course-files/python/02-pandas-for-manipulating-gff3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ff89b15",
   "metadata": {},
   "source": [
    "# Before you begin\n",
    "\n",
    "## Update your course repository\n",
    "\n",
    "You need to clone the course repository to Nova. You probably already have cloned this, so you can skip this step.\n",
    "\n",
    "```bash\n",
    "git clone git@github.com:EEOB-BioData/BCB546_Spring2024.git\n",
    "```\n",
    "\n",
    "You will still need to pull new changes to this repository at the beginning of class. This will enable you to access new data files and scripts needed for in-class activities.\n",
    "\n",
    "```bash\n",
    "cd BCB546_Spring2024\n",
    "git pull\n",
    "```\n",
    "\n",
    "Note that if you have modified any files in the repository, you will need to commit those changes before you can pull new changes. If you don't care about the changes, just delete and re-clone the repository.\n",
    "\n",
    "## Start Jupyter notebook on Nova on demand.\n",
    "\n",
    "You can start Jupyter notebook on Nova on demand. This will allow you to run Jupyter notebook on the server and access it from your local machine.\n",
    "\n",
    "1. Go to the [Nova OnDemand](https://nova-ondemand.its.iastate.edu/) and login\n",
    "2. Under the \"Interactive Apps\" tab, click on \"Jupyter Notebook\", request desired resources and click \"Launch\"\n",
    "3. Wait for the job to start and click on the \"Connect to Jupyter\" button\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31053aa9",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "For this short exercise, we will be using the `pandas` library in Python to manipulate a GFF3 file.\n",
    "GFF3 is a file format used for storing genomic features in a plain text format. It is a tab-delimited file that describes the features of a genome assembly. Each line in the file represents a feature, such as a gene, exon, or CDS. These features can be nested, eg: CDS, and exons are within the mRNA feature, mRNA is within the gene feature. \n",
    "The file is divided into nine columns (usually referred as attributes column), will contain feature specific information and is not standardized. The first eight columns are mandatory and are as follows:\n",
    "\n",
    "1. `seqid`: chromosome or scaffold the feature is located.  \n",
    "2. `source`: Source of the feature, eg: a database or tool that generated the feature.\n",
    "3. `type`: feature type, eg: `gene`, `mRNA`, `CDS`, `exon` etc.\n",
    "4. `start`: genomic start position of the feature.\n",
    "5. `end`: genomic end position of the feature.\n",
    "6. `score`: Feature score, can be a floating-point number, missing is represented by \"`.`\".\n",
    "7. `strand`: strand of the feature, can be + or -, missing is represented by \"`.`\".\n",
    "8. `phase`: usually for `CDS` feature, and can be `0`, `1`, or `2`. missing is represented by \"`.`\".\n",
    "9. `attributes`: a semicolon-separated list of tag-value pairs, where the tag and value are separated by `=`. The tag-value pairs are feature specific.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77fa4a2",
   "metadata": {},
   "source": [
    "## Importing the required libraries\n",
    "\n",
    "We will be using the `pandas` library to read and manipulate the GFF3 file. We will also use the `os` library to check if the file exists. Later for plotting, we will also use the `seaborn` library. And one specific function from `matplotlib` library to save the plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d6216b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af312afb",
   "metadata": {},
   "source": [
    "## Download the GFF3 files\n",
    "\n",
    "We will use maize annotation file for B73 downloaded from MaizeGDB for this tutorial. The file is loacted [here](https://download.maizegdb.org/Zm-B73-REFERENCE-NAM-5.0/) but it has been pre-downloaded to the working directory. \n",
    "\n",
    "The file name is `Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz` \n",
    "\n",
    "These are unix commands and you can simply prefix `!` to run them on Jupyter notebook.\n",
    "\n",
    "### in bash (note the exclamation point in front of standard commands)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a5c87e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-04-13 12:01:32--  https://download.maizegdb.org/Zm-B73-REFERENCE-NAM-5.0/Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz\n",
      "Resolving download.maizegdb.org (download.maizegdb.org)... 104.26.10.112, 172.67.74.80, 104.26.11.112, ...\n",
      "Connecting to download.maizegdb.org (download.maizegdb.org)|104.26.10.112|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 12251264 (12M) [application/x-gzip]\n",
      "Saving to: ‘Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz’\n",
      "\n",
      "Zm-B73-REFERENCE-NA 100%[===================>]  11.68M  75.8MB/s    in 0.2s    \n",
      "\n",
      "2024-04-13 12:01:32 (75.8 MB/s) - ‘Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz’ saved [12251264/12251264]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://download.maizegdb.org/Zm-B73-REFERENCE-NAM-5.0/Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz\n",
    "!gunzip Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f3f350",
   "metadata": {},
   "source": [
    "**Note**: If you rerun this cell, you will likely get an error since the file already exists in the directory. Be sure to either delete or skip this cell when rerunning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3681812f",
   "metadata": {},
   "source": [
    "## Reading the GFF3 file\n",
    "\n",
    "We will use the `pd.read_csv` function to read the GFF3 file. Since the file is tab-delimited, we will use the `sep` argument to specify the delimiter. We will also use the `comment` argument to skip the header lines in the file. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21bcce8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gff3_file = \"Zm-B73-REFERENCE-NAM-5.0_Zm00001eb.1.gff3\"\n",
    "columns = ['seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes']\n",
    "gff3_df = pd.read_csv(gff3_file, sep='\\t', comment='#', header=None, names=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df67c36",
   "metadata": {},
   "source": [
    "with `os` library, you can check if the file exists before reading it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcd1815d",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists(gff3_file):\n",
    "    gff3_df = pd.read_csv(gff3_file, sep='\\t', comment='#', header=None, names=columns)\n",
    "else:\n",
    "    print(f\"{gff3_file} does not exist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7fa873",
   "metadata": {},
   "source": [
    "## 1. Explore the data\n",
    "\n",
    "Let's take a look at the first few rows of the dataframe to understand the structure of the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb804303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>assembly</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>308452471</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=1;Name=chromosome:Zm-B73-REFERENCE-NAM-5.0:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>gene</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010;biotype=protein_coding;logi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>five_prime_UTR</td>\n",
       "      <td>34617</td>\n",
       "      <td>34721</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb000010_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>exon</td>\n",
       "      <td>34617</td>\n",
       "      <td>35318</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb000010_T001;Name=Zm00001eb0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid    source            type  start        end score strand phase  \\\n",
       "0  chr1  assembly      chromosome      1  308452471     .      .     .   \n",
       "1  chr1       NAM            gene  34617      40204     .      +     .   \n",
       "2  chr1       NAM            mRNA  34617      40204     .      +     .   \n",
       "3  chr1       NAM  five_prime_UTR  34617      34721     .      +     .   \n",
       "4  chr1       NAM            exon  34617      35318     .      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=1;Name=chromosome:Zm-B73-REFERENCE-NAM-5.0:...  \n",
       "1  ID=Zm00001eb000010;biotype=protein_coding;logi...  \n",
       "2  ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...  \n",
       "3                        Parent=Zm00001eb000010_T001  \n",
       "4  Parent=Zm00001eb000010_T001;Name=Zm00001eb0000...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gff3_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ec1392",
   "metadata": {},
   "source": [
    "and the last few rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73e10c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1143798</th>\n",
       "      <td>scaf_695</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>2336</td>\n",
       "      <td>4226</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb443030_T001;Parent=Zm00001eb443030...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143799</th>\n",
       "      <td>scaf_695</td>\n",
       "      <td>NAM</td>\n",
       "      <td>five_prime_UTR</td>\n",
       "      <td>4218</td>\n",
       "      <td>4226</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb443030_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143800</th>\n",
       "      <td>scaf_695</td>\n",
       "      <td>NAM</td>\n",
       "      <td>exon</td>\n",
       "      <td>2336</td>\n",
       "      <td>4226</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb443030_T001;Name=Zm00001eb4430...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143801</th>\n",
       "      <td>scaf_695</td>\n",
       "      <td>NAM</td>\n",
       "      <td>CDS</td>\n",
       "      <td>2898</td>\n",
       "      <td>4217</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=Zm00001eb443030_P001;Parent=Zm00001eb443030...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143802</th>\n",
       "      <td>scaf_695</td>\n",
       "      <td>NAM</td>\n",
       "      <td>three_prime_UTR</td>\n",
       "      <td>2336</td>\n",
       "      <td>2897</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb443030_T001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            seqid source             type  start   end score strand phase  \\\n",
       "1143798  scaf_695    NAM             mRNA   2336  4226     .      -     .   \n",
       "1143799  scaf_695    NAM   five_prime_UTR   4218  4226     .      -     .   \n",
       "1143800  scaf_695    NAM             exon   2336  4226     .      -     .   \n",
       "1143801  scaf_695    NAM              CDS   2898  4217     .      -     0   \n",
       "1143802  scaf_695    NAM  three_prime_UTR   2336  2897     .      -     .   \n",
       "\n",
       "                                                attributes  \n",
       "1143798  ID=Zm00001eb443030_T001;Parent=Zm00001eb443030...  \n",
       "1143799                        Parent=Zm00001eb443030_T001  \n",
       "1143800  Parent=Zm00001eb443030_T001;Name=Zm00001eb4430...  \n",
       "1143801  ID=Zm00001eb443030_P001;Parent=Zm00001eb443030...  \n",
       "1143802                        Parent=Zm00001eb443030_T001  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gff3_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd31980e",
   "metadata": {},
   "source": [
    "To examine the columns and the type of object they are assigned to, we can use the info method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "268444ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1143803 entries, 0 to 1143802\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count    Dtype \n",
      "---  ------      --------------    ----- \n",
      " 0   seqid       1143803 non-null  object\n",
      " 1   source      1143803 non-null  object\n",
      " 2   type        1143803 non-null  object\n",
      " 3   start       1143803 non-null  int64 \n",
      " 4   end         1143803 non-null  int64 \n",
      " 5   score       1143803 non-null  object\n",
      " 6   strand      1143803 non-null  object\n",
      " 7   phase       1143803 non-null  object\n",
      " 8   attributes  1143803 non-null  object\n",
      "dtypes: int64(2), object(7)\n",
      "memory usage: 78.5+ MB\n"
     ]
    }
   ],
   "source": [
    "gff3_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ae3496",
   "metadata": {},
   "source": [
    "Let's see the unique values in the `type` column. These are the features in the GFF3 file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c245818a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['chromosome', 'gene', 'mRNA', 'five_prime_UTR', 'exon', 'CDS',\n",
       "       'three_prime_UTR', 'scaffold'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gff3_df['type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed738c1",
   "metadata": {},
   "source": [
    "We can also see what the unique values in the `seqid` column are.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01db2e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8',\n",
       "       'chr9', 'chr10', 'scaf_22', 'scaf_23', 'scaf_30', 'scaf_32',\n",
       "       'scaf_33', 'scaf_34', 'scaf_36', 'scaf_37', 'scaf_45', 'scaf_48',\n",
       "       'scaf_49', 'scaf_54', 'scaf_56', 'scaf_61', 'scaf_66', 'scaf_67',\n",
       "       'scaf_72', 'scaf_77', 'scaf_82', 'scaf_87', 'scaf_88', 'scaf_94',\n",
       "       'scaf_100', 'scaf_103', 'scaf_106', 'scaf_107', 'scaf_110',\n",
       "       'scaf_113', 'scaf_114', 'scaf_126', 'scaf_136', 'scaf_138',\n",
       "       'scaf_139', 'scaf_143', 'scaf_146', 'scaf_153', 'scaf_155',\n",
       "       'scaf_158', 'scaf_160', 'scaf_161', 'scaf_173', 'scaf_174',\n",
       "       'scaf_178', 'scaf_179', 'scaf_181', 'scaf_183', 'scaf_186',\n",
       "       'scaf_187', 'scaf_190', 'scaf_191', 'scaf_194', 'scaf_196',\n",
       "       'scaf_198', 'scaf_200', 'scaf_203', 'scaf_209', 'scaf_210',\n",
       "       'scaf_212', 'scaf_214', 'scaf_219', 'scaf_221', 'scaf_222',\n",
       "       'scaf_223', 'scaf_224', 'scaf_228', 'scaf_229', 'scaf_231',\n",
       "       'scaf_232', 'scaf_235', 'scaf_237', 'scaf_241', 'scaf_243',\n",
       "       'scaf_246', 'scaf_250', 'scaf_253', 'scaf_258', 'scaf_259',\n",
       "       'scaf_264', 'scaf_273', 'scaf_274', 'scaf_275', 'scaf_277',\n",
       "       'scaf_278', 'scaf_279', 'scaf_281', 'scaf_282', 'scaf_288',\n",
       "       'scaf_298', 'scaf_300', 'scaf_301', 'scaf_304', 'scaf_307',\n",
       "       'scaf_309', 'scaf_311', 'scaf_313', 'scaf_316', 'scaf_317',\n",
       "       'scaf_328', 'scaf_332', 'scaf_339', 'scaf_345', 'scaf_348',\n",
       "       'scaf_350', 'scaf_355', 'scaf_357', 'scaf_359', 'scaf_360',\n",
       "       'scaf_362', 'scaf_370', 'scaf_372', 'scaf_375', 'scaf_380',\n",
       "       'scaf_381', 'scaf_382', 'scaf_383', 'scaf_386', 'scaf_388',\n",
       "       'scaf_398', 'scaf_399', 'scaf_409', 'scaf_410', 'scaf_412',\n",
       "       'scaf_420', 'scaf_435', 'scaf_443', 'scaf_444', 'scaf_445',\n",
       "       'scaf_446', 'scaf_461', 'scaf_463', 'scaf_465', 'scaf_469',\n",
       "       'scaf_475', 'scaf_487', 'scaf_489', 'scaf_490', 'scaf_494',\n",
       "       'scaf_498', 'scaf_507', 'scaf_512', 'scaf_514', 'scaf_516',\n",
       "       'scaf_518', 'scaf_519', 'scaf_521', 'scaf_524', 'scaf_527',\n",
       "       'scaf_532', 'scaf_538', 'scaf_539', 'scaf_542', 'scaf_545',\n",
       "       'scaf_554', 'scaf_556', 'scaf_562', 'scaf_568', 'scaf_576',\n",
       "       'scaf_579', 'scaf_582', 'scaf_585', 'scaf_587', 'scaf_592',\n",
       "       'scaf_604', 'scaf_605', 'scaf_612', 'scaf_621', 'scaf_623',\n",
       "       'scaf_625', 'scaf_640', 'scaf_644', 'scaf_651', 'scaf_656',\n",
       "       'scaf_659', 'scaf_664', 'scaf_666', 'scaf_670', 'scaf_674',\n",
       "       'scaf_675', 'scaf_692', 'scaf_695'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gff3_df['seqid'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0873a9b5",
   "metadata": {},
   "source": [
    "It looks like the GFF3 file has annotations for chromsomes and scaffolds. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c937fddf",
   "metadata": {},
   "source": [
    "## 2. Filtering the data\n",
    "\n",
    "Since we are interested in the chr features only, we can filter the dataframe to include `seqid` column that have a match to `chr`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d4ad414",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = gff3_df['seqid'].str.contains('chr')\n",
    "chr_gff3_df = gff3_df[filter]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06977cd2",
   "metadata": {},
   "source": [
    "Check the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f43292f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>assembly</td>\n",
       "      <td>chromosome</td>\n",
       "      <td>1</td>\n",
       "      <td>308452471</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=1;Name=chromosome:Zm-B73-REFERENCE-NAM-5.0:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>gene</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010;biotype=protein_coding;logi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>five_prime_UTR</td>\n",
       "      <td>34617</td>\n",
       "      <td>34721</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb000010_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>exon</td>\n",
       "      <td>34617</td>\n",
       "      <td>35318</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb000010_T001;Name=Zm00001eb0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  seqid    source            type  start        end score strand phase  \\\n",
       "0  chr1  assembly      chromosome      1  308452471     .      .     .   \n",
       "1  chr1       NAM            gene  34617      40204     .      +     .   \n",
       "2  chr1       NAM            mRNA  34617      40204     .      +     .   \n",
       "3  chr1       NAM  five_prime_UTR  34617      34721     .      +     .   \n",
       "4  chr1       NAM            exon  34617      35318     .      +     .   \n",
       "\n",
       "                                          attributes  \n",
       "0  ID=1;Name=chromosome:Zm-B73-REFERENCE-NAM-5.0:...  \n",
       "1  ID=Zm00001eb000010;biotype=protein_coding;logi...  \n",
       "2  ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...  \n",
       "3                        Parent=Zm00001eb000010_T001  \n",
       "4  Parent=Zm00001eb000010_T001;Name=Zm00001eb0000...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr_gff3_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ad0204aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1138113</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>exon</td>\n",
       "      <td>152286718</td>\n",
       "      <td>152287382</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb434590_T001;Name=Zm00001eb4345...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138114</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>CDS</td>\n",
       "      <td>152285016</td>\n",
       "      <td>152286200</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138115</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>CDS</td>\n",
       "      <td>152286281</td>\n",
       "      <td>152286588</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>2</td>\n",
       "      <td>ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138116</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>CDS</td>\n",
       "      <td>152286718</td>\n",
       "      <td>152287096</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138117</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>three_prime_UTR</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152285015</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>Parent=Zm00001eb434590_T001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid source             type      start        end score strand  \\\n",
       "1138113  chr10    NAM             exon  152286718  152287382     .      -   \n",
       "1138114  chr10    NAM              CDS  152285016  152286200     .      -   \n",
       "1138115  chr10    NAM              CDS  152286281  152286588     .      -   \n",
       "1138116  chr10    NAM              CDS  152286718  152287096     .      -   \n",
       "1138117  chr10    NAM  three_prime_UTR  152284763  152285015     .      -   \n",
       "\n",
       "        phase                                         attributes  \n",
       "1138113     .  Parent=Zm00001eb434590_T001;Name=Zm00001eb4345...  \n",
       "1138114     0  ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...  \n",
       "1138115     2  ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...  \n",
       "1138116     0  ID=Zm00001eb434590_P001;Parent=Zm00001eb434590...  \n",
       "1138117     .                        Parent=Zm00001eb434590_T001  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr_gff3_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a621e28",
   "metadata": {},
   "source": [
    "Does the output look as expected?\n",
    "\n",
    "Other common filters include filtering by feature type. For example, to filter only the `gene` features, we can use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1c0d569",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = chr_gff3_df['type'] == 'gene'\n",
    "gene_df = chr_gff3_df[filter]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea0260c",
   "metadata": {},
   "source": [
    "same can be done for `mRNA`, `CDS`, `exon` features as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4dd51eb",
   "metadata": {},
   "source": [
    "## 3. Format conversion\n",
    "\n",
    "A common task in bioinformatics is format conversion. We frequently need convert files from one format to another. \n",
    "The genomic coordinates in GFF3 files are 1-based, but other formats, such as [BED file format](https://useast.ensembl.org/info/website/upload/bed.html), is 0-based coordinates.\n",
    "\n",
    "![zero-based-and-one-based-coordinates](https://github.com/EEOB-BioData/BCB546_Spring2024/assets/4835524/17f19f18-06e0-442a-b5b5-d4cd285c4fdc)\n",
    "\n",
    "source: [biostars.org](https://www.biostars.org/p/84686/)\n",
    "\n",
    "We will extract only `mRNA` features from the GFF3 file and convert the coordinates to 0-based and save it as a bed file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6338a397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>41263</td>\n",
       "      <td>46050</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>41214</td>\n",
       "      <td>43902</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>41314</td>\n",
       "      <td>46039</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>41214</td>\n",
       "      <td>46762</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152010430</td>\n",
       "      <td>152012584</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152012893</td>\n",
       "      <td>152015823</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152200925</td>\n",
       "      <td>152201511</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152280251</td>\n",
       "      <td>152282615</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152287382</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71791 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid source  type      start        end score strand phase  \\\n",
       "2         chr1    NAM  mRNA      34617      40204     .      +     .   \n",
       "24        chr1    NAM  mRNA      41263      46050     .      -     .   \n",
       "43        chr1    NAM  mRNA      41214      43902     .      -     .   \n",
       "62        chr1    NAM  mRNA      41314      46039     .      -     .   \n",
       "85        chr1    NAM  mRNA      41214      46762     .      -     .   \n",
       "...        ...    ...   ...        ...        ...   ...    ...   ...   \n",
       "1138073  chr10    NAM  mRNA  152010430  152012584     .      -     .   \n",
       "1138079  chr10    NAM  mRNA  152012893  152015823     .      -     .   \n",
       "1138095  chr10    NAM  mRNA  152200925  152201511     .      +     .   \n",
       "1138101  chr10    NAM  mRNA  152280251  152282615     .      +     .   \n",
       "1138109  chr10    NAM  mRNA  152284763  152287382     .      -     .   \n",
       "\n",
       "                                                attributes  \n",
       "2        ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...  \n",
       "24       ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...  \n",
       "43       ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...  \n",
       "62       ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...  \n",
       "85       ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...  \n",
       "...                                                    ...  \n",
       "1138073  ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...  \n",
       "1138079  ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...  \n",
       "1138095  ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...  \n",
       "1138101  ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...  \n",
       "1138109  ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...  \n",
       "\n",
       "[71791 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'mRNA'\n",
    "mRNA_df = chr_gff3_df[filter]\n",
    "mRNA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e997f2b9",
   "metadata": {},
   "source": [
    "select the columns we need\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2937e01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>attributes</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41263</td>\n",
       "      <td>46050</td>\n",
       "      <td>ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>43902</td>\n",
       "      <td>ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41314</td>\n",
       "      <td>46039</td>\n",
       "      <td>ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>46762</td>\n",
       "      <td>ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152010430</td>\n",
       "      <td>152012584</td>\n",
       "      <td>ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152012893</td>\n",
       "      <td>152015823</td>\n",
       "      <td>ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152200925</td>\n",
       "      <td>152201511</td>\n",
       "      <td>ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152280251</td>\n",
       "      <td>152282615</td>\n",
       "      <td>ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152287382</td>\n",
       "      <td>ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71791 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid      start        end  \\\n",
       "2         chr1      34617      40204   \n",
       "24        chr1      41263      46050   \n",
       "43        chr1      41214      43902   \n",
       "62        chr1      41314      46039   \n",
       "85        chr1      41214      46762   \n",
       "...        ...        ...        ...   \n",
       "1138073  chr10  152010430  152012584   \n",
       "1138079  chr10  152012893  152015823   \n",
       "1138095  chr10  152200925  152201511   \n",
       "1138101  chr10  152280251  152282615   \n",
       "1138109  chr10  152284763  152287382   \n",
       "\n",
       "                                                attributes score strand  \n",
       "2        ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...     .      +  \n",
       "24       ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...     .      -  \n",
       "43       ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...     .      -  \n",
       "62       ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...     .      -  \n",
       "85       ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...     .      -  \n",
       "...                                                    ...   ...    ...  \n",
       "1138073  ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...     .      -  \n",
       "1138079  ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...     .      -  \n",
       "1138095  ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...     .      +  \n",
       "1138101  ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...     .      +  \n",
       "1138109  ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...     .      -  \n",
       "\n",
       "[71791 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mRNA_df = mRNA_df[['seqid', 'start', 'end', 'attributes', 'score', 'strand']]\n",
    "mRNA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb1fe6b",
   "metadata": {},
   "source": [
    "For the attribute column, we only need the first attribute which is the ID of the mRNA feature. We will extract this information and add it as a new column in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44782991",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/job-temp/arnstrm/5146429/ipykernel_842960/3649785668.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mRNA_df['mRNA_id'] = mRNA_df['attributes'].str.split(';', expand=True)[0].str.split('=', expand=True)[1]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>attributes</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>mRNA_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>Zm00001eb000010_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41263</td>\n",
       "      <td>46050</td>\n",
       "      <td>ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb000020_T002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>43902</td>\n",
       "      <td>ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb000020_T004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41314</td>\n",
       "      <td>46039</td>\n",
       "      <td>ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb000020_T003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>46762</td>\n",
       "      <td>ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb000020_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152010430</td>\n",
       "      <td>152012584</td>\n",
       "      <td>ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb434550_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152012893</td>\n",
       "      <td>152015823</td>\n",
       "      <td>ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb434560_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152200925</td>\n",
       "      <td>152201511</td>\n",
       "      <td>ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>Zm00001eb434570_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152280251</td>\n",
       "      <td>152282615</td>\n",
       "      <td>ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>Zm00001eb434580_T001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152287382</td>\n",
       "      <td>ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>Zm00001eb434590_T001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71791 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid      start        end  \\\n",
       "2         chr1      34617      40204   \n",
       "24        chr1      41263      46050   \n",
       "43        chr1      41214      43902   \n",
       "62        chr1      41314      46039   \n",
       "85        chr1      41214      46762   \n",
       "...        ...        ...        ...   \n",
       "1138073  chr10  152010430  152012584   \n",
       "1138079  chr10  152012893  152015823   \n",
       "1138095  chr10  152200925  152201511   \n",
       "1138101  chr10  152280251  152282615   \n",
       "1138109  chr10  152284763  152287382   \n",
       "\n",
       "                                                attributes score strand  \\\n",
       "2        ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...     .      +   \n",
       "24       ID=Zm00001eb000020_T002;Parent=Zm00001eb000020...     .      -   \n",
       "43       ID=Zm00001eb000020_T004;Parent=Zm00001eb000020...     .      -   \n",
       "62       ID=Zm00001eb000020_T003;Parent=Zm00001eb000020...     .      -   \n",
       "85       ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...     .      -   \n",
       "...                                                    ...   ...    ...   \n",
       "1138073  ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...     .      -   \n",
       "1138079  ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...     .      -   \n",
       "1138095  ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...     .      +   \n",
       "1138101  ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...     .      +   \n",
       "1138109  ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...     .      -   \n",
       "\n",
       "                      mRNA_id  \n",
       "2        Zm00001eb000010_T001  \n",
       "24       Zm00001eb000020_T002  \n",
       "43       Zm00001eb000020_T004  \n",
       "62       Zm00001eb000020_T003  \n",
       "85       Zm00001eb000020_T001  \n",
       "...                       ...  \n",
       "1138073  Zm00001eb434550_T001  \n",
       "1138079  Zm00001eb434560_T001  \n",
       "1138095  Zm00001eb434570_T001  \n",
       "1138101  Zm00001eb434580_T001  \n",
       "1138109  Zm00001eb434590_T001  \n",
       "\n",
       "[71791 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mRNA_df['mRNA_id'] = mRNA_df['attributes'].str.split(';', expand=True)[0].str.split('=', expand=True)[1]\n",
    "#mRNA_df.loc[:, 'mRNA_id'] = mRNA_df['attributes'].str.split(';', expand=True)[0].str.split('=', expand=True)[1]\n",
    "mRNA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e03c595",
   "metadata": {},
   "source": [
    "rearrange the columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1289a0f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>mRNA_id</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>Zm00001eb000010_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41263</td>\n",
       "      <td>46050</td>\n",
       "      <td>Zm00001eb000020_T002</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>43902</td>\n",
       "      <td>Zm00001eb000020_T004</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41314</td>\n",
       "      <td>46039</td>\n",
       "      <td>Zm00001eb000020_T003</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41214</td>\n",
       "      <td>46762</td>\n",
       "      <td>Zm00001eb000020_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152010430</td>\n",
       "      <td>152012584</td>\n",
       "      <td>Zm00001eb434550_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152012893</td>\n",
       "      <td>152015823</td>\n",
       "      <td>Zm00001eb434560_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152200925</td>\n",
       "      <td>152201511</td>\n",
       "      <td>Zm00001eb434570_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152280251</td>\n",
       "      <td>152282615</td>\n",
       "      <td>Zm00001eb434580_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152287382</td>\n",
       "      <td>Zm00001eb434590_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71791 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid      start        end               mRNA_id score strand\n",
       "2         chr1      34617      40204  Zm00001eb000010_T001     .      +\n",
       "24        chr1      41263      46050  Zm00001eb000020_T002     .      -\n",
       "43        chr1      41214      43902  Zm00001eb000020_T004     .      -\n",
       "62        chr1      41314      46039  Zm00001eb000020_T003     .      -\n",
       "85        chr1      41214      46762  Zm00001eb000020_T001     .      -\n",
       "...        ...        ...        ...                   ...   ...    ...\n",
       "1138073  chr10  152010430  152012584  Zm00001eb434550_T001     .      -\n",
       "1138079  chr10  152012893  152015823  Zm00001eb434560_T001     .      -\n",
       "1138095  chr10  152200925  152201511  Zm00001eb434570_T001     .      +\n",
       "1138101  chr10  152280251  152282615  Zm00001eb434580_T001     .      +\n",
       "1138109  chr10  152284763  152287382  Zm00001eb434590_T001     .      -\n",
       "\n",
       "[71791 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mRNA_df = mRNA_df[['seqid', 'start', 'end', 'mRNA_id', 'score', 'strand']]\n",
    "mRNA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e7e362",
   "metadata": {},
   "source": [
    "Now we will convert the start position to 0-based by subtracting 1 from the start position.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "393a748f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/job-temp/arnstrm/5146429/ipykernel_842960/3365232138.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mRNA_df['start'] = mRNA_df['start'] - 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>mRNA_id</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34616</td>\n",
       "      <td>40204</td>\n",
       "      <td>Zm00001eb000010_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41262</td>\n",
       "      <td>46050</td>\n",
       "      <td>Zm00001eb000020_T002</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41213</td>\n",
       "      <td>43902</td>\n",
       "      <td>Zm00001eb000020_T004</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41313</td>\n",
       "      <td>46039</td>\n",
       "      <td>Zm00001eb000020_T003</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>41213</td>\n",
       "      <td>46762</td>\n",
       "      <td>Zm00001eb000020_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152010429</td>\n",
       "      <td>152012584</td>\n",
       "      <td>Zm00001eb434550_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152012892</td>\n",
       "      <td>152015823</td>\n",
       "      <td>Zm00001eb434560_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152200924</td>\n",
       "      <td>152201511</td>\n",
       "      <td>Zm00001eb434570_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152280250</td>\n",
       "      <td>152282615</td>\n",
       "      <td>Zm00001eb434580_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>152284762</td>\n",
       "      <td>152287382</td>\n",
       "      <td>Zm00001eb434590_T001</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71791 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid      start        end               mRNA_id score strand\n",
       "2         chr1      34616      40204  Zm00001eb000010_T001     .      +\n",
       "24        chr1      41262      46050  Zm00001eb000020_T002     .      -\n",
       "43        chr1      41213      43902  Zm00001eb000020_T004     .      -\n",
       "62        chr1      41313      46039  Zm00001eb000020_T003     .      -\n",
       "85        chr1      41213      46762  Zm00001eb000020_T001     .      -\n",
       "...        ...        ...        ...                   ...   ...    ...\n",
       "1138073  chr10  152010429  152012584  Zm00001eb434550_T001     .      -\n",
       "1138079  chr10  152012892  152015823  Zm00001eb434560_T001     .      -\n",
       "1138095  chr10  152200924  152201511  Zm00001eb434570_T001     .      +\n",
       "1138101  chr10  152280250  152282615  Zm00001eb434580_T001     .      +\n",
       "1138109  chr10  152284762  152287382  Zm00001eb434590_T001     .      -\n",
       "\n",
       "[71791 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mRNA_df['start'] = mRNA_df['start'] - 1\n",
    "mRNA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd477035",
   "metadata": {},
   "source": [
    "Finally, we will save the dataframe as a bed file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbcbe511",
   "metadata": {},
   "outputs": [],
   "source": [
    "mRNA_df.to_csv('B73v5_mRNA.bed', sep='\\t', index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22138a5",
   "metadata": {},
   "source": [
    "Congratulations! You have successfully converted a GFF3 file to a BED file!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3069e762",
   "metadata": {},
   "source": [
    "## 4. Summary Statistics\n",
    "\n",
    "Another interesting task is to calculate summary statistics of various features in your GFF3 file. We can calculate the length of each feature by subtracting the start position from the end position (since it is 0-based).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a7e74ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/job-temp/arnstrm/5146429/ipykernel_842960/3516859215.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  chr_gff3_df['feature_length'] = chr_gff3_df['end'] - chr_gff3_df['start']\n"
     ]
    }
   ],
   "source": [
    "chr_gff3_df['feature_length'] = chr_gff3_df['end'] - chr_gff3_df['start']\n",
    "# chr_gff3_df.loc[:, 'feature_length'] = chr_gff3_df['end'] - chr_gff3_df['start'] # better way"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6153778c",
   "metadata": {},
   "source": [
    "Total sum of gene feature lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b18407ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176074616"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr_gff3_df[chr_gff3_df['type'] == 'gene']['feature_length'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f10362",
   "metadata": {},
   "source": [
    "This is the total length of all the genes in the GFF3 file. For a genome of 2.4Gb, this is a very small fraction of the genome. \n",
    "\n",
    "\n",
    "Using the `groupby` method, we can calculate various summary statistics for each feature type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2fd52197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</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>CDS</th>\n",
       "      <td>401806</td>\n",
       "      <td>2.156939e+02</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9095</td>\n",
       "      <td>3.014736e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chromosome</th>\n",
       "      <td>10</td>\n",
       "      <td>2.131847e+08</td>\n",
       "      <td>206081181.5</td>\n",
       "      <td>152435370</td>\n",
       "      <td>308452470</td>\n",
       "      <td>4.841196e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon</th>\n",
       "      <td>450442</td>\n",
       "      <td>2.899984e+02</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10978</td>\n",
       "      <td>3.813191e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five_prime_UTR</th>\n",
       "      <td>93346</td>\n",
       "      <td>1.974762e+02</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4010</td>\n",
       "      <td>2.005234e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <td>39035</td>\n",
       "      <td>4.510686e+03</td>\n",
       "      <td>2715.0</td>\n",
       "      <td>212</td>\n",
       "      <td>751400</td>\n",
       "      <td>8.883022e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>71791</td>\n",
       "      <td>5.404865e+03</td>\n",
       "      <td>3577.0</td>\n",
       "      <td>212</td>\n",
       "      <td>745091</td>\n",
       "      <td>8.351370e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_prime_UTR</th>\n",
       "      <td>81688</td>\n",
       "      <td>3.109432e+02</td>\n",
       "      <td>273.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4893</td>\n",
       "      <td>2.551815e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean       median        min        max  \\\n",
       "type                                                                       \n",
       "CDS              401806  2.156939e+02        121.0          0       9095   \n",
       "chromosome           10  2.131847e+08  206081181.5  152435370  308452470   \n",
       "exon             450442  2.899984e+02        147.0          0      10978   \n",
       "five_prime_UTR    93346  1.974762e+02        144.0          0       4010   \n",
       "gene              39035  4.510686e+03       2715.0        212     751400   \n",
       "mRNA              71791  5.404865e+03       3577.0        212     745091   \n",
       "three_prime_UTR   81688  3.109432e+02        273.0          0       4893   \n",
       "\n",
       "                          std  \n",
       "type                           \n",
       "CDS              3.014736e+02  \n",
       "chromosome       4.841196e+07  \n",
       "exon             3.813191e+02  \n",
       "five_prime_UTR   2.005234e+02  \n",
       "gene             8.883022e+03  \n",
       "mRNA             8.351370e+03  \n",
       "three_prime_UTR  2.551815e+02  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr_gff3_df.groupby('type')['feature_length'].agg(['count', 'mean', 'median', 'min', 'max', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f410de1f",
   "metadata": {},
   "source": [
    "\n",
    "change display options to show the values correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e1e73d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0ea126ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</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>CDS</th>\n",
       "      <td>401806</td>\n",
       "      <td>215.69</td>\n",
       "      <td>121.00</td>\n",
       "      <td>0</td>\n",
       "      <td>9095</td>\n",
       "      <td>301.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chromosome</th>\n",
       "      <td>10</td>\n",
       "      <td>213,184,679.50</td>\n",
       "      <td>206,081,181.50</td>\n",
       "      <td>152435370</td>\n",
       "      <td>308452470</td>\n",
       "      <td>48,411,960.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exon</th>\n",
       "      <td>450442</td>\n",
       "      <td>290.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10978</td>\n",
       "      <td>381.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five_prime_UTR</th>\n",
       "      <td>93346</td>\n",
       "      <td>197.48</td>\n",
       "      <td>144.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4010</td>\n",
       "      <td>200.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <td>39035</td>\n",
       "      <td>4,510.69</td>\n",
       "      <td>2,715.00</td>\n",
       "      <td>212</td>\n",
       "      <td>751400</td>\n",
       "      <td>8,883.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>71791</td>\n",
       "      <td>5,404.86</td>\n",
       "      <td>3,577.00</td>\n",
       "      <td>212</td>\n",
       "      <td>745091</td>\n",
       "      <td>8,351.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_prime_UTR</th>\n",
       "      <td>81688</td>\n",
       "      <td>310.94</td>\n",
       "      <td>273.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4893</td>\n",
       "      <td>255.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count           mean         median        min        max  \\\n",
       "type                                                                          \n",
       "CDS              401806         215.69         121.00          0       9095   \n",
       "chromosome           10 213,184,679.50 206,081,181.50  152435370  308452470   \n",
       "exon             450442         290.00         147.00          0      10978   \n",
       "five_prime_UTR    93346         197.48         144.00          0       4010   \n",
       "gene              39035       4,510.69       2,715.00        212     751400   \n",
       "mRNA              71791       5,404.86       3,577.00        212     745091   \n",
       "three_prime_UTR   81688         310.94         273.00          0       4893   \n",
       "\n",
       "                          std  \n",
       "type                           \n",
       "CDS                    301.47  \n",
       "chromosome      48,411,960.37  \n",
       "exon                   381.32  \n",
       "five_prime_UTR         200.52  \n",
       "gene                 8,883.02  \n",
       "mRNA                 8,351.37  \n",
       "three_prime_UTR        255.18  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr_gff3_df.groupby('type')['feature_length'].agg(['count', 'mean', 'median', 'min', 'max', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5b1ba3",
   "metadata": {},
   "source": [
    "To save them as a csv file, we can use the `to_csv` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d47d2159",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_stats = chr_gff3_df.groupby('type')['feature_length'].agg(['count', 'mean', 'median', 'min', 'max', 'std'])\n",
    "summary_stats.to_csv('summary_stats.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01bc6b1b",
   "metadata": {},
   "source": [
    "\n",
    "Also, if you want a specific feature stats, you can also do this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b1871daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    39,035.00\n",
       "mean      4,510.69\n",
       "std       8,883.02\n",
       "min         212.00\n",
       "25%       1,239.00\n",
       "50%       2,715.00\n",
       "75%       5,059.00\n",
       "max     751,400.00\n",
       "Name: feature_length, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'gene'\n",
    "gene_df = chr_gff3_df[filter]\n",
    "gene_df['feature_length'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f2bf73",
   "metadata": {},
   "source": [
    "This will give you the summary statistics for the gene features only.\n",
    "\n",
    "\n",
    "\n",
    "## 5. Visualizations\n",
    "\n",
    "\n",
    "Generating count plots is a great way to visualize the distribution of features in your GFF3 file. We can use the `seaborn` library to generate count plots.\n",
    "\n",
    "Some settings that can make the plot look good:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2997903b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['figure.figsize'] = 10,4\n",
    "plt.rcParams.update({'font.size': 8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "243782d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=chr_gff3_df, x='type');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8288d512",
   "metadata": {},
   "source": [
    "Maybe we should look at single feature across the `seqid`\n",
    "\n",
    "For `gene` features:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7adad0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'gene'\n",
    "sns.countplot(data=chr_gff3_df[filter], x='seqid');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee0cd95",
   "metadata": {},
   "source": [
    "For `mRNA` features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ed03b75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'mRNA'\n",
    "sns.countplot(data=chr_gff3_df[filter], x='seqid');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beed9a48",
   "metadata": {},
   "source": [
    "We also generate histograms to visualize the distribution of feature lengths.\n",
    "\n",
    "Let's check the `CDS` feature length distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "64a19f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'CDS'\n",
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73dd74f",
   "metadata": {},
   "source": [
    "To make the distribution more interpretable, we can use a log scale on the x-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "74e888c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=50, log_scale=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331335b5",
   "metadata": {},
   "source": [
    "you can adjust the bins for more granularity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d8d222e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=200, log_scale=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efbd4ac",
   "metadata": {},
   "source": [
    "or can also be plotted as a kernel density estimate plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0ee9137d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=200, kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5555019f",
   "metadata": {},
   "source": [
    "Similarly, you can generate histograms for other features as well.\n",
    "\n",
    "For `gene` features:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "56d33bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter = chr_gff3_df['type'] == 'gene'\n",
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=150, log_scale=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df317035",
   "metadata": {},
   "source": [
    "To save plots, you can either use the `%%` magic command in Jupyter notebook or use the `savefig` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3da651d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='feature_length', ylabel='Count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABqQAAALgCAYAAADyVhZRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AABlxklEQVR4nOzde5iXdZ0//ufADAcHRMUDMHIQRRFqLfHQF76ilnlIkdTcTNc8oGburrWCum2uea1uVoibbftbV9Ko/XoITVOyyDXRTSAzMVcDDA8IzuAhwkMyM8Dw+f3hxawIzAw492cGeDyua65r5n6/3vf9uj9C90XPeb/vilKpVAoAAAAAAAAUpEtHNwAAAAAAAMC2TSAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChKju6ATpWQ0NDnn766STJbrvtlspKfyQAAAAAAGB7tWbNmrz++utJkg9/+MPp0aNHu5xX+rCde/rpp3PIIYd0dBsAAAAAAEAn85vf/CYHH3xwu5xru9iy78UXX8zUqVNz/vnn54ADDkhlZWUqKipyzTXXtDp37ty5GT9+fHbbbbf07NkzI0aMyNVXX52GhoYW5y1YsCBnnHFG+vfvnx49emTvvffOpEmT8sYbb7Q4r7a2NhdccEEGDhyY7t27Z9CgQfnCF76Q2trazbllAAAAAACATqOiVCqVOrqJon35y1/ODTfcsMHxq6++OldcccUm5916660566yz0tTUlJqamuy+++555plnsnr16hx88MF5+OGHs8MOO2wwb9asWTn++ONTX1+f3XbbLQMHDszChQuzcuXKDB06NHPmzMkee+yxwbz58+fnsMMOy5/+9Kf06dMne++9d55//vm8+eab6du3bx599NEMHz78g30Y77N48eLstddeSd5NOvv379+u5wcAAAAAALYey5Yta95Z7cUXX8yQIUPa5bzbxZZ9u+66a0444YQccsghOfjgg/O9730vP/7xj1ucs3jx4kyYMCFNTU351re+lUmTJqWioiIvvfRSjjnmmDz++OO57LLL8t3vfne9eW+//XY++9nPpr6+PhdffHGuu+66VFVVZfny5Rk/fnxmz56dCRMm5Kc//el685qamnLqqafmT3/6U0455ZT88Ic/zA477JB33nknn//853P33Xfns5/9bJ588sl06dJ+C9ve+86o/v37Z88992y3cwMAAAAAAFuv92YIH9R2sWXfFVdckRkzZuQf//Efc+yxx6ZXr16tzpk8eXIaGxtz9NFH59JLL01FRUWSZPDgwbnllluSJDfddFNeffXV9ebdeOONef3117P//vvn+uuvT1VVVZKkb9++ue2221JZWZn7778/8+bNW2/e3Xffnfnz56dv3775/ve/37zyqrq6OtOmTUvfvn3zP//zP7n33ns/8OcBAAAAAABQTttFILW5SqVS7rnnniTJhAkTNhgfPXp0hg8fntWrV28QEN19991JkrPPPjtdu3Zdb2zQoEE56qijkiR33XXXRuf95V/+ZXr37r3eWO/evXPqqacmSe68884tvS0AAAAAAIAOIZDaiCVLlmTZsmVJkjFjxmy0Zt3xxx57rPnYmjVr8sQTT2z2vCT59a9/vUXzWvPyyy+3+LXuPgEAAAAAAIqyXbxDanMtWrQoSdK9e/cMGDBgozVDhw5drzZ5971Tq1evXm+8LfNWrVqVJUuWtGneumus2wqwNQMHDmxTHQAAAAAAQFGskNqIFStWJEl22mmn5ndHvd/OO++8Xu37v1833pZ5b775ZtauXdumeWvXrs1bb73VpvsAAAAAAADoDKyQ2oiGhoYkSbdu3TZZ07179yRJfX39BvNamvtB571/bmuWLl3a4viyZctyyCGHtPl8AAAAAAAAm0sgtRE9evRI8u5WepvS2NiYJOnZs+cG89bNfe/PmzOvpeu9f25r9txzzzbXAgAAAAAAFMGWfRuxbnu8N954I6VSaaM167bce+8We+/9/r1b8rU2r0+fPunSpUub5nXp0iU77rhjm+4DAAAAAACgMxBIbcSwYcOSvLsqqa6ubqM1L7zwwnq1STJkyJBUVVWtN96Wed26dcugQYPaNO+91wAAAAAAANgaCKQ2YtCgQenXr1+SZPbs2RutWXf80EMPbT5WWVmZAw88cLPnvffnzZ0HAAAAAADQ2QmkNqKioiInnXRSkuTmm2/eYHzOnDlZuHBhqqqqcuKJJ643dvLJJydJpk2blqampvXGlixZkgcffDBJcsopp2x03vTp0/P222+vN/b222/nzjvvTJJ85jOf2dLbAgAAAAAA6BACqU249NJL061btzzwwAOZPHly87ukXnrppZx77rlJkvPOO695JdU6F154YXbdddcsWLAgl1xySVavXp0kWb58eU4//fSsWbMmxx13XEaNGrXevFNOOSXDhw/P8uXLc84552TlypVJknfeeSfnnHNOli9fng996EP59Kc/XfCdAwAAAAAAtK+K0rqkZRs2e/bsjB8/vvnnP//5z2lsbMwOO+yQnj17Nh9/8sknM3DgwOaff/jDH+acc87J2rVrU1NTk9133z3PPPNMVq9enVGjRuWRRx5JdXX1Btf75S9/mRNOOCENDQ3ZbbfdMmjQoCxYsCArV67MkCFDMnfu3A2CrCR55plnMnbs2KxYsSJ9+vTJPvvsk+eeey5vvvlmdtlll/zqV7/KiBEj2vWzefnll5vveenSpdlzzz3b9fwAAAAAAMDWo6jcYLtYIbV69eosX768+auxsTFJsnLlyvWOv3+Lvc9//vP51a9+lRNOOCH19fWZP39+hg4dmquuuiqPPvroRsOoJPnEJz6R3/72tznttNNSUVGRp59+OnvssUcuueSSzJs3b6NhVJJ86EMfylNPPZXzzjsvvXr1ytNPP51evXrl/PPPz1NPPdXuYRQAAAAAAEA5bBcrpNg0K6QAAAAAAIB1rJACAAAAAABgqySQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUJUd3QAAAGyuYcNHpK62tsWaATU1WbRwfpk6AgAAAFoikAIAYKtTV1ubcVNmtlgzY+KxZeoGAAAAaI0t+wAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQlV2dAMAANCRhg0fkbra2hZrBtTUZNHC+WXqCAAAALY9AikAALZrdbW1GTdlZos1MyYeW6ZuAAAAYNtkyz4AAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFCVHd0AAAAUoaGxMdW9+7Re11Bfhm4AAABg+yaQAgBgm1Ra25RxU2a2Wjf9orFl6AYAAAC2b7bsAwAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKVdnRDQAAsH0YNnxE6mprW60bUFOTRQvnl6EjAAAAoFwEUgAAlEVdbW3GTZnZat2MiceWoRsAAACgnARSAAB8YG1Z/dTQUF+mbjpOWz4HK8AAAADYHgmkAAD4wNqy+mn6RWPbdK6GxsZU9+7Tck0nDbfa8jlYAQYAAMD2SCAFAECnUlrb1G7hFgAAANA5dOnoBgAAAAAAANi2CaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAAChUZUc3AAAAnV1DY2Oqe/dpva6hvgzdAAAAwNZHIAUAAK0orW3KuCkzW62bftHYMnQDAAAAWx9b9gEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhars6AYAAOgYw4aPSF1tbYs1A2pqsmjh/DJ1BAAAAGyrBFIAANuputrajJsys8WaGROPLVM3AAAAwLbMln0AAAAAAAAUSiDVitdeey2TJk3KyJEjs8MOO6RHjx7Ze++9c8EFF+S5557b5Ly5c+dm/Pjx2W233dKzZ8+MGDEiV199dRoaGlq83oIFC3LGGWekf//+zdeaNGlS3njjjXa+MwAAAAAAgPIQSLXg2WefzYc//OFMmTIlixYtyqBBgzJs2LDU1dVl6tSpOeCAA/LII49sMO/WW2/NYYcdlvvuuy/du3fP/vvvn+eeey5XXnllxo4dm5UrV270erNmzcqoUaNy2223pampKSNHjswrr7ySKVOmZNSoUXn11VeLvmUAAArW0NiY6t59WvwaNnxER7cJAAAA7co7pFrw13/913nttdcyZsyY3HHHHdlzzz2TJMuXL8+5556b++67L+ecc06ef/75VFRUJEkWL16cCRMmpKmpKd/61rcyadKkVFRU5KWXXsoxxxyTxx9/PJdddlm++93vrnett99+O5/97GdTX1+fiy++ONddd12qqqqyfPnyjB8/PrNnz86ECRPy05/+tOyfAwAA7ae0tsm7uwAAANjuWCG1CStXrsysWbOSJP/+7//eHEYlSd++fTNt2rRUVFTkxRdfzMKFC5vHJk+enMbGxhx99NG59NJLm4OqwYMH55ZbbkmS3HTTTRusdrrxxhvz+uuvZ//998/111+fqqqq5mvddtttqayszP3335958+YVet8AAAAAAADtTSC1CatWrcratWuTJEOHDt1gfOedd84uu+ySJFmzZk2SpFQq5Z577kmSTJgwYYM5o0ePzvDhw7N69erce++9643dfffdSZKzzz47Xbt2XW9s0KBBOeqoo5Ikd9111we5LQAAAAAAgLITSG3CTjvtlIEDByZJ5syZs8H4s88+m+XLl2ennXbKsGHDkiRLlizJsmXLkiRjxozZ6HnXHX/ssceaj61ZsyZPPPHEZs9ri5dffrnFr3X9AgAAAAAAFMU7pFpwzTXX5Kyzzsq5556bb3/72zniiCNSWVmZX//61/nyl7+cioqKfOtb30qPHj2SJIsWLUqSdO/ePQMGDNjoOdettlpXm7z73qnVq1evN96WeW2xLlQDANgSDY2Nqe7dp/W6hvoydAMAAABsrQRSLfj85z+fXr165eqrr85nPvOZ9cb+4i/+Ij/72c9y7LH/+8LpFStWJHl3ddW6d0e9384777xe7fu/XzfelnkAAEUrrW3KuCkzW62bftHYMnQDAAAAbK0EUi0olUp54YUXsnz58nTt2jV77bVXunXrlueeey7PPPNMbrrpphxyyCHN75JqaGhIknTr1m2T5+zevXuSpL7+f3+LeN28luZubF5bLF26tMXxZcuW5ZBDDtmscwIAAAAAAGwOgVQLLrzwwtx0000ZPXp0/vu//ztDhgxJkrz22muZMGFC7rnnnjz//POZN29eunbt2rx136pVqzZ5zsbGxiRJz549m4+tm7du7nt/bmleW+y5556bVQ8AAAAAANDeunR0A53VU089lalTp6aqqip33HFHcxiVJLvvvntuvfXW7Lrrrvmf//mfTJ8+Pcn/bqv3xhtvpFQqbfS867bce+/WfO/9flNb8m1sHgAAAAAAwNZAILUJs2fPTqlUyr777puBAwduML7jjjs2b3X329/+NkkybNiwJO+uZqqrq9voeV944YX1apNkyJAhqaqqWm+8LfMAAAAAAAC2BgKpTXj77bdbrVm3CmrdO6AGDRqUfv36JXk30NqYdccPPfTQ5mOVlZU58MADN3seAAAAAADA1kAgtQnrViL94Q9/yNKlSzcYf+utt/L4448nSfbdd98kSUVFRU466aQkyc0337zBnDlz5mThwoWpqqrKiSeeuN7YySefnCSZNm1ampqa1htbsmRJHnzwwSTJKaec8kFuCwAAAAAAoOwEUptw9NFHZ9ddd83q1atz2mmnZfHixc1jr732Ws4444z88Y9/TI8ePfKZz3ymeezSSy9Nt27d8sADD2Ty5MnNq6heeumlnHvuuUmS8847r3kl1ToXXnhhdt111yxYsCCXXHJJVq9enSRZvnx5Tj/99KxZsybHHXdcRo0aVfCdAwAAAAAAtC+B1Cb06tUrP/zhD9OjR4/MmTMn++yzT/bdd9+MHDkyAwcOzE9/+tNUVlbmxhtvTE1NTfO8vfbaK1OnTk2XLl1y2WWXZeDAgTnwwAMzbNiwPPvssxk1alQmT568wfV23HHH3HHHHenRo0e+853vpKamJgcddFAGDRqU2bNnZ8iQIbnlllvK+REAAAAAAAC0C4FUC4477rg89dRTueCCC7LXXntlyZIlee6559K/f/+ceeaZeeyxx3LWWWdtMO/zn/98fvWrX+WEE05IfX195s+fn6FDh+aqq67Ko48+murq6o1e7xOf+ER++9vf5rTTTktFRUWefvrp7LHHHrnkkksyb968DVZVAQAAAAAAbA0qO7qBzm7ffffNf/zHf2z2vNGjR2fGjBmbPW/kyJG5/fbbN3seAADbjobGxlT37tNq3YCamixaOL8MHQEAAMAHI5ACAIBOprS2KeOmzGy1bsbEY8vQDQAAAHxwtuwDAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAAClXZ0Q0AAPCuYcNHpK62tsWaATU1WbRwfpk6AgAAAGgfAikAgE6irrY246bMbLFmxsRj23SutoRbDQ31be4NAAAA4IMQSAEAfACddVVTW8Kt6ReNLVM3AAAAwPZOIAUA8AG056omAAAAgG1Vl45uAAAAAAAAgG2bQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKFRlRzcAANBZDRs+InW1tS3WNDTUl6kbAAAAgK2XQAoAYBPqamszbsrMFmumXzS2TN0AAAAAbL1s2QcAAAAAAEChrJACANiKNDQ2prp3n9brbCUIAAAAdCICKQBgu9OWd0MlnTPUKa1tanUbwcRWggAAAEDnIpACALY7bXk3VCLUAQAAAGgv3iEFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFKqyoxsAAGhPw4aPSF1tbYs1DQ31ZeoGAAAAgEQgBQBsJdoSNCXvhk2n/tsjLdZMv2hse7UFAAAAQBsIpACArUJdbW3GTZnZap2wCQAAAKDz8Q4pAAAAAAAACiWQAgAAAAAAoFC27AMAKFhDY2Oqe/dpva6hvgzdAAAAAJSfQAoAoGCltU3efwUAAABs12zZBwAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUqrKjGwAAALZMQ2Njqnv3abFmQE1NFi2cX6aOAAAAYOMEUgAAsJUqrW3KuCkzW6yZMfHYMnUDAAAAm2bLPgAAAAAAAAolkAIAAAAAAKBQtuwDAIBtWFveM5V41xQAAADFEkgBAMA2rC3vmUq8awoAAIBi2bIPAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCVXZ0AwAAQMdraGxMde8+LdYMqKnJooXzy9QRAAAA2xKBFAAAkNLapoybMrPFmhkTjy1TNwAAAGxrbNkHAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEqO7oBAIBhw0ekrra2xZqGhvoydQMAAABAexNIAQCFamvYdOq/PdJizfSLxrZnWwAAAACUkUAKAChUXW1txk2Z2WKNsAkAAABg2+YdUm3Q1NSUqVOn5vDDD8+uu+6aHj16ZPDgwfn0pz+de++9d6Nz5s6dm/Hjx2e33XZLz549M2LEiFx99dVpaGho8VoLFizIGWeckf79+6dHjx7Ze++9M2nSpLzxxhsF3BkAAAAAAEDxBFKtWLFiRf7v//2/ueCCC/KrX/0qu+66az70oQ9l9erVuffee/Of//mfG8y59dZbc9hhh+W+++5L9+7ds//+++e5557LlVdembFjx2blypUbvdasWbMyatSo3HbbbWlqasrIkSPzyiuvZMqUKRk1alReffXVom8XAAAAAACg3QmkWrB27dqceOKJ+fWvf52TTz45S5YsycKFC/Pb3/42dXV1Wbp0aS6++OL15ixevDgTJkxIU1NTvvWtb2Xp0qWZN29eFi1alP322y+PP/54Lrvssg2u9fbbb+ezn/1s6uvrc/HFF6e2tjZPPPFElixZkjFjxuSFF17IhAkTynXrAAAAAAAA7UYg1YKbbropjz76aI488sjceeed2XPPPdcb33PPPTN27PrvvJg8eXIaGxtz9NFH59JLL01FRUWSZPDgwbnllluaz/v+1U433nhjXn/99ey///65/vrrU1VVlSTp27dvbrvttlRWVub+++/PvHnzirpdAAAAAACAQgikWnDDDTckSa6++up06dL6R1UqlXLPPfckyUZXM40ePTrDhw9v3u7vve6+++4kydlnn52uXbuuNzZo0KAcddRRSZK77rpr828EgK3esOEjUt27T4tfw4aP6Og2AQAAAGCjKju6gc5q0aJFWbhwYXbZZZeMHj069957b+68884sW7Ysu+22W4466qiceeaZ6d69e/OcJUuWZNmyZUmSMWPGbPS8Y8aMycKFC/PYY4/lggsuSJKsWbMmTzzxRKvzZs6cmccee2yz7uPll19ucXxdvwB0bnW1tRk3ZWaLNTMmHlumbgAAAABg8wikNmFdQDR8+PCceeaZufXWW9cb/9GPfpQpU6Zk5syZGTx4cJJ3Q6wk6d69ewYMGLDR8w4dOnS92uTd906tXr16vfG2zGuLgQMHblY9AABsSkNjY6p792mxZkBNTRYtnF+mjgAAANhaCKQ2Yd3Koccffzxz5szJeeedlyuuuCL9+vXLo48+mgsuuCALFy7MKaeckt/85jfp0qVLVqxYkSTZaaedmt8d9X4777xzkjTXvv/7deNtmQcAAOVUWttktSYAAABbRCC1Ce+8806SZPXq1TnssMMyderU5rFPfOITufvuu/PRj340TzzxRO6///6MGzcuDQ0NSZJu3bpt8rzrtvirr69vPrZuXktzNzavLZYuXdri+LJly3LIIYds1jkBAAAAAAA2h0BqE3r06NH8/Ze+9KUNxg844IAceeSReeihhzJz5syMGzeuec6qVas2ed7GxsYkSc+ePTd6rVWrVq33c0vz2mLPPffcrHoA2s+w4SNSV1vbYo2trQAAAADYHgikNuG9W+cNHz58ozX7779/HnrooSxevHi9OW+88UZKpdJGt+1bt+Xee8//3u9XrFiR/v37t2keAJ1bXW2tra0AAAAAIEmXjm6gs9pvv/2av1+3Xd77rTve1NSUJBk2bFiSd1cz1dXVbXTOCy+8sF5tkgwZMiRVVVXrjbdlHgAAAAAAwNZAILUJH/3oR5u3zmstJKqpqUmSDBo0KP369UuSzJ49e6Nz1h0/9NBDm49VVlbmwAMP3Ox5AAAAAAAAWwOB1CZUV1fnU5/6VJLkBz/4wQbjr7zySn7xi18kST7+8Y8nSSoqKnLSSSclSW6++eYN5syZMycLFy5MVVVVTjzxxPXGTj755CTJtGnTmldcrbNkyZI8+OCDSZJTTjnlg9wWAAAAAABA2QmkWnDllVema9euueOOO9YLpd54442cffbZqa+vz9ChQ3Pqqac2j1166aXp1q1bHnjggUyePDmlUilJ8tJLL+Xcc89Nkpx33nnNK6nWufDCC7PrrrtmwYIFueSSS7J69eokyfLly3P66adnzZo1Oe644zJq1KiibxsAAAAAAKBdCaRacMABB+S73/1uSqVSzj777AwePDgHH3xwampq8otf/CK77rprfvzjH6dbt27Nc/baa69MnTo1Xbp0yWWXXZaBAwfmwAMPzLBhw/Lss89m1KhRmTx58gbX2nHHHXPHHXekR48e+c53vpOampocdNBBGTRoUGbPnp0hQ4bklltuKeftA7AdGzZ8RKp792nxa9jwER3dJgAAAABbicqObqCzu/DCCzNy5MhMnjw5c+fOzf/8z/9kwIABOf744/OVr3yl+f1R7/X5z38+++yzT6699trMmTMn8+fPz9ChQ/O5z30ul19+efO7qd7vE5/4RH7729/mmmuuyUMPPZSnn346NTU1Oemkk3LFFVdk5513Lvp2ASBJUldbm3FTZrZYM2PisWXqBgAAAICtnUCqDQ477LAcdthhmzVn9OjRmTFjxmZfa+TIkbn99ts3ex4AAAAAAEBnJZACALZIQ2Njqnv3ab2uob4M3QAAAADQmQmkAIAtUlrb1Oq2fkky/aKxZegGAAAAgM5MIAUAHcgqIwAAAAC2BwIpAOhAVhkBAAAAsD3o0tENAAAAAAAAsG0TSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIUSSAEAAAAAAFCoyo5uAAAor2HDR6SutrbFmoaG+jJ1AwAAAMD2QCAFANuZutrajJsys8Wa6ReNLVM3AAAAAGwPbNkHAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKHKEkj98Ic/zC9+8Ys21T7wwAP54Q9/WHBHAAAAAAAAlEtZAqmzzz47X//619tUe+211+acc84puCMAAAAAAADKpWxb9pVKpXatAwAAAAAAYOvQ6d4h9corr6S6urqj2wAAAAAAAKCdVBZx0iVLlmTx4sXrHXvzzTfz3//935ucU19fn0ceeSR/+MMfcuihhxbRFgAAAAAAAB2gkEDq+9//fv7pn/5pvWPPPPNMjjzyyBbnrduu78tf/nIRbQEAAAAAANABCgmkhgwZkrFjxzb//Mgjj2THHXfMRz7ykY3WV1RUpGfPnhk6dGg++9nP5v/+3/9bRFsA0G6GDR+RutraFmsaGurL1A0AAAAAdG6FBFJnnXVWzjrrrOafu3Tpkg9/+MOZNWtWEZcDgLKrq63NuCkzW6yZftHYFscBAAAAYHtRSCD1frNmzUqfPn3KcSkAAAAAAAA6mbIEUocffng5LgMAAAAAAEAnVJZA6r3efPPNvPDCC/nzn/+cUqm0ybr3voMKAAAAAACArVfZAqn//u//zt///d/nsccea7W2oqIia9asKUNXAAAAAAAAFK1s75A69thjs3r16nTv3j1DhgzJ7rvvni5dupTj8gAAAAAAAHSgsgRSX/va17J69eqcccYZ+fa3v52+ffuW47IAAAAAAAB0AmUJpObNm5eddtop06ZNS9euXctxSQAAAAAAADqJsuyZ161bt+yzzz7CKAAAAAAAgO1QWQKpQw89NIsXL06pVCrH5QAAAAAAAOhEyhJIfe1rX8ubb76Z6667rhyXAwAAAAAAoBMpyzukBgwYkOuvvz4TJ07MnDlzMmHChOy9996prq7e5JxBgwaVozUAAAAAAAAKVpZAasiQIamoqEipVMp9992X++67r8X6ioqKrFmzphytAQAAAAAAULCyBFKDBg1KRUVFOS4FANuthsbGVPfu03pdQ30ZugEAAACA/1WWQGrx4sXluAwAbNdKa5sybsrMVuumXzS2DN0AAAAAwP/q0tENAAAAAAAAsG0TSAEAAAAAAFCosmzZt2TJks2eM2jQoAI6AQAAAAAAoNzKEkgNGTIkFRUVba6vqKjImjVrCuwIAAAAAACAcilLIDVo0KBNBlLvvPNO/vjHPyZJqqqqMmDAgHK0BAAAAAAAQJmUJZBavHhxi+NvvfVWpk6dmquvvjqnn356/vmf/7kcbQGwjRg2fETqamtbrRtQU5NFC+eXoSMAAAAA4L3KEki1Zscdd8zEiRMzcuTIHH/88Rk+fHjOPPPMjm4LgK1EXW1txk2Z2WrdjInHtlrT1nCroaG+Tb0BAAAAAJ0kkFrn2GOPzeDBg3PDDTcIpADoEG0Nt6ZfNLYM3QAAAADAtqFLRzfwfjvttFMWLlzY0W0AAAAAAADQTjpVIPXaa69lwYIF6dGjR0e3AgAAAAAAQDvpFIHUH//4x/z85z/Pcccdl1WrVuWoo47q6JYAAAAAAABoJ2V5h1TXrl3bVFcqldKvX7984xvfKLgjAAAAAAAAyqUsgVSpVGpxvLq6OkOHDs1xxx2XSZMmZddddy1HWwAAAAAAAJRBWQKptWvXluMyAAAAAAAAdEJlCaQAoDNoaGxMde8+Ldc01JepGwAAAADYfgikANhulNY2ZdyUmS3WTL9obJm6AQAAAIDtR1kDqcbGxtx+++154IEH8oc//CFvv/12evfunX333TfHHHNMTjvttHTv3r2cLQEAAAAAAFCwsgVS8+bNy6mnnprFixenVCptMPajH/0oV199daZPn54DDzywXG0BAAAAAABQsLIEUi+//HI++clPZsWKFdl1111z/vnnZ+TIkdljjz3y6quv5ve//32+973v5YUXXsgxxxyT3/3ud6mpqSlHawAAAAAAABSsLIHUtddemxUrVuTkk0/Of/7nf6Znz54b1PzjP/5jzjzzzPz4xz/Otddem+9+97vlaA0AAAAAAICCdSnHRX7+85+nuro606ZN22gYlSQ9evTI97///VRXV+dnP/tZOdoCAAAAAACgDMoSSNXV1WX//fdPr169Wqzr1atX9t9//yxbtqwcbQEAAAAAAFAGZQmkevfunVdffbVNta+++mqqq6sL7ggAAAAAAIByKUsgNWrUqLz88su54447Wqy7/fbbs3Tp0hx00EHlaAsAAAAAAIAyKEsg9bd/+7cplUo566yzMnHixLz44ovrjb/44ou55JJLcs4556SioiIXX3xxOdoCAAAAAACgDMoSSB1//PG5/PLLs3r16nz729/OPvvskx122CGDBw/ODjvskH322Sc33HBDVq1alb//+7/Ppz71qXK0BQAAAAAAQBlUlutC1157bcaMGZNvfOMb+fWvf52GhoYsXbo0SdKlS5eMHj06l19+eY4//vhytQTAVmDY8BGpq61tsaahob5M3QDQmobGxlT37tNq3YCamixaOL8MHQEAANAZlC2QSpITTjghJ5xwQt55550899xz+fOf/5xevXpln332SXV1dTlbAWArUVdbm3FTZrZYM/2isWXqBoDWlNY2tfq/20kyY+KxZegGAACAzqKwQOr3v/99nn/++ey+++752Mc+tt5YdXV1DjjggPWO/frXv85rr72WffbZJyNGjCiqLQAAYCvRllWyidVWAAAAW4NCAqmVK1fm6KOPzh//+MfMmjWrTXPWrl2bz3zmMxkwYECeffbZdO/evYjWAACArURbVskmVlsBAABsDboUcdLbb789y5Yty4QJEzJ69Og2zRk9enTOP//8LF26NHfccUcRbQEAAAAAANABCgmkfvKTn6SioiIXX3zxZs378pe/nFKplB//+MdFtAUAAAAAAEAHKCSQevLJJ9O/f/8MHz58s+YNGzYsNTU1efLJJ4toCwAAAAAAgA5QSCD1xz/+MTU1NVs0d8CAAfnjH//Yzh0BAAAAAADQUQoJpHr06JH6+votmltfX59u3bq1c0cAAAAAAAB0lEICqf79++f5559PY2PjZs1rbGzM888/nwEDBhTRFgAAAAAAAB2gkEDqsMMOS0NDQ+66667NmnfnnXemvr4+hx12WBFtAQAAAAAA0AEKCaTOPvvslEqlXH755Vm6dGmb5ixZsiSXXXZZKioqctZZZxXRFgAAAAAAAB2gkEBq9OjROfXUU1NXV5dDDz00d955Z9auXbvR2rVr12b69On52Mc+lldffTWnnHJKxowZU0RbANutYcNHpLp3nxa/hg0f0dFtAgAAAADbqMqiTjxt2rTU1tZmzpw5Oe2007LbbrtlzJgx2WuvvVJdXZ133nknL774YubMmZPXXnstpVIp/+f//J9MmzatqJYAtlt1tbUZN2VmizUzJh5bpm4AAAAAgO1NYYFUz5498/DDD+eqq67Kv/7rv+a1117LPffck4qKiuaaUqmUJOnVq1f+9m//NldddVWqqqqKagkAAAAAAIAOUFgglSSVlZW55pprctlll+X+++/PnDlzUltbm7fffju9e/dOTU1NRo8enU996lPp06dPka0AAAAAAADQQQoNpNbZcccd87nPfS6f+9znynE5AAAAAAAAOpEuHd0AAAAAAAAA2zaBFAAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQqMqObgAAANj+NDQ2prp3n5ZrGurL1A0AAABFE0gBAABlV1rblHFTZrZYM/2isWXqBgAAgKLZsg8AAAAAAIBCCaQAAAAAAAAolC37AOgww4aPSF1tbYs13h8CQGva8j6qATU1WbRwfpk6AgAA4P0EUgB0mLraWu8PAeADa8v7qGZMPLZM3QAAALAxtuwDAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAqnNcMUVV6SioiIVFRW55pprNlk3d+7cjB8/Prvttlt69uyZESNG5Oqrr05DQ0OL51+wYEHOOOOM9O/fPz169Mjee++dSZMm5Y033mjnOwEAAAAAACgfgVQbLViwIJMnT2617tZbb81hhx2W++67L927d8/++++f5557LldeeWXGjh2blStXbnTerFmzMmrUqNx2221pamrKyJEj88orr2TKlCkZNWpUXn311fa+JQAAAAAAgLIQSLVBqVTKF77whVRVVeXjH//4JusWL16cCRMmpKmpKd/61reydOnSzJs3L4sWLcp+++2Xxx9/PJdddtkG895+++189rOfTX19fS6++OLU1tbmiSeeyJIlSzJmzJi88MILmTBhQpG3CAAAAAAAUBiBVBvcfPPN+dWvfpUrr7wyAwcO3GTd5MmT09jYmKOPPjqXXnppKioqkiSDBw/OLbfckiS56aabNljtdOONN+b111/P/vvvn+uvvz5VVVVJkr59++a2225LZWVl7r///sybN6+gOwQAAAAAACiOQKoVr7/+ei6//PKMGDEif/d3f7fJulKplHvuuSdJNrqaafTo0Rk+fHhWr16de++9d72xu+++O0ly9tlnp2vXruuNDRo0KEcddVSS5K677vpA9wIAAAAAANARKju6gc7u7/7u7/KnP/0pd999d/PKpY1ZsmRJli1bliQZM2bMRmvGjBmThQsX5rHHHssFF1yQJFmzZk2eeOKJVufNnDkzjz322Gb3//LLL7c4vq5nAAAAAACAogikWvDLX/4yt956a/7qr/4qhx9+eIu1ixYtSpJ07949AwYM2GjN0KFD16tN3n3v1OrVq9cbb8u8tmppi0EAAAAAAIBysGXfJjQ0NOTCCy9Mnz59ct1117Vav2LFiiTJTjvt1PzuqPfbeeed16t9//frxtsyDwAAAAAAYGthhdQmXHPNNXnuuefy3e9+N3vssUer9Q0NDUmSbt26bbKme/fuSZL6+voN5rU0d2Pz2mrp0qUtji9btiyHHHLIZp8XAAAAAACgrQRSG7FgwYJMnjw5Bx54YL74xS+2aU6PHj2SJKtWrdpkTWNjY5KkZ8+eG8xbN/e9P7c0r6323HPPzZ4DAAAAAADQnmzZtxEXXXRR1qxZk3//939Ply5t+4jWbav3xhtvpFQqbbRm3ZZ7792a773fb2pLvo3NAwAAAAAA2FoIpDbiySefTEVFRU488cT069dvva8f/ehHSZJvfvOb6devXw4++OAkybBhw5K8u5qprq5uo+d94YUX1qtNkiFDhqSqqmq98bbMAwAAAAAA2FoIpDahqakpr7766gZf69759Oc//zmvvvpqXn/99STJoEGD0q9fvyTJ7NmzN3rOdccPPfTQ5mOVlZU58MADN3seAAAAAADA1kIgtRHrtt3b2NdZZ52VJLn66qtTKpWyePHiJElFRUVOOumkJMnNN9+8wTnnzJmThQsXpqqqKieeeOJ6YyeffHKSZNq0aWlqalpvbMmSJXnwwQeTJKecckq73icAAAAAAEA5CKTa0aWXXppu3brlgQceyOTJk5vfJfXSSy/l3HPPTZKcd955zSup1rnwwguz6667ZsGCBbnkkkuyevXqJMny5ctz+umnZ82aNTnuuOMyatSo8t4QAAAAAABAOxBItaO99torU6dOTZcuXXLZZZdl4MCBOfDAAzNs2LA8++yzGTVqVCZPnrzBvB133DF33HFHevToke985zupqanJQQcdlEGDBmX27NkZMmRIbrnllg64IwAAAAAAgA9OINXOPv/5z+dXv/pVTjjhhNTX12f+/PkZOnRorrrqqjz66KOprq7e6LxPfOIT+e1vf5vTTjstFRUVefrpp7PHHnvkkksuybx58zZYVQXQ3hoaG1Pdu0+rX8OGj2j1XMOGj2jTuRoa6stwZwAAAABAR6vs6Aa2NtOmTcu0adNarBk9enRmzJix2eceOXJkbr/99i3sDOCDKa1tyrgpM1utmzHx2FZr6mpr23Su6ReNbVNvAAAAAMDWzQopAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKJZACAAAAAACgUAIpAAAAAAAACiWQAgAAAAAAoFACKQAAAAAAAAolkAIAAAAAAKBQAikAAAAAAAAKVdnRDQAAABStobEx1b37tFgzoKYmixbOL1NHAAAA2xeBFAAAsM0rrW3KuCkzW6yZMfHYMnUDAACw/bFlHwAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIWq7OgGAAAAOoOGxsZU9+7Tat2AmposWji/DB0BAABsOwRSAAAASUprmzJuysxW62ZMPLYM3QAAAGxbbNkHAAAAAABAoayQAmCztGU7o4aG+jJ1AwAAAABsDQRSAGyWtmxnNP2isWXqBgAAAADYGtiyDwAAAAAAgEJZIQUAALAZ2rJ97YCamixaOL9MHQEAAHR+AikAAIDN0Jbta2dMPLZM3QAAAGwdbNkHAAAAAABAoayQAgAA6ADDho9IXW1tq3W2/wMAALYFAikAAIAOUFdb2+rWf4nt/wAAgG2DLfsAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAAChUZUc3AMDGDRs+InW1ta3WDaipyaKF88vQEQDQVg2Njanu3aflmob6MnUDAADQ8QRSAJ1UXW1txk2Z2WrdjInHlqEbAGBzlNY2tfocn37R2DJ1AwAA0PFs2QcAAAAAAEChBFIAAAAAAAAUypZ9AFs576gAAAAAADo7gRTAVs47KgAAAACAzs6WfQAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUyjukADrAsOEjUldb22JNQ0N9mboBAAAAACiWQAqgA9TV1mbclJkt1ky/aGyZugEAAAAAKJYt+wAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQlV2dAMAAABsWkNjY6p792mxZkBNTRYtnF+mjgAAADafQAoAAKATK61tyrgpM1usmTHx2DJ1AwAAsGUEUgBtNGz4iNTV1rZY47eTAQAAAAA2JJACaKO62lq/nQwAAAAAsAW6dHQDAAAAAAAAbNuskAJI27bja2ioL1M3AAAAAADbFoEUQNq2Hd/0i8aWqRsAAAAAgG2LLfsAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCCaQAAAAAAAAolEAKAAAAAACAQlV2dAMAW2rY8BGpq61tsWZATU0WLZxfpo4AAAAAANgYgRSw1aqrrc24KTNbrJkx8dgydQMAAAAAwKYIpAAAALZyDY2Nqe7dp9W61U1NqeratcUaK8wBAIAiCKQAAAC2cqW1Ta2uHE+S6ReNzcn/33+3WGOFOQAAUIQuHd0AAAAAAAAA2zYrpADaUVu3y2loqC9DNwAAAAAAnYNACqAdbc52OQAAAAAA2wuBFAAAAM3asuJ7QE1NFi2cX6aOAACAbYFACgAAgGZtWfE9Y+KxZeoGAADYVnTp6AYAAAAAAADYtgmkAAAAAAAAKJRACgAAAAAAgEIJpAAAAAAAACiUQGoTSqVSHn300Vx66aX52Mc+lp122indunXLgAEDcsopp2TWrFktzp87d27Gjx+f3XbbLT179syIESNy9dVXp6GhocV5CxYsyBlnnJH+/funR48e2XvvvTNp0qS88cYb7Xh3AAAAAAAA5VPZ0Q10Vg899FCOOuqoJEmXLl2yzz77pLq6OosWLcrdd9+du+++O1dccUWuvvrqDebeeuutOeuss9LU1JSampoMHDgwzzzzTK688srMmDEjDz/8cHbYYYcN5s2aNSvHH3986uvrs9tuu2XkyJFZuHBhpkyZknvuuSdz5szJHnvsUfi9Q5GGDR+RutraFmsG1NRk0cL57XK9hsbGVPfu03pdQ327XA8AAAAAgA0JpDahVCpln332ySWXXJLTTjstO++8c5Jk1apVueqqq3LttdfmmmuuyaGHHpoTTjihed7ixYszYcKENDU15Vvf+lYmTZqUioqKvPTSSznmmGPy+OOP57LLLst3v/vd9a739ttv57Of/Wzq6+tz8cUX57rrrktVVVWWL1+e8ePHZ/bs2ZkwYUJ++tOflvVzgPZWV1ubcVNmtlgzY+Kx7Xa90tqmVq+XJNMvGttu1wQAAAAAYH227NuEQw45JAsWLMgXv/jF5jAqSbp165avf/3rOe6445IkU6dOXW/e5MmT09jYmKOPPjqXXnppKioqkiSDBw/OLbfckiS56aab8uqrr64378Ybb8zrr7+e/fffP9dff32qqqqSJH379s1tt92WysrK3H///Zk3b15h9wwAAAAAAFAEgdQm7Ljjjqms3PQCsk9+8pNJkj/84Q/Nx0qlUu65554kyYQJEzaYM3r06AwfPjyrV6/Ovffeu97Y3XffnSQ5++yz07Vr1/XGBg0a1Lx94F133bUFdwMAAAAAANBxBFJbqKGhIUnSs2fP5mNLlizJsmXLkiRjxozZ6Lx1xx977LHmY2vWrMkTTzyx2fPa4uWXX27xa12/AAAAAAAARfEOqS1QKpVy5513Jlk/QFq0aFGSpHv37hkwYMBG5w4dOnS92uTd906tXr16vfG2zGuLgQMHblY9AAAAAABAe7NCagtMnTo1Tz75ZLp165Yvf/nLzcdXrFiRJNlpp52a3x31fuveR7Wu9v3fv/d9Va3NAwAAAAAA2BpYIbWZ5s2bly996UtJkmuuuSZ7771389i6bfy6deu2yfndu3dPktTX128wr6W5G5vXFkuXLm1xfNmyZTnkkEM265wAAAAAAACbQyC1GV588cWccMIJaWhoyOmnn55JkyatN96jR48kyapVqzZ5jsbGxiTrv3tq3bx1c9/7c0vz2mLPPffcrHoAAAAAAID2Zsu+NnrllVfyyU9+MsuWLcvxxx+fadOmbbAt37pt9d54442USqWNnmfdlnvv3Zrvvd9vaku+jc0DAAAAAADYGlgh1QZ/+tOf8slPfjLPP/98Dj/88Nx5552pqqraoG7YsGFJ3l3NVFdXl5qamg1qXnjhhfVqk2TIkCGpqqrK6tWr88ILL6R///5tmgedzbDhI1JXW9tiTUPD5m07CQAAAADA1k8g1Yo///nP+dSnPpVnnnkmBx98cGbMmLHJbfMGDRqUfv365ZVXXsns2bPzl3/5lxvUzJ49O0ly6KGHNh+rrKzMgQcemMceeyyzZ8/OmDFj2jQPOpu62tqMmzKzxZrpF40tUzcAAAAAAHQWtuxrQWNjY8aPH5/HHnssI0eOzMyZM9O7d+9N1ldUVOSkk05Kktx8880bjM+ZMycLFy5MVVVVTjzxxPXGTj755CTJtGnT0tTUtN7YkiVL8uCDDyZJTjnllA90T7Alhg0fkerefVr9svoJAAAAAICNsUJqE5qamnLaaafloYceyt57753/+q//yi677NLqvEsvvTQ333xzHnjggUyePDmTJk1KRUVFXnrppZx77rlJkvPOOy/9+vVbb96FF16YyZMnZ8GCBbnkkkty3XXXpaqqKsuXL8/pp5+eNWvW5LjjjsuoUaMKuV9oSVtWPiVWPwEAAAAAsHECqU2YPn16fvKTnyRJunTpklNPPXWjdf3798+dd97Z/PNee+2VqVOn5pxzzslll12WG264IbvvvnueeeaZrF69OqNGjcrkyZM3OM+OO+6YO+64IyeccEK+853v5Pbbb8+gQYOyYMGCrFy5MkOGDMktt9xSyL0CAAAAAAAUSSC1CY2Njc3fL1q0KIsWLdpo3eDBgzc49vnPfz777LNPrr322syZMyfz58/P0KFD87nPfS6XX355evTosdFzfeITn8hvf/vbXHPNNXnooYfy9NNPp6amJieddFKuuOKK7Lzzzu1zcwAAAAAAAGUkkNqEs88+O2efffYWzx89enRmzJix2fNGjhyZ22+/fYuvCwAAAAAA0Nl06egGAAAAAAAA2LYJpAAAAAAAACiUQAoAAAAAAIBCeYcU0Ok0NDamunef1usa6svQDQAAAAAAH5RACuh0SmubMm7KzFbrpl80tgzdAAAAAADwQQmkYDs3bPiI1NXWtlhjJRIAAAAAAB+EQAq2c3W1ta2uRrISCQCALdGWX34aUFOTRQvnl6kjAACgowikAAAAKERbfvlpxsRjy9QNAADQkbp0dAMAAAAAAABs2wRSAAAAAAAAFMqWfQAAAHSYhsbGVPfu02qdd00BAMDWTSAFAABAhymtbWr1PVOJd00BAMDWzpZ9AAAAAAAAFEogBQAAAAAAQKFs2QcAAMBmaet7nxoa6svQDQAAsDUQSAEAALBZ2vrep+kXjS1DNwAAwNbAln0AAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFMo7pAAAANgmDBs+InW1tS3WDKipyaKF88vUEQAAsI5ACgAAgG1CXW1txk2Z2WLNjInHlqkbAADgvWzZBwAAAAAAQKGskAIAAKDTa2hsTHXvPi3XNNSXqRsAAGBzCaQAAADo9Eprm1rdjm/6RWPL1A0AALC5bNkHAAAAAABAoayQAgAAgC0wbPiI1NXWtlo3oKYmixbOL0NHAADQeQmkAAAAYAvU1da2uo1gksyYeGwZugEAgM7Nln0AAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEqO7oBAAAAKJeGxsZU9+7Tat2AmposWji/DB0BAMD2QSAFAADAdqO0tinjpsxste7Ovzmy1eCqoaG+vdoCAIBtnkAKAAAA3qctwdX0i8aWqRsAANj6eYcUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIXyDikAAAAoUENjY6p792mxZkBNTRYtnF+mjgAAoPwEUgAAAFCg0tqmjJsys8WaGROPLVM3AADQMWzZBwAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEqO7oBAAAA2N41NDamunefFmsG1NRk0cL5ZeoIAADal0AKAAAAOlhpbVPGTZnZYs2MiceWqRsAAGh/tuwDAAAAAACgUFZIAQAAwFagLdv6Jbb2AwCgcxJIAQAAwFagLdv6Jbb2AwCgc7JlHwAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUKjKjm4AKMaw4SNSV1vbal1DQ30ZugEAAMqlobEx1b37tFgzoKYmixbOL1NHAAAgkIJtVl1tbcZNmdlq3fSLxpahGwAAoFxKa5ta/bfAjInHtulcbflFt7aEW239hTlBGQDAtksgBQAAAGxUW37RrS3hVlt/Ya6tQRkAAFsf75ACAAAAAACgUFZIAQAAwHamLe+ZSrxzFgCA9iOQAgAAgO1MW94zlbTtnbNtCbcEWwAACKQAAACALdaWcKstwRYAANs275ACAAAAAACgUFZIQZkMGz4idbW1rdYNqKnJooXzy9ARAAAAAACUh0AK2kFbwqaGhvqc+m+PtHquGROPba+2AAAAAACgUxBIQTuoq621ZzoAAAAAAGyCd0gBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIWq7OgGAAAAAJKkobEx1b37tFgzoKYmixbOL1NHAAC0F4EUAAAA0CmU1jZl3JSZLdbMmHhsmboBAKA92bIPAAAAAACAQgmkAAAAAAAAKJQt+2ArNGz4iNTV1rZY09BQX6ZuAAAAAACgZQIp2ArV1da2uq/69IvGlqkbAACA8mlobEx17z6t1g2oqcmihfPL0BEAAG0hkAIAAAC2GqW1Ta3+gl6SzJh4bBm6AQCgrbxDCgAAAAAAgEIJpAAAAAAAACiUQAoAAAAAAIBCeYcUtGLY8BGpq61tsaahob5M3QAAANBe2vLvvQE1NVm0cH6ZOgIA2HYJpKAVdbW1rb4wd/pFY8vUDQAAAO2lLf/emzHx2DJ1AwCwbRNIAQAAAGxCQ2Njqnv3abXOSioAgJYJpAAAAAA2obS2qdVVVImVVAAArenS0Q0AAAAAAACwbbNCCgAAANjmtGWrvYaG+jJ1AwCAQAoAAADY5rRlq73pF40tUzebZ9jwEamrrW2xZnVTU6q6dm2xxnutAIDORCAFAAAA8AG1ZUVWW0Kk5N2VW6f+2yMt1ky/aGxO/v/+u8Ua77UCADoTgRQAAADAB9TWFVmthUjr6gAAtjUCKQAAAIBtUFtWbSW29gMAykMg1Qn97Gc/y/XXX5958+alsbEx++23X84555z89V//dbp06dLR7QEAAABbgbas2kps7QcAlIdAqpP5xje+ka985StJkqFDh6ZXr1556qmncvHFF+fBBx/MPffcI5QCAAAA2k17vf/KSisAoCUCqU5k7ty5+Yd/+Id06dIl/+///b987nOfS5I89dRTOeaYY3Lffffl+uuvz6RJkzq4UwAAAGBb0V7vv7rzb45s0xaBbQm32lKTCMEAYGsikOpErrnmmpRKpZx//vnNYVSSHHDAAbn++utzxhln5Bvf+Ea+9KUvpaqqqgM7pUht+c20hob6MnUDAAAAbdPWLQLbEm61pSax3SAAbE0EUp3EW2+9lQcffDBJMmHChA3GTz311Hzxi1/M8uXLM2vWrBx99NHlbpEyaetvpgEAAMD2ri2/1Nmeq6iGDR+Rutrasl0PALYlAqlO4sknn8yqVavSo0ePHHjggRuMV1VV5eCDD84vf/nLPPbYY20OpF5++eUWx5cuXdr8/bJlyzav6e3E2rVrs3LFay3WlEqldqlpz3OV+3rteS69d8y59N4x59J7571ee55L7x1zLr13zLn03nmv157n0nvHnEvvHXMuvXfe6yXJ2qY1+cQVP2qx5qdX/GV6VvdusaZfv3751SOzWr3ey0uX5pNX/r8PfL3NuWZ7OezwI/PKK6+0WFPunnhXW/7bJP77AOXz3qxgzZo17XbeilKpVGq3s7HFvve97+X888/Pvvvum2effXajNRdccEGmTp2aM888Mz/84Q/bdN6Kior2bBMAAAAAANhO/OY3v8nBBx/cLufq0i5n4QNbsWJFkmTnnXfeZM26sXW1AAAAAAAAWwNb9nUSDQ0NSZJu3bptsqZ79+5Jkvr6+jaf971b8m3qugsXLswee+yR3XbbLZWV2/4fiY9//ONJkoceeqiDO+lctpXPpbPeR0f3Va7rF3mdIs7dHudctmxZDjnkkCTv/sZI//7926U3OoeO/rvbWW0rn0tnvY+O7sszo7hzemZs2zr6725nta18Lp31Pjq6L8+M4s7pmbFt6+i/u53VtvK5dNb76Oi+PDOKO2cRz4w1a9bk9ddfT5J8+MMf/sDnW2fbTx+2Ej169EiSrFq1apM1jY2NSZKePXu2+bx77rlnqzX77LNPm8+3LaiqqkrSts9me7KtfC6d9T46uq9yXb/I6xRx7vY+Z//+/Tvdnz0+mI7+u9tZbSufS2e9j47uyzOjPOf0zNj2dPTf3c5qW/lcOut9dHRfnhnlOadnxrano//udlbbyufSWe+jo/vyzCjPOdvzmTFkyJB2Oc972bKvk2jLdnxt2dYPAAAAAACgsxFIdRLDhg1LkixZsiRr1qzZaM0LL7ywXi0AAAAAAMDWQCDVSXz0ox9NVVVVGhoaMm/evA3GV69enccffzxJcuihh5a7PQAAAAAAgC1WUSqVSh3dBO/61Kc+lZ///Oe54IIL8h//8R/rjd12220544wz0rdv39TV1aVbt24d1CUA7/Xyyy9n4MCBSZKlS5d2uj2iAeg8PDMAaCvPDADaamt6Zlgh1Yl89atfTUVFRb73ve/l9ttvbz7+1FNP5ZJLLkmSXHbZZcIoAAAAAABgqyKQ6kTGjBmTq6++OmvXrs3pp5+evffeOwcccEAOPPDAvPrqqzn++OMzceLEjm4TAAAAAABgswikOpmvfvWrmTFjRj7+8Y9n+fLlee655/LhD3843/72t3Pvvfema9euHd0iAAAAAADAZvEOKQAAAAAAAAplhRQAAAAAAACFEkgBAAAAAABQKIEUAAAAAAAAhRJIAQAAAAAAUCiBFAAAAAAAAIUSSAEAAAAAAFAogRQAAAAAAACFEkgBAAAAAABQKIEUAJTRL37xixxxxBHZfffd07179wwePDjnn39+amtrO7o1ADqxNWvW5C/+4i9SUVGRO+64o6PbAaCTefjhh1NRUbHB10EHHdTRrQHQSd1222056KCD0rNnz/Tt2zef/OQn88c//rHQa1YWenYAYD1/+tOfcsghh+Tiiy9O3759s2jRovzTP/1THnrooTzzzDPp2bNnR7cIQCd0ww035PXXX+/oNgDo5L73ve9l5MiRzT/36tWrA7sBoLP65je/mSuvvDKXXnppJk+enHfeeScPP/xwGhsbC71uRalUKhV6BQCgRQ888ECOOeaYzJw5M8ccc0xHtwNAJ1NbW5v9998/3/3ud3PWWWfl9ttvz2mnndbRbQHQiTz88MM58sgjM3fu3HzsYx/r6HYA6MT+8Ic/ZOTIkfm3f/u3XHDBBWW9ti37AKCD9e3bN0lSWWnhMgAb+tKXvpQTTzwxY8eO7ehWAACArdz3v//99OjRI2effXbZry2QAmC79+KLL2bq1Kk5//zzc8ABB6SysjIVFRW55ppr2jT/Zz/7WY466qjssssuqa6uzoEHHph//dd/zdq1azc5p6mpKY2NjZk/f34uvfTSfPSjH83hhx/eXrcEQEHK/cyYOXNmHnjggUyePLk9bwOAMuiIf2eMHz8+Xbt2Tb9+/fKFL3whK1asaK/bAaBA5XxmzJ07N/vtt19+8IMfZPDgwamsrMxHP/rR/OIXv2jv29qAX8UGYLt3ww035IYbbtiiud/4xjfyla98JUkydOjQ9OrVK0899VQuvvjiPPjgg7nnnnvSpcuGv/8xcuTIPPvss0mSgw46KD//+c+tkALYCpTzmdHQ0JC/+Zu/yde+9rX0798/ixcvbo9bAKBMyvnM6NOnTyZNmpTDDz88vXr1yty5c3PttdfmN7/5TX7zm9+kqqqqXe4JgGKU85nxyiuvpLa2Nl/72tfyzW9+M7vvvnv+5V/+JePGjcvTTz+d/fbbr13uaWOskAJgu7frrrvmhBNOyD/90z/l5z//eU455ZQ2zZs7d27+4R/+IV26dMltt92W559/Pk899VTmzZuXPfbYI/fdd1+uv/76jc798Y9/nLlz52batGl5++2388lPfjJvvfVWe94WAAUo5zPj61//erp165aLL764iFsBoGDlfGZ89KMfzeTJk3PCCSfkiCOOyFe+8pXceuut+d3vfpe77rqriNsDoB2V85nR1NSUP//5z/ne976XM888M8ccc0zuueee7LLLLrnuuuuKuL3/VQIA1nPWWWeVkpSuvvrqFus+9alPlZKULrjggg3Gbr311lKSUt++fUurVq1q8TxLly4tde3atTR58uQP1DcA5VfUM2Px4sWl7t27l+66667SihUrSitWrCg99dRTpSSlm2++ufTGG28Ucj8AFKfc/85Yu3Ztqbq6unTJJZd8oL4BKL8inxmHHHJIKUlp5cqV69WPHz++NHr06Pa5gU2wQgoAtsBbb72VBx98MEkyYcKEDcZPPfXU7Ljjjlm+fHlmzZrV4rn23HPP9OvXL88991whvQLQsbbkmfHiiy+msbExn/nMZ7Lzzjtn5513zgEHHNB8jj322KN8NwBA2bTnvzPWqaioaNceAegctvSZMXLkyI0+G0qlUhoaGoprOLbsA4At8uSTT2bVqlXp0aNHDjzwwA3Gq6qqcvDBBydJHnvssRbP9fzzz6euri577713Ib0C0LG25JnxkY98JLNmzVrv6/bbb0+S/OM//mMeeOCB8t0AAGXTnv/OuO+++/LOO+801wOwbdnSZ8YJJ5yQUqmUX/7yl83HVq5cmblz52bUqFGF9uzt6QCwBRYtWpQkGTRoUCorN/44HTp0aH75y1821ybJSSedlFGjRuUv/uIv0qtXr8yfPz/XXXdd9txzz43+NgsAW78teWbstNNOOeKII9arWbx4cZJkxIgRGTt2bGH9AtBxtvTfGX/1V3+VoUOH5sADD0yvXr0yd+7cfOtb38pBBx3U5veQALB12dJnxqc//ekccsghOe+88/KNb3wju+++e/7lX/4l77zzTiZOnFhozwIpANgCK1asSJLsvPPOm6xZN7auNkk+9rGP5Uc/+lEmT56cNWvWZNCgQTnllFNy+eWXZ5dddim2aQA6xJY+MwDY/mzpM2PkyJG57bbb8u1vfzsNDQ3Zc88984UvfCFXXnnlJv9PSgC2blv6zOjSpUvuv//+TJo0KZdccknq6+tz6KGHZtasWdlvv/0K7dkTCQC2wLo9dbt167bJmu7duydJ6uvrm49dfvnlufzyy4ttDoBOZUufGe83ZMiQlEql9m0OgE5lS58ZX/nKV/KVr3yl2OYA6FQ+yL8zdt1110ybNq2w3jbFO6QAYAv06NEjSbJq1apN1jQ2NiZJevbsWZaeAOicPDMAaCvPDADaamt8ZgikAGALtGVrpbYsnQZg2+eZAUBbeWYA0FZb4zNDIAUAW2DYsGFJkiVLlmTNmjUbrXnhhRfWqwVg++SZAUBbeWYA0FZb4zNDIAUAW+CjH/1oqqqq0tDQkHnz5m0wvnr16jz++ONJkkMPPbTc7QHQiXhmANBWnhkAtNXW+MwQSAHAFthxxx1z1FFHJUluvvnmDcbvvPPOvPXWW+nbt2+OOOKIMncHQGfimQFAW3lmANBWW+MzQyAFAFvoq1/9aioqKvK9730vt99+e/Pxp556KpdcckmS5LLLLku3bt06qkUAOgnPDADayjMDgLba2p4ZFaVSqdTRTQBAR5o9e3bGjx/f/POf//znNDY2ZocddkjPnj2bjz/55JMZOHDgenP/+Z//OVdccUWSZOjQoenVq1eeeeaZrF27Nscff3zuvffedO3atTw3AkDhPDMAaCvPDADaant5ZlR2dAMA0NFWr16d5cuXb3B85cqVWblyZfPPTU1NG9R89atfzQEHHJB/+Zd/yRNPPJFXXnklH/7wh3POOefkb/7mbzrNAx+A9uGZAUBbeWYA0FbbyzPDCikAAAAAAAAK5R1SAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAAAAAAAUSiAFAAAAAABAoQRSAAAAAAAAFEogBQAAAAAAQKEEUgAAAAAAABRKIAUAAAAAAEChBFIAAMA266mnnsoJJ5yQXXbZJV26dElFRUUefvjhjm5rm3L22WenoqIi06ZN6+hWClNRUZGKioqObgMAALZqlR3dAAAAQBFee+21HHnkkVmxYkVqamqy//77p6KiIn369ClrH9OmTcvixYtz9tlnZ8iQIWW9Nq37yU9+kt/97nf59Kc/nY985CMd3Q4AAGyzBFIAAMA26Y477siKFSsyfvz43H333enSpWM2iJg2bVoeeeSRHHHEEQKpTugnP/lJfvCDH2TIkCECKQAAKJAt+wAAgG3SwoULkyTHHHNMh4VRAAAAvMu/ygAAgG1SfX19kqRnz54d3AkAAAACKQAAYJty1VVXpaKiItOmTUuSnHPOOamoqEhFRUWOOOKI5rqVK1fmm9/8Zg466KDsuOOO2WGHHfKRj3wkkydPTmNj4wbnra+vz+23357TTjst++23X3r16pVevXrlIx/5SK655pq8884769U//PDDqaioyCOPPJIkOfLII5v7eG9/6+re29t7LV68OBUVFRts9/f+41OnTs3BBx+c3r17p6KiYr3al19+ORdffHH23Xff9OzZMzvttFOOPPLI3HXXXW37UD+AhQsX5txzz82QIUPSvXv39O3bN8cff3weeuihjdYPGTIkFRUVWbx4cX7961/nuOOOy84775zq6uocdthhm5yXvPvesC984QsZMGBAevTokeHDh+faa6/NmjVrcsQRR6SioiIPP/xwkv/9/H7wgx8kWf/PSUVFRa666qqNXuPnP/95xo4dm969e6dPnz457rjj8uSTT36gzwgAALYH3iEFAABsUwYNGpQxY8Zk0aJFee211zJs2LDsvvvuSZIPf/jDSZLa2tocffTRmT9/fiorKzNkyJBUVVXl97//fS677LLcd999eeCBB9ZbXfXEE0/k9NNPT2VlZfr165f9998/b775Zn7/+9/nqaeeyj333JNHH320eU6fPn0yZsyYPP3003nrrbfyoQ99KH369Gk+3x577NFu9/zFL34xN954YwYOHJjhw4fnueeeax575JFHMn78+Lz55pvp2bNnhg0bljfeeCMPP/xwHn744UycODHXXXddu/XyXtOnT8+ZZ56ZVatWpXfv3hkxYkReeeWV/OxnP8vPf/7z3HDDDfnbv/3bjc796U9/mksuuSQ77rhj9t577zz33HN59NFHc8wxx+S//uu/NgjwXn755YwZMyZLlixJVVVVPvShD+Wdd97JP/zDP+Sxxx7b4Pw9evTY5J+T5N0/R+9344035qKLLkq/fv2y77775tlnn83MmTPz6KOP5vHHH8/w4cM/2AcGAADbshIAAMA26KyzziolKX3/+99f73hTU1Np9OjRpSSl0047rfTKK680jy1durR02GGHlZKUJk2atN68xYsXl6ZPn156++231zu+bNmy0mc+85lSktJVV121QR+HH354KUlp1qxZG+1z1qxZpSSlww8/fKPjL774YilJafDgwRs93rVr11J1dXXp3nvvbR5buXJlqVQqlWpra0u77LJLqaKiovT1r3+91NDQ0Fwze/bsUk1NTSlJacaMGRu9dlts6nN+6qmnSt27dy/16NGjdNNNN5Wampqax+67777SjjvuWOratWvpd7/73XrzBg8eXEpSqqqqKl177bWlNWvWlEqlUmnVqlWlM844o5T/v717i2n6/OM4/inQovW42iG4RmUMWTCaMQcyLUoyyZjAEpZs7maLAWdMdjHlZhhiFvGIyYZ64TBZluk2N8xCwgwO4hIHGZKZGBcPZKBRFg+4mIIoOiiU/i9M+6fSyvGn/799v26Iz+H3fJ9fr8wnv+eRvEuXLh1SR05OjleS97XXXvNeu3bN397Q0OCdOXOm12w2B/0dQtU/mCSvJK/Vag0Yd/fuXe8bb7zhleRds2bNMG8KAAAACG8c2QcAAAAgrNTU1OjUqVNKTU3Vt99+G/ClksPhUGVlpaZOnaqKigr/PVSSNG/ePL377ruaOnVqwPNiY2N1+PBhWSwWff/9909sHz4ej0elpaV6++23/W2+r7Q+//xzdXR0aOPGjdq8ebOio6P9Y5YtW6aKigpJUnl5+YTXtXXrVvX29qqsrEwfffSRIiL++9/PvLw87dixQx6PR/v37w86Pzs7W8XFxYqMjJQkmc1m7d27V9HR0frjjz/U2dnpH9vS0qKamhqZzWYdPXpUDofD35eRkaHy8nL19fWNe0+FhYVau3at/9/Tpk3zv7va2tpxPx8AAAB4lhFIAQAAAAgrVVVVkqS1a9cqKmroKeZxcXFKTU1Vd3e3zpw5E9A3MDCg6upqffzxx3rrrbeUkZEhp9OprKwsmUwmXbp0SQ8ePHgi+xjsww8/DNru2+u6deuC9mdnZ8tisejUqVPq7++fsHrcbreOHz+uyMjIgABnMF+A5rtj61HBarbb7f47s65cueJvP3HihCQpMzNT8fHxQ+a9//77AccvjlWwmhYtWqRJkyapq6tLLpdr3GsAAAAAzyrukAIAAAAQVs6fPy9J+vLLL3XkyJGgY1pbWyU9vGvK586dO1q9erWampoe+/zOzk5ZrdYJqnZ4drtddrt9SHt3d7fa2tokSevXr3/sM3p6euRyuSbsXqvW1lb19PTIYrFo9erVQcd4vV5Jge94sISEhKDtMTExamlpUXd3t7/t0qVLkqTFixcHnTNp0iQlJibq3LlzI97DaGp6/vnnde3aNXV3d2vWrFnjWgMAAAB4VhFIAQAAAAgrXV1dkqQLFy4MO3bwkX1FRUVqampSUlKSdu7cqfT0dNntdlksFkkPj/u7cePGhBwNNxpTpkwJ2u7bpyQ1NjYO+5zBex0v39put3vYtXt6eoK2h9qX7+g/X6AlSffv35f08Ai9UB7XN1KjqQkAAABAIAIpAAAAAGHFdwfUiRMntGrVqhHN6e/v19GjRyVJ1dXVSkpKGtJ/69atMdVjMpkkhQ4zfGHLaA2+68rtdstsNo/pOeNZ+4UXXtD169cNX88XFA3+aupR9+7dM7wOAAAAAKFxhxQAAACAsJKcnCxpZF9I+dy+fVv379+XzWYbEkb5nuXxeILO9QVOofjClNu3bwftv3z58ojrHGzGjBmaM2eOJOnixYtjesZYJSYmymw2q729XR0dHYavt2DBAkkKeSRfb2+v/1i/Rw33+wAAAACYGARSAAAAAMLKO++8I0k6ePBgyOPiHjV58mRJ0t27d4Mebbdnz55h54Y6Eu/FF1+UJF25ckUul2tI/1dffTWiGoPx7XXv3r1jfsZYWK1WvfnmmxoYGND+/fsNXy8rK0uSdPLkSf39999D+isrK0O+/+F+HwAAAAATg0AKAAAAQFjJz89Xenq6/vrrL+Xl5Q35Aqm3t1c1NTUqKCjwt82cOVMLFy5Uf3+/Nm3aJLfbLUnyeDwqKytTZWWl/y6pR/kCp/r6+qD9NptNaWlp6u3tVVFRkf8OKo/Ho927d6uurm7Me/30009ls9l06NAhFRUV6c6dOwH9HR0d+vrrr7V9+/YxrxHKtm3bFB0dre3bt2v37t1DAp/29nbt27dPFRUV415rwYIFysnJUV9fn9577z3dvHnT39fY2KhNmzaFPLLQ9/s0NDRwBxQAAABgIAIpAAAAAGElIiJCVVVVSklJ0a+//qrExEQlJiYqPT1dCxcu1PTp05Wbm6vjx48HzNu1a5dMJpMOHjyouLg4paamKjY2VsXFxSopKVFcXFzQ9dasWSNJKisrU1JSklauXKnMzEzV1tb6x5SVlSkqKkqHDx9WTEyMUlNTNXv2bG3ZskVffPHFmPfqcDj0888/y263q7y8XDExMVq8eLHS09OVkJAgu92uwsLCUR1fOFKvvPKKfvjhB0VHR2vz5s2y2WxKSUnR0qVLNXfuXM2ZM0cbN25UW1vbhKxXUVGhuXPn6vTp05o/f76WLFmil19+WU6nU06nU6+//rokKTIyMmBefn6+LBaLfvzxR8XHx2vFihXKzMzUN998MyF1AQAAAHiIQAoAAABA2ImLi1NTU5MOHDigFStWyOVy6ezZs7p3757S0tK0detWnTx5MmBOXl6efvnlFy1btkz//vuvWlpa9NJLL+m7775TaWlpyLUyMjJ05MgRpaWl6caNG2poaFB9fb1u3brlH5OZmam6ujo5nU653W61trbq1Vdf1W+//abc3Nxx7XX58uVqbm5WSUmJkpOTdfXqVZ07d04RERHKzs7WgQMHtG/fvnGtEUp+fr6am5v1ySefaP78+WppaVFzc7OsVqvy8/N16NAhFRcXT8haDodDp0+f1vr16zVr1ixdvHhRAwMDKi0t1U8//aQHDx5IkqZNmxYwLyEhQceOHdPKlSvV2dmp33//XfX19RMWlAEAAAB4yOTlTAIAAAAAwDNsYGBANptNXV1d6ujo0HPPPfe0SwIAAADCDl9IAQAAAACeaVVVVerq6lJycjJhFAAAAPCUEEgBAAAAAP7v/fPPP9qzZ49cLldAe21trTZs2CBJ/r8AAAAAnjyO7AMAAAAASJKcTueIxxYUFKigoMDAakanra1N8fHxMplMcjgcio2N1fXr19Xe3i5JysnJUXV1tSIjI59ypQAAAEB4inraBQAAAAAA/jc0NjaOeOyqVasMrGT0YmJi9Nlnn6m2tlZXr17Vn3/+KavVquXLl+uDDz5QYWEhYRQAAADwFPGFFAAAAAAAAAAAAAzFHVIAAAAAAAAAAAAwFIEUAAAAAAAAAAAADEUgBQAAAAAAAAAAAEMRSAEAAAAAAAAAAMBQBFIAAAAAAAAAAAAwFIEUAAAAAAAAAAAADEUgBQAAAAAAAAAAAEMRSAEAAAAAAAAAAMBQBFIAAAAAAAAAAAAwFIEUAAAAAAAAAAAADEUgBQAAAAAAAAAAAEMRSAEAAAAAAAAAAMBQBFIAAAAAAAAAAAAwFIEUAAAAAAAAAAAADEUgBQAAAAAAAAAAAEMRSAEAAAAAAAAAAMBQBFIAAAAAAAAAAAAwFIEUAAAAAAAAAAAADPUfAT9LJAe0wCwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture --no-display fig.png\n",
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=150, log_scale=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa81b8f0",
   "metadata": {},
   "source": [
    "or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a790867f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=chr_gff3_df[filter], x='feature_length', bins=50, log_scale=True)\n",
    "plt.savefig('gene_feature_length_distribution.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca0e1a1",
   "metadata": {},
   "source": [
    "## 6. Advanced filtering\n",
    "\n",
    "Suppose you want to subset your GFF3 file to retain only primary transcripts. You can use the `attributes` column to filter the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "be139300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seqid</th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>phase</th>\n",
       "      <th>attributes</th>\n",
       "      <th>feature_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>34617</td>\n",
       "      <td>40204</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...</td>\n",
       "      <td>5587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>41214</td>\n",
       "      <td>46762</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...</td>\n",
       "      <td>5548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>108554</td>\n",
       "      <td>114382</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000050_T001;Parent=Zm00001eb000050...</td>\n",
       "      <td>5828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>188559</td>\n",
       "      <td>189581</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000060_T001;Parent=Zm00001eb000060...</td>\n",
       "      <td>1022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>chr1</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>190192</td>\n",
       "      <td>198832</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb000070_T001;Parent=Zm00001eb000070...</td>\n",
       "      <td>8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138073</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152010430</td>\n",
       "      <td>152012584</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...</td>\n",
       "      <td>2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138079</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152012893</td>\n",
       "      <td>152015823</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...</td>\n",
       "      <td>2930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138095</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152200925</td>\n",
       "      <td>152201511</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...</td>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138101</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152280251</td>\n",
       "      <td>152282615</td>\n",
       "      <td>.</td>\n",
       "      <td>+</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...</td>\n",
       "      <td>2364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138109</th>\n",
       "      <td>chr10</td>\n",
       "      <td>NAM</td>\n",
       "      <td>mRNA</td>\n",
       "      <td>152284763</td>\n",
       "      <td>152287382</td>\n",
       "      <td>.</td>\n",
       "      <td>-</td>\n",
       "      <td>.</td>\n",
       "      <td>ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...</td>\n",
       "      <td>2619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39035 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         seqid source  type      start        end score strand phase  \\\n",
       "2         chr1    NAM  mRNA      34617      40204     .      +     .   \n",
       "85        chr1    NAM  mRNA      41214      46762     .      -     .   \n",
       "107       chr1    NAM  mRNA     108554     114382     .      -     .   \n",
       "124       chr1    NAM  mRNA     188559     189581     .      -     .   \n",
       "132       chr1    NAM  mRNA     190192     198832     .      -     .   \n",
       "...        ...    ...   ...        ...        ...   ...    ...   ...   \n",
       "1138073  chr10    NAM  mRNA  152010430  152012584     .      -     .   \n",
       "1138079  chr10    NAM  mRNA  152012893  152015823     .      -     .   \n",
       "1138095  chr10    NAM  mRNA  152200925  152201511     .      +     .   \n",
       "1138101  chr10    NAM  mRNA  152280251  152282615     .      +     .   \n",
       "1138109  chr10    NAM  mRNA  152284763  152287382     .      -     .   \n",
       "\n",
       "                                                attributes  feature_length  \n",
       "2        ID=Zm00001eb000010_T001;Parent=Zm00001eb000010...            5587  \n",
       "85       ID=Zm00001eb000020_T001;Parent=Zm00001eb000020...            5548  \n",
       "107      ID=Zm00001eb000050_T001;Parent=Zm00001eb000050...            5828  \n",
       "124      ID=Zm00001eb000060_T001;Parent=Zm00001eb000060...            1022  \n",
       "132      ID=Zm00001eb000070_T001;Parent=Zm00001eb000070...            8640  \n",
       "...                                                    ...             ...  \n",
       "1138073  ID=Zm00001eb434550_T001;Parent=Zm00001eb434550...            2154  \n",
       "1138079  ID=Zm00001eb434560_T001;Parent=Zm00001eb434560...            2930  \n",
       "1138095  ID=Zm00001eb434570_T001;Parent=Zm00001eb434570...             586  \n",
       "1138101  ID=Zm00001eb434580_T001;Parent=Zm00001eb434580...            2364  \n",
       "1138109  ID=Zm00001eb434590_T001;Parent=Zm00001eb434590...            2619  \n",
       "\n",
       "[39035 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = chr_gff3_df['attributes'].str.contains('canonical_transcript')\n",
    "chr_gff3_df[filter]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dafb2d7",
   "metadata": {},
   "source": [
    "As you see, this will only fetch you `mRNA` features but not its child features like `CDS` or `exon`. If you want to fetch all the child features of the primary transcript, you can use the `mRNA_id` column to filter the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70c40139",
   "metadata": {},
   "outputs": [],
   "source": [
    "mRNA_id = chr_gff3_df[filter]['attributes'].str.split(';', expand=True)[0].str.split('=', expand=True)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e6880e",
   "metadata": {},
   "source": [
    "This list of ids are the primary transcripts. You can use this list to filter the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d2ce139",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# create a pattern to search for the IDs\n",
    "pattern = re.compile('|'.join(mRNA_id))\n",
    "# Define a function to check if a given attribute contains any of the IDs\n",
    "def contains_id(attribute):\n",
    "    return bool(pattern.search(attribute))\n",
    "# Apply the function to each row in the DataFrame\n",
    "primary_gff3_df = chr_gff3_df[chr_gff3_df['attributes'].apply(contains_id)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a649348",
   "metadata": {},
   "source": [
    "## 7. Iterating over the dataframe\n",
    "\n",
    "If you want to iterate over the dataframe, you can use the `iterrows` method.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6832fc47",
   "metadata": {},
   "source": [
    "This is useful when you want to perform some operation on each row of the dataframe.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86523115",
   "metadata": {},
   "source": [
    "Let's take just 250 lines for showing this example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f8aecac",
   "metadata": {},
   "outputs": [],
   "source": [
    "chr_gff3_test = chr_gff3_df.head(250)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3118f9af",
   "metadata": {},
   "source": [
    "iterate over each lines in gff3, select only \"mRNA\" entries, and print selective fields in preferred format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "10a346eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1:34617-40204\n",
      "chr1:41263-46050\n",
      "chr1:41214-43902\n",
      "chr1:41314-46039\n",
      "chr1:41214-46762\n",
      "chr1:108554-114382\n",
      "chr1:188559-189581\n",
      "chr1:190192-198832\n",
      "chr1:200262-203393\n",
      "chr1:206619-209723\n",
      "chr1:246422-247242\n",
      "chr1:315219-315846\n",
      "chr1:325307-326897\n",
      "chr1:328087-328525\n",
      "chr1:550834-564766\n",
      "chr1:551305-564761\n"
     ]
    }
   ],
   "source": [
    "for index, row in chr_gff3_test.iterrows():\n",
    "    if row['type'] == 'mRNA':\n",
    "        position = f\"{row['seqid']}:{row['start']}-{row['end']}\"\n",
    "        print(position)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0591efd",
   "metadata": {},
   "source": [
    "iterate over each lines in gff3, select only \"gene\" entries, and print selective fields in preferred format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "af40fd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1 gene 34617 40204\n",
      "chr1 gene 41214 46762\n",
      "chr1 gene 108554 114382\n",
      "chr1 gene 188559 189581\n",
      "chr1 gene 190192 198832\n",
      "chr1 gene 200262 203393\n",
      "chr1 gene 206619 209723\n",
      "chr1 gene 246422 247242\n",
      "chr1 gene 315219 315846\n",
      "chr1 gene 325307 326897\n",
      "chr1 gene 328087 328525\n",
      "chr1 gene 550834 564766\n"
     ]
    }
   ],
   "source": [
    "for index, row in chr_gff3_test.iterrows():\n",
    "    if row['type'] == 'gene':\n",
    "        print(row['seqid'], row['type'], row['start'], row['end'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce725155",
   "metadata": {},
   "source": [
    "This will print the genomic coordinates of all the genes in the GFF3 file.\n",
    "\n",
    "The above filtering can also be done using `iterrows` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0f6b3878",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zm00001eb000010_T001\n",
      "Zm00001eb000020_T001\n",
      "Zm00001eb000050_T001\n",
      "Zm00001eb000060_T001\n",
      "Zm00001eb000070_T001\n",
      "Zm00001eb000080_T001\n",
      "Zm00001eb000100_T001\n",
      "Zm00001eb000110_T001\n",
      "Zm00001eb000120_T001\n",
      "Zm00001eb000140_T001\n",
      "Zm00001eb000150_T001\n",
      "Zm00001eb000170_T004\n"
     ]
    }
   ],
   "source": [
    "for index, row in chr_gff3_test.iterrows():\n",
    "    if 'canonical_transcript' in row['attributes']:\n",
    "        mRNAid = row['attributes'].split(';')[0].split('=')[1]\n",
    "        print(mRNAid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e21065",
   "metadata": {},
   "source": [
    "This will print the genomic coordinates of all the primary transcripts in the GFF3 file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e21a672",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this short exercise, we learned how to read a GFF3 file using `pandas`, filter the data, convert the format, calculate summary statistics, and visualize the data. These are some of the common tasks you might encounter while working with GFF3 files.\n",
    "\n",
    "`pandas` makes working with non-standardized file formats like GFF3 easy and efficient. It provides a powerful set of tools to manipulate and analyze the data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3 (BCB/EEOB 546)",
   "language": "python",
   "name": "notebook"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}