_info + [num_isolates_with_both_variants] + strain_names + [strain_lineage_calls]\n",
    "                    variant_mutation_pair_df.loc[variant_mutation_pair_df_index, :] = co_occurring_SNP_i_and_SNP_or_indel_j_info\n",
    "                    variant_mutation_pair_df_index += 1\n",
    "\n",
    "            if variant_j.count('_') == 2: # if variant in regulon is an INDEL\n",
    "\n",
    "                #genotypes for each INDEL\n",
    "                genotypes_for_indel_j = INDEL_genotypes_array[INDEL_annotation_DF[INDEL_annotation_DF.key == variant_j].index[0] , :]\n",
    "\n",
    "                #boolean filter of isolates with both variants\n",
    "                isolates_with_both_variants_filter = [(indel_i_called and indel_j_called) for indel_i_called , indel_j_called in zip(list(genotypes_for_indel_i == 1) , list(genotypes_for_indel_j == 1))]\n",
    "\n",
    "                #number of isolates that have both variants\n",
    "                num_isolates_with_both_variants = np.sum(isolates_with_both_variants_filter)\n",
    "\n",
    "                #check to see if the number of isolates carrying both variants was >= 1\n",
    "                if num_isolates_with_both_variants >= 1:\n",
    "\n",
    "                    # get info for mutation in regulator\n",
    "                    regulator_INDEL_info = list(regulator_INDELs_DF[regulator_INDELs_DF.key == variant_i].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    # get info for mutation in regulon\n",
    "                    regulon_INDEL_info = list(regulon_INDELs_DF[regulon_INDELs_DF.key == variant_j].loc[:, ['pos','ref','alt','mut_type','codon_pos','gene_id','gene_name','gene_pos','num_isolates']].values[0])\n",
    "\n",
    "                    strain_names = [' - '.join(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].isolate_ID))]\n",
    "\n",
    "                    # sublineage calls\n",
    "                    strain_lineage_calls_count_dict = Counter(list(INDEL_isolate_annotation_DF[isolates_with_both_variants_filter].lineage_call))\n",
    "                    strain_lineage_calls_list = []\n",
    "                    for sublineage_i in strain_lineage_calls_count_dict.keys():\n",
    "                        sublineage_i_with_mutations = sublineage_i + '({0})'.format(str(strain_lineage_calls_count_dict[sublineage_i]))\n",
    "                        strain_lineage_calls_list.append(sublineage_i_with_mutations)\n",
    "                    strain_lineage_calls = ' - '.join(strain_lineage_calls_list)\n",
    "\n",
    "                    co_occurring_SNP_i_and_SNP_or_indel_j_info = ['INDEL'] + regulator_INDEL_info + ['INDEL'] + regulon_INDEL_info + [num_isolates_with_both_variants] + strain_names + [strain_lineage_calls]\n",
    "                    variant_mutation_pair_df.loc[variant_mutation_pair_df_index, :] = co_occurring_SNP_i_and_SNP_or_indel_j_info\n",
    "                    variant_mutation_pair_df_index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_type</th>\n",
       "      <th>A_pos</th>\n",
       "      <th>A_ref</th>\n",
       "      <th>A_alt</th>\n",
       "      <th>A_mut_type</th>\n",
       "      <th>A_codon_pos</th>\n",
       "      <th>A_gene_id</th>\n",
       "      <th>A_gene_name</th>\n",
       "      <th>A_gene_pos</th>\n",
       "      <th>A_num_isolates</th>\n",
       "      <th>...</th>\n",
       "      <th>B_alt</th>\n",
       "      <th>B_mut_type</th>\n",
       "      <th>B_codon_pos</th>\n",
       "      <th>B_gene_id</th>\n",
       "      <th>B_gene_name</th>\n",
       "      <th>B_gene_pos</th>\n",
       "      <th>B_num_isolates</th>\n",
       "      <th>num_isolates_co_occur</th>\n",
       "      <th>isolate_IDs</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAMN08376196</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>W13*</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAMN08709032</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>T</td>\n",
       "      <td>S</td>\n",
       "      <td>V1V</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>SAMN02419586 - SAMN02419559 - SAMN02419535 - S...</td>\n",
       "      <td>2.2.1.1.1.i3(5) - 1.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>GCT</td>\n",
       "      <td>ins</td>\n",
       "      <td>162</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>486</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3354 - Peru2946</td>\n",
       "      <td>4.1.i1.1.1.1(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>AT</td>\n",
       "      <td>ins</td>\n",
       "      <td>158</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>473</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAMN07956543</td>\n",
       "      <td>2.2.1.1.1.i3(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>del</td>\n",
       "      <td>150</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>448</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>SAMN02584676 - SAMEA1016073 - SAMN04633319 - S...</td>\n",
       "      <td>2.2.1.1.1.i3(7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>AC</td>\n",
       "      <td>ins</td>\n",
       "      <td>6</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAMN06210015</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SNP</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>AC</td>\n",
       "      <td>ins</td>\n",
       "      <td>5</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>eis</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAMN02584612</td>\n",
       "      <td>2.2.1.1.1(1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  A_type    A_pos A_ref A_alt A_mut_type A_codon_pos        A_gene_id  \\\n",
       "0    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "1    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "2    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "3    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "4    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "5    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "6    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "7    SNP  2715346     G     A          I        None  Rv2416c_Rv2417c   \n",
       "\n",
       "  A_gene_name A_gene_pos A_num_isolates              ...              B_alt  \\\n",
       "0        None       None            181              ...                  T   \n",
       "1        None       None            181              ...                  T   \n",
       "2        None       None            181              ...                  T   \n",
       "3        None       None            181              ...                GCT   \n",
       "4        None       None            181              ...                 AT   \n",
       "5        None       None            181              ...                  G   \n",
       "6        None       None            181              ...                 AC   \n",
       "7        None       None            181              ...                 AC   \n",
       "\n",
       "  B_mut_type B_codon_pos B_gene_id B_gene_name B_gene_pos B_num_isolates  \\\n",
       "0          N        W13*   Rv2416c         eis         39              1   \n",
       "1          N        W13*   Rv2416c         eis         38              1   \n",
       "2          S         V1V   Rv2416c         eis          3              6   \n",
       "3        ins         162   Rv2416c         eis        486              2   \n",
       "4        ins         158   Rv2416c         eis        473              1   \n",
       "5        del         150   Rv2416c         eis        448              7   \n",
       "6        ins           6   Rv2416c         eis         16              1   \n",
       "7        ins           5   Rv2416c         eis         14              1   \n",
       "\n",
       "  num_isolates_co_occur                                        isolate_IDs  \\\n",
       "0                     1                                       SAMN08376196   \n",
       "1                     1                                       SAMN08709032   \n",
       "2                     6  SAMN02419586 - SAMN02419559 - SAMN02419535 - S...   \n",
       "3                     2                                Peru3354 - Peru2946   \n",
       "4                     1                                       SAMN07956543   \n",
       "5                     7  SAMN02584676 - SAMEA1016073 - SAMN04633319 - S...   \n",
       "6                     1                                       SAMN06210015   \n",
       "7                     1                                       SAMN02584612   \n",
       "\n",
       "                    sublineages  \n",
       "0                  2.2.1.1.1(1)  \n",
       "1                  2.2.1.1.1(1)  \n",
       "2  2.2.1.1.1.i3(5) - 1.1.1.1(1)  \n",
       "3               4.1.i1.1.1.1(2)  \n",
       "4               2.2.1.1.1.i3(1)  \n",
       "5               2.2.1.1.1.i3(7)  \n",
       "6                  2.2.1.1.1(1)  \n",
       "7                  2.2.1.1.1(1)  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variant_mutation_pair_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 23)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(variant_mutation_pair_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save dataframe as CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "variant_mutation_pair_df.to_excel('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/co_occurring_mutations_within_isolates/eis_promoter_gene_body_co_occurence_mutations_in_31440_isolates.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2715346_A (*eis* -14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>array_index</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>2715346_A</td>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Rv2416c_Rv2417c</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "      <td>257</td>\n",
       "      <td>181</td>\n",
       "      <td>19</td>\n",
       "      <td>2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           key      pos ref alt gene_category gene_name          gene_id  \\\n",
       "273  2715346_A  2715346   G   A          None      None  Rv2416c_Rv2417c   \n",
       "\n",
       "    gene_pos mut_type codon_pos array_index  num_isolates  num_sublineages  \\\n",
       "273     None        I      None         257           181               19   \n",
       "\n",
       "                                           sublineages  \n",
       "273  2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) -...  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulator_SNPs_DF[(regulator_SNPs_DF.pos == 2715346) & (regulator_SNPs_DF.alt == 'A')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1.1.1.i3(89) - 4.3.i3.1(18) - 4.10.i2(4) - 4.1.i1.1.1.1(17) - 1.2.2.1(1) - 4.4.1.1(1) - 4.3.i4.2(1) - 4.2.1.1(1) - 2.2.1.1.1(27) - 3.1.1(2) - 2.2.1.1.2(2) - 4.1.i1.2.1(9) - 4.2.1.2.1.1.i4(2) - 4.1.i1.2.2(1) - 2.2.1.2(1) - 1.1.1.1(1) - 3.2.2(1) - 2.2.2(2) - 4.2.1.2.2.1(1)\n"
     ]
    }
   ],
   "source": [
    "print regulator_SNPs_DF[(regulator_SNPs_DF.pos == 2715346) & (regulator_SNPs_DF.alt == 'A')].sublineages.values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GA_2714885_G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>codon_pos</th>\n",
       "      <th>num_isolates</th>\n",
       "      <th>num_sublineages</th>\n",
       "      <th>sublineages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GA_2714885_G</td>\n",
       "      <td>2714885</td>\n",
       "      <td>GA</td>\n",
       "      <td>G</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>448</td>\n",
       "      <td>del</td>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2.1.1.1.i3(7)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             key      pos ref alt  gene_category gene_name  gene_id gene_pos  \\\n",
       "14  GA_2714885_G  2714885  GA   G  Non-Essential       eis  Rv2416c      448   \n",
       "\n",
       "   mut_type codon_pos  num_isolates  num_sublineages      sublineages  \n",
       "14      del       150             7                1  2.2.1.1.1.i3(7)  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regulon_INDELs_DF[regulon_INDELs_DF.key == 'GA_2714885_G']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [5] Get strains with *eis* promoter SNPs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Function* to get the strains with a specific mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_strain_names_for_SNV(SNP_i_pos, SNP_i_alt_allele):\n",
    "    \n",
    "    #get the number code that corresponds to the alt allele, 9 indicated bad quality call\n",
    "    base_code_dict = {'A':0, 'C':1, 'G':2, 'T':3}\n",
    "    \n",
    "    #get the number code that corresponds to the alternate allele, 9 indicated bad quality call\n",
    "    SNP_i_alt_allele_code = base_code_dict[SNP_i_alt_allele]\n",
    "\n",
    "    #genotypes for each SNP site\n",
    "    genotypes_for_SNP_i = SNP_genotypes_array[SNP_annotation_DF[SNP_annotation_DF.pos == SNP_i_pos].array_index.values[0] , :]\n",
    "\n",
    "    #boolean filter of isolates with variant\n",
    "    isolates_with_variant_filter = genotypes_for_SNP_i == SNP_i_alt_allele_code\n",
    "\n",
    "    #get list of isolates with variant\n",
    "    strain_names = list(SNP_isolate_annotation_DF[isolates_with_variant_filter].index)\n",
    "    \n",
    "    #get list of strains in phylogeny\n",
    "    strain_names = list(set(isolate_tags_in_phylogeny).intersection(set(strain_names)))\n",
    "    \n",
    "    #order strains according to terminal branches on phylogeny\n",
    "    isolate_tags_in_phylogeny_with_variant_filter = [(isolate_ID in strain_names) for isolate_ID in isolate_tags_in_phylogeny]\n",
    "    strain_names = np.array(isolate_tags_in_phylogeny)[isolate_tags_in_phylogeny_with_variant_filter]\n",
    "\n",
    "    return strain_names\n",
    "\n",
    "def get_strain_names_for_INDEL(INDEL_i_key):\n",
    "\n",
    "    #genotypes for each SNP site\n",
    "    genotypes_for_INDEL_i = INDEL_genotypes_array[INDEL_annotation_DF[INDEL_annotation_DF.key == INDEL_i_key].index[0] , :]\n",
    "\n",
    "    #boolean filter of isolates with variant\n",
    "    isolates_with_variant_filter = genotypes_for_INDEL_i == 1\n",
    "\n",
    "    #get list of isolates with variant\n",
    "    strain_names = list(INDEL_isolate_annotation_DF[isolates_with_variant_filter].isolate_ID)\n",
    "    \n",
    "    #get list of strains in phylogeny\n",
    "    strain_names = list(set(isolate_tags_in_phylogeny).intersection(set(strain_names)))\n",
    "    \n",
    "    #order strains according to terminal branches on phylogeny\n",
    "    isolate_tags_in_phylogeny_with_variant_filter = [(isolate_ID in strain_names) for isolate_ID in isolate_tags_in_phylogeny]\n",
    "    strain_names = np.array(isolate_tags_in_phylogeny)[isolate_tags_in_phylogeny_with_variant_filter]\n",
    "\n",
    "    return strain_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6] Explore the *eis* promoter SNP & *eis* mutations & explore phylogeny"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in phylogeny created by Luca tree pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to use Biopython's *Phylo* module to load phylogenetic trees created by Luca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#path to tree\n",
    "phylogeny_path = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/phylogenies/tree_output_files/phylogeny_lineage_2.2.1.1.1.i3_cluster/tree_lineage_2.2.1.1.1.i3_cluster_iqtree_FINAL.treefile'\n",
    "\n",
    "#parses and load tree\n",
    "tree = Phylo.parse(phylogeny_path , 'newick').next() \n",
    "\n",
    "#root the tree with the outgroup M. canettii [\"Normally you will want the outgroup to be a monophyletic group, rather than a single taxon.\"]\n",
    "tree.root_with_outgroup({\"name\":\"canettii\"})\n",
    "\n",
    "#flip branches so deeper clades are displayed at top\n",
    "tree.ladderize()\n",
    "\n",
    "'''\n",
    "Can choose to root the tree at the midpoint instead\n",
    "#root the tree at the midpoint [\"Root the tree at the midpoint of the two most distant taxa.\"]\n",
    "tree.root_at_midpoint()\n",
    "'''\n",
    "\n",
    "#retrieves the terminal branches of the tree\n",
    "terminal_branches = tree.get_terminals() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of branches on this tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(terminal_branches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the isolates that were used for this tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_tags_in_phylogeny = [terminal_branches[i].name for i in range(0 , len(terminal_branches))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(isolate_tags_in_phylogeny) #+1 for M. canetti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['canettii', 'SAMN08708358', 'SAMN08709231', 'SAMN08708977', 'SAMN08709035']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_tags_in_phylogeny[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use dataframe matrix to pull the isolate IDs that had specific mutations called"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different mutations that we're interested in - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [1] *promoter SNP* 2715346, 'A', -14A eis-Rv2417c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_2715346_A_strain_names = get_strain_names_for_SNV(2715346, 'A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SNP_2715346_A_strain_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SAMEA2535204' 'NLA000500538' 'NLA000401210' 'SAMEA3402474'\n",
      " 'SAMN08709254' 'IT634' 'SAMN04633299' 'SAMN08708268' 'SAMN08708369'\n",
      " 'SAMEA3558426' 'SAMN08708602' 'IT233' 'SAMEA1102554' 'SAMN08709192'\n",
      " 'SAMN09914650' 'SAMN04633291' 'IT77' 'SAMEA1102358' 'SAMEA1016073'\n",
      " 'SAMN02584676' 'SAMN04633319' 'SAMEA1403645' 'SAMN02414859'\n",
      " 'SAMN02419543' 'SAMN02419586' 'SAMN02419535' 'SAMN02419559'\n",
      " 'SAMN02419574' 'SAMN02419643' 'SAMN02419582' 'SAMN02419641'\n",
      " 'SAMN02419604' 'SAMN02419545' 'SAMN02419550' 'SAMN02628508'\n",
      " 'SAMN02628483' 'SAMN02628484' 'SAMN08347767' 'SAMEA3367276'\n",
      " 'SAMN08347734' 'SAMN09399503' 'SAMN09399504' 'IT947' '622-19'\n",
      " 'SAMEA3420946' 'SAMEA3443173' 'SAMEA3420955' 'SAMEA3444106'\n",
      " 'SAMEA3421124' 'SAMEA3442605' 'SAMEA3436395' 'SAMEA3443144' 'IT1070'\n",
      " 'SAMN07956560' 'SAMN07956543' 'SAMN07956617' 'SAMN08143531'\n",
      " '1479150601544T895915lib5741nextseqn0051151bp' 'SAMN08636976'\n",
      " 'SAMN13169633' 'SAMN06849040' 'SAMN09715570' 'SAMN09715516'\n",
      " 'SAMN06849035' 'SAMN12347358' 'SAMEA1403685' 'SAMN10593557'\n",
      " 'SAMEA1403638' 'SAMEA1707505' 'SAMEA1469523' 'SAMN05001043'\n",
      " 'SAMN05001051' 'SAMEA1404026' 'SAMEA1403691' 'SAMEA1403827']\n"
     ]
    }
   ],
   "source": [
    "print(SNP_2715346_A_strain_names) #blue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [2] *gene body SNP that abolishes START codon* 2715330, 'T', eis 3C>T V1V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_2715330_T_strain_names = get_strain_names_for_SNV(2715330, 'T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SNP_2715330_T_strain_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IT634' 'SAMN02419543' 'SAMN02419586' 'SAMN02419535' 'SAMN02419559']\n"
     ]
    }
   ],
   "source": [
    "print(SNP_2715330_T_strain_names) #green labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [3] *gene body deletion* 'GA_2714885_G', eis 448delA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEL_GA_2714885_A_strain_names = get_strain_names_for_INDEL('GA_2714885_G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(INDEL_GA_2714885_A_strain_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SAMEA1016073' 'SAMN02584676' 'SAMN04633319' 'IT1070' 'SAMN07956543'\n",
      " 'SAMEA1403685' 'SAMEA1403638']\n"
     ]
    }
   ],
   "source": [
    "print(INDEL_GA_2714885_A_strain_names) #pink labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4] *gene body insertion* 'A_2714860_AT', eis 473insT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEL_A_2714860_AT_strain_names = get_strain_names_for_INDEL('A_2714860_AT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(INDEL_A_2714860_AT_strain_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SAMN07956543']\n"
     ]
    }
   ],
   "source": [
    "print(INDEL_A_2714860_AT_strain_names) #orange labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approximate TMRCA for clusters of isolates that carry the *promoter SNP* 2715346, 'A', -14A eis-Rv2417c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 30,099 sites used to construct the **2.2.1.1.1.i3 cluster** tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sites_to_construct_tree = 30099.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def est_TMRCA_for_isolates(clade_isolate_IDs):\n",
    "\n",
    "    # Find the MRCA clade of this block of isolates\n",
    "    MRCA_of_children = tree.common_ancestor(clade_isolate_IDs)\n",
    "\n",
    "    Phylo.draw(MRCA_of_children) #visualize sub-tree\n",
    "    Phylo.draw_ascii(MRCA_of_children) #visualize sub-tree\n",
    "\n",
    "    # Find the distance between this MRCA & all terminal isolates within this clade\n",
    "    ## distance is given in *substitutions per sequence site* (SNPs / site)\n",
    "    distance_between_MRCA_and_terminal_nodes = []\n",
    "    for isolate_ID in clade_isolate_IDs:\n",
    "\n",
    "        #get the distance between MRCA node & the root (i.e. tree.distance({\"name\":'SAMN03647821'} , {\"name\":'SAMEA1485499'}))\n",
    "        #[\"Calculate the sum of the branch lengths between two targets. If only one target is specified, the other is the root of this tree.\"]\n",
    "        dist_between_MRCA_and_terminal_node = tree.distance(MRCA_of_children , isolate_ID)\n",
    "        distance_between_MRCA_and_terminal_nodes.append(dist_between_MRCA_and_terminal_node)\n",
    "\n",
    "    # Find median distances between the MRCA & the terminal nodes\n",
    "    median_distance_between_MRCA_and_terminal_nodes = np.median(distance_between_MRCA_and_terminal_nodes) #SNPs / site\n",
    "    median_SNP_distance_between_MRCA_and_terminal_nodes = median_distance_between_MRCA_and_terminal_nodes * num_sites_to_construct_tree #median distance in SNPs\n",
    "\n",
    "    # Aim to get an estimate for how long ago the MRCA existed in evolutionary time (Range of molecular clocks estimates: 0.3 - 0.6 SNPs/genome/year)\n",
    "\n",
    "    # using slower clock estimate\n",
    "    median_time_from_MRCA_to_tips_slow_clock = median_SNP_distance_between_MRCA_and_terminal_nodes / 0.3\n",
    "\n",
    "    # using faster clock estimate\n",
    "    median_time_from_MRCA_to_tips_fast_clock = median_SNP_distance_between_MRCA_and_terminal_nodes / 0.6\n",
    "    \n",
    "    print str(median_SNP_distance_between_MRCA_and_terminal_nodes)\n",
    "    print ''\n",
    "    print str(median_time_from_MRCA_to_tips_fast_clock) + ' - ' + str(median_time_from_MRCA_to_tips_slow_clock) + ' years'\n",
    "        \n",
    "    '''    \n",
    "    # Average the distances between the MRCA & the terminal nodes\n",
    "    mean_distance_between_MRCA_and_terminal_nodes = np.mean(distance_between_MRCA_and_terminal_nodes) #SNPs / site\n",
    "    mean_SNP_distance_between_MRCA_and_terminal_nodes = mean_distance_between_MRCA_and_terminal_nodes * num_sites_to_construct_tree #mean distance in SNPs\n",
    "\n",
    "    # Aim to get an estimate for how long ago the MRCA existed in evolutionary time (Range of molecular clocks estimates: 0.3 - 0.6 SNPs/genome/year)\n",
    "\n",
    "    # using slower clock estimate\n",
    "    mean_time_from_MRCA_to_tips_slow_clock = mean_SNP_distance_between_MRCA_and_terminal_nodes / 0.3\n",
    "\n",
    "    # using faster clock estimate\n",
    "    mean_time_from_MRCA_to_tips_fast_clock = mean_SNP_distance_between_MRCA_and_terminal_nodes / 0.6\n",
    "    \n",
    "    print str(mean_SNP_distance_between_MRCA_and_terminal_nodes)\n",
    "    print ''\n",
    "    print str(mean_time_from_MRCA_to_tips_fast_clock) + ' - ' + str(mean_time_from_MRCA_to_tips_slow_clock) + ' years'\n",
    "    \n",
    "    #return [mean_time_from_MRCA_to_tips_slow_clock, mean_time_from_MRCA_to_tips_fast_clock]\n",
    "    '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ___________________________________ IT947\n",
      "  |\n",
      "  | _____________________________ 622-19\n",
      "__||\n",
      "  ||                           __________ SAMEA3420946\n",
      "  ||             _____________|\n",
      "  ||            |             |_________________ SAMEA3443173\n",
      "   |   _________|\n",
      "   |  |         |           ___________ SAMEA3420955\n",
      "   |  |         |__________|\n",
      "   |  |                    |____________________________________ SAMEA3444106\n",
      "   |__|\n",
      "      |             __________________ SAMEA3421124\n",
      "      |      ______|\n",
      "      |     |      |_____________________ SAMEA3442605\n",
      "      |_____|\n",
      "            |         ______________ SAMEA3436395\n",
      "            |________|\n",
      "                     |____________________________________ SAMEA3443144\n",
      "\n",
      "38.290092196649994\n",
      "\n",
      "63.816820327749994 - 127.63364065549999 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['IT947','622-19','SAMEA3443144','SAMEA3436395','SAMEA3442605','SAMEA3421124','SAMEA3444106','SAMEA3420955','SAMEA3443173','SAMEA3420946']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **C** (Previously Cluster B), *double* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 _____________________________________________ IT1070\n",
      "________________|\n",
      "                |         ___________________________________ SAMN07956560\n",
      "                |________|\n",
      "                         | _______________________ SAMN07956543\n",
      "                         ||\n",
      "                          |_______________________________________ SAMN07956617\n",
      "\n",
      "35.681690282400005\n",
      "\n",
      "59.46948380400001 - 118.93896760800003 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['IT1070','SAMN07956560','SAMN07956617','SAMN07956543']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               _________________________________________________ SAMN08636976\n",
      "    __________|\n",
      "   |          |_______________ SAMN13169633\n",
      "   |\n",
      "___|             _________________ SAMN06849040\n",
      "   |      ______|\n",
      "   |     |      |_______________________ SAMN09715570\n",
      "   |_____|\n",
      "         |          _____________________________________________ SAMN09715516\n",
      "         |_________|\n",
      "                   |   __________________ SAMN06849035\n",
      "                   |__|\n",
      "                      |________________________________ SAMN12347358\n",
      "\n",
      "35.602072407600005\n",
      "\n",
      "59.33678734600001 - 118.67357469200002 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN13169633','SAMN08636976','SAMN09715570','SAMN06849040','SAMN09715516','SAMN12347358','SAMN06849035']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **A** (Previously Cluster D), *single* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               ____ SAMN02628508\n",
      "______________________________|\n",
      "                              |     ________________________ SAMN02628483\n",
      "                              |____|\n",
      "                                   |______________________________ SAMN02628484\n",
      "\n",
      "15.108463940999998\n",
      "\n",
      "25.180773234999997 - 50.36154646999999 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN02628483','SAMN02628484','SAMN02628508']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ______________ SAMN02419543\n",
      "  ______|\n",
      " |      |  _____________________________________________________ SAMN02419586\n",
      " |      |_|\n",
      " |        |  ___________________________ SAMN02419535\n",
      " |        |_|\n",
      "_|          |_______________________ SAMN02419559\n",
      " |\n",
      " |           ___________ SAMN02419574\n",
      " |    ______|\n",
      " |   |      |_____________ SAMN02419643\n",
      " |   |\n",
      " |___|      ______________ SAMN02419582\n",
      "     |   __|\n",
      "     |  |  |______________ SAMN02419641\n",
      "     |__|\n",
      "        |__________________________ SAMN02419604\n",
      "        |\n",
      "        |        _____________ SAMN02419545\n",
      "        |_______|\n",
      "                |______________ SAMN02419550\n",
      "\n",
      "65.6795937612\n",
      "\n",
      "109.465989602 - 218.931979204 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN02419545','SAMN02419550','SAMN02419604','SAMN02419582','SAMN02419641','SAMN02419574','SAMN02419643','SAMN02419535','SAMN02419559','SAMN02419586','SAMN02419543']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ___________________________________________ SAMEA1403645\n",
      "________|\n",
      "        |                                 ________________________ SAMN02414851\n",
      "        |________________________________|\n",
      "                                         |_______________________ SAMN02414859\n",
      "\n",
      "29.156750805\n",
      "\n",
      "48.59458467500001 - 97.18916935000001 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN02414859','SAMEA1403645']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **D** (Previously Cluster G), *double* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      __________ SAMEA1016073\n",
      "_____|\n",
      "     |    _________ SAMN02584676\n",
      "     |___|\n",
      "         |________________________________________________________ SAMN04633319\n",
      "\n",
      "25.413067284\n",
      "\n",
      "42.35511214 - 84.71022428 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN02584676','SAMN04633319','SAMEA1016073']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   _________________ SAMN09914650\n",
      "__|\n",
      "  |        _______________________________________________________ SAMN04633291\n",
      "  |_______|\n",
      "          |   , IT77\n",
      "          |___|\n",
      "              |__ SAMEA1102358\n",
      "\n",
      "17.348828827800002\n",
      "\n",
      "28.914714713000006 - 57.82942942600001 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['IT77','SAMEA1102358','SAMN04633291','SAMN09914650']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **E** (Previously Cluster I), *double* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ________________________ IT634\n",
      "       |\n",
      "_______|________________________________________________ SAMN04633299\n",
      "       |\n",
      "       |           __________________________ SAMN08708268\n",
      "       |    ______|\n",
      "       |   |      |______________________ SAMN08708369\n",
      "       |___|\n",
      "           |_____________________________________________________ SAMEA3558426\n",
      "           |\n",
      "           |         __________________ SAMN08708602\n",
      "           |________|\n",
      "                    |                  , IT233\n",
      "                    |__________________|\n",
      "                                       | SAMEA1102554\n",
      "\n",
      "32.562752140049994\n",
      "\n",
      "54.27125356674999 - 108.54250713349998 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['IT233','SAMEA1102554','SAMN08708602','SAMEA3558426','SAMN08708268','SAMN08708369','SAMN04633299','IT634']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster J)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gs2Yt2L17l798yJAh3HDDAObM+YBx48by6qtv5rqvlSuX0b79VQBUqlSZ9eu3cdFFYWzatIGbb+5LXNyaTKMcEZGCpkNY+axLl6tZvHgFixYt4+KL61K7dp3T1omN7c2iRQvz1O6yZUu46irn/EfJkiW56KIwAKKiLiciIpKffvrx7wcvIpIH5+wIZPbs2SQmphRoH7fc0v+0ZYmJiYSHh3Pw4AHeeWcSkyZNAWDHjh8JD78cgCVLPssysWTH6/Xy7bfbuPTSywDYv38/5cuXJygoiF27drJjx0/UqhXxt9+PSGFo0uRSLrusMe+88x4ACxbM5/PPF/PKKxOZOXM6Gzeu57nnXjyt3pYtm+jQoS0zZ871f5kCWL58CY899hBpaWn88583M3To/YBzTnLw4EEcOHCAZs2aMm7c65QoUYLjx49z992D2bRpAxdddBFvvTWFmjVrsWfPbtq0aUadOnV9cTbzX6CyadMGhg69k6NHj9KxY2eefnoMHo+HAweSue22Qfz8825q1KjFpElTKFeuPKtXxzFgwE3UrFkLgH/8owfDhj3MsWPH6NmzK8ePnwBOcfXVPXjooccA+O9/VzJq1AhOnTrFhRdeyIQJb+Tp70RRKJQRiDEmzBjzqTHGGmM2G2PmGmPCC6PvwjZ8+IO0adOM7t07M3Toff6dcfLkt2jYsCExMa2ZOPFVXnllIgAJCfHcdNN1/vqDBw+iW7eO/PjjD0RF1Wf69Kls2rSBRo0u8594//LL1bRv34r27a/kllsGMHbsS5QvfxEAo0aNICqqPkeP/kFUVH3GjHmmkLeAyJlt2rSB7du/y1OduXNn06JFK+bNm+1flpaWxkMPPcCMGXP44ouvmTt3tv8S+dGjn2Dw4CGsWbOR8uXLM336VACmT59K2bLlWLt2E4MHD2H06Cf87WV3deODD97HCy+8zJo1G9mx4yeWL18CwIQJ42nXLpo1azbSrl00EyaM99dp2bKVv61hwx4GnKMHc+Z8wsqV/2Pjxo2sWLGUdevW+vt4441JrFixmmuvvZ7x48fmafsUhcIagXiBMdbalQDGmLHAc8AthdR/oXnzzXeyXP7002MIDw89bVRUuXIVZsyYk2P9cePGZPrG1aNHT3r06JllP088MZonnhjtJnSRQnPXXffw0ksvMHHi5Fyt7/V6WbDgIz78cD7XXNOVY8eOUapUKdavX0dkZG0iIiIBiI29jsWLF1KvnuGLL1b527/55pt59NHhDBp0K4sXL+Tf/3Yuo+/RoxePPDIMr9ebbd/79iWQkpJCs2bOvVk33HATn366kA4dOrN48ULmz/8UgD59+tKrVzcef/zJbNvyeDyEhIQAcPLkSU6eTPV/MfR4PKSk/A5ASsrvVK4c+PdyFcoIxFqbnJ48fL4CahVG3+eC++9/kNjY3kUdhki+ueaaa9myZRM7dvyUq/XXrPmKWrVqERlZm9at27B06eeAM4KvVq26f70qVaoSH7+X5ORkypQpS3Cw8x25evXqJCTEn1YnODiY0NAyJCcnA/ivbuzZ82q++up/AMTH76VKlWr+PqpWrUZCwl7AOWSdftl8pUqV2b9/v3+9devW0r79ldx447WZRltpaWnExLSmYsWKREfH0KRJMwDGj3+Vvn17ExVVnw8/nMnQoffldnMWmUI/B2KMKQbcCXxc2H1L3mR1jqcwlCgRXOBX0LkRiHGdDTFNnjzttHWCgoIYMuReJkwYR4cOnc7Y5rx5H9Krl3Oot1ev3nz44Uy6d78my5GDx+PJdjmQTVn2Vzfm1FZ2Lrssim++2UZISAhLl37GzTffxJo1G/3vfcWK1RQvnkb37tfw3Xff0qDBJUyc+Brvvz+bJk2a8eqrL/P4448yfvyrZ9w2RakoTqK/AhwG8rRlwsJC8txReHhonuvkRYkSwXnup6BjciOrmHr37s0PP1gaNmxYBBH9uW0DTSDGFegx/XX/KlbMQ4UKIdx11228+up4mjRpTKlSxQkPDyU0tBQXXFAiU520tDQ+/XQBS5YsZsKEF/F6vSQlJVGqFFxySV0++GC6f/3ff0+iTp0I6tePICXld8qXv4Dg4GC+/HIrNWpUJzw8lFq1avLHHwcID69Pamoqhw+nUK9eLV9ScG4C7tixHXXrXkxycjyNGhl++y3e38fhw8lERNQkPDyUypUrkZp6mCpVqhAfH0+lShUJDw/NFP9NN/XmkUeG4fEc999knK5Tpw6sXRtHgwa12b59G127Opfq33LLALp27RqQfy8yKtQ9zxjzAlAX6GGtPZWXuklJhzl1KvvjlH+V1fmG/Jb+LSu3/RRGTHmVXUwnTqRSt67hjTeyPqdTFDEVtUCM62yI6a/xnTrlZf/+w3i9Jbn11jsZN248bdq0IzExhZSUYxw9eiJTneXLl9KgQUM++GC+f9nddw9m6tQZXHvt9Wzf/j3r1m2hSpWqvPfe+0ycOJn9+w9z5ZVt+c9/phEb25t3332Xq67qQmJiCjExnZk4cRIXX3wp8+bNpnXrduzff/i0qxut/Z4yZcIJDg7hggtKs2jRMpo0acakSf/h1lsHk5iYQseOXXn99bcYOvR+Xn/9LTp1uprExBT27dtHxYoV8Xg8rF+/jtTUNE6dKsF33+2kePFgypYtR0hIMIsWfcY99/wfqanBHDx4kK++Wk+dOnWZO3cBtWvX/VufbbFiHldfvPOi0BKIMeZpoAnwD2vt8cLqV0QCV79+A0672mjmzPcz3Sd18cX1TrtopHv3nkyZMokbbriJ554bS58+saSlpdG3b3/q128AwIgRoxg8eBDPPjuapk2bMHz4AH+fQ4bcTvPmUZQvX95/4cqXX65mzJinCQoKJigoKNPVjWPGjPdfxtuhQyc6dOgMwNCh93HbbQOZPn0q1avXYNKkdwH45JP5TJkymaCgYC64oBRvvvkOHo+HffsSuOeeO0hLS6NYMfjHP3rSufPVALz44iv861/98XiKUa5cOV566bX83tz5zpPT1Qf5xRjTENgKfA8c9S3eaa2NzUX1CGBnII5A0s8RZHWMNytnw7fFdHl9b/kpELcTBGZciil3zseYMoxAIoFdBdFHoYxArLXbgJzPOomIyFlFU5mIiIgrSiAiIuKKEoiIiLiiBCIiIq4ogYiIiCtKICIi4ooSiIiIuKIEIiIiruT6RkJjTDBwFxCNM+OY/8ZAa227/A9NREQCWV5GIOOBwcB/cea0mgNUBJYXQFwiIhLg8pJArgWutta+DKT6/u0FxBRIZCIiEtDykkBKAz/7fj9qjCltnYcPX57/YYmISKDLy2SK3wHNgLXAOmCkMeZ34NeCCExERAJbXhLIvUCa7/f7gTeAUOD2/A5KREQCX64TiLX26wy//wB0LJCIRETkrJDrcyDGmOHGGM9flpU2xryZ/2GJiEigy8tJ9K7AamNMbQBjTGtgM1CmIAITEZHAlpcE0g5YCHxtjJkGzANGWGtvKpDIREQkoOU6gVhrT+HcPPgb0BtYCXxUMGGJiEigy8s5kLuB1cBbQHXAC2wyxrQsoNhERCSA5eUy3luAdtbabb7XfYwx/YEFQHi+RyYiIgEtL+dAmmdIHgBYa6cBV+RvSCIicjbIy30gJwGMMaH8ZTZeERE5/+RlOvdLgOlAFM75D4/vX4Cg/A9NREQCWV4OYb0OrAAuAn4HygNvAjcXQFwiIhLg8pJAooCHrLUHAY+19hDwb2B0gUQmIiIBLS8J5BhQ3Pf7fmNMTV/9sHyPSgLS/PlziI5uRdu2zRk1aoR/+cyZ02nQIJKYmNbExLTmvffezbL+3LkfEh3dkujoVvTpE0tSUpK/7Ouv13D//feQnJxETEwMERFVePjhBzLV37RpA9HRLWnePIpHH/03Xq9zBPXAgWR69+5JixaN6d27JwcPHiiAdy8if5WXBBIH3OD7fTawCFgFLMvvoCTwJCcnMWrUCObMWUBc3FoSE3/jv/9d6S/v2fNaVqxYzYoVq/nnP08/qpmamsrw4Q8xd+5CVq36kksuuZTJk/+cRm358qXExHSkZMlSjB49mpEjnzqtjQcfvI8XXniZNWs2smPHTyxfvgSACRPG065dNGvWbKRdu2gmTBif/xtARE6TlwSy1lo7xff7o8CzwNvA19nWkHPG7t27qF37YipUqABAu3bt+eST3E9E4PV68Xq9/PHHEbxeLykpKVSuXMVfHhe3iujo9lx44YW0adOGUqVKZaq/b18CKSkpNGvWAo/Hww033MSnny4EYPHihfTp0xeAPn36smjRJ3/37YpILuTlRsLHgRfAP63JewDGmGTgmfwP7exg7XZuuaV/rtYtUSKYEydSCziivMkuJmu3Y0x9/+vIyNr8+OP37Nmzm6pVq7Fo0UJOnjzhL//kk4/58sv/UafOxYwe/SzVqlXP1F7x4sUZM2Y80dGtKF26NLVr1+H5518EICkpieLFi1OmTNls44yP30uVKtX8r6tWrUZCwl4AEhMTqVSpMgCVKlVm//79LraEiOTVGUcgxpirjDFXAcHGmJj0176fW4GUgg8zME2ePC3TH9lzWbly5RkzZjy33z6QHj26UKNGTYKCnO8fnTt35ZtvtrJq1Ze0a9eee+6547T6J0+eZMqUySxbFseWLd9zySUNefllJ4GsXLmM9u2vyrH/9PMdGXk8uhVJpCjlZgQy2fdvSeA/GZZ7gX3APfkd1Nlk8uRpuV43PDyUxMTAyrfZxZTVqKpLl6vp0uVqAKZOfYegIOf2n4su+vM6iv79BzJ69BOn1d26dTPgjGTAOWcyYcI4AJYtW8Kdd96dY5xVq1YjPv7Ppyfv3fsrlSpV8b2HcPbtS6BSpcrs25fgP8wmIgXrjAnEWhsJYIyZaq0dUPAhSaBKTEwkPDycgwcP8M47k5g0aQqA/483wOLFn1KvXr3T6lapUhVrt7N//34qVKjAqlXLqVvX4PV6+fbbbVx66WU59l2pUmVCQkJYt24tTZo044MPZnDrrYMB6NKlG7Nmvc/Qofcza9b7dO36j/x94yKSpbxMZaLkcZ4bPvxBtm3bCsADDzxEnTp1AXj77Yl89tmnBAUFU758eSZMmOivExPTmhUrVlO5chWGDXuYnj27EhxcnBo1ajBhwhts2rSBRo0uy3Q4KiIigkOHDnHixEkWLVrIBx/Mx5j6jBkznqFD7+To0aN06NCJDh06AzB06H3cdttApk+fSvXqNZg0KevLiEUkf3myOrYcYCKAnUlJhzl1Kvexnk2Hi4rSmQ5h5eUQnRvjxo0hMrI2sbG9zxhTUQvEuBRT7pyPMRUr5iEsLAQgEthVEH3k5SoskXx3//0PFnUIIuJSXu4DERER8VMCERERV5RAROSs0KTJpQwa9E//6wUL5vvvOZo5c/ppc6el27JlEx6Ph+XLl2Zavnz5Elq1uoLmzaP8l5SDM+tC164xtGjRmNtuG8iJE84Ns8ePH+e22wbSvHkUXbvGsGfP7kzt/fLLz0REVOG11yacsY/Jk9/k4osvpmLFMpnmhJs9exbR0a2Ijm5Ft24d2bp1i7/s3nvv4pJLatOuXYtM/RblXHCFlkCMMfONMZuMMRuMMXHGmMaF1beInBs2bdrA9u3f5anO3LmzadOmDfPmzfYvS0tL46GHHmDGjDl88cXXzJ07G2u3AzB69BMMHjyENWs2UrZsOaZPnwrA9OlTKVu2HGvXbmLw4CGn3e80YsQjdOjQKVd9NG/ekqVLl1KjRs1MbdSsGcFHH33KqlVf8sADDzJs2FB/2Y039mPmzLmnvb+inAuuMEcgN1tro6y1l+NMifKfM1UQEcnorrvu4aWXXsj1+l6vlwULPmLKlCmsXLmcY8eOAbB+/ToiI2sTERFJiRIliI29jsWLF+L1evnii1X06NELgD59bvLPrebMuXYTAD169CIubqV/hoRPP/2EWrUiMs1MkV0fAI0aRREREXFavM2bt6BcufIANGnSjL179/rLWrVq7S/LqCjngiu0BOJ7fki6ssCpwupbRM4N11xzLVu2bGLHjp9ytf6aNV9Rq1Yt6tSpQ+vWbVi69HMAEhLiM83XVqVKVeLj95KcnEyZMmUJDnYuUHXmXIs/rU5wcDChoWVITk7myJGg7Jp4AAAYkUlEQVQjvPLKeIYNezhT39n1kVvTp0/LNKLJTlHOBVeol/EaYyYBnXEeh9s1L3V91zPnSXh4aJ7rFLSzJaYSJYKzLSsMgbidIDDjOhdj6t3buS9o9uw/DzsVK+ahUqWyPPzwQ7z11itcffXVlCpVnPDwUEJDS3HBBSVO63fx4o/o378fADff3J9p06YxaFA/QkNL+esClClzAaVLlyQs7EKCgor5lx87FkJwcBDh4aH++yrSy9LXe/bZZ3nwwWFERlbhwgtLEhJS0h9TVn1kjLFYMQ8VKoRQoULmuFesWMGsWe/xxRdfEBb2Z9mRIyGZ4gPweMjxdUEq1ARirb0VwBjTHxgLdMttXd1IWDCyiyl9ht6iiDcQtxMEZlznakxZ7X+nTnnZv/8wXbv24qmnnqZWrYs5duwkiYkppKQc4+jRE5nWT0tL48MPZzN//kc8/fTTpKWdIjk5mZ0793LhheX56aed/vWt/YmyZcPwekty4MAB4uMPEBwczJYtlgoVKpKYmELFipXZvHk7JUuWJTU1lYMHD5KWVpwvvvgfH3zwIcOG/ZtDhw5RrJiH1FS47LLGWfaR/jo8PNT/nrzekv64t23byqBB/2LmzDmcOlUi03tKSjpMWtqpTMsqVAhn69Yf/HPBhYVVIDExJeONhAWmSK7CstZOA2KMMXqaoYjkSfHixRk8eAhvvfV6juutWrWChg0vZePG79i1axfr12+je/dr+PTTT7j88ibs2LGD3bt3ceLECebNm0OXLt3weDy0bt2OBQvmAzBr1gz/3GrOnGszAOcKsDZtovF4PCxY8BnffLOVb77Zyu2338m99w7jllsGZ9tHTn755WcGDerHa6+97Z8q6EzS54Jz4i3cueAKJYEYY0KMMTUyvO4BJPt+RETypF+/AaSmZn6OzcyZ7xMVVd//89prE+jWrUemdbp378ncuR8SHBzMc8+NpU+fWFq3bkrPnrHUr98AgBEjRjFx4qs0bx7FgQPJ9Os3wN/ngQPJNG8excSJrzJ8+MgcY8ypj7fffoPq1auzd++vtG/fivvuc2ajfvHF5zlw4AAPPXQ/MTGt6dQp2t/e4MGD6NatIz/++ANRUfX9V4cNHXofq1atoEWLxqxatYKhQ+9zv2HzqFDmwjLGVAI+Ai4E0nASxzBr7fpcVI9Ac2EVmKKeCysrgbidIDDjOldjyu/971zdTjk5Z+bCstbuA1oWRl8iIlI4dCe6iIi4ogQiIiKuKIGIiIgrSiAiIuKKEoiIiLiiBCIiIq4ogYiIiCtKICIi4ooSiIiIuKIEIiIiriiBiIiIK0ogIiLiihKIiIi4ogQiImeFZ555ksaNGxARUSXT8uPHj3PbbQNp3jyKrl1j2LNnt7/s5ZdfpHnzKIwxLF++NFO9Bx64lzVrvuLjj+fRtm1zKlUqy8aNmZ8wkV6/VasrMtVfvnwJrVpdQfPmUUyYMK4A3u3ZQQlERM4KnTt35bPPVpy2fPr0qZQtW461azcxePAQRo9+AgBrtzNv3hzi4tayePFiHnroftLS0vz11q9fR9Omzahf/xLeeWc6rVq1ztRuxvozZ871109LS+Ohhx5gxow5fPHF18ydOxtrtxfsmw9QSiAiclZo2rQ5lSpVPm354sUL6dPnJgB69OhFXNxKvF4vixcvJDb2OkqWLElkZCSRkbVZv34dAN9/b6lT52KCgoKoV89w8cWnPz42Y/1atSL89devX0dkZG0iIiIpUaIEsbHXsXjxwoJ98wFKCUREzmoJCfFUq1YdcB4jGxpahuTkZOLj91K1ajX/elWrViMhIR6AZcuWcNVVHXNsN7v6GfsDqFKlKvHxe/PzLZ01CuWJhHJ2sna7/9GihalEiWBOnEg984qFLBDjOldjsnY7xtTP1bpZPZbb48l6OXgAWLFiKRMmvJHndsGD13sqi/48uQn1nKMRiGRp8uRpuf4PLFKUqlSpyq+//gJAamoqKSm/U778RVStWo29e3/1r7d3769UrlyZP/74g99/P0TlylWyaxIg2/oZ+wNnpHKmts5VGoFItiZPnlYk/YaHh5KYmFIkfeckEOM6V2PKy8i3S5duzJo1g2bNWrBgwXzatInG4/HQpUs37rjjFu6442527tzPjh07uOKKpixfvoTWrdvlqt30+gkJ8f76Xq+XHTt2sHv3LqpUqcq8eXOYOHHy33m7Zy2NQETkrDBq1Aiioupz9OgfREXVZ8yYZwDo128ABw4k07x5FBMnvsrw4SMBqF+/AT17xtKmTTO6du3K88+/QFBQ0GnnPxYuXEBUVH3WrVtL377Xc8MNvU6rf+ON1/rrBwcH89xzY+nTJ5bWrZvSs2cs9es3KPTtEQg8WR/nCygRwM6kpMOcOpX7WM/Vb2b5TTHlXiDGda7GlD4Cya9RcMaYOnRoy+LFyylevHi+tJ0fMRWEYsU8hIWFAEQCuwqiDx3CEpHzyrJlcUUdwjlDh7BERMQVJRAREXFFCURERFxRAhEREVeUQERExBUlEBERcUUJREREXFECERERV5RARETEFSUQERFxRQlERERcUQIRERFXlEBERMQVJRAREXFFCUREzgvvvz+N6OiWREe3ol27FixatNBflpqaSv36ETz11MhMdXr16sbll1+S6fnoAwbcRESE8wjbPXt2U7NmRWJiWvt/Zs1637/uli2bqFixDMuXL/UvO3bsGF26tCcqKoq2bZvz/PNPnxbrI48M8/cBcPz4cW67bSDNm0fRtWsMe/bsBmDlyuV07NiO6OiWdOzYjri4Vae1VaZMmbc9Hs/W9Ncej2eWx+PZ6PvZ5fF4NmZc3+Px1PR4PIc9Hs+wHDcoRfA8EGPME8BIoJG1dusZVhcR+dt++eUXxo8fy7JlcZQpU5bDhw+TlLTfX75y5TLq1KnLRx/N5bHHnsDj8fjLypQpy5o1X9GyZSsOHTrIb78lZGo7IiKSFStWZ9nv3LmzadGiFfPmzfY/BbFkyZLMmfMJkZFV2Ls3mR49OtOhQyeaNm0OwMaN6zl06FCmdqZPn0rZsuVYu3YT8+bNZvToJ3j77SmEhYXx3nuzqFy5Ct999y19+sSyebPN0P9cgoKC/sjYltfr7ZP+u8fjeRHI3BmMBxadYZMChTwCMcZcAbQE9hRmvyJyfvvtt98ICQnlwgtDAAgJCaFWrQh/+dy5s7n99jupXr0G69atzVQ3NvY65s+fDTiPv+3W7Zpc9en1elmw4CMmTHiDlSuXc+zYMQA8Hg8hIU4cJ0+e5OTJVH/CSktLY9SoETzxxOhMbS1evJA+fW4CoEePXsTFrcTr9dKoURSVKzsjlfr1G3D8+DGOHz8OwOHDhxk3bhzXXXfdK1nF53E6vQGYkWFZL2AHsC0377HQRiDGmJLAa0BfYEVh9SsiZydrt/sfbetGxsfhRkVFER4eTtOmjWjbNpp//OMaunS5GoCjR48SF7eKF154mUOHDjFv3myaNWvhr9u2bTT33z+UtLQ05s2bzYsvTmDcuDH+8l27dhIT09r/+tlnx9Ky5ZWsWfMVtWrVIjKyNq1bt2Hp0s/p3t1JPmlpaTRu3JgffviRf/3rNpo0aeaL+U26dLmaSpUqZ3ovCQnxVKtWHYDg4GBCQ8uQnJxMWFiYf51PPvmISy+NomTJkr44nuKBBx5gzZo1x7LZRG2BfV6v9wcAj8dzIfAQ0Ak44+ErKNxDWE8C71lrdxpj8lzZ92zfPAkPD81znYKmmHInEGOCwIzrXIzp44/n07t373yNYfnypXz99dcsW7aMkSMf5ccfv2XkyJF8+OFiOnS4ilq1KjFwYD8aN27MxImvERQURPHiQVSoUIb27duxfPmnpKWdpEmTS/F4nPaPHAmhTp06bN26+bT+Fy/+iP79+xEeHsrNN/dn2rRpDBrUz1++ceNGDh48SGxsLPv27eaiiy5i0aIFrFy5kuDgYH8f8OfzzdNfBwUVIzw8lLAw5/W2bdt4+umRfP7554SHh7Jx40Z+/XU3sbGxrFmzJrtNdBMZRh/AKGC81+s9nPEQXk4KJYEYY1oBzYCH3baRlHSYU6e8Z17Rp6AfWO+GYsqdQIwJAjOuczmmN95452/VzxhDeHgo+/cfJjKyAbfe2oCmTa/k3nvvYsiQB5gyZRpr135FzZq1AEhKSmLevIVER8dw8mQaBw4coWvXaxg4sC///vcjJCam4PU67SclHSYt7dRp7zctLY0PP5zN/PkfMXr0U3i9XpKTk9m5cy8hIaH+mE6eDKJZs1bMnv0R9erV4/vvf6B27ToA/PHHH0RG1mbt2k1UrFiZzZu3U7JkWVJTUzl48CBpacVJTExh795fufbanrz88kTKlKlIYmIKn3++gq+/XkdERAS//PLLh0CYx+NZ6fV62wN4PJ5g4FqgSYawWwC9PR7PGKAccMrj8Rzzer2vZreNC+scSDRQH9hpjNkFVAc+M8Z0LqT+ReQ8tnfvXjZv/vNio61bt1C9eg1SUn5n7dov2bDhW775ZivffLOV5557kXnzZmeq37LllQwd+gCxsdfnqr9Vq1bQsOGlbNz4Hd98s5X167fRvfs1fPrpJ+zfv59Dhw4CzuGz//53JXXr1qVTp65s2/ajP44LLijN2rWbAOjSpRuzZjmDhQUL5tOmTTQej4dDhw7St+/1PPbYSFq0aOnvf9CgW9m27Qd27drFsGHDrge+T08ePh2B7V6v95f0BV6vt63X643wer0RwEvAMzklDyikEYi19jngufTXviTSXVdhiUhhOHnyJCNHDichIZ6SJUsRFlaBsWPHs3DhAtq0aec/bwDQtWs3nnxyhP9kNDgnvocMGZpl2389B9K37z/ZvHkT3br1yLRe9+49mTJlEg0bNuKee+7A4/Fy8mQq11wTS+fOV+cYf79+Axgy5HaaN4+ifPnyvPmmMzqbPPktdu3awbhxY/znZT74YD7h4eFn2iQ3kvnwlSuejNc3F5Y8JpAIYKcOYRUMxZR7gRiXYsqd8zGm9PMmQCSwqyD6KPT7QACstRFF0a+IiOQf3YkuIiKuKIGIiIgrSiAiIuKKEoiIiLiiBCIiIq4ogYiIiCtKICIi4ooSiIiIuKIEIiIiriiBiIiIK0ogIiLiihKIiIi4ogQiIiKuKIGIiIgrSiAiIuKKEoiIiLiiBCIiIq4ogYiIiCtKICIi4ooSiIiIuKIEIiIiriiBiIiIK0ogIiLiihKIiIi4ogQiIiKuKIGIiIgrSiAiIuKKEoiIiLiiBCIiIq4ogYiIiCtKICIi4kpwUQeQC0EAxYp58lzRTZ2CpphyJxBjgsCMSzHlzvkWU4a2gwqqD4/X6y2otvNLGyCuqIMQETlLtQW+KIiGz4YEUhJoBsQDaUUci4jI2SIIqAJ8DRwviA7OhgQiIiIBSCfRRUTEFSUQERFxRQlERERcUQIRERFXlEBERMQVJRAREXFFCURERFwJiKlMjDH1gHeBMCAJGGCt/eEv6wQBE4CugBd4zlo7qQDL6gMrgYtwbmAcba19phBiGgHcCKT6fh611n7mK5sL/MNXJw1401p7fyHENBK4C9jr62a1tXaIr+wyYDlQxhfTvdbatwohpqnAZRm6uQzoZa392BfvPUApnJupDgJt83Gf6gw8AzQCXrHWDstlmwW5T+UUU1HtUznFNJKi2adyiqko96mcPqPSwDtAE1/ZMGvtJ2cqKwyBMgKZCLxmra0HvAa8mcU6/YCLgbpAK2CkMSaiAMvmAr/h7DD3A49nKCvImNYCzay1UcC/gFnGmAt8Za2AmdbaUsBgoHEhbSeAqdbaxr6fIRmWzwe2WGtLAMOBV4wxIQUdk7V2QHo8wM3AAeCzDH0eAQb7ttUw8nef2gHcBozNY5sFuU/lFFNR7VM5xQRFs09lG1MR71M5fUbDgBRr7cVAD2BShu2RU1mBK/IEYoypCFwBzPAtmgFcYYwJ/8uqfYC3rbWnrLWJODvZ9QVR5oupLvCUtfYU8BbgAQYWdEzW2s+stX/41tvs6zfMF1MYsK4ItlOWfDHVAh7yLXoJZ5/qU8gx3QJMt9amT9dwIVCRAtqnrLU/Wms34Hzj+6si2adyiqmo9qkzbKcsFfQ+lYeYCnufyvIzylBvom+9H3A+r6tzUVbgijyBADWAX621aQC+f/f6lmdUE9id4fWeDOvkd1kNnKHzzgwxHQRMIcSU0QDgJ2vtL77yI8B9xpgtwBxgfxb1CiqmG40xm40xnxtjWvmWpZdn3E4pwCWFFBPGmBJAX+A/GRaXwTnMsMEY8znQnPzdp3JSVPtUbhXmPnUmRbFPnVEA7FMZP6P8arNABEICkSwYY6KB0cBNGRb/BlxsrW2EczikJoXzGU4EIq21l+EM/T8yxoSdoU5h6QXssdZuzLBsDvB9xngpwCmtzxbap3KtyPapbD6jgBUICeRnoJrvBFP6iaaqvuUZ7cEZ2qarmWGd/C77GWfniMwQUznAFkJM+L6NvYdzAi+9z5+BSjhDW4DpOBdB/HWG4nyPyVqbYK096ft9iW/5pRnqZtxOocC3BR1TBv8i8zdFgI349qkM8ebnPpWTotqnclRE+1S2inCfyo0i2aey+Yz+VpsFrcgTiLX2N5wPJz3j3gRs8B0jzOhD4DZjTDHfccdeON8K8r3MF9NPwHBjTDHgdpyrJqYWdEzGmGbALKC3tXb9X7bTtxm20zM4x3G3FEJM1dIbN8Y0BiKckOxvODvr877i//Ntpw8KOiZfLNVxnnXw/l/6K45vn/LFWw/YnI/7VE6Kap/KVhHuUznFVFT71JniKpJ9KrvPKEO9wb716uI83mJxLsoKXEBcxgvcAbxrjHkc58qHAQDGmE+Bx62164BpQAsg/bK5J621O3y/F0TZtcAK4BhwCnjaWrujEGJ6HbgAeNMY/+Hx/tba9P/UbxljJuPM7/9Pa21qIcT0jDGmCc430xO+eBJ8ZbHAUmPMCf685DKlEGIC50qZBdbaZDJ7Bufb4Zs43/p34XyrzJd9yhjTBpiJc1zcY4y5EbjFOpddFsk+dYaYimSfOkNMRbJPnSEmKKJ9ipw/o7HAFGPMj77tcbu1NsW3Tk5lBU7PAxEREVeK/BCWiIicnZRARETEFSUQERFxRQlERERcUQIRERFXlEAkYBljdhljOgZAHFOMMU/lct0IY4zXGFPol8gbYwYaY74o7H7l/BUo94GISB4YZxbXnUBxa22uJysUyU8agcg5oSi+8Yuc7/SfTgJdM2PMBKAKzvTXd1prjxlj2uPMG/QKcB+wxBgzlD/v9g0GVgN3pM9qaoxZCcQBV+E8LOhLoK+1dr+vvA0wBmfm1xRghLV2ii+O8saYhUA7nOk/+lprfzpT8MaYssA4oBvO3efvAE9Ya9OMMQOBW4GvcKYPPwjcZa1d5KsbifOgtcuBNTjzZpW11v4T+K+vi4O+O5c7ZejzhazaE8lvGoFIoOsHdAHq4Mw/NDxDWWWcp/vVwplbqhjOH+haOJPKHQVe/Ut7fYFBOM92KIHzQB6MMTWBRTgJKRznwUoZZ2O9CRgFlAd+BJ7OZfzv4swvdTFOIuiMkzTStcBJDBVwktdkY0z65Ibv4zxoKAwYCfTPUK+d799y1toQa+2XuWhPJF9pBCKB7lVr7c8Axpincf7ApyeRUzjf5tMf+nOUzJMtPo0z91RG71hrv/eVfwBc41veD1hqrU1/YFCS7yfdXGvtWl+96TijihwZYyrhPNynnLX2KHDEGDMeJ9mlP81ut7X2bd/67+LMiVTJOM+kaAZ0sNaeAL4wxnx8pj6zaw9IyLGWiAtKIBLoMk5NvRtnQrt0idbaY+kvjPN86PE4z5wu71scapwpuNOnKM/4h/QPIP3xnzVwZsvNTnb1clILZxbX+AwT5BUj83vyt2ut/cO3XgjOCCI5w1Pq8NU708OCsmtPJN8pgUigy/gHsybOU+DS/XUm0AdwnvDXwlqb4Jt2ewN/Pu8iJz/jPGUuP/2MM8NtBRdXSsUDFxljSmdIIhm3hWZBlSKncyAS6IYYY6obYy4CHsV5ZkJ2QnEOYx30rf9EHvqZDnQ0xtxgjAk2xoT5EpBr1tp44HPgRWNMGd9zIOoY56lzZ6q7G+f51iONMSWM87ChHhlWScQ5hFf778Qo8ncogUigex/nj/AO309ON/S9hPNMhf04Vzbl+sE61to9OFdKPQAk45xAj3IXciYDcE7Wf4vzrJvZOFeU5UY/oBXOuZincJLncV+8f+CcyF9tjDlojGmZD7GK5ImeByJyljDGzAK2W2vzMrISKTA6ByISoHyPOU3GueO8M9ATeK5IgxLJQAlEJHBVBubi3AfyC85NlBuKNiSRP+kQloiIuKKT6CIi4ooSiIiIuKIEIiIiriiBiIiIK0ogIiLiihKIiIi48v+RjByi2vDDUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ____________________ SAMEA2535204\n",
      "_______|\n",
      "       |              ____________________________________ NLA000500538\n",
      "       |_____________|\n",
      "                     |                                       , NLA000401210\n",
      "                     |_______________________________________|\n",
      "                                                             |____ SAMEA3402474\n",
      "\n",
      "42.165309852600004\n",
      "\n",
      "70.275516421 - 140.551032842 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['NLA000401210','SAMEA3402474','NLA000500538','SAMEA2535204']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ________________________________________________________ SAMEA1404026\n",
      "______|\n",
      "      |         _____________________________________ SAMEA1403691\n",
      "      |________|\n",
      "               |__________________________________________________ SAMEA1403827\n",
      "\n",
      "39.9375745062\n",
      "\n",
      "66.562624177 - 133.125248354 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMEA1403691','SAMEA1403827','SAMEA1404026']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **B** (Previously Cluster L), *single* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         _____________ SAMN05001043\n",
      "________________________________________|\n",
      "                                        |__________________________ SAMN05001051\n",
      "\n",
      "13.18763756295\n",
      "\n",
      "21.97939593825 - 43.9587918765 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMN05001043','SAMN05001051']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Previously Cluster M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ____________________________________________________ SAMEA1403638\n",
      "_________|\n",
      "         |_________________________________________________________ SAMEA1707505\n",
      "\n",
      "35.040351325050004\n",
      "\n",
      "58.40058554175001 - 116.80117108350002 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMEA1403638','SAMEA1707505']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster **F** (Previously Cluster N), *double* mutant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 ____________________________________ SAMEA1403685\n",
      "________________|\n",
      "                |__________________________________________________ SAMN10593557\n",
      "\n",
      "30.6063397143\n",
      "\n",
      "51.0105661905 - 102.021132381 years\n"
     ]
    }
   ],
   "source": [
    "clade_isolate_IDs = ['SAMEA1403685','SAMN10593557']\n",
    "print(len(clade_isolate_IDs))\n",
    "est_TMRCA_for_isolates(clade_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [7] Annotate new variants in additional 12 eis C-14T mutants with AG MICs in the *eis* gene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate INDELs w/ H37Rv Locus Info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve information for H37Rv Reference Genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:18: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "####################################################################################################################################################################################\n",
    "#load reference genome and reference annotation\n",
    "reference_genome = '/home/rv76/Farhat_Lab/Reference_Seqs/H37Rv/h37rv.fasta'\n",
    "for reference_genome in SeqIO.parse(reference_genome, \"fasta\"):\n",
    "    reference_genome.seq.alphabet = IUPAC.unambiguous_dna\n",
    "\n",
    "reference_genome_annotation = pd.read_csv('/home/rv76/Farhat_Lab/CSV_files/h37rv_genome_summary.txt', '\\t').set_index('name')\n",
    "\n",
    "####### Make a dictionary of all H37Rv reference positions and corresponding genes #######\n",
    "ReferencePosition_Gene_mapping = {} #keys: H37Rv Reference Positions , values: gene_ids (may be multiple genes for some Reference Positions)\n",
    "\n",
    "#store a list corresponding to every Reference Position (to store all genes that map to the Reference Position)\n",
    "for H37Rv_RefPos in range(0 , len(reference_genome.seq) + 1):\n",
    "    ReferencePosition_Gene_mapping[H37Rv_RefPos] = []\n",
    "\n",
    "for gene_id_index in range(0 , len(reference_genome_annotation.index)):\n",
    "\n",
    "    gene_id_info = reference_genome_annotation.ix[gene_id_index , :]\n",
    "    gene_id = gene_id_info.name\n",
    "\n",
    "    chrom_start = gene_id_info.chromStart\n",
    "    chrom_end = gene_id_info.chromEnd\n",
    "\n",
    "    #find the position of the first base relative to H37Rv in 5' -> 3'\n",
    "    H37Rv_start = min(chrom_start , chrom_end)\n",
    "    H37Rv_end = max(chrom_start , chrom_end)\n",
    "\n",
    "    #store all corresponding H37Rv Reference Positions to gene_id in dictionary\n",
    "    for H37Rv_RefPos in range(H37Rv_start+1 , H37Rv_end+1):\n",
    "\n",
    "        ReferencePosition_Gene_mapping[H37Rv_RefPos].append(gene_id) #append gene_id to list already in dict\n",
    "        \n",
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Annotate INDELs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To annotate INDELs, we get gene_ids that the INDELs 'land in' and the gene-relative coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCGGCGATGCGT_2715054_C</td>\n",
       "      <td>2715054</td>\n",
       "      <td>CCGGCGATGCGT</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A_2714706_AC</td>\n",
       "      <td>2714706</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714933_CG</td>\n",
       "      <td>2714933</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      key      pos           ref alt\n",
       "0  CCGGCGATGCGT_2715054_C  2715054  CCGGCGATGCGT   C\n",
       "1            A_2714706_AC  2714706             A  AC\n",
       "2            C_2714933_CG  2714933             C  CG"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load INDEL annotation file (rows of Genotype Matrix)\n",
    "INDEL_annotation_DF = pd.DataFrame()\n",
    "INDEL_annotation_DF.loc[:,'key'] = ['CCGGCGATGCGT_2715054_C', 'A_2714706_AC', 'C_2714933_CG']\n",
    "INDEL_annotation_DF.loc[:,'pos'] = [2715054, 2714706, 2714933]\n",
    "INDEL_annotation_DF.loc[:,'ref'] = ['CCGGCGATGCGT', 'A', 'C']\n",
    "INDEL_annotation_DF.loc[:,'alt'] = ['C', 'AC', 'CG']\n",
    "\n",
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get Gene Categories\n",
    "gene_categories = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/gene_categories/gene_categories.csv').set_index('name')\n",
    "\n",
    "#create dictionary, keys: H37Rv locus, values: gene category\n",
    "gene_categories_map = dict( zip( list(gene_categories.gene_id) , list(gene_categories.Gene_Category) ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse through Mycobrowser (tuberculist) to collect gene names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycobrowser_table = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/H37Rv/Mycobrowser_Release3/Mycobacterium_tuberculosis_H37Rv_txt_v3.txt' , sep = '\\t')\n",
    "\n",
    "#retreive columns for locus tag & gene name\n",
    "mycobrowser_table = mycobrowser_table.loc[: , ['Locus' , 'Name']]\n",
    "\n",
    "#create dictionary, keys: H37Rv loucs, values: gene category\n",
    "gene_names_map = dict( zip( list(mycobrowser_table.Locus) , list(mycobrowser_table.Name) ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterate through each INDEL to annotate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some INDELs are present in more than 1 gene, have to account for this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.333333333333\n",
      "0.666666666667\n"
     ]
    }
   ],
   "source": [
    "#create dictionaries for each columsn of information\n",
    "locus_tag_dict = {} #H37Rv id\n",
    "genomic_coord_dict = {} #coordinate on gene, 5' -> 3'\n",
    "gene_categ_dict = {} #gene Category\n",
    "gene_name_dict = {} #gene Name\n",
    "\n",
    "for INDEL_index in INDEL_annotation_DF.index:\n",
    "\n",
    "    #reference position, ref allele and alt allele for INDEL\n",
    "    INDEL_info = INDEL_annotation_DF.loc[INDEL_index , :]\n",
    "\n",
    "    #find the gene that INDEL occurs on\n",
    "    ref_seq_position = INDEL_info.pos\n",
    "\n",
    "    #check list corresponding to H37Rv coordinate to see if there are any genes associated with RefPosition\n",
    "    if len(ReferencePosition_Gene_mapping[ref_seq_position]) == 1:\n",
    "        \n",
    "        #get gene that ReferencePosition is mapped to (i.e. INDEL filtering ensures that INDEL corresponds to only 1 gene)\n",
    "        gene_intergenic_id = ReferencePosition_Gene_mapping[ref_seq_position][0]\n",
    "\n",
    "        #find genomic coordinate of INDEL relative to gene (subtract 1 since reference seq starts counting at 1)\n",
    "        gene_relative_coord = (ref_seq_position - 1) - min( reference_genome_annotation.loc[gene_intergenic_id , 'chromStart'] , reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd'] )\n",
    "\n",
    "        #store all information for INDEL in relevant dictionaries\n",
    "        locus_tag_dict[INDEL_index] = gene_intergenic_id\n",
    "        genomic_coord_dict[INDEL_index] = gene_relative_coord\n",
    "\n",
    "        #use gene id to get category and names from mappings\n",
    "        if gene_intergenic_id in gene_categories_map.keys():\n",
    "            gene_categ_dict[INDEL_index] = gene_categories_map[gene_intergenic_id]\n",
    "        else:\n",
    "            gene_categ_dict[INDEL_index] = 'None'\n",
    "\n",
    "        if gene_intergenic_id in gene_names_map.keys():\n",
    "            gene_name_dict[INDEL_index] = gene_names_map[gene_intergenic_id] \n",
    "        else:\n",
    "            gene_name_dict[INDEL_index] = 'None'\n",
    "            \n",
    "    #H37Rv RefSeq position was not found in dictionary so Reference position must be intergenic    \n",
    "    elif len(ReferencePosition_Gene_mapping[ref_seq_position]) == 0:\n",
    "\n",
    "        #store all information for INDEL in relevant dictionaries\n",
    "        locus_tag_dict[INDEL_index] = 'intergenic'\n",
    "        genomic_coord_dict[INDEL_index] = np.nan\n",
    "        gene_categ_dict[INDEL_index] = 'None'\n",
    "        gene_name_dict[INDEL_index] = 'None'\n",
    "\n",
    "    #keep track of progress of loop\n",
    "    num_INDELs = float( np.shape(INDEL_annotation_DF)[0] )\n",
    "    if INDEL_index % np.ceil(0.20 * num_INDELs) == 0:\n",
    "        print float(INDEL_index) / num_INDELs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update INDEL annotation DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#append columns for info from annotation\n",
    "INDEL_annotation_DF['gene_id'] = pd.Series(locus_tag_dict)\n",
    "INDEL_annotation_DF['gene_pos'] = pd.Series(genomic_coord_dict)\n",
    "INDEL_annotation_DF['gene_category'] = pd.Series(gene_categ_dict)\n",
    "INDEL_annotation_DF['gene_name'] = pd.Series(gene_name_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 8)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape( INDEL_annotation_DF )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCGGCGATGCGT_2715054_C</td>\n",
       "      <td>2715054</td>\n",
       "      <td>CCGGCGATGCGT</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>930</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A_2714706_AC</td>\n",
       "      <td>2714706</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>582</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_2714933_CG</td>\n",
       "      <td>2714933</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>809</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      key      pos           ref alt  gene_id  gene_pos  \\\n",
       "0  CCGGCGATGCGT_2715054_C  2715054  CCGGCGATGCGT   C  Rv2416c       930   \n",
       "1            A_2714706_AC  2714706             A  AC  Rv2416c       582   \n",
       "2            C_2714933_CG  2714933             C  CG  Rv2416c       809   \n",
       "\n",
       "   gene_category gene_name  \n",
       "0  Non-Essential       eis  \n",
       "1  Non-Essential       eis  \n",
       "2  Non-Essential       eis  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This block of code will determine the functional consequence of each INDEL in our genotypes array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a dataframe for each unique _reference_ allele - _alternate_ allele pairing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEL_functional_annotation_DF = INDEL_annotation_DF.loc[: , ['key' , 'pos' , 'ref' , 'alt' , 'gene_category' , 'gene_name']]\n",
    "INDEL_functional_annotation_DF.set_index('key' , inplace = True , drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCGGCGATGCGT_2715054_C</th>\n",
       "      <td>2715054</td>\n",
       "      <td>CCGGCGATGCGT</td>\n",
       "      <td>C</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_2714706_AC</th>\n",
       "      <td>2714706</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_2714933_CG</th>\n",
       "      <td>2714933</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            pos           ref alt  gene_category gene_name\n",
       "key                                                                       \n",
       "CCGGCGATGCGT_2715054_C  2715054  CCGGCGATGCGT   C  Non-Essential       eis\n",
       "A_2714706_AC            2714706             A  AC  Non-Essential       eis\n",
       "C_2714933_CG            2714933             C  CG  Non-Essential       eis"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_functional_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_functional_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert **gene_category** and **gene_name** columns so that the elements are lists only when there is more than 1 label in a cell (when a Reference Position corresponds to two overlapping genes, these positions have been filtered out in the most recent version of analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_category_list = []\n",
    "gene_name_list = []\n",
    "\n",
    "for INDEL_i in INDEL_functional_annotation_DF.index:\n",
    "    \n",
    "    gene_category_INDEL_i = INDEL_functional_annotation_DF.loc[INDEL_i , 'gene_category']\n",
    "    gene_name_INDEL_i = INDEL_functional_annotation_DF.loc[INDEL_i , 'gene_name']\n",
    "    \n",
    "    if len(gene_category_INDEL_i) == 1:\n",
    "        \n",
    "        gene_category_list.append(gene_category_INDEL_i[0])\n",
    "        gene_name_list.append(gene_name_INDEL_i[0])\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        gene_category_list.append(gene_category_INDEL_i)\n",
    "        gene_name_list.append(gene_name_INDEL_i)\n",
    "        \n",
    "INDEL_functional_annotation_DF['gene_category'] = gene_category_list\n",
    "INDEL_functional_annotation_DF['gene_name'] = gene_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCGGCGATGCGT_2715054_C</th>\n",
       "      <td>2715054</td>\n",
       "      <td>CCGGCGATGCGT</td>\n",
       "      <td>C</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_2714706_AC</th>\n",
       "      <td>2714706</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_2714933_CG</th>\n",
       "      <td>2714933</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            pos           ref alt  gene_category gene_name\n",
       "key                                                                       \n",
       "CCGGCGATGCGT_2715054_C  2715054  CCGGCGATGCGT   C  Non-Essential       eis\n",
       "A_2714706_AC            2714706             A  AC  Non-Essential       eis\n",
       "C_2714933_CG            2714933             C  CG  Non-Essential       eis"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_functional_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_functional_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load in work-horse for functional INDEL annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relevant Information for H37Rv sequence and INDEL functional annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### Collect all DNA and Amino Acid sequences corresponding to genes on H37Rv #######\n",
    "#load reference genome and reference annotation\n",
    "reference_genome = '/n/data1/hms/dbmi/farhat/bin/work-horse/bin/h37rv.fasta'\n",
    "for reference_genome in SeqIO.parse(reference_genome, \"fasta\"):\n",
    "    reference_genome.seq.alphabet = IUPAC.unambiguous_dna\n",
    "\n",
    "reference_genome_annotation = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/H37Rv/h37rv_genome_summary.txt', '\\t').set_index('name')\n",
    "\n",
    "####### Load in dictionaries for SNP annotation #######\n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/pickled_files/dicts_for_SNP_annotation/H37Rv_gene_seq_records.pickle', 'rb') as handle:\n",
    "    ref_gene_sequences_records = pickle.load(handle)\n",
    "    \n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/pickled_files/dicts_for_SNP_annotation/H37Rv_protein_seq_records.pickle', 'rb') as handle:\n",
    "    ref_protein_sequences_records = pickle.load(handle)\n",
    "    \n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/pickled_files/dicts_for_SNP_annotation/H37Rv_coord_gene_mapping.pickle', 'rb') as handle:\n",
    "    ReferencePosition_Gene_mapping = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to annotate Intergenic INDELs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_flanking_genes_for_intergenic_region(intergenic_ref_pos): \n",
    "\n",
    "    #this function finds the genes flagging an intergenic region given a reference position\n",
    "\n",
    "    #find gene immediately in the 5' direction\n",
    "    for i in range(0 , 100000):\n",
    "\n",
    "        #move toward 5' direction\n",
    "        if ReferencePosition_Gene_mapping[intergenic_ref_pos - i] != []:\n",
    "\n",
    "            gene_to_left = ReferencePosition_Gene_mapping[intergenic_ref_pos - i][0]\n",
    "            break\n",
    "\n",
    "    #find gene immediately in the 3' direction       \n",
    "    for i in range(0 , 100000):\n",
    "\n",
    "        #move toward 3' direction\n",
    "        try:\n",
    "            if ReferencePosition_Gene_mapping[intergenic_ref_pos + i] != []:\n",
    "\n",
    "                gene_to_right = ReferencePosition_Gene_mapping[intergenic_ref_pos + i][0]\n",
    "                break\n",
    "        \n",
    "        #KeyError means we have hit the 'end' of the chromosome, the intergenic region at then end of H37Rv in 5' > 3' orientation \n",
    "        #since TB chromosome is circular the gene to the 'right' is Rv0001    \n",
    "        except KeyError:\n",
    "            \n",
    "            gene_to_right = 'Rv0001'\n",
    "            break\n",
    "    \n",
    "    return gene_to_left + '_' + gene_to_right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to determine whether INDELs are *Inframe* or *Frameshift*; Return codon position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def INDEL_type(ref_allele_i , ref_seq_position , alt_allele_i):\n",
    "    \n",
    "    '''\n",
    "    This function takes as input a reference position on H37Rv located within a \n",
    "    gene, a reference allelel and an alternate allele and returns whether the indel\n",
    "    would correspond to an inframe or frameshift indel.\n",
    "    \n",
    "    '''\n",
    "    gene_intergenic_id_list = []\n",
    "    genomic_coord_list = []\n",
    "    IF_FS_list = []\n",
    "    INS_DEL_list = []\n",
    "    codon_coord_list = []\n",
    "    \n",
    "    #find the gene that INDEL occurs on\n",
    "    \n",
    "    #check list corresponding to H37Rv coordinate to see if there are any genes associated with RefPosition\n",
    "    if len(ReferencePosition_Gene_mapping[ref_seq_position]) > 0:\n",
    "\n",
    "        #iterate through all genes that ReferencePosition is mapped to (i.e. SNP might correspond to 2 genes)\n",
    "        for gene_intergenic_id in ReferencePosition_Gene_mapping[ref_seq_position]:\n",
    "\n",
    "            #find genomic coordinate of SNP relative to gene (subtract 1 since reference seq starts counting at 1)\n",
    "            gene_relative_coord = (ref_seq_position - 1) - min( reference_genome_annotation.loc[gene_intergenic_id , 'chromStart'] , reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd'] )\n",
    "\n",
    "            #find out if indel is inframe or frameshift\n",
    "            indel_length = abs(len(ref_allele_i) - len(alt_allele_i))\n",
    "            \n",
    "            if indel_length % 3 == 0:\n",
    "                INDEL_type = 'inframe'\n",
    "                \n",
    "            elif indel_length % 3 != 0:\n",
    "                INDEL_type = 'frameshift'\n",
    "                \n",
    "            #find out whether insertion or deletion\n",
    "            if len(ref_allele_i) > len(alt_allele_i):\n",
    "                INS_or_DEL = 'del'\n",
    "                \n",
    "            elif len(ref_allele_i) < len(alt_allele_i):\n",
    "                INS_or_DEL = 'ins'\n",
    "            \n",
    "            #get the codon number where the SNP occurs within\n",
    "            ## take the genomic coordinate (relative to the gene, in the 5' to 3' direction), divide by 3, then take the ceiling of this number (will be fraction if SNP occurs in 1st or 2nd position on codon)\n",
    "            strand = reference_genome_annotation.loc[gene_intergenic_id, 'strand']\n",
    "            if strand == '+':\n",
    "                 genomic_5_to_3_coord = (ref_seq_position) - reference_genome_annotation.loc[gene_intergenic_id , 'chromStart']\n",
    "\n",
    "            elif strand == '-':\n",
    "                 genomic_5_to_3_coord = (reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd']) - (ref_seq_position-1)\n",
    "\n",
    "            codon_coord = int(np.ceil( float( genomic_5_to_3_coord) / 3.0 ))\n",
    "            \n",
    "            #store relevant info in lists    \n",
    "            gene_intergenic_id_list.append(gene_intergenic_id)\n",
    "            genomic_coord_list.append(genomic_5_to_3_coord)\n",
    "            IF_FS_list.append(INDEL_type)\n",
    "            INS_DEL_list.append(INS_or_DEL)\n",
    "            codon_coord_list.append(codon_coord)\n",
    "    \n",
    "    #if no gene in H37Rv corresponds to the Reference Position for INDEL, then INDEL must be intergenic\n",
    "    else:\n",
    "        \n",
    "        gene_intergenic_id = find_flanking_genes_for_intergenic_region(ref_seq_position)\n",
    "        \n",
    "        #find out if indel is inframe or frameshift\n",
    "        indel_length = abs(len(ref_allele_i) - len(alt_allele_i))\n",
    "\n",
    "        if indel_length % 3 == 0:\n",
    "            INDEL_type = 'inframe'\n",
    "\n",
    "        elif indel_length % 3 != 0:\n",
    "            INDEL_type = 'frameshift'\n",
    "            \n",
    "        #find out whether insertion or deletion\n",
    "        if len(ref_allele_i) > len(alt_allele_i):\n",
    "            INS_or_DEL = 'del'\n",
    "\n",
    "        elif len(ref_allele_i) < len(alt_allele_i):\n",
    "            INS_or_DEL = 'ins'\n",
    "                \n",
    "        genomic_5_to_3_coord = 'None'\n",
    "        codon_coord = 'None'\n",
    "        \n",
    "        #store relevant info in lists    \n",
    "        gene_intergenic_id_list.append(gene_intergenic_id)\n",
    "        genomic_coord_list.append('None')\n",
    "        IF_FS_list.append('I')\n",
    "        INS_DEL_list.append(INS_or_DEL)\n",
    "        codon_coord_list.append('None')\n",
    "    \n",
    "    #if there is only a single gene associated with this INDEL, just return the individual elememts\n",
    "    if len(gene_intergenic_id_list) == 1:\n",
    "        return [gene_intergenic_id , genomic_5_to_3_coord , INS_or_DEL , INDEL_type , codon_coord]\n",
    "    \n",
    "    #else if there are two genes associated with this SNP, return elements for each SNP annotation in a list\n",
    "    elif len(gene_intergenic_id_list) > 1:\n",
    "        return [gene_intergenic_id_list , genomic_coord_list , INS_DEL_list , IF_FS_list , codon_coord_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterate through each unique Reference Allele - Alternate Allele pair and get relevant annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create lists for each new column of the INDEL functional annotation DF\n",
    "gene_intergenic_id_list = []\n",
    "gene_pos_list = []\n",
    "INS_DEL_list = []\n",
    "INDEL_type_list = []\n",
    "codon_coord_list = []\n",
    "\n",
    "for INDEL_i in INDEL_functional_annotation_DF.index:\n",
    "    \n",
    "    #get the reference allele , reference position & alternate allele from index key\n",
    "    ref_allele_i , ref_pos_i , alt_allele_i = INDEL_i.split('_')\n",
    "    \n",
    "    #get functional annotation for INDEL\n",
    "    gene_intergenic_id_i , gene_pos_i , INS_or_DEL_i , INDEL_type_i , codon_coord_i = INDEL_type(ref_allele_i , int(ref_pos_i) , alt_allele_i)\n",
    "    \n",
    "    #store functional annotation info in lists\n",
    "    gene_intergenic_id_list.append(gene_intergenic_id_i)\n",
    "    gene_pos_list.append(gene_pos_i)\n",
    "    INS_DEL_list.append(INS_or_DEL_i)\n",
    "    INDEL_type_list.append(INDEL_type_i)\n",
    "    codon_coord_list.append(codon_coord_i)\n",
    "    \n",
    "#append new columns to INDEL functional annotation\n",
    "INDEL_functional_annotation_DF['gene_id'] = gene_intergenic_id_list\n",
    "INDEL_functional_annotation_DF['gene_pos'] = gene_pos_list\n",
    "INDEL_functional_annotation_DF['ins_del'] = INS_DEL_list\n",
    "INDEL_functional_annotation_DF['INDEL_type'] = INDEL_type_list\n",
    "INDEL_functional_annotation_DF['codon_pos'] = codon_coord_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 10)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_functional_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>ins_del</th>\n",
       "      <th>INDEL_type</th>\n",
       "      <th>codon_pos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CCGGCGATGCGT_2715054_C</th>\n",
       "      <td>2715054</td>\n",
       "      <td>CCGGCGATGCGT</td>\n",
       "      <td>C</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>279</td>\n",
       "      <td>del</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A_2714706_AC</th>\n",
       "      <td>2714706</td>\n",
       "      <td>A</td>\n",
       "      <td>AC</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>627</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_2714933_CG</th>\n",
       "      <td>2714933</td>\n",
       "      <td>C</td>\n",
       "      <td>CG</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>400</td>\n",
       "      <td>ins</td>\n",
       "      <td>frameshift</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            pos           ref alt  gene_category gene_name  \\\n",
       "key                                                                          \n",
       "CCGGCGATGCGT_2715054_C  2715054  CCGGCGATGCGT   C  Non-Essential       eis   \n",
       "A_2714706_AC            2714706             A  AC  Non-Essential       eis   \n",
       "C_2714933_CG            2714933             C  CG  Non-Essential       eis   \n",
       "\n",
       "                        gene_id  gene_pos ins_del  INDEL_type  codon_pos  \n",
       "key                                                                       \n",
       "CCGGCGATGCGT_2715054_C  Rv2416c       279     del  frameshift         93  \n",
       "A_2714706_AC            Rv2416c       627     ins  frameshift        209  \n",
       "C_2714933_CG            Rv2416c       400     ins  frameshift        134  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_functional_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__: \n",
    "\n",
    "The gene position/coordinate for the original INDEL annotation file (whose rows map to the rows of the Genotypes Matrix) starts counting from 0, this is to accomodate the functional INDEL annotation. \n",
    "\n",
    "The gene position/coordinate for the functional INDEL annotation (above) starts counting from 1 which is more intuitive for reporting purposes."
   ]
  }
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