a_inornata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A_inornata Genome Quality Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to assess the quality of the A. inornata genome that was produce by Dovetail Genomics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Transcriptome Assembly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I am using Trinity to adapter trim, normalize and assemble a transcriptome for A inornata. Five different tissue samples will be used (blood, brain, heart, germinal bed, and testes).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Trinity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I concatinated all of the reads from each tissue together by R1 and R2 (PE read designation). Leaving a single R1 and R2 for heart, blood, brain, testes, and germinal bed (blood_R1.fastq.gz, blood_R2.fastq.gz, brain_R1.fastq.gz, brain_R2.fastq.gz, germinalbed_R1.fastq.gz, germinalbed_R2.fastq.gz, heart_R1.fastq.gz, heart_R2.fastq.gz, testes_R1.fastq.gz, testes_R2.fastq.gz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#author: tfording\n",
    "\n",
    "#FILES: Blood\n",
    "#Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/blood_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/blood_R2.fastq.gz --CPU 24 --max_memory 600G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_blood --trimmomatic --quality_trimming_params \"ILLUMINACLIP:/n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\"\n",
    "\n",
    "#FILES: Brain\n",
    "#Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/RNAseq_data/brain_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/brain_R2.fastq.gz --CPU 24 --max_memory 600G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_brain --quality_trimming_params \"ILLUMINACLIP:/n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\"\n",
    "\n",
    "#FILES: GerminalBed\n",
    "#Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/RNAseq_data/germinalbed_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/germinalbed_R2.fastq.gz --CPU 24 --max_memory 600G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_germinalbed --quality_trimming_params \"ILLUMINACLIP:/n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\"\n",
    "\n",
    "#FILES: Heart\n",
    "#Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/RNAseq_data/heart_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/heart_R2.fastq.gz --CPU 24 --max_memory 600G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_heart --quality_trimming_params \"ILLUMINACLIP:/n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\"\n",
    "\n",
    "#FILES: Testes\n",
    "#Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/RNAseq_data/testes_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/testes_R2.fastq.gz --CPU 24 --max_memory 600G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes --quality_trimming_params \"ILLUMINACLIP:/n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I normalized by tissue type to a min kmer count of 2, max coverage of 50 (see http://ivory.idyll.org/blog/trinity-in-silico-normalize.html for how trinity's in silico normalization works) and minimum contig length of 200. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Post Trinity Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Script:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# author: tfording\n",
    "\n",
    "import numpy\n",
    "import argparse\n",
    "import os\n",
    "import pandas as pd\n",
    "from scipy import stats, integrate\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "def parse_trinity_fasta(input_file_path):\n",
    "    '''\n",
    "    This function will iterate through a trinity fasta and pull lengths from header lines to pass to various functions\n",
    "    :param input_file_path: Directory to file\n",
    "    :return: Above parameters in a table format\n",
    "    '''\n",
    "    length_array = []\n",
    "    fh = open(input_file_path, \"r\")\n",
    "\n",
    "    for line in fh:\n",
    "        if str(line[0]) == '>':\n",
    "            line = line.split()\n",
    "            length = line[1]\n",
    "            length = length[4:]\n",
    "            length_array.append(length)\n",
    "    length_array_int = []\n",
    "\n",
    "    for item in length_array:\n",
    "        item = int(item)\n",
    "        length_array_int.append(item)\n",
    "\n",
    "    min = min_contig(length_array_int)\n",
    "    max = max_contig(length_array_int)\n",
    "    total_size = total_size_in_bp(length_array_int)\n",
    "    file_name = parse_file_name(input_file_path)\n",
    "    num_contigs = len(length_array_int)\n",
    "    median = median_contig(length_array_int)\n",
    "    average = average_contig(length_array_int)\n",
    "    n_50 = N50_func(length_array_int, total_size)\n",
    "\n",
    "    print 'File Name:', file_name\n",
    "    print 'Number of Contigs:', num_contigs\n",
    "    print 'Total Size:', total_size\n",
    "    print 'Min Contig Size:', min\n",
    "    print 'Max Contig Size:', max\n",
    "    print 'Average Contig Size:', average\n",
    "    print 'Median Contig Size:', median\n",
    "    print 'N50:', n_50\n",
    "    contig_length_dist(length_array_int, file_name)\n",
    "\n",
    "\n",
    "def contig_length_dist(length_array, file_name):\n",
    "    '''\n",
    "    This function plots a length distribution\n",
    "    :param length_array:\n",
    "    :return:\n",
    "    '''\n",
    "    #plt.show(sns.distplot(length_array, bins=40, kde=False, rug=True, axlabel='Contig Length', label=file_name+' contig dist'))\n",
    "\n",
    "def N50_func(length_array, total_len):\n",
    "    '''\n",
    "    Calculates N50 of given array\n",
    "    :param length_array: array of integers\n",
    "    :return: N50 of array\n",
    "    '''\n",
    "    new_list = sorted(length_array)\n",
    "    len_count = 0\n",
    "    pos_count = 0\n",
    "    for i in new_list:\n",
    "        len_count += i\n",
    "        if len_count >= (total_len/2.0):\n",
    "            return i\n",
    "\n",
    "\n",
    "def average_contig(length_array):\n",
    "    '''\n",
    "    :param length_array: Array of integers\n",
    "    :return: mean of array\n",
    "    '''\n",
    "    return numpy.mean(length_array)\n",
    "\n",
    "\n",
    "def median_contig(length_array):\n",
    "    '''\n",
    "    :param length_array: array of integers\n",
    "    :return: median of array\n",
    "    '''\n",
    "    return numpy.median(length_array)\n",
    "\n",
    "\n",
    "def parse_file_name(path_to_file):\n",
    "    '''\n",
    "    :param path_to_file: directory (str)\n",
    "    :return: Just the file name\n",
    "    '''\n",
    "    path_to_file= path_to_file.split('/')\n",
    "    return path_to_file[-1]\n",
    "\n",
    "\n",
    "def min_contig(length_array):\n",
    "    '''\n",
    "    :param length_array: array of integers\n",
    "    :return: min of array\n",
    "    '''\n",
    "    min_contig = min(length_array)\n",
    "    return min_contig\n",
    "\n",
    "\n",
    "def max_contig(length_array):\n",
    "    '''\n",
    "    :param length_array: array of integers\n",
    "    :return: max of array\n",
    "    '''\n",
    "    max_contig = max(length_array)\n",
    "    return max_contig\n",
    "\n",
    "\n",
    "def total_size_in_bp(length_array):\n",
    "    '''\n",
    "    :param length_array: array of integers\n",
    "    :return: sum of integers in array\n",
    "    '''\n",
    "    total_size = 0\n",
    "    for item in length_array:\n",
    "        total_size += item\n",
    "    return total_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: blood_Trinity.fasta\n",
      "Number of Contigs: 289077\n",
      "Total Size: 302896770\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 21467\n",
      "Average Contig Size: 1047.80653597\n",
      "Median Contig Size: 439.0\n",
      "N50: 2301\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_blood/blood_Trinity.fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: brain_Trinity.fasta\n",
      "Number of Contigs: 69560\n",
      "Total Size: 44777863\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 14145\n",
      "Average Contig Size: 643.73006038\n",
      "Median Contig Size: 355.0\n",
      "N50: 978\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_brain/brain_Trinity.fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: germinalbed_Trinity.fasta\n",
      "Number of Contigs: 72688\n",
      "Total Size: 41130150\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 10914\n",
      "Average Contig Size: 565.845118864\n",
      "Median Contig Size: 342.0\n",
      "N50: 750\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_germinalbed/germinalbed_Trinity.fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: heart_Trinity.fasta\n",
      "Number of Contigs: 48047\n",
      "Total Size: 30567142\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 25486\n",
      "Average Contig Size: 636.192519824\n",
      "Median Contig Size: 350.0\n",
      "N50: 956\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_heart/heart_Trinity.fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: testes_Trinity.fasta\n",
      "Number of Contigs: 52506\n",
      "Total Size: 36439377\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 14746\n",
      "Average Contig Size: 694.004056679\n",
      "Median Contig Size: 375.0\n",
      "N50: 1145\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes/testes_Trinity.fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Troubleshooting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "Duncan and my data sets are drastically different in number of reads retained after normalization and trimmomatic. I am going to investigate which step within trinity cause the massive loss in reads in my data set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Trimmomatic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#java -jar /n/apps/CentOS7/bin/trinityrnaseq-2.1.1/trinity-plugins/Trimmomatic/trimmomatic-0.32.jar PE -threads 4 \\\n",
    "#/n/projects/tfording/a_inornata/RNAseq_data/testes_R1.fastq.gz \\\n",
    "#/n/projects/tfording/a_inornata/RNAseq_data/testes_R2.fastq.gz \\\n",
    "#-baseout testesdiagnosis \\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "After running trimmomatic on the testes data set, 115,305,156 reads were retained from the original 115,660,629 reads. This is a net loss of 355,473 reads. Trimmomatic is not responsible for the massive loss in data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "##### Trinity (2.3.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Installed trinity 2.3.2 via anaconda.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#/n/projects/tfording/anaconda2/bin/Trinity --seqType fq --SS_lib_type RF --left /n/projects/tfording/a_inornata/RNAseq_data/testes_R1.fastq.gz --right /n/projects/tfording/a_inornata/RNAseq_data/testes_R2.fastq.gz --CPU 16 --max_memory 400G --normalize_reads --verbose --min_kmer_cov 2 --min_contig_length 200 --output /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2 --quality_trimming_params \"ILLUMINACLIP:/n/projects/tfording/anaconda2/pkgs/trinity-2.3.2-0/opt/trinity-2.3.2/trinity-plugins/Trimmomatic-0.32/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: testes_Trinity_2.3.2.fasta\n",
      "Number of Contigs: 52123\n",
      "Total Size: 36838397\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 14825\n",
      "Average Contig Size: 706.75895478\n",
      "Median Contig Size: 380.0\n",
      "N50: 1180\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2/testes_Trinity_2.3.2.fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### BUSCO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "After running Trinity 2.3.2 I got a very similar set of data, so I ran BUSCO (at Duncan's request) on both of my generated testes data sets, as well as on his. I ran BUSCO on both CEGs and the Tetrapod references. I installed BUSCO and its supporting programs via anaconda.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## EUKARYOTA\n",
    "# Trinity 2.1.1\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes/testes_Trinity.fasta -o EUK_trinity_2.1.1 -l /n/projects/tfording/BUSCO_lineages/eukaryota_odb9 -m tran -c 4\n",
    "\n",
    "# Trinity 2.3.2\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2/testes_Trinity_2.3.2.fasta -o EUK_trinity_2.3.2 -l /n/projects/tfording/BUSCO_lineages/eukaryota_odb9 -m tran -c 4\n",
    "\n",
    "# Duncan's Testes (Trinity 2.3.2)\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/dut/a_marmorata/teste_transcriptome_sex_determ/data/trinity_out/Trinity.fasta -o EUK_duncans_testes_trinity_2.3.2 -l /n/projects/tfording/BUSCO_lineages/eukaryota_odb9 -m tran -c 4\n",
    "\n",
    "## TETRAPODA\n",
    "# Trinity 2.1.1\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes/testes_Trinity.fasta -o TET_trinity_2.1.1 -l /n/projects/tfording/BUSCO_lineages/tetrapoda_odb9 -m tran -c 4\n",
    "\n",
    "# Trinity 2.3.2\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2/testes_Trinity_2.3.2.fasta -o TET_trinity_2.3.2 -l /n/projects/tfording/BUSCO_lineages/tetrapoda_odb9 -m tran -c 4\n",
    "\n",
    "# Duncan's Testes (Trinity 2.3.2)\n",
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/dut/a_marmorata/teste_transcriptome_sex_determ/data/trinity_out/Trinity.fasta -o TET_duncans_testes_trinity_2.3.2 -l /n/projects/tfording/BUSCO_lineages/tetrapoda_odb9 -m tran -c 4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Results from BUSCO: (TESTES)**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* INORNATA TRINITY 2.1.1 BUSCO: CEGs\n",
    "\t* 196\tComplete BUSCOs (C) **64.7%**\n",
    "\t* 150\tComplete and single-copy BUSCOs (S) **49.5%**\n",
    "\t* 46\tComplete and duplicated BUSCOs (D) **15.2%**\n",
    "\t* 71\tFragmented BUSCOs (F) **23.4%**\n",
    "\t* 36\tMissing BUSCOs (M) **11.9%**\n",
    "\t* 303\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "* INORNATA TRINITY 2.3.2 BUSCO: CEGs\n",
    "\t* 198\tComplete BUSCOs (C) **65.3%**\n",
    "\t* 151\tComplete and single-copy BUSCOs (S) **49.8%**\n",
    "\t* 47\tComplete and duplicated BUSCOs (D) **5.5%**\n",
    "\t* 69\tFragmented BUSCOs (F) **22.8%**\n",
    "\t* 36\tMissing BUSCOs (M) **11.9%**\n",
    "\t* 303\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "* MARMORATA TRINITY 2.3.2 BUSCO: CEGs\n",
    "\t* 296\tComplete BUSCOs (C) **97.7%**\n",
    "\t* 64\tComplete and single-copy BUSCOs (S) **21.1%**\n",
    "\t* 232\tComplete and duplicated BUSCOs (D) **76.6%**\n",
    "\t* 7\tFragmented BUSCOs (F) **2.3%**\n",
    "\t* 0\tMissing BUSCOs (M) **0.0%**\n",
    "\t* 303\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "* INORNATA TRINITY 2.1.1 BUSCO: TETRAPOD \n",
    "\t* 1384\tComplete BUSCOs (C) **35.0%**\n",
    "\t* 909\tComplete and single-copy BUSCOs (S) **23.0%**\n",
    "\t* 475\tComplete and duplicated BUSCOs (D) **12.0%**\n",
    "\t* 761\tFragmented BUSCOs (F) **19.3%**\n",
    "\t* 1805\tMissing BUSCOs (M) **45.7%**\n",
    "\t* 3950\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "* INORNATA TRINITY 2.3.2 BUSCO: TETRAPOD\n",
    "\t* 1402\tComplete BUSCOs (C) **35.5%**\n",
    "\t* 903\tComplete and single-copy BUSCOs (S) **22.9%**\n",
    "\t* 499\tComplete and duplicated BUSCOs (D) **12.6%**\n",
    "\t* 753\tFragmented BUSCOs (F) **19.1%**\n",
    "\t* 1795\tMissing BUSCOs (M) **45.4%**\n",
    "\t* 3950\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "* MARMORATA TRINITY 2.3.2 BUSCO: TETRAPOD\n",
    "\t* 3207\tComplete BUSCOs (C) **81.2%**\n",
    "\t* 856\tComplete and single-copy BUSCOs (S) **21.7%**\n",
    "\t* 2351\tComplete and duplicated BUSCOs (D) **59.5%**\n",
    "\t* 501\tFragmented BUSCOs (F) **12.7%**\n",
    "\t* 242\tMissing BUSCOs (M) **6.1%**\n",
    "\t* 3950\tTotal BUSCO groups searched\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "My data set continues to appear to be drastically less informative. I am going to run Trinity (2.3.2) on the testes data set, without doing a normalization in an attempt to get more contigs and contigs of higher quality.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Kmer Distributions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "def histogram(file_path):\n",
    "    '''\n",
    "    This function takes a jellyfish.kmers.fa.histo file from Trinity and plots a histogram.\n",
    "    :param file_path: Path to jellyfish.kmers.fa.histo\n",
    "    :return: Nothing is returned, a histogram is plotted\n",
    "    '''\n",
    "    fh = open(file_path, \"r\")\n",
    "    kmer_freq = []\n",
    "    num_kmers = []\n",
    "\n",
    "    for line in fh:\n",
    "        line = line.split()\n",
    "        kmer_freq.append(int(line[0]))\n",
    "        num_kmers.append(math.log10(int(line[1])))\n",
    "    #print kmer_freq, num_kmers\n",
    "    # the histogram of the data\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "\n",
    "    fig.set_size_inches(10,12)\n",
    "\n",
    "    ax.bar(kmer_freq, num_kmers, width=100, color='b', edgecolor='b')\n",
    "\n",
    "    ax.set_xlabel('Freq of Kmer')\n",
    "    ax.set_ylabel('Num of Kmer (Log10)')\n",
    "    ax.set_title('Kmer Dist')\n",
    "\n",
    "    ax.grid(True)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I decided to do kmer distributions (at the reccomendation of both Duncan and Aaron) to see how my seemingly problematic datasets compared to Duncan's data set which performed very well in BUSCO\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**A. inornata Trinity 2.1.1 (Testes)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAAPzCAYAAAC5k+1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc303cae150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**A. inornata Trinity 2.3.2 (Testes)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAPzCAYAAACweE0LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABgQhYqAACACVmoAAAAJvT/AxKL5j3fIqMiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3280791d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**A. marmorata Trinity 2.3.2 (Testes) DUNCAN'S DATA SET**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAPzCAYAAACweE0LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4199b63090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/dut/a_marmorata/teste_transcriptome_sex_determ/data/trinity_out/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Trinity 2.1.1 Brain**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAAPzCAYAAAC5k+1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAgJIYoAAAAACgJAYoAAAAACjp/wPBOnNyYClMjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f419194a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_brain/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Trinity 2.1.1 Germinal Bed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAAPzCAYAAAC5k+1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41c3b1f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_germinalbed/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Trinity 2.1.1 Heart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAAPzCAYAAAC5k+1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4196816f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_heart/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "My distributions seem to drop off rather quickly (with the exception of the blood library which is below), while Duncan's spread much more nicely. This implies a loss of complexity in the data set. I am going to talking to Rachel and see if she did anything differently with these data sets, and run the entire set of data together through Trinity 2.3.2 and see how it looks.\n",
    "\n",
    "After talking to Rachel, there wasn't anything notable done differently with the data set. I am hoping that a non-normalized run will restore the kmer complexity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Blood Library**\n",
    "\n",
    "Looking at the blood library, it seems to have done better through the Trinity normalization step.\n",
    "* raw blood reads: 90,758,319\n",
    "* normalized: 19,709,140\n",
    "\n",
    "I also ran it through BUSCO and got the following numbers back:.\n",
    "\n",
    "* CEGs    \n",
    "    * 294 Complete BUSCOs (C) **97.0%**\n",
    "    * 97 Complete and single-copy BUSCOs (S) **32.0%**\n",
    "    * 197 Complete and duplicated BUSCOs (D) **65.0%**\n",
    "    * 9 Fragmented BUSCOs (F) **3.0%**\n",
    "    * 0 Missing BUSCOs (M) **0.0%**\n",
    "    * 303 Total BUSCO groups searched\n",
    "* TET\n",
    "\t* 2865\tComplete BUSCOs (C) **72.5%**\n",
    "\t* 971\tComplete and single-copy BUSCOs (S) **24.6%**\n",
    "\t* 1894\tComplete and duplicated BUSCOs (D) **47.9%**\n",
    "\t* 326\tFragmented BUSCOs (F) **8.3%**\n",
    "\t* 759\tMissing BUSCOs (M) **19.2%**\n",
    "\t* 3950\tTotal BUSCO groups searched\n",
    "\n",
    "\n",
    "**Trinity 2.1.1 Blood**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAPzCAYAAACweE0LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wO2UFp2IMyc7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3044fc690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_blood/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Testes Trinity (2.3.2) Without Normalization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAP3CAYAAAAx1e+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41bd747c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/not_normalized_Trinity_testes_2.3.2/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "BUSCO Results for Non-normalized testes:\n",
    "\n",
    "* CEGS\n",
    "    * 234 Complete BUSCOs (C) **77.2%**\n",
    "    * 178 Complete and single-copy BUSCOs (S) **58.7%**\n",
    "    * 56 Complete and duplicated BUSCOs (D) **18.5%**\n",
    "    * 51 Fragmented BUSCOs (F) **16.8%**\n",
    "    * 18 Missing BUSCOs (M) **6.0%**\n",
    "    * 303 Total BUSCO groups searched\n",
    "* TETRAPOD\n",
    "    * 768 Complete BUSCOs (C) **44.7%**\n",
    "    * 1146 Complete and single-copy BUSCOs (S) **29.0%**\n",
    "    * 622 Complete and duplicated BUSCOs (D) **15.7%**\n",
    "    * 810 Fragmented BUSCOs (F) **20.5%**\n",
    "    * 1372 Missing BUSCOs (M) **34.8%**\n",
    "    * 3950 Total BUSCO groups searched\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: Trinity.fasta\n",
      "Number of Contigs: 81023\n",
      "Total Size: 54860022\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 15982\n",
      "Average Contig Size: 677.091961542\n",
      "Median Contig Size: 353.0\n",
      "N50: 1145\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/not_normalized_Trinity_testes_2.3.2/Trinity.fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### All Tissue Trinity (2.3.2) Assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAP1CAYAAABmIa4WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQAApPT/A/gtYaFtrqRRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4cc8164d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_alltissues_2.3.2/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This distribution seems normal in complexity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: Trinity.fasta\n",
      "Number of Contigs: 1662368\n",
      "Total Size: 1111513484\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 28295\n",
      "Average Contig Size: 668.632627673\n",
      "Median Contig Size: 379.0\n",
      "N50: 988\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/tfording/a_inornata/RNAseq_data/Trinity_alltissues_2.3.2/Trinity.fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* BUSCO CEGs Results:\n",
    "    * 273 Complete BUSCOs (C) **90.1%**\n",
    "    * 53 Complete and single-copy BUSCOs (S) **17.5%**\n",
    "    * 220 Complete and duplicated BUSCOs (D) **72.6%**\n",
    "    * 29 Fragmented BUSCOs (F) **9.6%**\n",
    "    * 1 Missing BUSCOs (M) **0.3%**\n",
    "    * 303 Total BUSCO groups searched\n",
    "* BUSCO Tetrapod Results:\n",
    "    * 3136 Complete BUSCOs (C) **79.4%**\n",
    "    * 878 Complete and single-copy BUSCOs (S) **22.2%**\n",
    "    * 2258 Complete and duplicated BUSCOs (D) **57.2%**\n",
    "    * 700 Fragmented BUSCOs (F) **17.7%**\n",
    "    * 114 Missing BUSCOs (M) **2.9%**\n",
    "    * 3950 Total BUSCO groups searched\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**DUNCAN'S A.marmorata**\n",
    "\n",
    "* BUSCO CEGs Results:\n",
    "    * 284 Complete BUSCOs (C) **93.8%**\n",
    "    * 82 Complete and single-copy BUSCOs (S) **27.1%**\n",
    "    * 202 Complete and duplicated BUSCOs (D) **66.7%**\n",
    "    * 18 Fragmented BUSCOs (F) **5.9%**\n",
    "    * 1 Missing BUSCOs (M) **0.3%**\n",
    "    * 303 Total BUSCO groups searched\n",
    "* BUSCO Tetrapod Results:\n",
    "    * 3255 Complete BUSCOs (C) **82.5%**\n",
    "    * 1037 Complete and single-copy BUSCOs (S) **26.3%**\n",
    "    * 2218 Complete and duplicated BUSCOs (D) **56.2%**\n",
    "    * 600 Fragmented BUSCOs (F) **15.2%**\n",
    "    * 95 Missing BUSCOs (M) **2.3%**\n",
    "    * 3950 Total BUSCO groups searched\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAP1CAYAAABmIa4WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f070f567d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram('/n/projects/dut/a_marmorata/marmorata_comprehensive_transcriptome/data/trinity_out/jellyfish.kmers.fa.histo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name: Trinity.fasta\n",
      "Number of Contigs: 2218131\n",
      "Total Size: 1413658492\n",
      "Min Contig Size: 201\n",
      "Max Contig Size: 24998\n",
      "Average Contig Size: 637.319658758\n",
      "Median Contig Size: 377.0\n",
      "N50: 908\n"
     ]
    }
   ],
   "source": [
    "parse_trinity_fasta('/n/projects/dut/a_marmorata/marmorata_comprehensive_transcriptome/data/trinity_out/Trinity.fasta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Summary\n",
    "* Total bp in transcriptome: 1130843204\n",
    "* Total transcripts in transcriptome: 1662367\n",
    "* The mean transcript length is: 680.260859365\n",
    "\n",
    "The A. inornata transcriptome is comparable to the A. marmorata transcriptome in terms of BUSCOs. So I will continue forward with the genome annotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#BUSCO SCRIPT\n",
    "python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/transcriptome/inornataTranscriptome.fasta -o ino_trans_VERT_BUSCO.out -l /n/projects/tfording/BUSCO_lineages/vertebrata_odb9 -m tran -c 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### BUSCO Script example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/RNAseq_data/Trinity_testes_2.3.2/testes_Trinity_2.3.2.fasta -o EUK_trinity_2.3.2 -l /n/projects/tfording/BUSCO_lineages/eukaryota_odb9 -m tran -c 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Genome Mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "Goal of this portion is to mask out repeats in the genome using repeat modeler and repeat masker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#BuildDatabase -name AspInornata -engine ncbi AspIno_2.0.fasta \n",
    "#RepeatModeler -engine ncbi -pa 16 -database AspInornata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Note to self:** Repeat Modeler takes a LLLLOOOONNNNNGGGG time. 10+ days. After repeatmodeler sat at the same step for over 3 days, I decided to kill the script and start it with more threads. The script would not restart so I had to start it over.\n",
    "\n",
    "* Step 5 took 120 hours\n",
    "* Step 6 took 240+"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "RepeatModeler did not output the file I needed. Instead of spending more time on this I'm going to move forward using the masked inornata genome Rutendo created. File loc: '/n/projects/tfording/a_inornata/repeatmodeler/RM_42558.SunJun260011112016/consensi.fa.classified'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#RepeatMasker -xsmall -gff -s -pa 20 -lib /n/projects/tfording/a_inornata/repeatmodeler/RM_42558.SunJun260011112016/consensi.fa.classified /n/projects/tfording/genomes/AspIno_2.0.fasta -dir /n/projects/tfording/a_inornata > repeatmasker.out "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**File Name: AspIno_2.0.fasta**\n",
    "* sequences: 1172\n",
    "* total length: 1528101006 bp (1511313112 bp excl N/X-runs)\n",
    "* GC level: **42.46 %**\n",
    "* bases masked: 646865925 bp (42.33 %)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "            Number of Elements   |   Length Occupied       | Percentage of Sequence    |\n",
    "* SINEs:             295887   |   49230611 bp |     3.22 %     |\n",
    "    * ALUs            12600   |    1175022 bp |     0.08 %     | \n",
    "    * MIRs           189271   |   37285204 bp |     2.44 %     | \n",
    "\n",
    "\n",
    "* LINEs:             757284   |  168150683 bp |    11.00 %     |\n",
    "    * LINE1           26898   |    9823120 bp |     0.64 %     | \n",
    "    * LINE2          334498   |   73271199 bp |     4.79 %     |\n",
    "    * L3/CR1         261685   |   60605970 bp |     3.97 %     |\n",
    "    \n",
    "    \n",
    "* LTR elements:      103909   |   27998616 bp |     1.83 %     |\n",
    "    * ERVL             7774   |    1288103 bp |     0.08 %     |\n",
    "    * ERVL-MaLRs          0   |          0 bp |     0.00 %     |\n",
    "    * ERV_classI       1107   |     319068 bp |     0.02 %     |\n",
    "    * ERV_classII      1132   |     289710 bp |     0.02 %     |\n",
    "\n",
    "\n",
    "* DNA elements:      301265   |   54417934 bp |     3.56 %     |\n",
    "   * hAT-Charlie      78883   |   16228489 bp |     1.06 %     |\n",
    "   * TcMar-Tigger     21762   |    3160903 bp |     0.21 %     |\n",
    "\n",
    "\n",
    "* Unclassified:     1475208   |  314017294 bp |    20.55 %     |\n",
    "\n",
    "\n",
    "* Total interspersed repeats: 613815138 bp |    40.17 %     \n",
    "* Small RNA:          17706   |    2689236 bp |     0.18 % \n",
    "\n",
    "\n",
    "* Satellites:         7089   |     975113 bp |     0.06 %     |\n",
    "* Simple repeats:    429526   |   34238343 bp |     2.24 %     |\n",
    "* Low complexity:     32771   |    1713435 bp |     0.11 %     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Mapping RNAseq to Masked Genome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "Following the pipeline that was used for the marmorata genome, I am mapping the reads back using STAR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### STAR Genome Index Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#STAR --runThreadN 8 --runMode genomeGenerate --genomeDir /n/projects/tfording/a_inornata/genome --genomeFastaFiles /n/projects/tfording/a_inornata/AspIno_2.0.fasta.masked --limitGenomeGenerateRAM 200000000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Index Out:**\n",
    "\n",
    "* Apr 03 13:49:27 ..... started STAR run\n",
    "* Apr 03 13:49:27 ... starting to generate Genome files\n",
    "* Apr 03 13:50:07 ... starting to sort Suffix Array. This may take a long time...\n",
    "* Apr 03 13:50:18 ... sorting Suffix Array chunks and saving them to disk...\n",
    "* Apr 03 14:01:00 ... loading chunks from disk, packing SA...\n",
    "* Apr 03 14:02:15 ... finished generating suffix array\n",
    "* Apr 03 14:02:15 ... generating Suffix Array index\n",
    "* Apr 03 14:07:51 ... completed Suffix Array index\n",
    "* Apr 03 14:07:51 ... writing Genome to disk ...\n",
    "* Apr 03 14:08:00 ... writing Suffix Array to disk ...\n",
    "* Apr 03 14:08:38 ... writing SAindex to disk\n",
    "* Apr 03 14:08:44 ..... finished successfully"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### STAR Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#STAR --readFilesIn /n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CAGATC_2_R1_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CAGATC_2_R2_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CAGATC_R1_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CAGATC_R2_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CGATGT_2_R1_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CGATGT_2_R2_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CGATGT_R1_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_CGATGT_R2_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_GTGAAA_2_R1_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_GTGAAA_2_R2_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_GTGAAA_R1_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1655_GTGAAA_R2_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ACAGTG_2_R1_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ACAGTG_2_R2_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ACAGTG_R1_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ACAGTG_R2_brain.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ATGTCA_2_R1_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ATGTCA_2_R2_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ATGTCA_R1_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_ATGTCA_R2_testes.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_GCCAAT_2_R1_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_GCCAAT_2_R2_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_GCCAAT_R1_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1656_GCCAAT_R2_heart.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1684_CCGTCC_2_R1_germinalbed.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1684_CCGTCC_2_R2_germinalbed.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1684_CCGTCC_R1_germinalbed.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG1684_CCGTCC_R2_germinalbed.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG897_CGTACG_2_R1_blood.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG897_CGTACG_2_R2_blood.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG897_CGTACG_R1_blood.fastq.gz,/n/projects/tfording/a_inornata/data/raw_data/RNA_seq/MOLNG897_CGTACG_R2_blood.fastq.gz --readFilesCommand gunzip -c --genomeDir /n/projects/tfording/a_inornata/genome/STAR_genome --outFileNamePrefix /n/projects/tfording/a_inornata/data/STAR_alignments_run1_april17 --runThreadN 8 --twopassMode Basic --alignSJoverhangMin 8 --alignSJDBoverhangMin 5 --outSAMtype BAM SortedByCoordinate --limitBAMsortRAM 50000000000 --outFilterType Normal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "STAR --readFilesIn --readFilesCommand “cat” --genomeDir ../data/marm_ref --outFileNamePrefix ../data/100_million_alignments_2ndpass/merged_samples_ --runThreadN 8 --alignSJoverhangMin 8 --alignSJDBoverhangMin 5 --sjdbFileChrStartEnd ../data/100_million_alignments/merged_samples_SJ.out.tab --outSAMtype BAM SortedByCoordinate --limitBAMsortRAM 50000000000 --outFilterType Normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Masked Genome BUSCO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/genome/inornata_09Jan2016_DgKHC_uppercase.fasta. masked -o ino_masked_vert_BUSCO.out -l /n/projects/tfording/BUSCO_lineages/vertebrata_odb9 -m genome -c 4  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**NOTE TO SELF:** BUSCO requires a path to Augustus to be set. This is done by typing the following into the command line environment:\n",
    "\n",
    "export AUGUSTUS_CONFIG_PATH=/n/projects/tfording/anaconda2/pkgs/augustus-3.2.3-boost1.60_0/config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**BUSCO OUTPUT:**\n",
    "* 2480\tComplete BUSCOs (C) **95.9%**\n",
    "* 2465\tComplete and single-copy BUSCOs (S) **95.3%**\n",
    "* 15\tComplete and duplicated BUSCOs (D) **0.6%**\n",
    "* 65\tFragmented BUSCOs (F) **2.5%**\n",
    "* 41\tMissing BUSCOs (M) **1.6%**\n",
    "* 2586\tTotal BUSCO groups searched\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BRAKER: Augustus Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Installed using Anaconda command:** conda install -c bioconda braker=1.9\n",
    "\n",
    "Lots of perl dependencies were missing. After trying to install the needed dependencies, Anaconda informed me of conflicts between packages of different versions. It appears as if I need to use an older version of perl to allow for the Logger::Simple module to be installed from CPAN. To do this I created a separate Conda Environment named Braker1Perl. This is where I am installing the needed packages for Braker.\n",
    "\n",
    "5May2017: Installing in a new environment didn't work. Duncan said that he used the site wide version of perl. This resolved the Logger::Simple issue, but BRAKER still failed after trying to run it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Command to run BRAKER1:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "/n/projects/tfording/anaconda2/bin/perl /n/projects/tfording/anaconda2/pkgs/braker-1.9-1/bin/braker.pl --genome=/n/projects/tfording/a_inornata/star/AspIno_2.0.fasta.masked --species=AspIno --bam=/n/projects/tfording/a_inornata/data/STAR_alignments_run1_april17Aligned.sortedByCoord.out.bam --cores 10 --filterOutShort on --gff3 --overwrite &>AspIno_braker1_Check3_stdout.txt &l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have installed all the perl dependencies that maker is currently throwing errors for. Perl however isn't utilizing them currently. I am going to try and switch back to my conda installation of perl. If it doesn't work I'll need to look into how to point my CPAN installations to perl.\n",
    "\n",
    "After seemingly endless dependency errors I finally got BRAKER to work. I ended up putting the paths to Augustus, Bamtools and GeneMark in my .bash_profile. This fixed BRAKER's inability to find these programs despite the paths being passed to it in the command originally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BRAKER V1 BUSCO RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/bin/braker/AspIno/augustus.aa -o EUK_maker_1_BUSCO.out -l /n/projects/tfording/BUSCO_lineages/vertebrata_odb9 -m proteins -c 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EUKARYOTIC BUSCOs**\n",
    "C:77.8%[S:75.2%,D:2.6%],F:15.5%,M:6.7%,n:303\n",
    "\n",
    "    236    Complete BUSCOs (C)\n",
    "    228    Complete and single-copy BUSCOs (S)\n",
    "    8    Complete and duplicated BUSCOs (D)\n",
    "    47    Fragmented BUSCOs (F)\n",
    "    20    Missing BUSCOs (M)\n",
    "    303    Total BUSCO groups searched\n",
    "\n",
    "\n",
    "**Vertabrate BUSCOs** \n",
    "C:51.8%[S:47.6%,D:4.2%],F:27.7%,M:20.5%,n:2586\n",
    "\n",
    "    1339 Complete BUSCOs (C)\n",
    "    1231 Complete and single-copy BUSCOs (S)\n",
    "    108 Complete and duplicated BUSCOs (D)\n",
    "    717 Fragmented BUSCOs (F)\n",
    "    530 Missing BUSCOs (M)\n",
    "    2586 Total BUSCO groups searched"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BRAKER V2 BUSCO RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#python /n/projects/tfording/anaconda2/bin/BUSCO.py -i /n/projects/tfording/a_inornata/bin/braker/AIno/augustus.aa -o EUK_maker_1_BUSCO.out -l /n/projects/tfording/BUSCO_lineages/vertebrata_odb9 -m proteins -c 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vert BUSCOs BRAKER V2**\n",
    "* C:73.7%[S:66.5%,D:7.2%],F:17.8%,M:8.5%,n:2586\n",
    "    * 1906 Complete BUSCOs (C) **73.7%**\n",
    "    * 1720 Complete and single-copy BUSCOs (S) **66.5%**\n",
    "    * 186 Complete and duplicated BUSCOs (D) **7.2%**\n",
    "    * 461 Fragmented BUSCOs (F) **17.8%**\n",
    "    * 219 Missing BUSCOs (M) **8.5%**\n",
    "    * 2586 Total BUSCO groups searched \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAKER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAKER Cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "/n/projects/tfording/a_inornata/bin/maker/bin/maker"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Repeat Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Plot Script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#author=Tyler Fording\n",
    "\n",
    "from __future__ import division\n",
    "import argparse\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "def plot_repeats(file_path1):\n",
    "    fh_in = open(file_path1, 'r')\n",
    "    line_list = []\n",
    "\n",
    "\n",
    "    ############################################  Read in file\n",
    "    for line in fh_in:\n",
    "        line = line.strip('\\n')\n",
    "        line = line.split()\n",
    "        for i in line:\n",
    "            i = i.strip\n",
    "        if float(line[3]) > 0.0010:\n",
    "            line_list.append(line)\n",
    "\n",
    "    ############################################  Populate data structures for plotting\n",
    "    x_tick_labels = [i[0].strip() for i in line_list]\n",
    "\n",
    "    x_list = [i+1 for i in range(len(line_list))]\n",
    "\n",
    "    y_list = [float(i[3].strip()) for i in line_list]\n",
    "\n",
    "    data = []\n",
    "    new_line_list = []\n",
    "\n",
    "    for item in line_list:\n",
    "        data.append({'label':item[0], 'color':'maroon', 'height':float(item[3])})\n",
    "\n",
    "\n",
    "    ############################################  Plot data\n",
    "    figure, ax = plt.subplots()\n",
    "    i = 1\n",
    "    for bar in data:\n",
    "        ax.bar(i, bar['height'], align='center', color=bar['color'])\n",
    "        i += 1\n",
    "\n",
    "    plt.xticks(x_list, x_tick_labels, rotation=45, ha='right')\n",
    "    plt.ylabel('% Abundence')\n",
    "    plt.xlabel('Repeat Class')\n",
    "    plt.title('Inornatus Repeat Abundence by Class')\n",
    "\n",
    "    for i in x_list:\n",
    "        ax.text(i, y_list[i-1]+2, str(y_list[i-1]), fontweight='bold', ha='center', rotation=90)\n",
    "\n",
    "\n",
    "    plt.show()\n",
    "    #plt.savefig('ino_repeats.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAGaCAYAAAAW6tqeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f30115ab0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_repeats('/n/projects/tfording/a_inornata/repeatmasker/repeat_masker_rutendo/inornata/inornata.table.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "# Transcriptome Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Annotating The AspIno Transcriptome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Trinnotate Pipeline Scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "I started this process by running Trinnotate on the Inornata transcriptome. Trinnotate is apart of an established pipeline that can be found (https://trinotate.github.io/). The commands are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#blastx -query Trinity.fasta -db uniprot_sprot.pep -num_threads 8 -max_target_seqs 1 -outfmt 6 > blastx.outfmt6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This command uses blastx to search the Trinity transcriptome assembly for transcripts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#blastp -query transdecoder.pep -db uniprot_sprot.pep -num_threads 8 -max_target_seqs 1 -outfmt 6 > blastp.outfmt6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This command uses blastp to seach Swissprot database for Transdecoder-predicted proteins. It creates a database of proteins to be used later in the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#hmmscan --cpu 8 --domtblout TrinotatePFAM.out Pfam-A.hmm transdecoder.pep > pfam.log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "HMMER identifies protein domains and outputs a log of all the protein domains found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#./signalp -f short -n signalp_short.out /n/projects/tfording/a_inornata/transcriptome/Transcriptome_annotation/ino_trans_longestorfs.pep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "SignalP predicts signal peptides in the transcriptome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#tmhmm --short < transdecoder.pep > tmhmm.out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "tmHMM predicts transmembrane regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#/n/projects/tfording/a_inornata/transcriptome/bin/Trinnotate/util/rnammer_support/RnammerTranscriptome.pl --transcriptome inornataTranscriptome.fasta --path_to_rnammer /n/projects/tfording/a_inornata/transcriptome/bin/rnammer-1.2.src/rnammer "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "RNAMMER identifies rRNA transcripts it outputs a file called inornataTranscriptome.fasta.rnammer.gff\n",
    "\n",
    "\"RNAMMER was originally developed to identify rRNA genes in genomic sequences. To have it identify rRNA sequences among our large sets of transcriptome sequences, we first concatenate all the transcripts together into a single super-scaffold, run RNAMMER to identify rRNA homologies, and then transform the rRNA feature coordinates in the super-scaffold back to the transcriptome reference coordinates. The following script will perform all of these operations:\" -(https://trinotate.github.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Note to self:** The installation of RNAmmer requires some hacking. I attempted to do as trinotate's website instructed, but the code seems to have been changed since they wrote their hack instructions. They note that this is not a required step to finish annotation, but I would like to produce as complete an annotation as is possible, so I am going to spend some more time trying to a solution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "## Annotation Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### BlastX Hits by %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 100% BlastX hits: 13103**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#awk 'BEGIN {count=0 ;} $3 == \"100.000\" {count ++ ;} END {print count ;}' inornata_blastx.outfmt6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 95% BlastX hits: 21676**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 90% BlastX hits: 55868**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 85% BlastX hits: 89550**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 80% BlastX hits: 122962**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 75% BlastX hits: 154266**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "**Number of 70% BlastX hits: 184451**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "174px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}