{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IsoMut example run\n", "\n", "### Accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut\n", "\n", "##### O. Pipek, D. Ribli, J. Molnár, Á. Póti, M. Krzystanek, A. Bodor, G. E. Tusnády, Z. Szallasi, I. Csabai, and D. Szüts\n", "\n", "\n", "\n", "---\n", "\n", "## Overview:\n", "\n", "- The ipython notebook below contains everything (including the download and indexing of data BAM files and the reference fasta file) to run IsoMut on some of the samples described in the article. After slight modifications to input and output directories, it can be run on any data, whenever multiple isogenic samples are available.\n", "\n", "- The dataset used below is a reduced version of the one used in the article and is available online at the website [genomics.hu](http://www.genomics.hu/tools/isomut/IsoMut_example_dataset.tar.gz). The BAM files of 10 samples contain only a 10 MB length portion of the first chromosome of the genome, allowing for a rapid analysis for testing purposes. Also, the Gallus gallus reference genome in fasta format can be downloaded from [ensembl.org](ftp://ftp.ensembl.org/pub/release-84/fasta/gallus_gallus/dna/). \n", "\n", "- Besides identifying unique mutations with predefined filters, this notebook also demonstrates the tuning of the mutation quality score (score), when control samples are available. In these samples no unique, treatment-induced mutations should be present, thus the score can be tuned by minimizing the number of detected mutations in these samples, while maintaining satisfyingly high numbers in samples that underwent strong mutagenic treatments. The tuning procedure can be individually carried out for SNVs, insertions and deletions, achieving optimized results for all types of mutations.\n", "\n", "- The value of the score is related to the probability of correctly categorizing a candidate mutation as a true positive. More precisely, the probability *p* of incorrectly detecting a difference in the genotypes of the two noisiest samples in the given position can be calculated by Fisher’s exact test on the two samples. This gives the probability of being wrong to state that the reads found in the two noisiest samples are from different nucleotide distributions. The score is calculated as the negative logarithm of *p*. Thus a higher score value gives a ‘surer’ mutation.\n", "\n", "- The notebook contains shell and python commands combined.\n", "\n", "\n", "\n", "Note The score optimisation for insertions and deletions are skipped in the notebook below, due to the lack of sufficiently high numbers of indels in the dataset. However, the commands are included in the notebook and can be run readily on any data very similarly to the score optimisation process for SNVs.\n", "\n", "Warning We strongly advise against the biological interpretation of the data below. As IsoMut was run on 10 samples only and on a very short portion of the whole genome, any conclusions drawn from the results are unreliable. The notebook below serves demonstration purposes only.\n", "\n", "\n", "## Steps:\n", "\n", "- 1) Obtain and prepare data BAM files: download from [genomics.hu](http://www.genomics.hu/tools/isomut/IsoMut_example_dataset.tar.gz), unzip, index\n", "- 2) Obtain and prepare the reference genome: download from [ensembl.org](ftp://ftp.ensembl.org/pub/release-84/fasta/gallus_gallus/dna/), unzip, index\n", "- 3) Obtain and prepare the IsoMut tool: download from [github](https://github.com/genomicshu/isomut), compile\n", "- (4) Modify example script if necessarry)\n", "- 5) Run the script\n", "- 6) Analyze results, optimise score value\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparations (steps 1, 2 and 3)\n", "\n", "#### Importing necessarry python modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import subprocess\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1) Downloading, unzipping, indexing of BAM files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%bash\n", "\n", "wget -q http://www.genomics.hu/tools/isomut/IsoMut_example_dataset.tar.gz\n", "tar -zxf IsoMut_example_dataset.tar.gz\n", "for file in IsoMut_example_dataset/*\n", "do\n", " gunzip $file\n", "done\n", "for file in IsoMut_example_dataset/*\n", "do\n", " samtools index $file\n", "done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2) Downloading, unzipping, indexing of reference fasta file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%bash\n", "\n", "mkdir chrom1_ref_fasta\n", "cd chrom1_ref_fasta\n", "wget -q ftp://ftp.ensembl.org/pub/release-84/fasta/gallus_gallus/dna/Gallus_gallus.Galgal4.dna.chromosome.1.fa.gz\n", "gunzip Gallus_gallus.Galgal4.dna.chromosome.1.fa.gz\n", "samtools faidx Gallus_gallus.Galgal4.dna.chromosome.1.fa\n", "cd .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3) Downloading and compiling IsoMut" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Cloning into 'isomut'...\n" ] } ], "source": [ "%%bash \n", "\n", "########################################\n", "# Download from git\n", "git clone https://github.com/genomicshu/isomut.git\n", "cd isomut/src\n", "\n", "# compile\n", "gcc -c -O3 isomut_lib.c fisher.c -W -Wall\n", "gcc -O3 -o isomut isomut.c isomut_lib.o fisher.o -lm -W -Wall\n", "\n", "cd ../.." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "os.chdir('isomut/')\n", "os.mkdir('output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Viewing (and modifying) example script (step 4)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting isomut_example_script.py\n" ] } ], "source": [ "%%writefile isomut_example_script.py\n", "#!/usr/bin/env python\n", "\n", "#################################################\n", "# importing the wrapper\n", "#################################################\n", "import sys,os\n", "#add path for isomut_wrappers.py\n", "#\tif not running it from the isomut directory\n", "#\tchange os.getcwd for the path to it\n", "sys.path.append(os.getcwd()+'/src')\n", "#load the parallel wrapper function\n", "from isomut_wrappers import run_isomut\n", "\n", "#add path for isomut, if its in the path comment/delete this line\n", "#\tif not running it from the isomut directory\n", "#\tchange os.getcwd for the path to it\n", "os.environ[\"PATH\"] += os.pathsep + os.getcwd() +'/src'\n", "\n", "\n", "#################################################\n", "# defining administrative parameters\n", "#################################################\n", "#using parameter dictionary, beacause there are awful lot of parameters\n", "params=dict()\n", "#minimum number of blocks to run\n", "# usually there will be 10-20 more blocks\n", "params['n_min_block']=100\n", "#number of concurrent processes to run\n", "params['n_conc_blocks']=4\n", "#genome\n", "params['ref_fasta']=\"/\".join(os.getcwd().split(\"/\")[:-1])+'/chrom1_ref_fasta/Gallus_gallus.Galgal4.dna.chromosome.1.fa'\n", "#input dir output dir\n", "params['input_dir']=\"/\".join(os.getcwd().split(\"/\")[:-1])+'/IsoMut_example_dataset/'\n", "params['output_dir']='output/'\n", "#the bam files used\n", "samples=['S07','S09','S12','S13','S15','S22','S24','S27','S28','S30']\n", "params['bam_filenames']=[sample+'_reduced.bam' for sample in samples ]\n", "#limit chromosomes (for references with many scaffolds)\n", "# just comment/delete this line if you want to analyze all contigs in the ref genome\n", "#params['chromosomes']=map(str,range(1,29))+ ['32','W','Z','MT']\n", "params['chromosomes']=['1']\n", "\n", "#################################################\n", "# defining mutation calling parameters\n", "# default values here ...\n", "#################################################\n", "params['min_sample_freq']=0.21\n", "params['min_other_ref_freq']=0.93\n", "params['cov_limit']=5\n", "params['base_quality_limit']=30\n", "params['min_gap_dist_snv']=0\n", "params['min_gap_dist_indel']=20\n", "\n", "#################################################\n", "# and finally run it\n", "#################################################\n", "run_isomut(params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running IsoMut (step 5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defining parallel blocks ...\n", "Done\n", "\n", "blocks to run: 101\n", "running: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 \n", "Done\n", "\n", "Preparing for second round...\n", "Defining parallel blocks ...\n", "Done\n", "\n", "blocks to run: 101\n", "running: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 \n", "Done\n", "\n", "Finalizing output...\n", "Cleaning up...\n", "\n", "Done\n" ] } ], "source": [ "%%bash\n", "\n", "chmod +x isomut_example_script.py\n", "./isomut_example_script.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing results (step 6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Collecting results into one file" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%bash\n", "\n", "head -n1 output/all_SNVs.isomut > all_output.csv\n", "cat output/all_SNVs.isomut | grep -v sample >> all_output.csv\n", "cat output/all_indels.isomut | grep -v sample >> all_output.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output=pd.read_csv('all_output.csv',sep='\\t',header=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the number of mutations found in each sample for different score thresholds" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "##define sample groups\n", "samples=['S07','S09','S12','S13','S15','S22','S24','S27','S28','S30']\n", "control_idx=[2,4]+ [7,9]\n", "weak_idx=[0,5]\n", "strong_idx=[1,3,6,8]\n", "\n", "def plot_tuning_curve(output,ymax):\n", " #set cols\n", " cols=['lightgreen' for i in xrange(10)]\n", " for i in control_idx:\n", " cols[i]='dodgerblue' \n", " for i in weak_idx:\n", " cols[i]='salmon' \n", "\n", " fig,ax=plt.subplots()\n", " fig.set_size_inches(12,9)\n", " for i,col in zip(range(10),cols):\n", " score=output[(output['#sample_name']==samples[i]+'_reduced.bam')].sort(['score'])['score']\n", " ax.plot(score,len(score)-np.arange(len(score)),c=col,lw=4,label='')\n", " ax.set_xlabel(r'score threshold',fontsize=16)\n", " ax.set_ylabel(r'Mutations found',fontsize=16)\n", " ax.set_ylim(1,ymax)\n", " ax.set_xlim(0,4)\n", " ax.grid()\n", " dump=ax.legend(loc='upper right',fancybox='true',fontsize=16)\n", "\n", "\n", " fig,ax=plt.subplots()\n", " fig.set_size_inches(12,9)\n", " for i,col in zip(range(30),cols):\n", " score=output[(output['#sample_name']==samples[i]+'_reduced.bam')].sort(['score'])['score']\n", " ax.plot(score,len(score)-np.arange(len(score)),c=col,lw=4,label='')\n", "\n", " ax.set_xlabel(r'score threshold',fontsize=16)\n", " ax.set_ylabel(r'Mutations found',fontsize=16)\n", " ax.set_ylim(1,ymax)\n", " ax.set_xlim(0,4)\n", " ax.set_yscale('log')\n", " ax.grid()\n", " #dump=ax.legend(loc='upper right',fancybox='true',fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting SNV counts\n", "\n", "- It can be seen that by setting the score threshold to ~ 2 there are no mutations in the control samples, while the treated samples still have reasonably high numbers of them." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python2.7/dist-packages/matplotlib/axes/_axes.py:476: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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LrpYxyy/8yRVGWh2tZbNKhpQS+6f3473oe2mP7fLswo2eG8vuFw4iIlr72GNNOSnXPiyn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\n4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pnBhzZ9MMAtFQRBEEJRSpFj5VDsKmbeiHlcPfJqbhp5E2dknEGelRfxvBbdwtqOtTzU\n/BAPND3AmvY1x7SypCBWkJTn7+vgZ28E+2PBTIC84zxIk8w7QpQcaoV590Gb21/204XwmVngWb0C\n+8Vn6DHSph0HmzcEFCic3/oxKltmoguCICQDWmvqPHVUdVaxuXNzVJaRYmcxlWmVVKRVkK7SB6GV\nyYd4rIcxL2yFr73Yc4n0M4rhz5fAqOH5mxD6wc/egL+t8+8XZcPyz0C6E+yqD/H860FwdwWflJYO\nnf7lfK1FV+A49YxBarEgCIIQLba22e3eTVVnFds6t9FFV6/1HTgod5VTmVZJiatkWOXJFo/1MOai\nCnj4CsgLTjPMW3vgysdhb1PPc8TXFl+GSn9+fjakB/zd3N8C/9pk3luVM3F85jYYkR18UoCohvjY\nQYZKfyYL0p/xQ/oyvkh/xpe++tNSFiWuEj6W9TE+N/pzfCzrY5Q4S1CE148ePFR3VbPk6BL+duRv\nvN76Ovvd+5EgZ2REWA8RZhfBU1dD6ajg8i31cNljsEHmJQhRUJAF1x0fXHbnGrNwEYBVXILz5q9A\n3piI19A129Ct/VtNTBAEQRgcXMpFZVoll+Vcxs2jbmZ+5nzGOcZFrN+u2/mg4wMea36M+5vuZ1Xb\nKg57Ykv7NxwY0lYQj21S080qgJJhkgGsoQ1uXtJzUZksF9y1CBaWJqRZQgqxvxnm3w+dAdaiX50H\nVx/n39etLXgeuQ+9uybsNRyXXo118mkD21BBEAQh7jR4GtjcuZmqziqa7DCPvEMYqovQiMc6ALcN\nz26GP71rVpS7agb8+vxBaGCS0O42nusXtwWXOxScOgEqcqEiD6Z4t3FZoGIeOsJQ5odL4YGAzHml\no+C1G8AZ8IxLd3Xhefoh9MYw1o9xRbhuk8ViBEEQUhWtNfs8+6jqqKK6q5oO3dFrfQuLYmcx5a5y\nytPKybFSe0loEdYBPF8Ntz3v33daJvfzpFGRzxlqeGz4+UqTNSQc7dXLyJiyEICcNCO0fdsUr/Au\nHgkOMQtFxbJly1i4cGGimxE39jTBgvvNTaqP22bD9+YF19Paxn7l39hvL+9xDTV3Po4LFqP6cdc2\n1Poz0Uh/xg/py/gi/RlfBqo/3dpNTVcNVZ1V1HTV4MHT5znjHOMoc5Ux2TWZMQPPwr8AACAASURB\nVI4x/fq/IJH0V1g7B6IxieZjk6F8NGxvNPtuG+5eA784N7HtGkwcFvx4PkwcaVbV6+12pLkT1h0w\nWyDpDpicGyK680webUnjN7QpHglXTodHAjLp3b0WKsfAZZX+MqUsHBcshlG52C8+HXQNveoNPEdb\ncCy+BuUckn9qBEEQhgVO5aQirYKKtAra7Xa2dm2lqrOKve69Ec+p9dRS66lldftqslU25WnllLvK\nKXYWD+nsIkMyYg3wxEb41iv+fZcFb9wI41P7yUS/eHkb3L4c9sYp57tDQckov9D2ie7JuZCVFp/P\nEBJP7VG4+GHz6iPdAU9cBScU9KzvfvIB9Ic9H5Go0sk4rr4RlTl0vHeCIAgCNHmauvNjN9gNUZ2T\nRholrhLK08opdZaSYWUMcCv7h1hBQujywDn/hF1H/GU3nAA/O3uAGpfkeGzTF1sPw9YGqG4wr1sb\nzHLo8WJCjldo5wYL79zMvs8Vko91B+CaJ4JzpBdmw5JrjTc/EH1gL+6//Db8hcYU4Lz+FtToyCuA\nCYIgCKmJ1prD9mG2d21ne+d29nv2930SoFBMcE4wvmxXOaMcyePZFWEdhoc/gu+95t9Pd8CKG6Eg\nO9IZwwefD0trONASLLS3Hjb7DW3x+7z8TL/gnpLvF96F2UNj4uRQ9gn+axN88+XgspOL4JHLzcIx\nPrTWuP/4CzhcH/5C2Tk4P3kLqqi4z88cyv2ZCKQ/44f0ZXyR/owvydKfR+2j1HTVsL1rO7u6duHG\n3fdJQL4jv1tkFzgKEurLFo91GK6YDn98x2+B6PDAX9bCjxcktl3JhFJQlGO2+SXBxxraeka3tzbA\nvn6kKK5vg/q9sDrEjpWd5hfZgT7uiTJxMmm4YjpsrAtekfG9/fCfr8Mvz/PfGCmlsGacgP3m6+Ev\n1NKM+747cVx1A9aU6QPfcEEQBCEhZFlZHJd+HMelH0eX7mJ3124Tze7aTpuOHLWr99RT76nn3fZ3\nyVJZlLnKKE8rZ6JzIk6VGpJ1SEesAf75gREAPjKcsPJGGJsV8RShD1o6YVuD31biE987j4Adp+GU\n7oCy0T193GWjg6OkwuDgtuGmZ+CNXcHlty+Am07079t7d+H52x/8BUpB6G9WWTguvhxr9ukD12BB\nEAQh6dBac8BzoNsyEq0v24WLSa5JlLvKKXOVkWkNvL9UrCARaHfD/H/AwYAJWF+YDT+YF/EUoZ90\nuKGm0WslqfcL7+2Hgz26x4IVMHEy0Mc9Oc9Ev4WB40g7fPxR2NHoL3Mo+L/LYN4ks6+1xv2Hn8MR\n/2pcauoM9JaNPa5nzTsX65wLUUoeTQiCIAxHGj2N3ZHsfe596F5zmBkUiiJnUbdlJNeROyBtE2Hd\nC/etN1kxfIxwwZs3Qd4wnlA3mD4sjw27m4Kj2z4vd0tn/D6nKLtnhLsiF/IHIRlFsvjaBprqBrjs\n0eB/t1HpZjKjb3VTz0vPYq/y/+DUcSeiJpWZdHwhv1818yQci6/tkY5vuPTnYCH9GT+kL+OL9Gd8\nSeX+bLPbun3ZO7t20kV0mRVyrdzuRWkKHYVYcQrWiMe6F647Hu58F+pazX5rF/zPCvjaXOPlFQYW\nh2VyX5eOhvPK/eVamycJgdFt33aoHxMn97eYbUWIXSEv0wjsEwrg7FKYM0HycPeXKXnwvxfCzc/6\nc6Mf6YCbl8BTV0NOOqgZMyFAWOstG3F8/FrUqNF4nngA3P4/lvrDdXiamnBc91lUenKmXBIEQRAG\nnkwrk+np05mePh23drPHvYftXdvZ0bmDFh15ctdh+zBrO9aytmMtmSqTYmcxI62RZFvZ5Fg5ZFvZ\nZFvZjFAjBmUy5LCIWAP89T34+Yqe5eWjzaS9BSUwt9hEs4XEc7gtIENJgPCORy7uLJexLpxTCmeX\nQYH47WPmrnfhjreCy2460XiutbZx/+5n0NzUfcxxzY1YlTONB/uhv0Nr8B9JddJpOBdfPRhNFwRB\nEFIIrTW1ntpuy8ghz6F+XceBgywryy+4VbDwzrFyyFSZ3eJbrCB90NoFZ97Xewq5NAecOt4I7YUl\nMC1/aKSCG0oc7YRth0NSA9abiZOefg7l48bCOWUmmn1igWQjiQat4asvwrNb/GWjM2DNLeBygOeF\np7DfWdl9TJ0wG+cnPmnOPVyP+8F7oL7Of7LTifPbP0WlpQ/WVxAEQRBSkCZPEzu6drCtaxt73Xux\nseN2bQeObqF91cirRFj3xb82wbde7n1570AKsmD+JCO0z5o0tBY5SWUfVjg63EZch6YG3BbjxMm8\nTPP04uxS8zo6SnfCUOvPaGjrgrn3QmO7v+yBy+CsErBrtuG5/y7/gfQMnP/xX91eat16FPdffgNN\n/hWcHFd+Gus4k2JkOPbnQCL9GT+kL+OL9Gd8GW792aE72Nm1k22d26hx19Cp4zdx6+t5XxePdV9c\nMR1mFcBL2+CNnbBmv0kjFomDR+HxTWZTmHPnl5jtpEJwSmQzaUh3wtR8swXisWFPE3xUB6/XwLIa\nv9c+HA1t8FSV2SwFs4uMyD6nFCrHyBOMQDJdcH45PB6Q8OOFrUZYq0llkJUNR72Wj4529I5qlDd/\ntRqRhTXjxKBJjvamD7qFtSAIgiD0RbpKZ2raVKamTcWjPRxwH6DRbqTFbqHFbqHZbjbvdQsdumNQ\n2jSsItahNHfA23tg+U6z7W7q+xwfI9PgzEkmqjl/EkyQSZApga1hQy0srTFCe/2B6J9gFGV7RXYZ\nnDlR/PgAS3fATc/698dkwju3GDuN599PYK99u/uYOvFUnB+/tnvf3l2D594/+k92pRk7iEs6VhAE\nQYgvnbozWGyHiO9m3RwU8e5vxHpYC+tAtDY5mJfvMtHst/cYX3a0VOTBAq9tZG6xWYhGSH4OtZp/\n76U15uaqKcob2jQHzJ3g92aXjh7ARiYxHW445R5oCnj69ugV5jdgb9+C559/8R/IHIHzW7ejHCYl\ni5nk+N/QHGAH8U5yFARBEITBJlB8l6aV9ktYi5nBi1JQlgs3zoJ7F8P6z8NDl8Ots2HGmL7P39oA\nf18Pn3kGTvgzfPopuOc92FLfc+G5ZGDZsmWJbkJSMGYEXD4d/nQRrPs8PH4l3DbbTFztjU6PWYXw\n9uWw4H448XvL+OkbsHKXOTZcSHfCueXBZc9vNa+qZDJkBiQRb2tF12zt3lXKwpoeLKLtjR8AMj7j\njfRn/JC+jC/Sn/FF+vPYSFNp5DnyKHGV9PsaEleNQLrTPO4/cyJ8f57xW6/cZTy6K3f3nl2kwyu6\n3tgF/73CWAh8Kf3mTYRRkq43KXFaJsf1nAnwvXmwt8nYRZbWwJu7zSqekdjfAn9fZzZfOr+zS403\nuyB7UJqfMBZVGE+6jxe2mrR7lsOBmnY8ev073cf0xg9g8rTufTXjBAjIHqK3bES7e+loQRAEQUhi\nxArSD2wNH9X6vdnv7Y8+1ZulzCTIhd5JkLMkvVtK0O6GVXv8QnvXkb7O8HPcWL83eyim82t3w8l/\nhaMB1ql/XQWnjAe7ehOeh/7mPzAiG+e3foKyTCdo28b925/CUX+Ccsd1N2NNnTFYzRcEQRCEHkge\n6wTS1AFv7TZe3eU7YU8Mi5iMSjfRzfPK4dIpJgewkNxobdL4vV5jJu+9s6/37DKB5GaYJxcXVcAF\nk82N1lDgyy/AkoCc1jefBD+eD9rjxv2rn0CHPyef44bbsMoquvd7THKccQKOS65CZQ7CWvSCIAiC\nEAYR1kmC1rC90S+y397Tu4UgkBPGwV0XD84y68Mt1+VA0twBdz2xjEPjFvJ6Te/p/AJZUAK//5jJ\nnZ3qPFcNX3zevz8+G9640dwoup96CP3BWv9BpxNVUYk1/QTU1BnofbuDJzkCy3fsZsGsmaiCIlTB\neO9rEeSPRVly9xkr8nuPH9KX8UX6M75If8aP/gpr8VjHGaVgcq7ZbjrRiOo1+4zIfmMnVNVHPveD\nWrj4Ifjdx+DcssFrs3Bs5KTDaRNg4UJvOr86E8nuK53f8p2w6CG4axGcXDSIDR4Azi41mXB8N5H7\nWuCv78GXTgVrxgl4AoW1242u+ghP1UdgWVBaAa406ApJ7N/UiG5qRFdv8pc5nDC2IERwj0dlDXEj\nuyAIgpASSMR6kDnQYgT2G7tgxa7gVesC+dIp8M3TZRGaVKe+1Qjo12tgWYR0fi4LfniWyUiTygvQ\nfPsVeCxgsZg0Bzz/SagY6cZ996+g4dDAfXh2jhHZ4/xim7HjUA6JHQiCIAixI1aQFMRjw4e1cOe7\n8PL2nsfnFsMfL4RxWYPfNiH+uG0z0fWON82qn6FcPAXuONdEwFOR+lY474HgjDknF8ETV4LV2oT9\n1jKTTu/I4cFpkGXBmIJuG4kvwk32SFQq38EIgiAIA44I6xRGa/PY/I43e2YXGTsC7lxkrAbxRHxY\n8SWW/uzywP97E/62ruex8tFw98Vm+fRU5NnN8JUXg8t+Mh8+e5J5r7WGA3uxN36AvekDqK8Le53l\nO3azoGyivyAtzbx2doatHxOZI4J92wXjUWMLh/SKj/J7jx/Sl/FF+jO+SH/GD/FYpzBKwRdmw0mF\n8KUXoPao/1hdK1z3L/jOGfD52UMni8RwxuWAH8036ei+/Qo0B2jF7Y3w8Ufhf86BK6Ynro395dKp\n8OwWeCXgCcwv3zJZbyaNMn+oKCrGUVSM49xF6LoD2Js+NCL7wL7IFw4U1DkjUfnjwOVCtzRB7UHw\nxJD72rtQTeBiNShlJkYG2UmKYFSuRLcFQRCEqJGIdZJRdxS++iK8tafnsfPK4LcXyAIzQ4maRrjt\nOdgYxn583fFmoZWMFLv9PdgC5/0zeJnzM4rNSqa9aVR9uB570wfoTR+i9+yM7sNGjkZVHo8aPxGU\nBXUH0Af3oQ/uh6bGY/siAOkZXrFdhCocb6Lb4wpRaSnq1xEEQRCiQqwgQwiPDb9bDX98p+ex4pHw\n50Uws2Dw2yUMDO1u+PEyeHRDz2PHjYW7F0HJ6EFv1jHx6Ab4zqvBZf/vXHOzEA266Qh21YdGZO/c\nZvxSfZGVg1V5PGr6TFRpBXR2oGv3ow+ajYP70LUHemYf6Q+5+UF2ElUwHnLzUEpmGwuCIAwFRFgP\nQZbugG+83DNzSJrDRDI/eXz/s0iIDyu+xKM/H9sA//k6dHiCy0emwa8vgI9NPqbLDypaw6efNplv\nfOSkwSufgqKcvs8P7E/d2oKu2mCE9rYtYHt6PxkgIxM17Tis6TNR5dO6/dNa23C4AX1gn4lse4U3\nh3vJgxktrrTu6DaB6QAzEp+oXH7v8UP6Mr5If8YX6c/4IR7rIcg5ZfDcdWbhjfcP+ss7PfCDpfDu\nPuPFHdGPOVedHvj8v+Ezs+DMiX3XFwaeq4+DmePg1uegJmDJ9KZO82/1+ZON1z4VVudUCn5xLlzw\nALR6lzpv7oQfvg5/vzS2G0I1Iht18mlYJ5+Gbm9DV28ylpHqKnB3hT+pvQ39/ho8768xgnfqdKzK\nmajiEhg1GitvDMw4obu67mhH1x4IiGx7BXdHhHyY4ejqRO/Z2dPGMirXbycpKEJNmITKzY/+uoIg\nCELKIBHrFKDDDT9fCfe/3/PYzHHw+JWQGaO4vuc9+O8V5v3cYvjWXJgT58wjQv9o6jA2ihe29jy2\noATuXZw6+c3vWw+3Lw8u++OFsHjasV9bd3Wit1Zhb/oQvWVjbCJ4RLaZBJkz0vs6qvvVlI1Cj8hC\nNR/p9mybbZ/Jx32Mf4fUcbNwLLocNUIWthEEQUhGxAoyDHh2M3z3NX8E0Mfdi2DRlOivc7QTzvoH\n1LcFl581Cb45N/VXARwKaG1E6c9XmvzXgXz/TLj1lMS0K1ZsDVc9Hpy3e3YRPHl1fD9Hu93oHdUm\nkr15A7Qe7fukvlDKLDzjE93ZRnAzYgTYNrS3opuboL7ORLfbolzL3kdWNo5Lr8KaFqXxXBAEQRg0\nRFgPE6ob4OZnYWeAVeAX58AnZ0Z/jSVb4Jb/XUbGlIVhj59dagT2CTJBMmoGyte2dj986XnY3+Iv\nS3fAi9dDeW7cP25A2FIP5z/g3x/hgg239Z468lj6U9se9M4d6E0fYFd9CM1N/bpO1DgckJUDWVng\ndKG0iabTdhSam0HbvZ6uZp2C48LLBtSLLb7L+CF9GV+kP+OL9Gf8EI/1MGFKHpwxMVhYx8qlU+Fn\nZ8MKyyytHsrrNWa7oBy+MRdmjO3/ZwnHxuwieOZa41X2TWLt8BiryGNXpkZe8yl5MDLdv5x7axfs\nazYZbgYCZTlQZRVQVoF10WXovbvRGz9A79yGbmqElhYgjjfoHo9J7edN7xfxypZlouCe4MmX+v01\nuKs3YZ1/KdZxs1CutPi1TRAEQRhUJGKdgnzvNXj4I/9+rBHrQN7ZC79ZBavC5M32sajCCOypMt8q\nYTxZBd94KbjsvxbCjbMS0pyYufwxE3338Y+PmycjiUDbHmhpQTcfgeYmY+doPtJjP2ZrR7xIS4dR\no7u93saCEugD9+47JC4iCIIwUEjEWugXcybAo1fAW7vhN28He2F9PL/VTKRbPA2+dhpMThELwlDi\nE9OMx/71Gn/ZHW/CuWUwcYAiv/FkSl6wsN7akDhhrSwHjByFGjmq13ra3QUtzX7B3eR9bfEJ8SZj\nM4ll0mQ0dHZA3UF0nUkFFDGUEHECpu/9KMjKRlkpMtNVEARhCCDCepgS6sM6YyKcXmysIb99G9Yf\nDK6vgWc2G3/25ZXw1Tmpt2jJQDLQvjalzJOJ8x/wL4He2gXffw3+eVn/85kPFlPygverG3qvnww+\nQeV0weg81Oi8Xuvpzg5vpPtIyGtTwP4RcMew7Ho0tLZAa4vJVEIEAe6dgPnGvjoWnHJy9wRMlTPS\n3Fx4RTmZWbJ0e5Qkw9gcSkh/xhfpz8QjwlroRimTzm3+JLM4zW9WwYa64Dq2hic2wVNVcNUM+Mqc\ngfPKCsEU5cAP5sH3l/rLVuyCxzbCNcclrl3RUBGjsE4lVFo65I9F5UeejKC1hva2MML7CHbtfti7\nK/7C23yw+axDtejNGyJHvx0OyB7pt58Evfqj4aRniAAXBEHoBfFYpyDx9Fj3htbw0jb47SrYHGFh\nOpdlRN1X5kChpOQdcLSG656EtwM88SPT4NVPQ0ES9//eJjjjPv/+yDT44Nbkj7QPFrqzA/vV57Df\nfTPMUQVFEyA7B1qaTfQ73hMwo8WVFmA/iSzAZQKmIAipjqTbG0YMlrD2YWt4vtoI7G2Hw9dJd5g2\nfPEUGJc1cG0RYGcjXPAgtAcEOM8vh3suSV6hqjUcdzccDcjB/s4tUCBjJQh7+xY8zz4GR8L80Aon\n4PzEdahxRck/ATM9wwjukTIBUxCE1ESE9TAiVFj/8Cyz3HUs9MeH5bHhmS3wh1XBS24Hku6AEwth\n+hioHGNep+b3b9n1VGKwfW1/ew9+tiK4LF4rGg4Uix+B9wO8+w9+AuZNCl93OPsEdXsbnpeeRa9/\np+dBZcGILHC5wOkClwvldHa/7y5zOMHlQjscKNtm2br1LJg+Dbq6oLPD+MLb26CtzYjvrs7B/6IA\nI0ejyqdiTZmOmjwVlZ6RmHbEwHAemwOB9Gd8kf6MH5IVZBjz8xUmXd4XT4FTxg/c5zgsM3Fx8VR4\nchP84R3YE7L2RocHVu81mw8FlI72C23fa/HI1MjDnIzcdCL8uxrWHfCX3fEmXDI1eft0Sl6wsK5u\niCyshzMqIxPnx6/Bnj4Tz5LHjP3Dh7bhaHNQ/XChBh3yXu/Yje7ox0I5gY9ABiKo0dSIXv8OnvXv\nmJuGkjKsKTOwpk6H/HHi5xYEIeWQiHUK8qd34Fdvhz82Zzx88VRYWDLwtoBODzyxEf74Duxr6bt+\nKNlpMC3fL7Yrx0BlPuSkx7+tQ5HqBlj0kPl38PHU1cm7JP2v3zZjxcc355r0jUJkdOtRPM8/id6w\nPtFNGXyUMp7ujAzIHGF82wFReZxmU04XuJzd+yaKH1jH2f1ehSnD5TIpGAVBEAIQK8gwoqENPvts\ncLQylBlj4LZTYNEUcA5wGtsONzyyAf68pn8CO5TikcGR7coxUDrKRMyFYG57zuQZ9/H5k401KBn5\nzdvwvyKs+4W9YT2epS9Aw6FEN2VoYllBwtwnvJXT1cNmY8R8GPtNmDKczmCRH3hD4BAxLwjJjAjr\nYYat4ZXtcNe7PXNOBzJpFNw6G66YDhkBxp+B8GHZ2lhDNh2CqkP+15rGY89fkOE00e1AsV2ZD7mZ\ncWn6MZMoX9uSLfDlF/z7xSNh5Y3JOYkxFmEtPsGeaK3NYjRdXeDugq4us4iN933gq3a7g8qWrX2P\nhTMqTXnoOW43uqtnGV1dJCTzSJKzfMduFpRNPPYLKSv6qHp3ebB4VwHHo7khwLKSzl4jv/X4Iv0Z\nP8RjPcywFHxsMlxQDm/tgbvXmJzGoew6Aj9YCr9bBbecBNfPHDirhaWMkJ80yrTNR2sXbKk3QjtQ\ndDd1RH/tdrfx574fchNRmG0EdmCEuzwXXMMkGHR2qZkw2uG1g+xpgo9qYWZBIlslDARKKcjINJuv\nLMpzHVYmjhj/s9Vag8cTIrgDBH2XV4D3KAsQ5u4uv2gPqBtc7i3z7Q8XtG1W2ewM/kMY6VYmLrc4\nSkUZVXcG2GxCxXt48a/CnI/LBZYj6cS8IAwkErEeQnxw0AjsF7ZG/iM8Mg0+PQs+eyKMGTGozQtC\na9jf4hfaPrG9/TB4jvGfOM1hFiTpFtveSPfYIZra7XNL4OXt/v0vnQLfOTNx7YmEWEGEvtBag+1B\nHz2K3r4ZvW0Lekc1HI3BY5aVbVL8ZWUbYReF+B+QiZmCwSfmQ33tPaLqAWI+SvEfyWaDQ8S8cOyI\nFUToZtth+Mtak7mjyw5fJ91hFnb5/GyYmEQrJ7a7zaS8qpDodkPbsV97TKZ/kqRPdFfkBVtkUpEn\nq+AbL/n3y0bD6zcknx1EhLXQH7TWULsfu3oTunoTendN9EI4OwdVUYk1ZUbEdH4+MR8age8RbY8U\naQ+y5bjDlPcsEzE/0Kgga0ywnSYkqt7bpNaobghEzA9VRFgLPdjfDPesMzmvW0OesLZXLyNjykIc\nyuQ+vm02TBuTmHb2hdZQ19pTbG9tiHzjEC0OBZPzetpJCrNjE6aJ9LU1dcDJfw3ui5euN98lmRCP\ndeIYSv2p21rR2zYbob21ClqPRneiZaEmlaOmTMeaMh3G9C+dX7z6UsdgswkS9AHHgkR+JPtN4Hn6\nGP9gDgBx86wnHNVDhHdPdO0RVe9lUqvL2TMCH5TpJuC4w9ljDA+l33qiEY+10IOiHPjxfPjKqXD/\n+3Df+9DYHlzHo+GpKrOdV2ZS9c1OsnRtSpnVHMdlwfwSf3mnx1hHQidLHozy/1kw339Lvdme3eIv\nH5UenAZw+hgzeTLTFb/vFS9Gppt80K/X+Muer04+YS0I8UBljkAdfxLW8SehbRu9bze6ehN29SbY\nvyfyibaNrtmKrtmK/coSGJ1nFqaZMh1VWmGiloOIcjjA4TCrVAaWD+Bndov5qKLq7mDrTODE2LDl\nAaI/UPDbySfmBwbt/87BpZFqxwGvmA8Q7+4du3BXrw+y1/hFfi9ZbsKJ/3A2G2dPMS8EIxHrYURr\nl4le3/Oe8TdH4rQJJovIuWWJ9WH3l4Y2v9D2ie0t9f4Jfv1FYWwWMwvMYjzJJFwf2wDfftW/PzUf\nXvlU4toTjtCI9TdOg6/PTVx7hKGHbj6C3lplotnbtvSYGBgRpws1eSqO8y5GjZGZv/FEd9tsoouq\n98x04/aXRbLfhNpsho2YTxChqSWDbDZOk38+PQOVlm5uHNPSIT3dWLHSvWWBx9PTwZWWdIJdrCBC\n1HR64Okq+PNa48eOhMIsNnJ+OZxXDhW5yefbjRa3bdL+hdpJ9jb3fW44RqbDkmvNipLJwOE2mH1P\n8MTPyyvh9oUm+p4MhArrdAdcPMVkqpldlLpjS0hOtMeN3rUDvWUTdvVGqK/r+6TMETi/9ROzJLyQ\nsmjbE0Zwh4/AR7LZ9BT57l7rYh9j5Ga4o1S3ACc9w4jwAOGt0nyivOdxlR4g0NMy4hZVF2EtxMSy\nZcs4a/5CXvbmwv6gtu9zykYbgX1BuRHcA73wzGBwpAM2h4jtzfU9PenhqMiDp6826QuTwdf2qad6\nplwszIZfnRdsoUkUf18HP30j/LHpY4zA/kSlWZEzGfpzKCH9CbrhkH8CZM028LjD1nPc8jWsCZMi\nXkf6Mr4Mlf7Uth0SVXfHlGu+O6d81HW7TDrMEIaOZ/0YsKyASHmo8PZFzjMC3ocT8BlYWdnisRZi\nw2HBRRVw4WR4czfctca8RmJHo7GR3PMe5GbAOWUmmj1/EmSlDV6748modJgzwWw+bA27j/itJD7R\nvfNI8LlbG+DrL8E9lw5umyPx/XnwzqPBlpcDLfDpp+FTM+EH8xL777SowojrcE8JNh2C/3wdfrES\nLpsGU/v5JEEQIqHyxuA47Sw47Sx0Zwd6x1bjzf7wvWDLiARvhH6gLMsIubTgR4QD6pkPI+Ydy5fj\nmHNq5Ah8uImxYSfRunuURRLzSYdtQ1ur2ejpZx/oX7hErIUgNtXBS9tMXuQNUTw5BfNI/4yJXstI\nGRRkD2wbE8XRTvjea8GTHAG+dCp854zEtCmUj2rhmy+bqHsoE0fCby4wHvpE0dgOj3wED3wIu5t6\nr3tSIXz6BGMXSfWUiELy4v7bH9B7/Y96HDd/Fas4CR7xCEISom3bPO3pLare2Qmd7eiODrNarPdV\nd/red6A7fce8ZUm4OFTa7b8VK4gQX/Y1m2XTX90Ob++JPrXdrAK/ZWRa/tDyzrZ1wZVPGAEbyJ8u\ngkunJqZNoXS44ferjYfeDvm5KODmk+DbZyRWrNoa3tgJD34Ir+7o2c5ARmfAldONVaQ8d/DaKAwP\nRFgLQuLRHo95cuQV2rqjvee+t6xbsHuP6wDxTkdH3PzuIqyFmIjV19bc56puHAAAFUNJREFUAct3\nGqG9tCb65ciLR5pI9vnlMGf80FhqfF8zLH7E5Nb2YW9fxvM/XMjMcYlrVyhr98O3XjYWnlAm58Lv\nLoBZhYPfrlD2NZso9sMboNabKtGXZz2UM4rhUyeYm7ahMJYGi6HiYx0IYhXW0pfxRfozvkh/YqLn\nEYS37uiAwMh5R3vAvu+4eZ/2vZ+Lx1oYOHLS4ZKpZuvywLv7TCT75e29P9Lf0wT3rTfbyHQ4u9SI\n7AUlZj8VGZ8Dd18M1/3LH8Xv9MDnl8Cz1ybP0umzi+CFT8Idb5n+D2TbYfjEYyZv+VfnmGXgE8X4\nHPjm6fCVOSZ6/cAH8Gp1+Lpv7THb2BFw7fFw3XEwIYlWDhUEQRASi3I6wZkNWcaX2u+H5t/7ef8+\nP5UiwBKxTj60d4GVV7abbf3B6M5zWTC32FhGzi9LTXH0yEfw3deCy04pgoevSKxQDcebu+E7r8Ce\nMJMCZ4w10etkysu94zA8+BE8vrHnokaBWArOKTVR7PmTzIRcQYgFsYIIghAOSbcnJAUHj8LSHfDy\nNiPmol2UZcZYI7DPnwzHj00dX/bty3tGg687Hn5xTvJ9h+YO+NkKeHRDz2Muyywx/oXZySVO293w\n/FYTxV67v/e6xSPh+uPh6uNSc2EjITGIsBYEIRwirIWYGAwfVmuXmaD26nZ4rcasiBgNRdlm1cfT\nJpjJauW5MCIJlxIHs/DMDU/Da0uDPcFfPAVOLzaWhbFZkJdpoqvJwGs7THaT2jBLv59UCD+cBycl\nOE95uPG5qc5kE3mqCo72MoHc5UsjWWGi8KWjkutmIRGI7zIy4rFOLNKf8UX6M370V1iLx1oYMEa4\njLi5sAI8Nry3H17ZAa9sg+1hJtT52N9iBNQDH/rLxmf7RXZ5rlkFsjwXinISK1idFtx5Ecx/BwKt\n5netMZsPh4L8EUZoj8vyC+6gfW9Z9gDnmj63zCx3/qPXe6YOXHfAZD3JSTM3BmdOgnkTzWTHREfg\np4+Fn59j8nU/s9lEsTce6lmvyzbfy/fd0h0wJR8q881S75VjzPtxWYn/TkLyoffvBYlYC4LQTyRi\nLSSEbYf9qfzW7Ot/wvYMJ5SPNiJ7cq7/tSx34AVqIFvq4bJHe4+kRssIV7DQDhLh3vfjsiA/89gz\nYzxXDT9cCod78TGDWcFx3kQjtM+cCAVJMEFTa3Mj8MCH8O8t0duOfIzOMOkgp3nFtu99TopOqhX6\nh/u+O9G7tgeVqRNm47jwMlSmeIoEYbgiVhAhZalvNSn8XtlurCNt4VcajpmCLL/QnhwQ7Z6QMzDW\ngJe3wRee6z0nc7zJy+xFhAe8jkqPHJ2tOwrfX2r6P1qm5sNZXqF92oTBvYkJR2O7mej44Ifh0wvG\nwoScYLFdOcaMm2SbkCrEB8/KpdivPdfzQM5IHIuvwaqoHPxGCYKQcERYCzGRrD6sdreZ9PjWbhPV\n3n7YpPOLp1hNd0Dp6OAIt0909zcFoK8/1+yDF7Ya/3JtqxGtda3R5/0eKFxWT/E9LmB/zAizWuOb\nu2DVXqiP0g8Pxg5zUqE/on1iwbFH0vs7PrU242fpDrNM+pZ6OBTDd4mE0zLjozIgwj0130yYTBbv\nfG8k6+89GdDaxl6xFHv5y2EXlrBOnot1waWo9AxA+jLeSH/GF+nP+CEea2FIkOE0HuBzy/xlHW7Y\necSIbJ/Y9r0e6Ydg7fAYERlu2e+xI4KFtk94F4+MbjLfKePNFkq7Gw61GsFdFyC4u1+972tbTU7s\neNNlw74Ws/VFTpo3qq/M04OGNvD0cmPjtk1e83f3we9Wm+j13Akwz2sbmZI3eF5mpcznzpvkLzvU\nClWHzL93lVdsb66P7cmI2zbnbQkZM1kuv2870FaSlxmf7yMMPEpZOOafhzV1Ou6nH4aDweln7PdW\nYW/fguPj12KVTk5QKwVBSBUkYi2kLFob0bctQGj7tp1HeheDsZLmgJJRPSPck3ONVzdeaG2i27W9\niO86r0BvaOu/N30wGTPC2EbOKjFCuzA70S0yT0B2H4Eqr8jefMi833E4PuNm7IieYntKHmQmaXYb\nwaDdbuzlL2O/udT8GINQWHPPwjpnEcol/5CCMNQRK4ggBNDpgV1H/EI7MNLd10S9WMnP7BnhnpwL\nE0cO7LLbXR4jrsOJ8NDIeDwmVcaLLJfpm2ljjG1k4ii/LWXMiMR6mdvdZox0i21vpHt/FJH+vlAY\nC9JUb4aSaWMkHWCyYu/Ziefph6G+rufB/LE4PvFJrAmTeh4TBGHIIMJaiInh7MM63BYS4W6ErQ1G\niPuWKI+V9urgPNZgrCO+KHd5rsleMjnPiO7Btgoc7fRaUXoR33Wtpo67n30QL0ZngHPnMqbOXhjs\nCff5wr37ozMGz998pN3YQKrq/WJ78yFo6jz2a6c7oCIveLJkvNMBDuffe3/RXZ3Yrz2PvXpFUPny\nHbtZUD4Ja965WAvORznEUXksyNiML9Kf8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Uiwuu6nk4eJiHIg/JTDg5SI+3WDVKedXqbVXw0QPetoEYvHZrNoi/M8SSSw/0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\npSfBFYeZx15YGBufNgghhIhfk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R3T4PWgEpMjOuqV7f35YgX8aQqUV5vHu2TC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\nM7N3PUdq5J5MGk7yBmm6wKkR0QlpDLOk3UlTCX6fNN56KekBw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_tuning_curve(output[output['type']=='SNV'],50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting insertion and deletion counts\n", "\n", "- The above plotting function can be readily applied in the case of indels as well, using the parameters below. However, as the investigated portion of the genome contains very few indels, no further insight can be gained by optimizing the score value. In general, for real datasets, the above practise is applicable for all types of detected mutations." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_tuning_curve(output[output['type']=='INS'],30)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_tuning_curve(output[output['type']=='DEL'],40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting a quasi-ROC curve for score optimization\n", "\n", "- False positive rate: the ratio of mutations found in control samples to the length of the genome\n", "- For a fast and convenient optimization process, true positives were estimated as the mutations found in all treated samples. Some of these may in fact be false positives, but as the mutation rate in treated samples are higher by a factor of 100-1000 than in control samples, this estimation still gives reasonable results.\n", "- The ROC curves below show how the change in the selected score threshold affects the number of false and true positive results. For our purposes, very low false positive rates were necessary, thus we chose fairly strict filtering values to achieve this.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_roc(output,score0,score_sel):\n", "\n", " fig,ax=plt.subplots()\n", " fig.set_size_inches(12,9)\n", " fp, tp = [0 for i in xrange(100) ],[0 for i in xrange(100) ]\n", " for score_lim,j in zip(np.linspace(score0,10,100),range(100)):\n", " muts=[]\n", " for i in xrange(10):\n", " filt_idx = output['#sample_name'] == samples[i]+'_reduced.bam'\n", " filt_idx = filt_idx & ((output['score']>score_lim))\n", " muts.append(len(output[filt_idx]))\n", " muts=np.array(muts)\n", " fp[j] ,tp[j]=1e-3*np.mean(muts[control_idx]),1e-3*np.mean(muts[weak_idx+strong_idx])\n", " ax.step(fp,tp,c='magenta',lw=4,label='quasy ROC, scannning the tuning parameter')\n", " \n", " muts=[]\n", " for i in xrange(10):\n", " filt_idx = output['#sample_name'] == samples[i]+'_reduced.bam'\n", " filt_idx = filt_idx & ((output['score']>score_sel))\n", " muts.append(len(output[filt_idx]))\n", " muts=np.array(muts)\n", "\n", " ax.plot(1e-3*np.mean(muts[control_idx]),1e-3*np.mean(muts[weak_idx+strong_idx]),\n", " 'o',mec='dodgerblue',mfc='dodgerblue',ms=15,mew=3,label='selected parameter')\n", " ax.legend(fancybox=True,loc='lower right',fontsize=16)\n", "\n", " #ax.set_xlim(0,30)\n", " ax.set_ylim(ymin=0)\n", " ax.set_xlim(xmin=-0.0001)\n", " ax.set_xlabel('false positive rate 1/Mbp ',fontsize=16)\n", " dump=ax.set_ylabel('mutation rate 1/Mbp ',fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SNVs" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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oBAjwAAAAQIgQ4AEAAIAQIcADAAAAIUKABwAAAEKEAA8AAACECAEeAAAACBEC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\nNm2atm3bppYtW2rMmDGaNGlSRZdWJSQ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7EqsxJiL1798fgJtuuokdO3ZQUFBQwTUyxhhT\nmUVFRZGUlMTUqVM9/wdVFJuFpjKyWWiMMcYYYyqMzUJjjDHGGGOM8Qh7AC8i6SKyWUS2isjoAHn+\nz92+QUQuKm1fEWkgIitE5CsReVtEgnzgeSWlPosxxhhjjKk0whrAi0g1YDqQDrQD+ovI+T55egKt\nVLU18AfgqSD2HQOsUNU2wLvuepW3cuXKiq6CCQPr56rB+rlqsH6u/KyPTXkI9x34TsA2Vd2hqieA\nRcC1Pnl+B8wDUNV1QD0RaVTKvp593H97hbYZkcHeJKoG6+eqwfq5arB+rvysj015CHcAfy6Q67W+\n000LJs85JezbUFX3uq/3Ag3Lq8LGGGOMMcacScIdwAc7IjuYX+2Kv/LcaWZs5LcxxhhjjKmUwjqN\npIh0Bsararq7PhYoUNUsrzyzgJWqushd3wx0BZID7evmSVXVPSLSGPinqrb1c3wL7I0xxhhjTMiF\nchrJcD/IaT3QWkSSgP8CNwC+M+G/AQwHFrkB/yFV3SsieSXs+wYwGMhy/13s7+ChPJHGGGOMMcaE\nQ1gDeFU9KSLDgX8A1YA5qvpvEbnd3T5bVd8SkZ4isg34AbilpH3doh8BXhKR3wM7gOvD2S5jjDHG\nGGPCpUo9idUYY4wxxphIZ09iPcOF+8FXIjLWzb9ZRLqHtnUGwtvHIpImIutF5HP33ytC30IDFfMQ\nOxFpJiJHROTu0LXMeKuA9+wLRCRbRDa6f9c1QttCA2F/344VkYVu/24SEXvWTRiEqI/7isiXInJK\nRC72Kats8Zeq2nKGLjhDhbYBSUA08Blwvk+ensBb7utLgQ9L2xd4FLjXfT0aeMR93c7NF+3utw2I\nqujzUJmXCujjC4FG7uv2wM6KPgdVYQl3P3uV+QrwInB3RZ+DqrBUwN9zdWAD0MFdr2/v2ZWyn28G\nFrqvawI5QLOKPg+VeQlhH7cF2gD/BC72KqvM8ZfdgT+zhfvBV9fivEmcUNUdOBdQp5C0zBQKax+r\n6mequsdN3wTUFJHo0DTNeAn7Q+xEpBewHaefTXiEu5+7A5+r6hdueQdVtSA0TTNewt3Pu4Ha4jyR\nvjaQDxwOSctMoZD0sapuVtWv/ByvzPGXBfBntnA/+OocN19JxzPlqyIfbpYJfOK+wZjQCms/i0gd\n4F5gfDnU3QQv3H/PbQAVkeUi8omI3HP6TTBBCGs/q+o/cAL23TgTdTymqodOuxWmJKHq40DKHH+F\nexpJUzZhefCVlDw/vv3KObQqpI9FpD3O7E1pQR7fnJ5w9/N44C+q+qOI2PS54RPufq4OXAb8CjgK\nvCsin6jqe0HWw/w8Ye1nERmIM3SmMdAAWC0i76pqTpD1MGVXnn0ckjpYAH9m2wU09VpvStFPaP7y\nNHHzRPtJ3+W+3isijfSnB1/tK6GsXZhQCncfIyJNgNeAm+w/gLAJdz93AjJF5FGgHlAgIkdVdWa5\ntMYEEu5+zgVWqeoBABF5C7gYsAA+tMLdz12A11X1FLBfRD7A+dBm79+hU5597G/f0o5XavxlQ2jO\nbJ4HX4lIDM7Dq97wyfMGMAg8T7o95H4FV9K+hQ++gqIPvnoD6CciMSKSDLQGPgpN04wrrH3szmrw\nd2C0qmaHrlnGR1j7WVVTVDVZVZOBJ4DJFryHRbjfs98GOohITRGpjvPU8i9D0zTjJdz9vBno5pZV\nG+gMFD4Hx4RGqPrYm/fd+7LHXxX5K19bgvoldA9gC84PGsa6abcDt3vlme5u30DRXzUX29dNbwC8\nA3yF8x9APa9t49z8m4GrKrr9VWEJZx8D9wNHgE+9lviKPgdVYQn337JXnoeAURXd/qqyVMB79gBg\nI/AFPrMQ2VI5+hmoASxw+/hLbFapSO7j63C+OTsK7AGWeW0rU/xlD3IyxhhjjDEmgtgQGmOMMcYY\nYyKIBfDGGGOMMcZEEAvgjTHGGGOMiSAWwBtjjDHGGBNBLIA3xhhjjDEmglgAb4wxxhhjTASxAN4Y\nc8YSkVtFZKuIHBeRg2Xct0BEHgpV3Sqa274HvdZ7ichIP/lS3bwp4a3h6RGRm0XklnIus46I/K+I\nrBSRw+556VrKPptFZJz7eqW7zwcB8j7vbs/1SS8QkT+XX0uMMVWdBfDGmDOSiJwDPA2sAa4Afvsz\niqnMD7roDDzrtd4LGOUn3ydu3k/DUalydDNwazmXGQ/cAuTjPCgHSrhGRKQt0IafnoipwPdAZxFp\n6ZO3FtDH3e6vzMp8LRpjwswCeGPMmao1znvUfFVdq6r/qugKnUlU9SNV/W8Q+b53834fjnoFIiI1\nKvL4AKq6Q1XjVLU7MCuIXXoBW1V1k1fa58DXwECfvL1xgvR/UPQR6cYYU+4sgDfGnHFEZC7wT3f1\nXXcIwnPutn4i8p6I7BOR70XkXyIyKIgy24jI6yKyV0SOisg3IvKSiFTzypMgIrNEZKeIHBORf4vI\nbUGUXThMpbeIzBWRAyLynYgsEJEGPnnrish0Efmve4zNInKXT546IjLNreMxt84rROQ8rzyeITTu\n+RoEnOumF4jIdp+6pbjrM0Rkj3e73fQaInJQRP5SjufjOhF5RkT24zw2HBFpJSJ/FZHtIvKjiHwt\nIjNFpJ7X/iuBFOA3Xu15z2t7soi84F4Dx0TkUxHpVVq9foZewBLvprn//pXiAfwg4FXghwBlRYnI\nfe65/FFE3heRjt4Z3CE6q0XkWhHZ6HXO+5ZHY4wxlUf1iq6AMcb4MRFYD/wfMAz4F7Df3dYCeA14\nBDgJdAWeFZGaqjq7hDL/DuQBdwDfAk2AHjg3Mk6JSF2c4To1gIeAHCAdeEpEaqjq9CDq/QSwAuiH\nM/RiCnAO0A1ARKLcelwEPAB8AWQAU0UkQVXvc8v5C3ANMBbYijP0owvgCXJ9THTzXOLuB3A8QN75\nwFCgO7DMKz0DONvdTjmdj2nAW8AAINZNawzsxBnuk4fTn+PcfF3cPEOBBTh9c7ubdtitV1NgHc4H\ngrtwrot+wKsi0ktVlwZRr1KJSGOc8+k7LElxAvjxIvJrVc12h3t1A9KAwQGKHAR8g3M9x+L02bsi\n0lpVC3/foUAr4Emcc77Pzb9IRPar6sryaJsxphJQVVtsscWWM24BrgQKgJQS8kTh3Ih4BvjMZ1sB\n8KD7Ot5dzyihrAeAo0BLn/SncYLEqBL2TXXLf8sn/UY3vZu7nuGuD/LJ9wxwDGjgrn8B/G8p58fT\nPnd9LpBbQt1SvNK2AH/zybcY2FjO5+PVIPq5OnCZm/9Cr/SVwCo/+ecAe4H6PulvA5+W1/WF80Fv\nt0+ap07A+8BT7ut7gR04d+iL9YN7nH1ATa+05jhj8Sf6lF8AdPK5xv/t71zYYostVXexITTGmIgi\nIq1FZKGI7MQJgPKB3+Pc8Q4kD9gOZInIEBFp7SdPOvAhsENEqhcuOIFhHNAuiOq95LP+Ck5A1tld\nT3HX/+aT7wUgBvi1u/4xcIuIjBWRX/kOdykHfwWuFZE6ACISh/NtxF+98pTH+XjdN0FEYkRknDt0\n6Eec/lvlbi6pD73r9RZw2E+9Oha2qRz0Akq6mz8fuF5EYnDurr+gqiX9UPUtVT1auKKq3+Cc31/7\n5PuPqn7kla8A5zrqVMb6G2MqMQvgjTERww3OVgAdgNE4d25/BTzHT0M0inEDqzScYTkPA1vcsdd3\neGVLxBmOc4KfPhjk4wTlihO0lmavz3HzgYPAuW5SA+CAqp702W+P13aAPwKzcWZh+QjYKyJTRaRm\nEHUIxgKc89XHXb8B5074Aq885XE+dvtJexhneMh8oCfOMJXe7raAfehTr8F+6vVoGepVInf4UCo/\nzT7jzytATZy2tMMdelSCvX7S9uEMsSot314gRkQSSjmGMaaKsDHwxphI8mugGXCZqq4tTBSR6NJ2\nVNUc3PHJ7o8HhwMzRWSHqi7HGRe/BxgRoIivgqhfI+8V9+5sfWCXm3QAaCAi1X2C+EZe21HVH3DG\nhY9zx3z3xRnznw+MCaIeJVLVHeLMZT4QZ8jHQOCfqrrLK1t5nA9/d6T7AfNUdUphghswB+tbnDv2\nWQG2+/vQUFY9cc71O4EyqOp3IrIE54Pkx6q6pZQyGwZI2+WT1ihAvnxV3e9nmzGmCrIA3hgTSWq5\n/3qCXxGpD1xLGebZVtUNInI3ztCb9sByd/kjzvjlnxsoXQ8877XeF+ebzmx3fSXwJzef9zCaATg/\nOs3Gh6rm4vzIdaBb10CO49wRDtZ8YJaIpOIM8fF9aFJ5nA9/auLVfy5/D2w6jv+76ctxPshtUtVj\n5Vgvb72A5e43KCWZjvMj3xd80v1diz1FpJaq/gggIkk4532KT76mInKpqq5z81XDuY7WlakFxphK\nzQJ4Y0wk+QBnNpIZ4jxltQ5wP86PKgPexRWRC3Bm9liEM4d3NZwHBZ0ACqcn/AvOUJLV7lSKXwG1\ngbY4d/yDmaawnTjTXb6IM557Ms6d7cIpMZfhzOwyyx0OsQnnbu/vgSmqesCtbzbO9IUbgSM4Q1ku\noOiHA19fAre5w4I+AY6p6hcl5H8ZZ5aYBcCPOENCvJXH+fBnOTBYRL7A6YveFB8HXtieYSJyPc7v\nFw6r6lfAgzjDilaJyHScmV3qA78AklX19yUdXER6uO3o4Calikgi8IOqLnO/NUnHmf3FbxGFL1T1\nA5xrMmAeL8eAt0XkMZyhQhOAQzjn2dte4EX3+v4WZ0aeVvw0G48xxlgAb4w5oxW5k6mq34rIdcDj\nOAHnLpzAPA4nsAtkN06gNwpn+shjOA/kyVDVT92yD4tIF7ec0Tjj1g8Bm3Hm9w7GCJxvAxbhfEh4\nA/gfr/qriFyNc9d1tFvvHGCkqj7pVc77OHfpx+C8T38N3KU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_roc(output[output['type']=='SNV'],score0=1.5,score_sel=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indels\n", "\n", "- Please note, that the selected score thresholds for insertions and deletions are usually very different than the score threshold chosen for SNVs, because these types of mutations appear both with different mutation and different noise frequencies. The example dataset is not suitable for determining these threshold values, but in general, the same steps should be carried out as in the case of SNVs." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_roc(output[output['type']=='INS'],score0=0.5,score_sel=1)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_roc(output[output['type']=='DEL'],score0=0.5,score_sel=1.35)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Plotting the final results with optimized score thresholds\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#define plotting func\n", "def plot_mutres(table):\n", " #group mutations per samples\n", " sample_counts=table.groupby(['#sample_name']).count().reset_index()[['#sample_name','mut']]\n", " sample_counts.columns=['sample','count']\n", "\n", " #add zeroes if a sample is missing\n", " for i in samples:\n", " if (not i+'_reduced.bam' in set(sample_counts['sample'])):\n", " sample_counts=pd.concat([sample_counts,pd.DataFrame({'sample':[i+'_reduced.bam'], 'count' : [0]})])\n", " sample_counts=sample_counts.sort('sample').reset_index()[['sample','count']]\n", "\n", " #plot\n", " fig,ax=plt.subplots()\n", " fig.set_size_inches(12,9)\n", "\n", " #starting clones and controls\n", " ax.bar(control_idx,[sample_counts.loc[i,'count'] for i in control_idx],\n", " facecolor='dodgerblue',edgecolor='none',label='starting clone and controls')\n", " #weak treatment\n", " ax.bar(weak_idx,[sample_counts.loc[i,'count'] for i in weak_idx],\n", " facecolor='salmon',edgecolor='none',label='weak mutagenic treatment')\n", " #strong treatment\n", " ax.bar(strong_idx,[sample_counts.loc[i,'count'] for i in strong_idx],\n", " facecolor='lightgreen',edgecolor='none',label='strong mutagenic treatment')\n", "\n", " #samples labels\n", " names=samples\n", "\n", " ax.set_xticks(0.4+np.arange(len(names)))\n", " ax.set_xticklabels(names,rotation='vertical',fontsize=14)\n", " #axis, and legend\n", " ax.set_xlabel(r'samples',fontsize=18)\n", " ax.set_ylabel(r'Mutations detected',fontsize=18)\n", " dump=ax.legend(loc='best',fancybox='true',fontsize=16)\n", " \n", " #print the table\n", " sample_counts['code']=names\n", " return sample_counts[['code','count']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SNVs" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codecount
0 S07 4
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" ], "text/plain": [ " code count\n", "0 S07 4\n", "1 S09 32\n", "2 S12 0\n", "3 S13 11\n", "4 S15 0\n", "5 S22 3\n", "6 S24 31\n", "7 S27 0\n", "8 S28 33\n", "9 S30 0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NbB8/J9dAUlISKSkpOBwODh48yG+//eb25sj0a/linvS5iIhITimRLiDq1atH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\nB5ZvB/6yvUiSJEmryryHdiQ5Nskec2zfPcmrFqY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_mutres(output[(output['type']=='SNV' ) & (output['score']> 2)] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Insertions and deletions" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_mutres(output[(output['type']=='INS' ) & (output['score']> 1)] )" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_mutres(output[(output['type']=='DEL' ) & (output['score']> 1.35)] )" ] } ], "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }