Github-Ribo_Density.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Public License Disclaimer\n",
    "\n",
    "    Script to run ribosome analysis\n",
    "    Copyright (C) 2019  Fuad Mohammad, fuadm424@gmail.com\n",
    "\n",
    "\n",
    "    This program is free software: you can redistribute it and/or modify\n",
    "    it under the terms of the GNU General Public License as published by\n",
    "    the Free Software Foundation, either version 3 of the License, or\n",
    "    (at your option) any later version.\n",
    "\n",
    "    This program is distributed in the hope that it will be useful,\n",
    "    but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "    GNU General Public License for more details.\n",
    "\n",
    "    You should have received a copy of the GNU General Public License\n",
    "    along with this program.  If not, see <http://www.gnu.org/licenses/>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FASTQ processing script\n",
    "\n",
    "This notebook will take FASTQ files from Ribo-seq or RNA-seq and convert them to density files aligned to a genome using the 3' end of reads. \n",
    "\n",
    "- Filtering is done using skewer v0.2.2 to remove low quality reads and reads outside of size threshold.\n",
    "- Bowtie v0.12.7 is used to remove any contaminating ladder, tRNA, and rRNA reads ny making indices for all tRNAs, rRNA, and ladder sequences separately. Aligns reads with an allowed mismatch of 2.\n",
    "- Density is made using 3' alignment. It is stored as a pickled dictionary and is size separated. It is also saved as a .wig file and a .bin file for convenience. \n",
    "- Script will output library statistics including filtering and bowtie read allocation, as well as some custom QC information. \n",
    "\n",
    "Input and paths are placed in dictionaries. Libraries are numbered as initials + library number (FM1, FM2...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Settings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CW63']\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import ribo_util\n",
    "import ribo_main\n",
    "import ribo_analysis\n",
    "import ribo_plot\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "'''Files and Input Settings'''\n",
    "\n",
    "path_pc     = '/Volumes/HDD/Mohammad_2018/'  # location of folder for ribosome profiling\n",
    "                                             # should have subfolder '/libraries/FASTQ/' containing FASTQ file\n",
    "                                             # should have subfolder '/annotations/Coli/' containing GFF\n",
    "        \n",
    "path_script = '/Users/fuad/Dropbox/scripts/' # Location of the pipeline scripts\n",
    "\n",
    "# File names can be manually added to list, or read as .csv with header == Library\n",
    "# Library names must be the same as the fastq file name\n",
    "\n",
    "all_files  = []                              # sample name, same as FASTQ filename\n",
    "file_csv   = path_pc  + 'Library_names.csv'  # alternatively, provide .csv with filenames\n",
    "\n",
    "library_id = pd.read_csv(file_csv)\n",
    "\n",
    "for fname in library_id.Library:\n",
    "    all_files.append(fname)\n",
    "    \n",
    "# inputs dict will have all input info used in FASTQ processing\n",
    "\n",
    "inputs = {\n",
    "    \n",
    "    'files' : all_files,                     # Files to analyze\n",
    "    'data_type'  : 'ribo_seq',               # Type of library: ribo_seq or RNA_seq\n",
    "    \n",
    "    # select which functions to run: 'yes' and 'no' \n",
    "    'run_filtering'         : 'yes',\n",
    "    'run_filter_UMI'        : 'no' ,         # set to yes only if using UMI (Unique Molecular Identifiers)\n",
    "    'run_bowtie'            : 'yes',\n",
    "    'run_density'           : 'yes',\n",
    "    'run_readQC'            : 'yes',\n",
    "    \n",
    "    # cuttoff for read length and quality\n",
    "    'minlength'    : 15,\n",
    "    'maxlength'    : 45,\n",
    "    'phred_cutoff' : 10,\n",
    "    \n",
    "    # linker sequence to be removed during filtering                        \n",
    "    'linker'       :   'CTGTAGGCACCATCAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCA',\n",
    "                      \n",
    "    # UMI lengths, ignore if UMIs are not used\n",
    "    'linker_UMI'   : 0, \n",
    "    'RT_UMI'       : 0,\n",
    "    \n",
    "    # CPU information for multithreading applications, \n",
    "    'multiprocess' : 'yes',\n",
    "    'threads'      : 8,\n",
    "    'cores'        : 4,     \n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'''Input directories'''\n",
    "\n",
    "# Can be customized to your liking \n",
    "\n",
    "\n",
    "# paths_in must be created prior to running, and filled with the relevant files:\n",
    "paths_in = {\n",
    "    'path_fastq'      : path_pc + 'libraries/FASTQ/',                             # location of FASTQ files    \n",
    "    'path_bowtie'    : path_script + 'ribo_seq/bowtie/bowtie',                    # location of bowtie\n",
    "    'btindex_ladder' : path_script + 'ribo_seq/bowtie/indexes/ladder/ladder',     # bowtie index for ladders used in profiling\n",
    "    'btindex_trna'   : path_script + 'ribo_seq/bowtie/indexes/Coli/Coli_tRNA',    # bowtie index for tRNA\n",
    "    'btindex_rrna'   : path_script + 'ribo_seq/bowtie/indexes/Coli/Coli_rRNA',    # bowtie index for rRNA\n",
    "    'btindex_chr'    : path_script + 'ribo_seq/bowtie/indexes/Coli/Coli_genome',  # bowtie index for genome\n",
    "    }\n",
    "\n",
    "# Output directories\n",
    "paths_out = {\n",
    "    'path_filter'       : path_pc  + 'libraries/density/filtering_bowtie/filterdata/',\n",
    "    'path_ladder'       : path_pc  + 'libraries/density/filtering_bowtie/alignments/ladder/',\n",
    "    'path_trna'         : path_pc  + 'libraries/density/filtering_bowtie/alignments/tRNA/',\n",
    "    'path_rrna'         : path_pc  + 'libraries/density/filtering_bowtie/alignments/rRNA/',\n",
    "    'path_chr'          : path_pc  + 'libraries/density/filtering_bowtie/alignments/chr/',\n",
    "    'path_temp'         : path_pc  + 'libraries/density/filtering_bowtie/tmpds/',\n",
    "    'path_density'      : path_pc  + 'libraries/density/density/',\n",
    "    'path_log'          : path_pc  + 'libraries/density/logs/',\n",
    "    'path_analysis_log' : path_pc  + 'libraries/analysis/logs/',\n",
    "    'path_analysis'     : path_pc  + 'libraries/analysis/individual/',\n",
    "    'path_figures'      : path_pc  + 'libraries/figures/',\n",
    "    }\n",
    "\n",
    "# Check inputs, create output paths, and make gff dictionary if needed\n",
    "step = 'density'                                 # density or analysis\n",
    "ribo_util.check_inputs(inputs, paths_in, step)   # will remove file from analysis for not having a FASTQ file\n",
    "ribo_util.createpath(inputs, paths_out)          # create output paths\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----FILTER-----\n",
      "\n",
      "Files to filter: CW63\n",
      "Filter parameters are: \n",
      "min length = 15 \n",
      "max length = 45 \n",
      "phred cutoff = 10 \n",
      "\n",
      "\tStarted filtering at 2019-05-29 05:44:48.536525\n",
      "\tFinished filtering at 2019-05-29 05:45:32.867782\n",
      "\tCOMPLETED FILTERING\n",
      "\n",
      "\tStarted UMI removal at 2019-05-29 05:45:32.868485\n",
      "CW63will not be filtered for a UMI\n",
      "\tFinished UMI removal at 2019-05-29 05:45:32.868618\n",
      "\tCOMPLETED UMI REMOVAL\n",
      "\n",
      "------ALIGN------\n",
      "\n",
      "Files to align: CW63\n",
      "\n",
      "\tStarted Bowtie alignment at 2019-05-29 05:45:32.869351\n",
      "\tFinished ladder removal at 2019-05-29 05:49:39.155360\n",
      "\tFinished tRNA removal at 2019-05-29 05:56:31.425884\n",
      "\tFinished rRNA removal at 2019-05-29 06:03:40.100346\n",
      "\tFinished chromosome alignment at 2019-05-29 06:07:13.849068\n",
      "\tCOMPLETED ALIGNING\n",
      "\n",
      "-----DENSITY-----\n",
      "\n",
      "Files to condense: CW63\n",
      "\n",
      "\tStarted density at 2019-05-29 06:07:13.850185\n",
      "\tFinished density at 2019-05-29 06:09:58.285403\n",
      "\tCOMPLETED DENSITY\n",
      "\n",
      "\tStarted readQC at 2019-05-29 06:09:58.304155\n",
      "\tFinished readQC at 2019-05-29 06:13:36.597788\n"
     ]
    }
   ],
   "source": [
    "filterreads  = ribo_main.run_filter(inputs, paths_in, paths_out)\n",
    "remove_UMI   = ribo_main.run_filter_UMI(inputs, paths_in, paths_out)\n",
    "bowtiereads  = ribo_main.run_align(inputs, paths_in, paths_out)\n",
    "densityreads = ribo_main.run_density(inputs, paths_in, paths_out)\n",
    "analyzereads = ribo_analysis.readQC(inputs, paths_in, paths_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read allocation after filtering and bowtie alignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>CW63</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>total_reads</th>\n",
       "      <td>36601050</td>\n",
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       "      <td>31373629</td>\n",
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       "      <th>ladder</th>\n",
       "      <td>114019</td>\n",
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       "      <th>tRNA</th>\n",
       "      <td>8184259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rRNA</th>\n",
       "      <td>1901308</td>\n",
       "    </tr>\n",
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       "      <th>chromosome</th>\n",
       "      <td>20143914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Total mapped</th>\n",
       "      <td>55</td>\n",
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       "      <th>unaligned</th>\n",
       "      <td>1030129</td>\n",
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       "                    CW63\n",
       "total_reads     36601050\n",
       "filtered_reads  31373629\n",
       "ladder            114019\n",
       "tRNA             8184259\n",
       "rRNA             1901308\n",
       "chromosome      20143914\n",
       "% Total mapped        55\n",
       "unaligned        1030129"
      ]
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R13IUiEGDBnHq1ClOnTpFYGAgVatWfexrH/R2HT16NA0aNMgyzdPTk7i4OG7f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\nBEIIDQmEEBoSCCE0JBBCaPx/JFLI9E4mGwUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b70fdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_data = ribo_util.get_filter_bowtie_log(inputs, paths_in, paths_out)\n",
    "ribo_plot.plot_alignment_allocation(inputs, paths_in, paths_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read size distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KqaBboCv2liaPfW6vRu5sPBLHL9FJ9GpUsesgCyHKl7QwDFRUwv86vN2erYUBYGqkYlAz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b5ea410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_size_distribution = ribo_plot.size_dist(inputs, paths_in, paths_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average nucleotide composition of reads:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1f8f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZU2aQTjrpfNKrU98mXfh8Ijpd/JRVZ8vTl9rtFYc88iOblvhRMGBnWHyXlJDfWuYH5/iY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\nA4GrA6X3mwZgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2415d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_composition = ribo_plot.read_comp(inputs, paths_in, paths_out)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}