Bat_Project_MISC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2945acb1",
   "metadata": {},
   "source": [
    "## Takes in output file from KAAS and writes file that includes only the trascripts assigned to a KEGG ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b01ebf6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('mobl_KO_only.txt', 'w') as f:\n",
    "    with open ('mobl_ko.txt', 'r') as file:\n",
    "        for line in file:\n",
    "            if \"K\" in line:\n",
    "                anno = line\n",
    "                f.write(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b48b8dc",
   "metadata": {},
   "source": [
    "## Counting number of each ORF type from Transdcoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18c5b657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete ORF Type: 34558\n",
      "Internal ORF Type: 10500\n",
      "3 prime ORF Type: 5996\n",
      "5 prime ORF Type: 17070\n"
     ]
    }
   ],
   "source": [
    "icount = 0\n",
    "tcount = 0\n",
    "fcount = 0\n",
    "ccount = 0\n",
    "with open ('mobl.transdecoder_CDS.fasta', 'r') as file:\n",
    "    for line in file:\n",
    "        if \"internal\" in line:\n",
    "            icount += 1\n",
    "        elif '3prime_partial' in line:\n",
    "            tcount += 1\n",
    "        elif '5prime_partial' in line:\n",
    "            fcount += 1\n",
    "        elif 'complete' in line:\n",
    "            ccount += 1\n",
    "print(\"Complete ORF Type: \" + str(ccount))\n",
    "print(\"Internal ORF Type: \" + str(icount))\n",
    "print(\"3 prime ORF Type: \" + str(tcount))\n",
    "print(\"5 prime ORF Type: \" + str(fcount))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbd6929",
   "metadata": {},
   "source": [
    "## Busco graph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f33e7bc",
   "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>Complete and single-copy</th>\n",
       "      <th>Complete and duplicated</th>\n",
       "      <th>Fragmented</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H.thomasi</th>\n",
       "      <td>35.6</td>\n",
       "      <td>62.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. geoffroyi</th>\n",
       "      <td>44.6</td>\n",
       "      <td>50.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. soricina</th>\n",
       "      <td>53.8</td>\n",
       "      <td>42.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. brevicauda</th>\n",
       "      <td>46.9</td>\n",
       "      <td>49.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. villosum</th>\n",
       "      <td>34.7</td>\n",
       "      <td>64.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. ludovici</th>\n",
       "      <td>38.3</td>\n",
       "      <td>59.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. jamaicensis</th>\n",
       "      <td>47.2</td>\n",
       "      <td>48.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. rotundus</th>\n",
       "      <td>41.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. blainvillei</th>\n",
       "      <td>36.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. waterhousi</th>\n",
       "      <td>47.2</td>\n",
       "      <td>48.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P. pusillus</th>\n",
       "      <td>53.7</td>\n",
       "      <td>34.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N. leporinus</th>\n",
       "      <td>43.9</td>\n",
       "      <td>53.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Complete and single-copy  Complete and duplicated   \\\n",
       "Species                                                              \n",
       "H.thomasi                           35.6                      62.4   \n",
       "A. geoffroyi                        44.6                      50.8   \n",
       "G. soricina                         53.8                      42.6   \n",
       "C. brevicauda                       46.9                      49.5   \n",
       "C. villosum                         34.7                      64.3   \n",
       "S. ludovici                         38.3                      59.1   \n",
       "A. jamaicensis                      47.2                      48.8   \n",
       "D. rotundus                         41.6                      53.8   \n",
       "M. blainvillei                      36.0                      63.0   \n",
       "M. waterhousi                       47.2                      48.8   \n",
       "P. pusillus                         53.7                      34.1   \n",
       "N. leporinus                        43.9                      53.5   \n",
       "\n",
       "                Fragmented   Missing   \n",
       "Species                                \n",
       "H.thomasi               2.0       0.0  \n",
       "A. geoffroyi            4.3       0.3  \n",
       "G. soricina             3.6       0.0  \n",
       "C. brevicauda           3.3       0.3  \n",
       "C. villosum             1.0       0.0  \n",
       "S. ludovici             2.6       0.0  \n",
       "A. jamaicensis          3.6       0.4  \n",
       "D. rotundus             4.6       0.0  \n",
       "M. blainvillei          1.0       0.0  \n",
       "M. waterhousi           4.0       0.0  \n",
       "P. pusillus             9.4       2.8  \n",
       "N. leporinus            2.6       0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_excel('busco_stats.xlsx').set_index(\"Species\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4a63c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd4a4e4d8e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df.plot.barh(stacked=True, cmap=\"ocean\")\n",
    "plot.set_title(\"Assembly Completeness\")\n",
    "plot.set_xlabel(\"% \")\n",
    "plot.legend(bbox_to_anchor=(1.0, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e79fee8",
   "metadata": {},
   "source": [
    "## Transdecoder Graph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4432b58e",
   "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>Complete</th>\n",
       "      <th>Internal</th>\n",
       "      <th>3 Prime</th>\n",
       "      <th>5 Prime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H.thomasi</th>\n",
       "      <td>30887</td>\n",
       "      <td>9692</td>\n",
       "      <td>5491</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. geoffroyi</th>\n",
       "      <td>23548</td>\n",
       "      <td>10062</td>\n",
       "      <td>4446</td>\n",
       "      <td>12859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. soricina</th>\n",
       "      <td>22455</td>\n",
       "      <td>10089</td>\n",
       "      <td>4498</td>\n",
       "      <td>12650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. brevicauda</th>\n",
       "      <td>26470</td>\n",
       "      <td>10508</td>\n",
       "      <td>5142</td>\n",
       "      <td>13929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. villosum</th>\n",
       "      <td>35183</td>\n",
       "      <td>9618</td>\n",
       "      <td>5780</td>\n",
       "      <td>16616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. ludovici</th>\n",
       "      <td>28862</td>\n",
       "      <td>9954</td>\n",
       "      <td>5497</td>\n",
       "      <td>13689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. jamaicensis</th>\n",
       "      <td>26951</td>\n",
       "      <td>10813</td>\n",
       "      <td>4771</td>\n",
       "      <td>15687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. rotundus</th>\n",
       "      <td>27238</td>\n",
       "      <td>11661</td>\n",
       "      <td>4724</td>\n",
       "      <td>17027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. blainvillei</th>\n",
       "      <td>34558</td>\n",
       "      <td>10500</td>\n",
       "      <td>5996</td>\n",
       "      <td>17070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. waterhousi</th>\n",
       "      <td>22370</td>\n",
       "      <td>9570</td>\n",
       "      <td>4675</td>\n",
       "      <td>12181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P. pusillus</th>\n",
       "      <td>17601</td>\n",
       "      <td>8952</td>\n",
       "      <td>3964</td>\n",
       "      <td>10411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N. leporinus</th>\n",
       "      <td>27901</td>\n",
       "      <td>9260</td>\n",
       "      <td>4820</td>\n",
       "      <td>14555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Complete  Internal  3 Prime  5 Prime \n",
       "Species                                              \n",
       "H.thomasi          30887      9692     5491     14999\n",
       "A. geoffroyi       23548     10062     4446     12859\n",
       "G. soricina        22455     10089     4498     12650\n",
       "C. brevicauda      26470     10508     5142     13929\n",
       "C. villosum        35183      9618     5780     16616\n",
       "S. ludovici        28862      9954     5497     13689\n",
       "A. jamaicensis     26951     10813     4771     15687\n",
       "D. rotundus        27238     11661     4724     17027\n",
       "M. blainvillei     34558     10500     5996     17070\n",
       "M. waterhousi      22370      9570     4675     12181\n",
       "P. pusillus        17601      8952     3964     10411\n",
       "N. leporinus       27901      9260     4820     14555"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = pd.read_excel('transdecoder_done_stats.xlsx').set_index(\"Species\")\n",
    "trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb9b208a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa6942ff10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = trans.plot.bar(stacked=True, cmap = \"Pastel1\")\n",
    "plot2.set_title(\"Identified CDS (TransDecoder)\")\n",
    "plot2.set_ylabel(\"Number of Sequences\")\n",
    "plot2.legend(bbox_to_anchor=(1.0, 1.0), title='ORF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ae99d19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59df33f3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}