ATACseq_Tutorial3_Downstream.ipynb

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    "# ATAC-seq Module3: Downstream Analysis\n",
    "\n",
    "<img src=\"https://storage.googleapis.com/unmc_atac_data_examples/Tutorial3/images/ATACseqWorkflowLesson3.jpg\" alt=\"Drawing\" style=\"width: 1000px;\"/>\n",
    "\n",
    "## Overview & Purpose\n",
    "In the previous sections of this module we performed preprocessing quality control, mapping, deduplication, visualization, profiling aroud TSSs, and peak identification. In this section we will focus on differential peak identification, motif footprinting, and annotation of nearby genomic features. \n",
    "\n",
    "### Required Files\n",
    "In this stage of the module you will use several of the files that we prepared in the previous sections. Don't worry if you are just jumping in now, we have examples of these files saved and will include a step that copies them for your use. You can also use this module on your own data or any published ATAC-seq dataset, but you should complete the mappping and deduplication steps first.\n",
    "\n",
    "<div class=\"alert-info\" style=\"font-size:200%\">\n",
    "STEP1: Setup Environment\n",
    "</div>\n",
    "\n",
    "Initial items to configure your google cloud environment. In this step we will use conda to install the following packages:\n",
    "\n",
    "Differential Peak Idnetification:\n",
    "[manorm](https://anaconda.org/bioconda/manormfast)\n",
    "\n",
    "Genome Annotation:\n",
    "[homer](https://anaconda.org/bioconda/homer)\n",
    "\n",
    "Motif Analysis:\n",
    "[tobias](https://anaconda.org/bioconda/tobias)\n"
   ]
  },
  {
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   "execution_count": 32,
   "id": "f2074873-7df9-46e7-9922-f1b1bb26f7c4",
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    {
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     "text": [
      "Warning: 'bioconda' already in 'channels' list, moving to the top\n",
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Retrieving notices: ...working... done\n",
      "Requirement already satisfied: jupyterquiz in /opt/conda/lib/python3.7/site-packages (2.0.7)\n"
     ]
    }
   ],
   "source": [
    "#!python -m ipykernel install --user --name ATACtraining\n",
    "numthreads=!lscpu | grep '^CPU(s)'| awk '{print $2-1}'\n",
    "numthreadsint = int(numthreads[0])\n",
    "!conda config --prepend channels bioconda\n",
    "!conda install -y -c bioconda manorm tobias homer\n",
    "!pip install jupyterquiz\n",
    "from jupyterquiz import display_quiz\n",
    "from IPython.display import IFrame\n",
    "from IPython.display import display\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f8e28a-543c-4eb9-8c6a-6107ce0613a8",
   "metadata": {},
   "source": [
    "## Setup FileSystem\n",
    "Now lets create some folders to stay organized and copy over our prepared fastq files. We're going to create a directory called \"Tutorial1\" which we'll use for this module. We'll then create subfolders for our InputFiles and for the files that we'll be creating during this module. We'll also copy over the fasta file for chromosome 4 as well as some bowtie2 index files (don't worry we'll teach you how to create these index files)."
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a730ba8-82e0-4ab2-a0ef-118a6910cc92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/CTL_dedup.bam.bai...\n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/CTL_dedup.bam...      \n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/Mutant_peaks.narrowPeak...\n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/Mutant_dedup.bam.bai...\n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/CTL_peaks.narrowPeak...\n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/chr4.fa...\n",
      "Copying gs://unmc_atac_data_examples/Tutorial3/InputFiles/Mutant_dedup.bam...   \n",
      "\\ [7/8 files][227.7 MiB/227.7 MiB]  99% Done                                    \r"
     ]
    }
   ],
   "source": [
    "#These commands create our directory structure.\n",
    "#!cd $HOMEDIR\n",
    "#!mkdir -p Tutorial3\n",
    "#!mkdir -p Tutorial3/InputFiles\n",
    "!mkdir -p Tutorial3/GenomeAnnotation\n",
    "!mkdir -p Tutorial3/DiffPeaks\n",
    "!mkdir -p Tutorial3/MotifFootprinting\n",
    "#!mkdir -p Tutorial3/LessonImages\n",
    "!mkdir -p Tutorial3/Plots\n",
    "#!cd ./Tutorial3\n",
    "#!echo $PWD\n",
    "\n",
    "#These commands help identify the google cloud storage bucket where the example files are held.\n",
    "project_id = \"nosi-unmc-seq\"\n",
    "original_bucket = \"gs://unmc_atac_data_examples/Tutorial3\"\n",
    "#!gsutil -m cp $original_bucket/images/* Tutorial3/LessonImages\n",
    "#!gsutil -m cp $original_bucket/Annotations/* Tutorial3/GenomeAnnotations\n",
    "#This command copies our example files to the Tutorial1/Inputfiles folder that we created above.\n",
    "! gsutil -m cp $original_bucket/InputFiles/* Tutorial3/InputFiles\n",
    "\n"
   ]
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   "cell_type": "markdown",
   "id": "51fc4b08-4f02-4f49-9a8b-48b02a122455",
   "metadata": {},
   "source": [
    "### OK\n",
    "Let's make sure that the files copied correctly. You should see 2 .bam files, 2 .bai files, and 2 .narrowPeak files after running the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2d9b02b6-3f5a-4880-a533-71804ec7365d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tutorial3/InputFiles/CTL_dedup.bam\n",
      "Tutorial3/InputFiles/CTL_dedup.bam.bai\n",
      "Tutorial3/InputFiles/CTL_peaks.narrowPeak\n",
      "Tutorial3/InputFiles/Mutant_dedup.bam\n",
      "Tutorial3/InputFiles/Mutant_dedup.bam.bai\n",
      "Tutorial3/InputFiles/Mutant_peaks.narrowPeak\n",
      "Tutorial3/InputFiles/chr4.fa\n",
      "Tutorial3/InputFiles/chr4.fa.fai\n"
     ]
    }
   ],
   "source": [
    "!ls Tutorial3/InputFiles/*"
   ]
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   "cell_type": "markdown",
   "id": "cf7aea89-8610-4430-a802-6f7d387deb8b",
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   "source": [
    "<div class=\"alert-info\" style=\"font-size:200%\">\n",
    "Differential Peak Identification\n",
    "</div>\n",
    "If you have two or more samples and desire to discover differential peaks, we recommend using manorm. Novices may be tempted to simply intersect the two peak lists to find the overlap, however this is highly inadvisable. \n",
    "\n",
    "<div class=\"alert-info\" style=\"font-size:200%\">\n",
    "Interactive Quiz Question 1: Click on the correct answer in following cell.\n",
    "</div>"
   ]
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   "id": "1ddac73d-8512-4697-aceb-150ae417d476",
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       "        \"question\": \"Please click on all the answers that you think apply (there can be more than one). What are some limitations of simply intersecting two peak lists to identify differential peaks?\",\n",
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       "            },\n",
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       "                \"answer\": \"Different samples may have distinct immunoprecipitation (IP) effiencies\",\n",
       "                \"correct\": true,\n",
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       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            //console.log(responsesContainer);\n",
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       "            if (label.querySelector(\".QuizCode\")){\n",
       "                response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n",
       "            }\n",
       "            console.log(response);\n",
       "            //console.log(document.getElementById(\"quizWrap\"+id));\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            if (label.dataset.correct == \"true\") {\n",
       "                if (typeof(responses[qnum]) == \"object\"){\n",
       "                    if (!responses[qnum].includes(response))\n",
       "                        responses[qnum].push(response);\n",
       "                } else{\n",
       "                    responses[qnum]= [ response ];\n",
       "                }\n",
       "            } else {\n",
       "                responses[qnum]= response;\n",
       "            }\n",
       "            console.log(responses);\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End save responses stuff\n",
       "\n",
       "\n",
       "\n",
       "        var numcorrect = fb.dataset.numcorrect;\n",
       "        var answeredcorrect = fb.dataset.answeredcorrect;\n",
       "        if (answeredcorrect >= 0) {\n",
       "            fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n",
       "        } else {\n",
       "            fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n",
       "        }\n",
       "\n",
       "\n",
       "    }\n",
       "\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        var version = MathJax.version;\n",
       "        console.log('MathJax version', version);\n",
       "        if (version[0] == \"2\") {\n",
       "            MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "        } else if (version[0] == \"3\") {\n",
       "            MathJax.typeset([fb]);\n",
       "        }\n",
       "    } else {\n",
       "        console.log('MathJax not detected');\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n",
       "    var shuffled;\n",
       "    if (shuffle_answers == \"True\") {\n",
       "        //console.log(shuffle_answers+\" read as true\");\n",
       "        shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n",
       "    } else {\n",
       "        //console.log(shuffle_answers+\" read as false\");\n",
       "        shuffled = qa.answers;\n",
       "    }\n",
       "\n",
       "\n",
       "    var num_correct = 0;\n",
       "\n",
       "\n",
       "\n",
       "    shuffled.forEach((item, index, ans_array) => {\n",
       "        //console.log(answer);\n",
       "\n",
       "        // Make input element\n",
       "        var inp = document.createElement(\"input\");\n",
       "        inp.type = \"radio\";\n",
       "        inp.id = \"quizo\" + id + index;\n",
       "        inp.style = \"display:none;\";\n",
       "        aDiv.append(inp);\n",
       "\n",
       "        //Make label for input element\n",
       "        var lab = document.createElement(\"label\");\n",
       "        lab.className = \"MCButton\";\n",
       "        lab.id = id + '-' + index;\n",
       "        lab.onclick = check_mc;\n",
       "        var aSpan = document.createElement('span');\n",
       "        aSpan.classsName = \"\";\n",
       "        //qDiv.id=\"quizQn\"+id+index;\n",
       "        if (\"answer\" in item) {\n",
       "            aSpan.innerHTML = jaxify(item.answer);\n",
       "            //aSpan.innerHTML=item.answer;\n",
       "        }\n",
       "        lab.append(aSpan);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeSpan;\n",
       "        if (\"code\" in item) {\n",
       "            codeSpan = document.createElement('span');\n",
       "            codeSpan.id = \"code\" + id + index;\n",
       "            codeSpan.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeSpan.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = item.code;\n",
       "            lab.append(codeSpan);\n",
       "            //console.log(codeSpan);\n",
       "        }\n",
       "\n",
       "        //lab.textContent=item.answer;\n",
       "\n",
       "        // Set the data attributes for the answer\n",
       "        lab.setAttribute('data-correct', item.correct);\n",
       "        if (item.correct) {\n",
       "            num_correct++;\n",
       "        }\n",
       "        if (\"feedback\" in item) {\n",
       "            lab.setAttribute('data-feedback', item.feedback);\n",
       "        }\n",
       "        lab.setAttribute('data-answered', 0);\n",
       "\n",
       "        aDiv.append(lab);\n",
       "\n",
       "    });\n",
       "\n",
       "    if (num_correct > 1) {\n",
       "        outerqDiv.className = \"ManyChoiceQn\";\n",
       "    } else {\n",
       "        outerqDiv.className = \"MultipleChoiceQn\";\n",
       "    }\n",
       "\n",
       "    return num_correct;\n",
       "\n",
       "}\n",
       "function check_numeric(ths, event) {\n",
       "\n",
       "    if (event.keyCode === 13) {\n",
       "        ths.blur();\n",
       "\n",
       "        var id = ths.id.split('-')[0];\n",
       "\n",
       "        var submission = ths.value;\n",
       "        if (submission.indexOf('/') != -1) {\n",
       "            var sub_parts = submission.split('/');\n",
       "            //console.log(sub_parts);\n",
       "            submission = sub_parts[0] / sub_parts[1];\n",
       "        }\n",
       "        //console.log(\"Reader entered\", submission);\n",
       "\n",
       "        if (\"precision\" in ths.dataset) {\n",
       "            var precision = ths.dataset.precision;\n",
       "            // console.log(\"1:\", submission)\n",
       "            submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n",
       "            // console.log(\"Rounded to \", submission, \" precision=\", precision  );\n",
       "        }\n",
       "\n",
       "\n",
       "        //console.log(\"In check_numeric(), id=\"+id);\n",
       "        //console.log(event.srcElement.id)           \n",
       "        //console.log(event.srcElement.dataset.feedback)\n",
       "\n",
       "        var fb = document.getElementById(\"fb\" + id);\n",
       "        fb.style.display = \"none\";\n",
       "        fb.textContent = \"Incorrect -- try again.\";\n",
       "\n",
       "        var answers = JSON.parse(ths.dataset.answers);\n",
       "        //console.log(answers);\n",
       "\n",
       "        var defaultFB = \"\";\n",
       "        var correct;\n",
       "        var done = false;\n",
       "        answers.every(answer => {\n",
       "            //console.log(answer.type);\n",
       "\n",
       "            correct = false;\n",
       "            // if (answer.type==\"value\"){\n",
       "            if ('value' in answer) {\n",
       "                if (submission == answer.value) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "                // } else if (answer.type==\"range\") {\n",
       "            } else if ('range' in answer) {\n",
       "                //console.log(answer.range);\n",
       "                if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n",
       "                    fb.textContent = jaxify(answer.feedback);\n",
       "                    correct = answer.correct;\n",
       "                    //console.log(answer.correct);\n",
       "                    done = true;\n",
       "                }\n",
       "            } else if (answer.type == \"default\") {\n",
       "                defaultFB = answer.feedback;\n",
       "            }\n",
       "            if (done) {\n",
       "                return false; // Break out of loop if this has been marked correct\n",
       "            } else {\n",
       "                return true; // Keep looking for case that includes this as a correct answer\n",
       "            }\n",
       "        });\n",
       "\n",
       "        if ((!done) && (defaultFB != \"\")) {\n",
       "            fb.innerHTML = jaxify(defaultFB);\n",
       "            //console.log(\"Default feedback\", defaultFB);\n",
       "        }\n",
       "\n",
       "        fb.style.display = \"block\";\n",
       "        if (correct) {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"correctButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"correct\");\n",
       "        } else {\n",
       "            ths.className = \"Input-text\";\n",
       "            ths.classList.add(\"incorrectButton\");\n",
       "            fb.className = \"Feedback\";\n",
       "            fb.classList.add(\"incorrect\");\n",
       "        }\n",
       "\n",
       "        // What follows is for the saved responses stuff\n",
       "        var outerContainer = fb.parentElement.parentElement;\n",
       "        var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n",
       "        if (responsesContainer) {\n",
       "            console.log(submission);\n",
       "            var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n",
       "            //console.log(\"Question \" + qnum);\n",
       "            //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n",
       "            var responses=JSON.parse(responsesContainer.dataset.responses);\n",
       "            console.log(responses);\n",
       "            if (submission == ths.value){\n",
       "                responses[qnum]= submission;\n",
       "            } else {\n",
       "                responses[qnum]= ths.value + \"(\" + submission +\")\";\n",
       "            }\n",
       "            responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n",
       "            printResponses(responsesContainer);\n",
       "        }\n",
       "        // End code to preserve responses\n",
       "\n",
       "        if (typeof MathJax != 'undefined') {\n",
       "            var version = MathJax.version;\n",
       "            console.log('MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([fb]);\n",
       "            }\n",
       "        } else {\n",
       "            console.log('MathJax not detected');\n",
       "        }\n",
       "        return false;\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "function isValid(el, charC) {\n",
       "    //console.log(\"Input char: \", charC);\n",
       "    if (charC == 46) {\n",
       "        if (el.value.indexOf('.') === -1) {\n",
       "            return true;\n",
       "        } else if (el.value.indexOf('/') != -1) {\n",
       "            var parts = el.value.split('/');\n",
       "            if (parts[1].indexOf('.') === -1) {\n",
       "                return true;\n",
       "            }\n",
       "        }\n",
       "        else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 47) {\n",
       "        if (el.value.indexOf('/') === -1) {\n",
       "            if ((el.value != \"\") && (el.value != \".\")) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 45) {\n",
       "        var edex = el.value.indexOf('e');\n",
       "        if (edex == -1) {\n",
       "            edex = el.value.indexOf('E');\n",
       "        }\n",
       "\n",
       "        if (el.value == \"\") {\n",
       "            return true;\n",
       "        } else if (edex == (el.value.length - 1)) { // If just after e or E\n",
       "            return true;\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else if (charC == 101) { // \"e\"\n",
       "        if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n",
       "            // Prev symbol must be digit or decimal point:\n",
       "            if (el.value.slice(-1).search(/\\d/) >= 0) {\n",
       "                return true;\n",
       "            } else if (el.value.slice(-1).search(/\\./) >= 0) {\n",
       "                return true;\n",
       "            } else {\n",
       "                return false;\n",
       "            }\n",
       "        } else {\n",
       "            return false;\n",
       "        }\n",
       "    } else {\n",
       "        if (charC > 31 && (charC < 48 || charC > 57))\n",
       "            return false;\n",
       "    }\n",
       "    return true;\n",
       "}\n",
       "\n",
       "function numeric_keypress(evnt) {\n",
       "    var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n",
       "\n",
       "    if (charC == 13) {\n",
       "        check_numeric(this, evnt);\n",
       "    } else {\n",
       "        return isValid(this, charC);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "function make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n",
       "\n",
       "\n",
       "\n",
       "    //console.log(answer);\n",
       "\n",
       "\n",
       "    outerqDiv.className = \"NumericQn\";\n",
       "    aDiv.style.display = 'block';\n",
       "\n",
       "    var lab = document.createElement(\"label\");\n",
       "    lab.className = \"InpLabel\";\n",
       "    lab.textContent = \"Type numeric answer here:\";\n",
       "    aDiv.append(lab);\n",
       "\n",
       "    var inp = document.createElement(\"input\");\n",
       "    inp.type = \"text\";\n",
       "    //inp.id=\"input-\"+id;\n",
       "    inp.id = id + \"-0\";\n",
       "    inp.className = \"Input-text\";\n",
       "    inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n",
       "    if (\"precision\" in qa) {\n",
       "        inp.setAttribute('data-precision', qa.precision);\n",
       "    }\n",
       "    aDiv.append(inp);\n",
       "    //console.log(inp);\n",
       "\n",
       "    //inp.addEventListener(\"keypress\", check_numeric);\n",
       "    //inp.addEventListener(\"keypress\", numeric_keypress);\n",
       "    /*\n",
       "    inp.addEventListener(\"keypress\", function(event) {\n",
       "        return numeric_keypress(this, event);\n",
       "    }\n",
       "                        );\n",
       "                        */\n",
       "    //inp.onkeypress=\"return numeric_keypress(this, event)\";\n",
       "    inp.onkeypress = numeric_keypress;\n",
       "    inp.onpaste = event => false;\n",
       "\n",
       "    inp.addEventListener(\"focus\", function (event) {\n",
       "        this.value = \"\";\n",
       "        return false;\n",
       "    }\n",
       "    );\n",
       "\n",
       "\n",
       "}\n",
       "function jaxify(string) {\n",
       "    var mystring = string;\n",
       "\n",
       "    var count = 0;\n",
       "    var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "\n",
       "    var count2 = 0;\n",
       "    var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "\n",
       "    //console.log(loc);\n",
       "\n",
       "    while ((loc >= 0) || (loc2 >= 0)) {\n",
       "\n",
       "        /* Have to replace all the double $$ first with current implementation */\n",
       "        if (loc2 >= 0) {\n",
       "            if (count2 % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n",
       "            }\n",
       "            count2++;\n",
       "        } else {\n",
       "            if (count % 2 == 0) {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n",
       "            } else {\n",
       "                mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n",
       "            }\n",
       "            count++;\n",
       "        }\n",
       "        loc = mystring.search(/([^\\\\]|^)(\\$)/);\n",
       "        loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n",
       "        //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n",
       "    }\n",
       "\n",
       "    //console.log(mystring);\n",
       "    return mystring;\n",
       "}\n",
       "\n",
       "\n",
       "function show_questions(json, mydiv) {\n",
       "    console.log('show_questions');\n",
       "    //var mydiv=document.getElementById(myid);\n",
       "    var shuffle_questions = mydiv.dataset.shufflequestions;\n",
       "    var num_questions = mydiv.dataset.numquestions;\n",
       "    var shuffle_answers = mydiv.dataset.shuffleanswers;\n",
       "\n",
       "    if (num_questions > json.length) {\n",
       "        num_questions = json.length;\n",
       "    }\n",
       "\n",
       "    var questions;\n",
       "    if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n",
       "        //console.log(num_questions+\",\"+json.length);\n",
       "        questions = getRandomSubarray(json, num_questions);\n",
       "    } else {\n",
       "        questions = json;\n",
       "    }\n",
       "\n",
       "    //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n",
       "\n",
       "    // Iterate over questions\n",
       "    questions.forEach((qa, index, array) => {\n",
       "        //console.log(qa.question); \n",
       "\n",
       "        var id = makeid(8);\n",
       "        //console.log(id);\n",
       "\n",
       "\n",
       "        // Create Div to contain question and answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        //iDiv.id = 'quizWrap' + id + index;\n",
       "        iDiv.id = 'quizWrap' + id;\n",
       "        iDiv.className = 'Quiz';\n",
       "        iDiv.setAttribute('data-qnum', index);\n",
       "        mydiv.appendChild(iDiv);\n",
       "        // iDiv.innerHTML=qa.question;\n",
       "\n",
       "        var outerqDiv = document.createElement('div');\n",
       "        outerqDiv.id = \"OuterquizQn\" + id + index;\n",
       "\n",
       "        iDiv.append(outerqDiv);\n",
       "\n",
       "        // Create div to contain question part\n",
       "        var qDiv = document.createElement('div');\n",
       "        qDiv.id = \"quizQn\" + id + index;\n",
       "        //qDiv.textContent=qa.question;\n",
       "        qDiv.innerHTML = jaxify(qa.question);\n",
       "\n",
       "        outerqDiv.append(qDiv);\n",
       "\n",
       "        // Create div for code inside question\n",
       "        var codeDiv;\n",
       "        if (\"code\" in qa) {\n",
       "            codeDiv = document.createElement('div');\n",
       "            codeDiv.id = \"code\" + id + index;\n",
       "            codeDiv.className = \"QuizCode\";\n",
       "            var codePre = document.createElement('pre');\n",
       "            codeDiv.append(codePre);\n",
       "            var codeCode = document.createElement('code');\n",
       "            codePre.append(codeCode);\n",
       "            codeCode.innerHTML = qa.code;\n",
       "            outerqDiv.append(codeDiv);\n",
       "            //console.log(codeDiv);\n",
       "        }\n",
       "\n",
       "\n",
       "        // Create div to contain answer part\n",
       "        var aDiv = document.createElement('div');\n",
       "        aDiv.id = \"quizAns\" + id + index;\n",
       "        aDiv.className = 'Answer';\n",
       "        iDiv.append(aDiv);\n",
       "\n",
       "        //console.log(qa.type);\n",
       "\n",
       "        var num_correct;\n",
       "        if (qa.type == \"multiple_choice\") {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "        } else if (qa.type == \"many_choice\") {\n",
       "            num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n",
       "        } else if (qa.type == \"numeric\") {\n",
       "            //console.log(\"numeric\");\n",
       "            make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n",
       "        }\n",
       "\n",
       "\n",
       "        //Make div for feedback\n",
       "        var fb = document.createElement(\"div\");\n",
       "        fb.id = \"fb\" + id;\n",
       "        //fb.style=\"font-size: 20px;text-align:center;\";\n",
       "        fb.className = \"Feedback\";\n",
       "        fb.setAttribute(\"data-answeredcorrect\", 0);\n",
       "        fb.setAttribute(\"data-numcorrect\", num_correct);\n",
       "        iDiv.append(fb);\n",
       "\n",
       "\n",
       "    });\n",
       "    var preserveResponses = mydiv.dataset.preserveresponses;\n",
       "    console.log(preserveResponses);\n",
       "    console.log(preserveResponses == \"true\");\n",
       "    if (preserveResponses == \"true\") {\n",
       "        console.log(preserveResponses);\n",
       "        // Create Div to contain record of answers\n",
       "        var iDiv = document.createElement('div');\n",
       "        iDiv.id = 'responses' + mydiv.id;\n",
       "        iDiv.className = 'JCResponses';\n",
       "        // Create a place to store responses as an empty array\n",
       "        iDiv.setAttribute('data-responses', '[]');\n",
       "\n",
       "        // Dummy Text\n",
       "        iDiv.innerHTML=\"<b>Select your answers and then follow the directions that will appear here.</b>\"\n",
       "        //iDiv.className = 'Quiz';\n",
       "        mydiv.appendChild(iDiv);\n",
       "    }\n",
       "//console.log(\"At end of show_questions\");\n",
       "    if (typeof MathJax != 'undefined') {\n",
       "        console.log(\"MathJax version\", MathJax.version);\n",
       "        var version = MathJax.version;\n",
       "        setTimeout(function(){\n",
       "            var version = MathJax.version;\n",
       "            console.log('After sleep, MathJax version', version);\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            }\n",
       "        }, 500);\n",
       "if (typeof version == 'undefined') {\n",
       "        } else\n",
       "        {\n",
       "            if (version[0] == \"2\") {\n",
       "                MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n",
       "            } else if (version[0] == \"3\") {\n",
       "                MathJax.typeset([mydiv]);\n",
       "            } else {\n",
       "                console.log(\"MathJax not found\");\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return false;\n",
       "}\n",
       "\n",
       "        {\n",
       "        show_questions(questionsYOaxnBOqdiAA,  YOaxnBOqdiAA);\n",
       "        }\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_quiz(\"Tutorial3/LessonImages/DiffPeaks.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21691ec7-e2d0-474f-939e-ddc84b046452",
   "metadata": {},
   "source": [
    "### Consider the below peak which was identified in both the control and mutant sample. A simple intersect would result in this peak being reported as unchanged between the two samples. To represent the differences we will use [manorm](https://anaconda.org/bioconda/manormfast).\n",
    "\n",
    "<img src=\"Tutorial3/LessonImages/PeakOverlapProblem.jpg\" alt=\"Drawing\" style=\"width: 100px;\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "17cea9d5-2b65-4513-bba3-7e2970c90aa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "#We specify several non-default parameters to better reflect ATAC-seq data\n",
    "!manorm --p1 Tutorial3/InputFiles/CTL_peaks.narrowPeak --p2 Tutorial3/InputFiles/Mutant_peaks.narrowPeak --r1 Tutorial3/InputFiles/CTL_dedup.bam --r2 Tutorial3/InputFiles/Mutant_dedup.bam --rf bam --n1 CTL --n2 Mutant --pe -w 1000 -o Tutorial3/DiffPeaks --wa 2> Tutorial3/DiffPeaks/log_manorm.txt\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e61bd4a-26e2-4ce6-a719-6b8c51ed9dca",
   "metadata": {},
   "source": [
    "The above command will write out several files including the differential peaks for each sample as well as the unchanged peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a51f1ba8-3bf0-4715-bb97-4a029352a3cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CTL_vs_Mutant_M_above_1.0_biased_peaks.bed   CTL_vs_Mutant_unbiased_peaks.bed\n",
      "CTL_vs_Mutant_M_below_-1.0_biased_peaks.bed\n"
     ]
    }
   ],
   "source": [
    "!ls Tutorial3/DiffPeaks/output_filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4d7d7656-9e43-4cce-a173-fee9b0a82dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr4\t52059325\t52059732\tCTL_unique\t2.20155\n",
      "chr4\t52298589\t52298799\tCTL_unique\t1.09775\n",
      "chr4\t52550105\t52550494\tCTL_unique\t1.29536\n",
      "chr4\t52698223\t52698464\tCTL_unique\t1.84416\n",
      "chr4\t52834103\t52834470\tCTL_unique\t1.26119\n",
      "chr4\t52884232\t52884622\tCTL_unique\t1.09835\n",
      "chr4\t52968329\t52968671\tCTL_unique\t1.41519\n",
      "chr4\t52993914\t52994157\tCTL_unique\t1.22576\n",
      "chr4\t53595301\t53595477\tCTL_unique\t1.20393\n",
      "chr4\t53702525\t53703113\tCTL_unique\t1.07373\n"
     ]
    }
   ],
   "source": [
    "#Let's also check the format of these files\n",
    "!head Tutorial3/DiffPeaks/output_filters/CTL_vs_Mutant_M_above_1.0_biased_peaks.bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "42d1dbc3-7708-4907-8089-a76fee983a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  124 Tutorial3/DiffPeaks/output_filters/CTL_vs_Mutant_M_above_1.0_biased_peaks.bed\n",
      "   74 Tutorial3/DiffPeaks/output_filters/CTL_vs_Mutant_M_below_-1.0_biased_peaks.bed\n",
      "  590 Tutorial3/DiffPeaks/output_filters/CTL_vs_Mutant_unbiased_peaks.bed\n",
      "  788 total\n"
     ]
    }
   ],
   "source": [
    "#We can also count how many are in each.\n",
    "!wc -l Tutorial3/DiffPeaks/output_filters/*bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ff40bca1-bef8-48c8-8c9b-033ac6fec8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Stats ====\n",
      "Total read pairs of sample 1: 167,920\n",
      "Total read pairs of sample 2: 219,380\n",
      "Total peaks of sample 1: 650 (unique: 277 common: 373)\n",
      "Total peaks of sample 2: 560 (unique: 190 common: 370)\n",
      "Number of merged common peaks: 369\n",
      "M-A model: M = -0.04460 * A +0.18904\n",
      "590 peaks are filtered as unbiased peaks\n",
      "124 peaks are filtered as sample1-biased peaks\n",
      "74 peaks are filtered as sample2-biased peaks\n"
     ]
    }
   ],
   "source": [
    "#Our log file tells us this information as well\n",
    "!tail Tutorial3/DiffPeaks/log_manorm.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2832435-d335-476e-9bef-1c71797f10c7",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\" style=\"font-size:100%\">\n",
    "Annotating Peaks\n",
    "</div>\n",
    "\n",
    "Let's take the differential peaks and annotate them with nearby genes and perform gene ontology using [homer](https://anaconda.org/bioconda/homer).\n",
    "\n",
    "First we need to reformat the differential peaks file to the format required by homer.\n",
    "\n",
    "In an earlier command, we examined the format of manorm's ouput using head and saw that it outputs a five column format. We will change this to a 6 column bed format including a unique name for each peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7029964f-05bb-40e1-a007-69387a33485e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr4\t52059325\t52059732\tCTL_unique_1\t2.20155\t+\n",
      "chr4\t52298589\t52298799\tCTL_unique_2\t1.09775\t+\n",
      "chr4\t52550105\t52550494\tCTL_unique_3\t1.29536\t+\n",
      "chr4\t52698223\t52698464\tCTL_unique_4\t1.84416\t+\n",
      "chr4\t52834103\t52834470\tCTL_unique_5\t1.26119\t+\n",
      "chr4\t52884232\t52884622\tCTL_unique_6\t1.09835\t+\n",
      "chr4\t52968329\t52968671\tCTL_unique_7\t1.41519\t+\n",
      "chr4\t52993914\t52994157\tCTL_unique_8\t1.22576\t+\n",
      "chr4\t53595301\t53595477\tCTL_unique_9\t1.20393\t+\n",
      "chr4\t53702525\t53703113\tCTL_unique_10\t1.07373\t+\n"
     ]
    }
   ],
   "source": [
    "#This command will reformat the peaks file including the line number in naming the peaks (NR) as well as a place-holder strand in the 6th column (note that peaks don't necessarily have a strand, but the format requires this column). The -F \\t tells awk that the file is tab delimited.\n",
    "!awk '{print $1\"\\t\"$2\"\\t\"$3\"\\t\"$4\"_\"NR\"\\t\"$5\"\\t+\"}' Tutorial3/DiffPeaks/output_filters/CTL_vs_Mutant_M_above_1.0_biased_peaks.bed > Tutorial3/GenomeAnnotation/CTL_specific_peaks.bed\n",
    "#Let's head this to compare\n",
    "!head Tutorial3/GenomeAnnotation/CTL_specific_peaks.bed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0996284a-06bd-43f2-ae7b-ed6807e5623c",
   "metadata": {},
   "source": [
    "Now let's configure homer to recognize our genome build. We aligned our reads to hg38, so we'll have homer use that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "32334764-854f-4739-a178-4273997248a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "!perl /opt/conda/share/homer/configureHomer.pl -install hg38 2> Tutorial3/DiffPeaks/homer_log1.txt\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f00783-28c0-4032-a841-42a5da716536",
   "metadata": {},
   "source": [
    "Let's use that reformatted peak file to get nearby genes and perform gene onotology analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a79558d5-24a2-4b4c-9795-48af65cf6545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tPeak file = Tutorial3/GenomeAnnotation/CTL_specific_peaks.bed\n",
      "\tGenome = hg38\n",
      "\tOrganism = human\n",
      "\tWill perform Gene Ontology analysis - output to directory = Tutorial3/GenomeAnnotation/CTL_GO\n",
      "\tPeak/BED file conversion summary:\n",
      "\t\tBED/Header formatted lines: 124\n",
      "\t\tpeakfile formatted lines: 0\n",
      "\t\tDuplicated Peak IDs: 0\n",
      "\n",
      "\tPeak File Statistics:\n",
      "\t\tTotal Peaks: 124\n",
      "\t\tRedundant Peak IDs: 0\n",
      "\t\tPeaks lacking information: 0 (need at least 5 columns per peak)\n",
      "\t\tPeaks with misformatted coordinates: 0 (should be integer)\n",
      "\t\tPeaks with misformatted strand: 0 (should be either +/- or 0/1)\n",
      "\n",
      "\tPeak file looks good!\n",
      "\n",
      "\tReading Positions...\n",
      "\t-----------------------\n",
      "\tFinding Closest TSS...\n",
      "\tAnnotating:.\n",
      "\t\tAnnotation\tNumber of peaks\tTotal size (bp)\tLog2 Ratio (obs/exp)\tLogP enrichment (+values depleted)\n",
      "\t\t3UTR\t0.0\t1226327\t-0.852\t0.802\n",
      "\t\tmiRNA\t0.0\t3258\t-0.003\t0.002\n",
      "\t\tncRNA\t0.0\t315963\t-0.271\t0.206\n",
      "\t\tTTS\t1.0\t1306500\t0.231\t-0.554\n",
      "\t\tpseudo\t0.0\t40049\t-0.037\t0.026\n",
      "\t\tExon\t0.0\t1490268\t-0.985\t0.975\n",
      "\t\tIntron\t33.0\t73166083\t-0.532\t5.597\n",
      "\t\tIntergenic\t84.0\t111121736\t0.213\t-3.865\n",
      "\t\tPromoter\t5.0\t1403974\t2.450\t-6.038\n",
      "\t\t5UTR\t1.0\t114432\t3.745\t-2.632\n",
      "\tNOTE: If this part takes more than 2 minutes, there is a good chance\n",
      "\t\tyour machine ran out of memory: consider hitting ctrl+C and rerunning\n",
      "\t\tthe command with \"-noann\"\n",
      "\tAnnotating:.\n",
      "\t\tAnnotation\tNumber of peaks\tTotal size (bp)\tLog2 Ratio (obs/exp)\tLogP enrichment (+values depleted)\n",
      "\t\t3UTR\t0.0\t1226327\t-0.852\t0.802\n",
      "\t\tRetroposon\t0.0\t200838\t-0.178\t0.131\n",
      "\t\tRC?\t0.0\t2850\t-0.003\t0.002\n",
      "\t\tRNA\t0.0\t6910\t-0.006\t0.005\n",
      "\t\tmiRNA\t0.0\t3258\t-0.003\t0.002\n",
      "\t\tncRNA\t0.0\t315963\t-0.271\t0.206\n",
      "\t\tTTS\t1.0\t1306500\t0.232\t-0.554\n",
      "\t\tLINE\t18.0\t45014382\t-0.705\t4.777\n",
      "\t\tsrpRNA\t0.0\t13945\t-0.013\t0.009\n",
      "\t\tSINE\t6.0\t18936280\t-1.041\t3.470\n",
      "\t\tRC\t0.0\t20949\t-0.020\t0.014\n",
      "\t\ttRNA\t0.0\t2852\t-0.003\t0.002\n",
      "\t\tDNA?\t0.0\t27318\t-0.025\t0.018\n",
      "\t\tpseudo\t0.0\t40049\t-0.037\t0.026\n",
      "\t\tDNA\t1.0\t6724040\t-2.132\t2.750\n",
      "\t\tExon\t0.0\t1490268\t-0.985\t0.975\n",
      "\t\tIntron\t25.0\t38474934\t-0.005\t0.605\n",
      "\t\tIntergenic\t39.0\t48657251\t0.298\t-2.484\n",
      "\t\tPromoter\t5.0\t1403974\t2.450\t-6.038\n",
      "\t\t5UTR\t1.0\t114432\t3.745\t-2.632\n",
      "\t\tLTR?\t0.0\t92183\t-0.084\t0.060\n",
      "\t\tscRNA\t0.0\t6881\t-0.006\t0.004\n",
      "\t\tCpG-Island\t0.0\t373419\t-0.315\t0.244\n",
      "\t\tLow_complexity\t0.0\t365373\t-0.309\t0.238\n",
      "\t\tLTR\t27.0\t20757981\t0.997\t-7.957\n",
      "\t\tSimple_repeat\t1.0\t2265635\t-0.563\t0.572\n",
      "\t\tsnRNA\t0.0\t18664\t-0.017\t0.012\n",
      "\t\tUnknown\t0.0\t49238\t-0.046\t0.032\n",
      "\t\tSINE?\t0.0\t130\t-0.000\t0.000\n",
      "\t\tSatellite\t0.0\t2306654\t-1.335\t1.513\n",
      "\t\trRNA\t0.0\t7760\t-0.007\t0.005\n",
      "\tPerforming Gene Ontology Analysis...\n",
      "rm: cannot remove '0.318584714640991.bg.tmp': No such file or directory\n",
      "\tCounting Tags in Peaks from each directory...\n",
      "\tOrganism: human\n",
      "\tLoading Gene Informaiton...\n",
      "\tOutputing Annotation File...\n",
      "\tDone annotating peaks file\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!annotatePeaks.pl Tutorial3/GenomeAnnotation/CTL_specific_peaks.bed hg38 -go Tutorial3/GenomeAnnotation/CTL_GO -annStats Tutorial3/GenomeAnnotation/CTL_annStats.txt > Tutorial3/GenomeAnnotation/CTL_specific_Annotated.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb425f8-7be0-4854-9344-48479b57ab70",
   "metadata": {},
   "source": [
    "Let's look at the output files. First, let's look at the first 2 lines of at our annotation stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "efa56f92-b6be-42dc-a3df-65a03b201e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annotation</th>\n",
       "      <th>peakcount</th>\n",
       "      <th>size</th>\n",
       "      <th>foldenrichment</th>\n",
       "      <th>log10significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5UTR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114432</td>\n",
       "      <td>3.745</td>\n",
       "      <td>-2.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Promoter</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1403974</td>\n",
       "      <td>2.450</td>\n",
       "      <td>-6.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LTR</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20757981</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-7.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Intergenic</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48657251</td>\n",
       "      <td>0.298</td>\n",
       "      <td>-2.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TTS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1306500</td>\n",
       "      <td>0.232</td>\n",
       "      <td>-0.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TTS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1306500</td>\n",
       "      <td>0.231</td>\n",
       "      <td>-0.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Intergenic</td>\n",
       "      <td>84.0</td>\n",
       "      <td>111121736</td>\n",
       "      <td>0.213</td>\n",
       "      <td>-3.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SINE?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>tRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2852</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>miRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3258</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RC?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2850</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Intron</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38474934</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>scRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6881</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6910</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>rRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7760</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>srpRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13945</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>snRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18664</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20949</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNA?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27318</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>pseudo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40049</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49238</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LTR?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92183</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Retroposon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200838</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ncRNA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315963</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Low_complexity</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365373</td>\n",
       "      <td>-0.309</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CpG-Island</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373419</td>\n",
       "      <td>-0.315</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Intron</td>\n",
       "      <td>33.0</td>\n",
       "      <td>73166083</td>\n",
       "      <td>-0.532</td>\n",
       "      <td>5.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Simple_repeat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2265635</td>\n",
       "      <td>-0.563</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LINE</td>\n",
       "      <td>18.0</td>\n",
       "      <td>45014382</td>\n",
       "      <td>-0.705</td>\n",
       "      <td>4.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3UTR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1226327</td>\n",
       "      <td>-0.852</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Exon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1490268</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SINE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18936280</td>\n",
       "      <td>-1.041</td>\n",
       "      <td>3.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Satellite</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2306654</td>\n",
       "      <td>-1.335</td>\n",
       "      <td>1.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DNA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6724040</td>\n",
       "      <td>-2.132</td>\n",
       "      <td>2.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        annotation  peakcount       size  foldenrichment  log10significance\n",
       "1             5UTR        1.0     114432           3.745             -2.632\n",
       "14        Promoter        5.0    1403974           2.450             -6.038\n",
       "11             LTR       27.0   20757981           0.997             -7.957\n",
       "6       Intergenic       39.0   48657251           0.298             -2.484\n",
       "24             TTS        1.0    1306500           0.232             -0.554\n",
       "23             TTS        1.0    1306500           0.231             -0.554\n",
       "7       Intergenic       84.0  111121736           0.213             -3.865\n",
       "20           SINE?        0.0        130          -0.000              0.000\n",
       "33            tRNA        0.0       2852          -0.003              0.002\n",
       "26           miRNA        0.0       3258          -0.003              0.002\n",
       "16             RC?        0.0       2850          -0.003              0.002\n",
       "8           Intron       25.0   38474934          -0.005              0.605\n",
       "30           scRNA        0.0       6881          -0.006              0.004\n",
       "17             RNA        0.0       6910          -0.006              0.005\n",
       "29            rRNA        0.0       7760          -0.007              0.005\n",
       "32          srpRNA        0.0      13945          -0.013              0.009\n",
       "31           snRNA        0.0      18664          -0.017              0.012\n",
       "15              RC        0.0      20949          -0.020              0.014\n",
       "4             DNA?        0.0      27318          -0.025              0.018\n",
       "28          pseudo        0.0      40049          -0.037              0.026\n",
       "25         Unknown        0.0      49238          -0.046              0.032\n",
       "12            LTR?        0.0      92183          -0.084              0.060\n",
       "18      Retroposon        0.0     200838          -0.178              0.131\n",
       "27           ncRNA        0.0     315963          -0.271              0.206\n",
       "13  Low_complexity        0.0     365373          -0.309              0.238\n",
       "2       CpG-Island        0.0     373419          -0.315              0.244\n",
       "9           Intron       33.0   73166083          -0.532              5.597\n",
       "22   Simple_repeat        1.0    2265635          -0.563              0.572\n",
       "10            LINE       18.0   45014382          -0.705              4.777\n",
       "0             3UTR        0.0    1226327          -0.852              0.802\n",
       "5             Exon        0.0    1490268          -0.985              0.975\n",
       "19            SINE        6.0   18936280          -1.041              3.470\n",
       "21       Satellite        0.0    2306654          -1.335              1.513\n",
       "3              DNA        1.0    6724040          -2.132              2.750"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Clean up duplicate entries\n",
    "!sort -u Tutorial3/GenomeAnnotation/CTL_annStats.txt | grep -v Annotation > Tutorial3/GenomeAnnotation/CTL_annStats_clean.txt\n",
    "\n",
    "#Load results into a pandas table\n",
    "annstats = pd.read_csv(\"Tutorial3/GenomeAnnotation/CTL_annStats_clean.txt\", sep='\\t', header=None, names=['annotation','peakcount','size','foldenrichment','log10significance'])\n",
    "\n",
    "#View entries sorted by enrichment\n",
    "annstats_sorted = annstats.sort_values(by=[\"foldenrichment\"], ascending=False)\n",
    "display(annstats_sorted)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74893643-1429-4894-afeb-e4af6d745a1d",
   "metadata": {},
   "source": [
    "From this we can see highest enrichment in 5' UTRs and promoters.\n",
    "\n",
    "Let's plot the results as a barplot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4fe8630e-b47a-4bb4-b9c1-80c6e93f1644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='annotation'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IgVGjRqFhw4aoUqWK2n1SU1ORmpqqeJ6UlJRb4REREZEEcq1FxM/PD9HR0diyZYvGfWbMmAFHR0fFw83NLbfCIyIiIgnkSiLi7++Pffv24ciRIyhVqpTG/SZMmIDExETFIyEhITfCIyIiIomY9NaMEAL+/v7YvXs3IiIi4OnpqXV/KysrWFlZmTIkIiIiykNMmogMHToUmzdvxt69e2Fvb49Hjx4BABwdHWFjY2PKqomIiCgfMOmtmeXLlyMxMRG+vr4oUaKE4vHnn3+asloiIiLKJ0x+a4aIiIhIE641Q0RERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSMWkicuzYMfzvf/+Dq6srZDIZ9uzZY8rqiIiIKJ8xaSLy9u1bVK9eHUuWLDFlNURERJRPFTDlm7dt2xZt27Y1ZRVERESUj5k0Ecmp1NRUpKamKp4nJSVJGA0RERGZWp5KRGbMmIGgoCCpw1DhMf6AxtfiZn6di5EQERF9WvLUqJkJEyYgMTFR8UhISJA6JCIiIjKhPNUiYmVlBSsrK6nDICIiolySp1pEiIiI6PNi0haRN2/e4O7du4rnsbGxiIqKQuHChVG6dGlTVk1ERET5gEkTkQsXLqBp06aK56NGjQIA9O3bF+vXrzdl1URERJQPmDQR8fX1hRDClFUQERFRPsY+IkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJJkCUgfwqfMYf0Dja3Ezv87FSIiIiPIetogQERGRZJiIEBERkWSYiBAREZFk2Eckj9LWtwTQ3r+E/VKIiCi/YCJCCoYkP0RERPrgrRkiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSTK4kIsuWLYOnpyesra1Ru3ZtHD9+PDeqJSIiojzO5InIn3/+iREjRmDSpEm4dOkSGjVqhLZt2yI+Pt7UVRMREVEeZ/JEZN68eRgwYAAGDhyISpUqYcGCBXBzc8Py5ctNXTURERHlcSZNRN6/f4+LFy+iVatWSttbtWqFU6dOqeyfmpqKpKQkpQcRERF9umRCCGGqN3/w4AFKliyJkydPokGDBort06dPx4YNG3Dr1i2l/QMDAxEUFKTyPomJiXBwcDBVmESfLEMWQNS3rBQLNkq1SGR+ivdT+ht9St+LIWXzWp2ZyyYlJcHR0VGn63euLHonk8mUngshVLYBwIQJEzBq1CjF86SkJLi5uZk8PqJPlSELFepbVoo6iSj/MmkiUrRoUZibm+PRo0dK2588eQIXFxeV/a2srGBlZWXKkIjoE8Tkhyj/MmkiYmlpidq1a+Pw4cPo2LGjYvvhw4fRvn17U1ZNRGRyTGKIDGfyWzOjRo1C7969UadOHdSvXx+///474uPjMXjwYFNXTURERHmcyRORrl274vnz55gyZQoePnyIKlWq4O+//4a7u7upqyYiypPYkkL0/+VKZ9UhQ4ZgyJAhuVEVERER5SNca4aIiIgkw0SEiIiIJMNEhIiIiCSTK31EiIjIONjRlT41bBEhIiIiyTARISIiIskwESEiIiLJMBEhIiIiyTARISIiIslw1AwR0WeAo20or2KLCBEREUmGLSJERKSVIa0pbImh7LBFhIiIiCTDRISIiIgkw0SEiIiIJMNEhIiIiCTDRISIiIgkw0SEiIiIJMNEhIiIiCTDRISIiIgkw0SEiIiIJMNEhIiIiCTDRISIiIgkw0SEiIiIJMNF74iIKM/hYnmfDyYiRET0SWESk7/w1gwRERFJhokIERERSYaJCBEREUmGiQgRERFJhokIERERSYaJCBEREUmGiQgRERFJhvOIEBERgfOPSIUtIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGSYiREREJBkmIkRERCQZJiJEREQkGU5oRkREZCBOhqY/togQERGRZJiIEBERkWSYiBAREZFkTJqIBAcHo0GDBrC1tYWTk5MpqyIiIqJ8yKSJyPv37/Hdd9/hp59+MmU1RERElE+ZdNRMUFAQAGD9+vWmrIaIiIjyqTw1fDc1NRWpqamK50lJSRJGQ0REZHqf+9DfPNVZdcaMGXB0dFQ83NzcpA6JiIiITCjHiUhgYCBkMpnWx4ULF/QKZsKECUhMTFQ8EhIS9HofIiIiyh9yfGvGz88P3bp107qPh4eHXsFYWVnByspKr7JERESU/+Q4ESlatCiKFi1qiliIiIhIR59K3xKTdlaNj4/HixcvEB8fj/T0dERFRQEAypYtCzs7O1NWTURERPmASRORyZMnY8OGDYrnNWvWBAAcOXIEvr6+pqyaiIiI8gGTjppZv349hBAqDyYhREREBOSx4btERET0eWEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSMenqu0RERJT3xM38WuoQFNgiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkjFZIhIXF4cBAwbA09MTNjY2KFOmDAICAvD+/XtTVUlERET5TAFTvfHNmzeRkZGBlStXomzZsrh69SoGDRqEt2/fYs6cOaaqloiIiPIRkyUibdq0QZs2bRTPvby8cOvWLSxfvpyJCBEREQHI5T4iiYmJKFy4cG5WSURERHmYyVpEsoqJicHixYsxd+5cjfukpqYiNTVV8TwpKSk3QiMiIiKJ5LhFJDAwEDKZTOvjwoULSmUePHiANm3a4LvvvsPAgQM1vveMGTPg6OioeLi5ueX8ExEREVG+IRNCiJwUePbsGZ49e6Z1Hw8PD1hbWwP4mIQ0bdoUX375JdavXw8zM825j7oWETc3NyQmJsLBwSEnYRIREZGReYw/oPX1uJlfA/h4/XZ0dNTp+p3jWzNFixZF0aJFddr3/v37aNq0KWrXro1169ZpTUIAwMrKClZWVjkNiYiIiPIpk/URefDgAXx9fVG6dGnMmTMHT58+VbxWvHhxU1VLRERE+YjJEpFDhw7h7t27uHv3LkqVKqX0Wg7vBhEREdEnymTDd/v16wchhNoHEREREcC1ZoiIiEhCuTaPCBEREeVv8lExxsQWESIiIpIMExEiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSDBMRIiIikgwTESIiIpIMExEiIiKSDBMRIiIikkwBqQPQRggBAEhKSpI4EiIiItKV/Lotv45rk6cTkdevXwMA3NzcJI6EiIiIcur169dwdHTUuo9M6JKuSCQjIwMPHjyAvb09ZDKZyutJSUlwc3NDQkICHBwcdH5ffcuxzk8vXv6N8mad+S3ez6XO/BYv/0bS1SmEwOvXr+Hq6gozM+29QPJ0i4iZmRlKlSqV7X4ODg45/gMaUo51mrbs51KnIWU/lzoNKcs682bZz6VOQ8p+KnVm1xIix86qREREJBkmIkRERCSZfJ2IWFlZISAgAFZWVrlSjnWatuznUqchZT+XOg0pyzrzZtnPpU5Dyn4udWaVpzurEhER0actX7eIEBERUf7GRISIiIgkw0SEiIiIJMNEhIiIiCTDRCQPOXv2LA4ePKi0LSQkBJ6ennB2dsYPP/yA1NRUo5WjvOHp06dSh5AjhsQbFRVlvECI1JgyZQqSk5NVtr979w5TpkyRICLKDhORPCQwMBDR0dGK51euXMGAAQPQokULjB8/Hvv378eMGTOMVs5Y3r17hwcPHhjt/V6+fInFixejRo0aRnvPvEYIgb///hudOnXSafZgTdLT07Fnzx69yuYkoTAk3sTERCxbtgy1atVC7dq1cxom5QERERE67xsdHa3zwxSCgoLw5s0ble3JyckICgoySZ25zdvbGy9evFA8/+GHH5SO5ydPnsDW1jbb97l79y7CwsLw7t07ALotUKeJIeciiE/Ezp07RdWqVdW+9uHDB2Fubi6uXLli1DofPHgghg4dqnWfb7/9VsyYMUNl+6xZs0Tnzp2VthUvXlycP39e8XzixInCx8dH8Xzbtm2iUqVKKu+lbzlDYpU7fPiwsLOzE2ZmZqJNmzbi9evX2dajyeHDh0W3bt2EtbW1KFWqlBg2bFiO3yM9PV3s27dPtG/fPtv9pBATEyMmTZokSpUqJZycnETPnj3Frl27cvw+N27cEGPGjBHOzs7CwsJC53IZGRniwIEDomPHjsLS0tKk8YaHh4uePXsKGxsbUbFiRTFp0iQRGRmpcd9KlSqJxMRElddevXolvL29xbFjx1Re+++//0SzZs1EuXLlxMyZM3WKS527d+8KPz8/0bx5c9GiRQvh7+8v7t69q1PZly9firCwMLFx40axYcMGpYc6ISEhIjg4WNy5c0evWNPS0sTq1atF9+7dRfPmzUXTpk2VHjmh6/FiZWUlvLy8xNSpU0V8fLzWfWUymTAzM1P8V9vDFGQymXjy5InK9vDwcFG0aFG1ZSpVqiSeP3+ueD5o0CCl93j8+LGwsbHRKx5t1ya5nH6nMplMPH78WPHc3t5exMTEKJ4/evRIyGQyjfU9e/ZMNG/eXPEdycv2799fjBo1KkefT99zUWb5KhH5/fffRefOnUX37t3FmTNnhBAf/3HVqFFD2NjYiB9++EFjWS8vLxEVFZXjOq9duyaWLFkiVq5cKV6+fCmEEOLp06dixIgRwtraOtsLfNGiRUV0dLTK9ujoaOHs7Ky0zcrKSukg9/HxEVOnTlU8j42NFXZ2dirvpW85Q2KVq169uhg8eLCIjIwUrVq1Eg0bNlR7MdHk3r17IjAwULi7u4siRYoIMzMzsWPHDp3Ly92+fVuMHz9elChRQlhbW2s8sUZGRgovLy9hY2MjBg8eLNLS0nJUz5s3b8Qvv/wi6tevL8qUKSM8PT2VHuq8e/dObNy4UTRp0kRYWVmJdu3a6ZUYv3nzRqxZs0Y0aNBAmJmZiebNm4tVq1aJp0+fZls2JwmFIfEmJCSIqVOnCk9PT+Hs7Cz8/PxEgQIFxLVr17SW+9///ifmzZun8fWFCxeKDh06qGxv1aqV8PHxEfPmzRNubm5i+PDh2caYVWhoqLC0tBR169YVI0eOFCNGjBB169YVVlZW4tChQ1rL7tu3T9jb2wszMzPh6OgonJycFI9ChQqp7D9+/HhhY2MjqlWrJhwcHMTx48dzHO/QoUNFwYIFRZcuXcTw4cPFiBEjlB660PV4kXv+/LlYuHChqFmzpjA3NxetWrUSf/75p0hNTVXZNy4uTvHYvXu3KFOmjFixYoW4fPmyuHz5slixYoUoV66c2L17t9Y6c3qsyf/mZmZmiv+XPxwcHISZmZkYMmSI2roMvbAbcm0SIuffadZ47ezsVOLVluj17t1btG7dWiQkJCiVDQsLE97e3lpjFcKwc5E6+SYRmT17trCwsBC1a9cWtra2wtbWVgQHB4siRYqIwMDAbP8Aa9euFW3btlXKerOzf/9+YWlpKWQymZDJZKJMmTLi33//FUWLFhW+vr5i//792b6HtbW1uHnzpsr2GzduCGtra6VtpUuXFkePHhVCCJGamipsbGzEP//8o3g9Ojpa7clN33KGxCpna2sr/vvvP0XdrVq1EnZ2dsLT01NcvXpVNG/eXO1J488//xQtW7YUtra2onPnzmLPnj0iNTVVp4uWXHJysli/fr1o1KiRsLCwEGZmZmLhwoVaW2UaNGggOnToIHbt2iVq1KghOnfuLD58+KBTfUII0a1bN1GiRAkxduxYMX/+fLFgwQKlR1Y//fSTKFSokKhXr55YsmSJePbsmRBC5Ohznjp1SvTv31/Y2dmJmjVrijlz5ghzc/Nsy+uTUBgSb9u2bYW9vb3o3r27+OuvvxRJni5lS5cuLa5fv67x9Rs3bgg3NzeV7XZ2dorPc+/ePeHu7i58fHzE999/L+7duycmTpwovv/+e61116hRQ4wbN05l+7hx40TNmjW1li1XrpwYPny4ePv2rdb95IoXLy727NkjhPh4TnJ0dBSrVq0SR48eFW/evBHnz59XHMuaFClSRBw4cECn+jLT53hR59KlS8Lf318ULVpUFC5cWPj7+2v8kffFF1+ojfXAgQOiVq1aWuvJ6bG2fv16sW7dOiGTycTChQvF+vXrFY/NmzeLU6dOaazLkAu7odcmIXL+nRqaiLi4uCi+s8xl//vvP1GwYEGN5fQ9F2Un3yQiFStWFGvWrBFCCHHkyBEhk8lE8+bNFa0U2alRo4aws7MTVlZWonz58qJmzZpKD3Xq1asnhg0bJl6/fi3mzp0rZDKZKF++fLYniszq1KkjgoKCVLYHBASoHIg//PCDqF+/vjh27JgYNWqUKFKkiNIvjk2bNok6deqovJe+5QyJVa5cuXLixIkTiudpaWli7969YuHCheLJkydiyZIlIjAwUKWcubm5mDBhgkhKSlLarstF6+zZs2LQoEHCwcFB1KlTRyxYsEA8evRIp7IFCxYUt2/fFkJ8bFKvWbOm8PDwEE2bNhW3b98Wffr00dq87ejoqPR5s2Nubi4mTpyo1+cU4mOTsbu7u5gwYYLS/tmV1zehMCRec3NzMXLkSMXfNydlraystN6quHPnjtpkuGTJkkoXwf/7v/8TI0eOFB07dhQxMTGiT58+wtfXN9u6s8YshBC3bt0SVlZWWsva2toqXQCy4+DgoLT/+vXrFS2BFy9eFBUrVsz2lkWJEiXErVu3dK7TkONFk/v374uAgABhZWUlChYsKMzNzUXDhg3F1atXlfaztrZWm2Bev35d448buZwea3IRERHi/fv3OSpjyIXd0GuTEDn/Ts3MzJRuHdnZ2Sl+EGYXr3x/+b/5zJ/13LlzonDhwmrL6Hsu0kW+SURsbGzEvXv3FM8tLS0VTWC6CAwM1PpQx9HRUfGPQ97P5O+//85R3Hv37hUFChQQffr0UWTnvXv3FgUKFFBpmnzy5Ilo2LChkMlkwt7eXqXpvFmzZmLixIkqdehbzpBY5SZOnCh69+6d7XtnNWjQIOHo6CgaNGggli9fLl68eCGE0P2CN2LECJXWG13Kuru7K/WnSUpKEgsXLhQjR44UCQkJYsKECaJfv34ay3t4eGj95Z7VH3/8IVq0aKFodt2/f7/48OGDzgevhYWF6N27tzh06JDIyMhQbDdVQmFIvKdOnRIDBw4UDg4Oom7dumLx4sXiyZMnOpX18vLS2vdk586dalvW+vTpIyZPnqz1vbNTqlQpsW3bNpXtf/75p9pWmMw6duwo/vzzT53ratiwodi8ebPK9sTERJGeni7u378v4uLitL7HnDlzxJAhQ5T+PWhjyPGS2fv378X27dtF27ZtRYECBUS9evXEqlWrxJs3b0R8fLzo3r27yq3qmjVrih49eoh3794ptqWkpIgePXpk29qU02NNneTkZJGYmKj0UMeQC7uh1yYhcv6dymQyUbVqVcUPaXNzc1G5cmXF86pVq2pNRL766ivxyy+/CCH+/2dNT08X3333nfj222/VltH3XKSLfJOIZJex5ladunZgy+yvv/4SDRo0ELa2tqJIkSKiadOmIiIiQuP+r169Utt34fnz52rvyRpazpBYU1JSsu28pom8qbhx48bCyspKfPPNNzr1RWjZsqWwt7cXPXr0EAcPHlQcFLocEEOHDhX+/v56xSuEEBs3bhSdO3fWuSleLjY2VkyePFmULl1aFC1aVJiZmYnt27dnW+7//u//xLRp00SZMmWEq6ur+Pnnn0VkZKSwsLAwWUJhSLxCCPH27VuxZs0a4ePjo7gFsGDBApWkKDM/Pz9RpUoVpQuWXHJysqhSpYra7+3evXti586dOsWlSVBQkHBychIzZ84Ux44dE8ePHxczZswQTk5OSn2t1Fm9erUoXbq0CAgIEDt27BB79+5VemS1d+9e4efnZ1C8HTp0EI6OjsLT01O0a9dOdOzYUemRlSHHi5yfn58oUqSIKFKkiBg+fLjaY/TevXsq/SjOnj0rnJ2dRdGiRUXz5s1F8+bNRdGiRUWxYsXE2bNntdap77H29u1bMXToUFGsWDGdO8gacmE3xrUpp99pdj+stf3AFuJj38dixYqJNm3aCEtLS9G5c2dRqVIl4eLiovEap++5SBf5ZtE7MzMzTJs2DXZ2dgCAcePGYcyYMShatKjSfsOGDdP4Hq9evcKOHTsQExODMWPGoHDhwoiMjISLiwtKliypts5///0XhQsXBgA0aNAA27ZtUxm+WK1aNUM/HgDA3NwcDx8+hLOzc66Uy0vu3LmDtWvXIiQkBG/evMHXX3+Nzp07o1OnTmr3T0hIwLp167Bu3Tq8e/cOXbt2xbJlyxAdHY1KlSpprOfFixe4ffs26tWrp1ecNWvWRExMDIQQ8PDwgIWFhdLrkZGRWssLIRAWFoa1a9di3759KFq0KDp16oRFixZlW/e///6LtWvXYteuXUhJScHo0aMxcOBAlC9fXmOZuLg4rFu3DuvXr0dycjJevHiBP//8E507d9bp8xoSLwDcunULa9aswcaNG/Hq1Su0bNkS+/btU9nv8ePHqFWrFszNzeHn54cKFSpAJpPhxo0bWLp0KdLS0nDp0iW4uLjoVK9ceno69u/fjw4dOmj9jAsWLMDcuXMVw9BdXV0xZswYDBs2DDKZTGNZMzPNMyDIZDKkp6fnKF5dfP/991pfX7dunco2fY8XuebNm2PgwIH49ttvYWlpqXaftLQ0nDx5Ek2aNFHanpycjE2bNuHmzZsQQsDb2xs9evRAwYIFtdap77E2dOhQHDlyBFOmTEGfPn2wdOlS3L9/HytXrsTMmTPRs2dPlTK6DusNCAhQ2WaMa5M+36mhHj16hOXLl+PixYvIyMhArVq1MHToUJQoUSLbsvqci7TJN4mIh4eH1hMC8PHA/++//9S+Fh0djRYtWsDR0RFxcXG4desWvLy88Ouvv+LevXsICQlRKWNmZgaZTKZ2bLV8uzFPNmZmZnj06FGOEwp9yxmDt7c3Tpw4oUjWfvjhBwQHB6NYsWIAPo5n9/DwUDvBkDoZGRk4cOAA1qxZg4MHD+o0Edvhw4exdu1a7NmzB25ubujcuTM6d+6MWrVq6f/BNMjuhKXuRKXJixcvEBISgnXr1uHy5cs6l0tMTMQff/yBtWvXIjIyElWqVMl2TgZDEwpD4gX+f0Igr1+duLg4DBkyBGFhYYpjTiaToXXr1li+fDkKFCig9geDOjdv3sTatWuxYcMGvHz5Eu/fv9ep3OvXrwEA9vb2Ou1vbA8fPkRwcDCWLFlisjr0OV6OHTuGBg0aoECBAkrb09LScOrUKTRu3Njocep7rJUuXRohISHw9fWFg4MDIiMjUbZsWWzcuBFbtmzB33//bdQ4Db02SSE+Ph5ubm5q446Pj0fp0qV1eh99zkXq5JtExFAtWrRArVq1MGvWLNjb2+Py5cvw8vLCqVOn0KNHD8TFxamUuXfvnk7v7e7urvS8cOHCuH37NooWLYpChQpp/UeaeVIaKRIRfWPVVLeDgwOioqLg5eUF4OMv3RIlSiAjIyPHsT158iRHn+nly5fYtGkT1q5di+joaLUJ4r///gs/Pz+cOXMGDg4OSq8lJiaiQYMGWLFiBRo1apTjeKUQFRWFtWvX5lpCkRtevnyJu3fvQgiBcuXKITU1FcHBwVi9erVi4iV13r59iz///BNr1qzBmTNn0LRpU3Tr1g0dOnRQ+XVqLMnJyTpNHJXZ9evXceTIEVhYWKBLly5wcnLCs2fPEBwcjBUrVsDT0xPXr1/P9n2ePn2KW7duQSaToXz58orkX1e6HC9ymlpdnz9/DmdnZ61lb9++jYiICDx58kTlPDB58uQcxawLOzs7XLt2De7u7ihVqhR27dqFunXrIjY2FlWrVlU72Vleoet32rRpU52Sn/DwcLWvGfJ9aqLPuUiuQPa75A3NmjXDrl274OTkpFf58+fPY+XKlSrbS5YsiUePHqkts2HDBowePTrHJ5r58+crflEtWLAgR2XDwsLg6OiodZ9vvvnGaOUMiVUdTa1HWR07dkyn98tJIlKoUCH4+/vD399fY7PtggULMGjQIJUkBAAcHR3x448/Yt68edkmIhcvXsSNGzcgk8ng7e2NmjVrqt1PlymlZTIZfv3112z3U6dGjRo5PvALFy6MESNGYMSIESqvGRJv//79dSq7Zs0ale2vXr3C0KFDcejQIVhaWmLcuHHw8/NDUFAQ5syZA29vb6xdu1bte54+fRqrV6/Gtm3bUK5cOfTs2RNnz57FokWL4O3trbZMzZo1sz2Ry2m73ebk5IQ6derA19cXTZo0QcOGDbXecvjrr7/w7bff4sOHDwCAWbNmYdWqVejSpQuqVKmC7du3o127dlrjefv2Lfz9/RESEqK4sJubm6NPnz5YvHixzucrXY4XOXnrb1bPnz/X+nlXrVqFn376CUWLFkXx4sWV3kMmk+mUiOh6rMl5eXkhLi4O7u7u8Pb2xrZt21C3bl3s379f4/XDkAu7odcmIOffqbZZp5OSkrBlyxatrcmavs83b97A2tpar8+gz7lILt+0iBh6+8HFxQWhoaGoWbOmUovIoUOHMGDAACQkJKiUye2+F9ruN8upuxWkbzljyPq9ZP7bAh9bRFxdXXMUs/wAkclkSEtLU3k9Pj5ep9jUNS+6u7sjNDRU433xmzdvolWrVhrrePLkCbp164aIiAg4OTlBCIHExEQ0bdoUW7duVfkFo+2kKZPJcOvWLaSkpGj8bvQ9QeqbUBgSb8eOHTWWTU9Pxz///IPU1FS1ZYcMGYL9+/eja9euCA0NxY0bN9C6dWukpKQgICBApd+BnLe3N5KTk9GjRw/06tVLkXhYWFjg8uXLGhORzM3+KSkpWLZsGby9vVG/fn0AwJkzZ3Dt2jUMGTJE6/IIp0+fxtGjRxEREYFTp04hJSUFtWrVUiQmbdu2Vdq/fv36qFu3LoKDg/H7779j9OjRKFeuHFatWqXz7Y0ff/wR//zzD5YsWQIfHx8AwIkTJzBs2DC0bNkSy5cvV9rfkONF3kdr7969aNOmDaysrBSvpaenIzo6GhUqVEBoaKja93R3d8eQIUMwbtw4nWLILKfHmtz8+fNhbm6OYcOG4ciRI/j666+Rnp6OtLQ0zJs3D8OHD1cpM3LkSI1xZL6wq/u3a4xb4zn9TtVJS0vD0qVLERwcDEdHR0ydOhXdunVT2mfUqFEAgIULF2LQoEFKCU56ejrOnj0Lc3NznDx5UuX9DW2F0Vruc0lE5HPxb9u2DYULF0Z0dDTMzc3RoUMHNG7cWG1rgLH6XmRkZODu3btqmyYzn3zyQh8RXWOVMzc3x6NHjxQnBXt7e0RHR8PT0xOA5kQkMTFRbf3JyclYuHAhFi1aBC8vL1y9elVlH3nfnawyZ/makhhra2tcvXoVZcuWVVv/3bt3UbVqVY23ALp27YqYmBhs3LhRkcxcv34dffv2RdmyZbFlyxa15bKKiorC+PHj8e+//6J///5YsWKF2v30PUEamgDpG686e/fuxcSJE/HgwQOMGzcO48ePV9nH3d0da9asQYsWLfDff/+hbNmyGDZsWLatdJaWlujWrRt69+6NFi1aKL7/7BKRzAYOHIgSJUpg6tSpStsDAgKQkJCgsSUmq/T0dJw/fx4rVqzAH3/8gYyMDJW/r5OTE86dO4fy5csjLS0N1tbW2L9/v0rCok3RokWxY8cO+Pr6Km0/cuQIunTporKGkCHHi7wT5YYNG9ClSxfY2NgoXrO0tISHhwcGDRqk8dZX1lu1OWGsYy0+Ph4XLlxAmTJlUL16dZ3r1+XCDhjn/JvT7zSrP/74A5MnT8a7d+/wyy+/4IcfflDpzwN8TCYA4OjRo6hfv75Sx2P59ylPjrMyJFnLlkFjbnKRTCYTd+/eVRkTrssYcSE+jtP38fERTk5OwtzcXLi5uQkLCwvRuHFj8ebNG411qluzICdOnz4tPD09FWsvZH5kHQ5mZmamNAxMV/qWMyRWOUPHs8ulp6eLVatWiVKlSonSpUuLtWvXalwPJioqSu3j0qVLYty4ccLGxkYUK1ZMbVl956uQc3BwEOfOnVPZfvbsWeHo6Kj9Q4qPMxf27NlTFChQQHTp0kXtRFrZ+fDhg1iwYIEoVqyYKFu2rNiyZYvOZS9duiRat24tLCwsxI8//mjSeE+cOCF8fHyEra2tGDt2rGKuGHUKFCgg7t+/r3huY2Oj05TyxhhS6ODgoPZz3b59Wzg4OGRb/saNG2L58uWiW7duonjx4qJIkSKiY8eOamf/NMaUADY2Nmrn17h69aqwtbVV2W7I8SIXGBio8TypTf/+/cXy5ctzXE4Iw481IYTa4eC62LRpk/Dy8hIlSpQQS5cu1Tr7sqHXJiFy/p3KHTx4UFSvXl04ODiIKVOm6Pwd9evXL0dLcWhiyLkos3yViGhbPEnbxTKz8PBwMXv2bPHbb7+Jw4cPZ1tn5ouspoc21atXF9999524fv26ePnypXj16pXSI2t9+iQU+pYzJFY5Q8ezC/Hx4l+hQgVRuHBhMXv2bJGSkpLj2A8fPixq164t7O3tRUBAgMZpq/Wdr0LOzs5OXLp0SWV7ZGSksLe311ju6dOnws/PT1haWopmzZqpPcHqIicnyMxymlAYEu/Vq1dFu3btRIECBUT//v1FQkJCtmWym1BKF5kX2ZPJZGLMmDE6zVbp4uIi1q5dq7J97dq1GtdYyly2cOHConPnzmLJkiVq12rKTCaTiSNHjijWXSlYsKA4cOCA4rn8oU2zZs3Ed999p/RvODk5WXz33XeiefPmWsvK6Xq8GGr69OmiaNGiom/fvmLOnDli4cKFSg9t9D3W0tLSxJQpU4Srq6swNzdXzOnxyy+/iNWrV2utU58LuzGuTTn9Ts+ePSt8fX2FtbW1GDFihN5rvBhC33OROvnq1szOnTsVw0Q10XQvOSQkBF27dlW6xwkA79+/x9atW9GnTx+1df7888+K8eHqZNfhqmDBgrh8+bLGWwGZff/991i0aFGOhw7qWy6rnMRqDEePHsW4ceNw5coVDB8+HOPGjcu2w21WFy9exPjx43H8+HEMHDgQkydP1tpEauh8Fe3bt8erV6+wZcsWuLq6AgDu37+Pnj17olChQti9e7fS/m/fvsWcOXMwb948lC1bFjNmzECrVq1y9BkBIDQ0FOPHj0dsbCxGjx6NUaNGZTsPAwA8e/YMQUFB+P3339GwYUPMnDkTX3zxhcb9DYk3ISEBkydPxqZNm9CuXTtMnz5dpzkqgI/HWtu2bRXH5/79+9GsWTOVz7hr165s3yunQwpnzpyJwMBADBw4UDG/zJkzZ7B27VpMnjxZ7a0kuRo1auDGjRuoUaMGfH194evri0aNGmk8ZxhjSoCrV6+iTZs2SElJQfXq1SGTyRAVFQVra2uEhYWhcuXKGsvm5HipVasWwsPDUahQoWw792rq7Cq/RatOdkNac3qsyU2ZMgUbNmzAlClTMGjQIFy9ehVeXl7Ytm0b5s+fj9OnT6uUOXfuHMaNG4czZ85g8ODBmDRpks4jrQy9NgE5/07NzMxgY2ODH3/8ER4eHhrfN/PcJZ06dcL69evh4OCgcX4mOW3Hmb7nIm3yVSJiyH04fYYrZVdnQkICAgICtN5DbtasGcaOHYs2bdroHaOpymWVk1gN9dVXXyE8PBzff/89AgMDUbx48RyVv3v3LiZNmoSdO3eiS5cumDZtms73oQ2ZryIhIQHt27fH1atXFePw4+PjUbVqVezdu1dlsrvixYvj9evX8Pf3R/fu3TWezDVNiqfvCVLfhMKQeG1tbSGTyeDv748GDRporEPd6K3sJnSSy+nETroOKdy2bRsWLlyIGzduAAAqVaqE4cOHo0uXLtnW8erVKxw7dgxHjx7F0aNHce3aNVSrVg1NmzbFzJkzlfbVd0qArN69e6cySVjPnj2V+nBkps/xEhQUhDFjxsDW1haBgYFaE5GczJ+jq5wea3Jly5bFypUr0bx5c6XO8zdv3kT9+vXx8uVLlTL6XNgzlzVGH72cfKf6zF2S+QerPhOoGZKsZeezSUTMzMzw+PFjlZ7Wly9fRtOmTdXOkZHdBf7y5cuoVauW1l8vu3fvxi+//IIxY8agatWqKrMDZj6hS91ZNSexyunbk9rMzAwFChRAwYIFczx3yZAhQ7BmzRrFiV7bUDZt9J2vAvg4KVTmE0aLFi3U7pd5dFDWX8K6/ALW9wSpb0JhSLxSjt7KC168eIGIiAjs3bsXmzdvVttZdcqUKXpNCWAIYx0vUtH1WJOzsbHBzZs34e7urpSIXL9+HXXr1lU7j4ghk5IZ4/z7+PFjja2w0dHRRpu92xCGJGvZyTeJiKenJy5cuIAiRYrkqJy8SfHy5cuoXLmyUk/i9PR0xMbGok2bNti2bZtK2ez+gemSiKg7OWs6oWtKlrKjbzlDYpXTtyf1hg0bdIqpb9++auO0trZGxYoVtZZV11ysy3wVo0aNQvfu3VXKykc5REVFoUqVKjrFb+gvYH1PkPomFMb6xZ5bTDmkUBe7d+9GREQEIiIicO3aNRQpUgSNGjWCr68vmjZtqtKkbozWyw0bNqBo0aL4+uuvAQBjx47F77//Dm9vb2zZskXluzHkeJH7559/NCYAK1euxI8//qix7P/93/9h3759iI+PV5nhdt68eWrL6HOsydWpUwcjRoxAr169lBKRoKAg/PPPPzh+/HiO3i87+l6bMnN2dsbq1atVWgvnzJmDX3/9NdsfRrnBlDPI5psJzWJjY/UqJ19jIioqCq1bt1a6dysfrvTtt99qrNPQpqecxl2+fPlsv2x1rQT6lstMn7/x/PnzVbZlHvZWsmRJlWGRgPoEQ1eGNANPnDgRx44dQ9++fREaGoqRI0ciNDQUKSkp+Pvvv7Xexy1QoADc3d1z9Ive0Au2uhl/s7p//77KNn2PF1MnGPrMRKqNoRM7AZqHt8pp+75//PFHNG7cGIMGDYKvr2+2F01j/O6bPn26Yl6J06dPY8mSJViwYAH++usvjBw5UuX+vjFum3z99dfw8/PDjBkzFEM+nz59iv79++PkyZMaE5Hw8HB888038PT0xK1bt1ClShXExcVBCKF1Snl9jjW5gIAA9O7dG/fv30dGRgZ27dqFW7duISQkBH/99VeO3y87+h5rmY0bNw5du3ZF3759MX/+fLx48QK9e/fGtWvX8Oeff6rsr+vEYZlbJwydxE+Xc5G+8lWLiLo/oqOjIypUqIDRo0ejTp06Gstv2LABXbt21XvWOHV0aRHJCTMzMyxYsCDbDptZL+L6ljMFXcezZ8dUa27oO1+F3Lp167B9+3Zs2rQp285puti1axcCAwP1Wp/h0aNHmD59OlatWpVrv5j0jTclJQVLly7F7NmzNc5kbCy6zv8gt3fvXqXnHz58wKVLl7BhwwYEBQVhwIABRovNGK2Xtra2uHnzJkqXLo1x48bh4cOHCAkJwbVr1+Dr65vtnBP6OHv2LHr37g0bGxts3rwZcXFx6N+/P7y9vRESEgI3Nze15erWrYs2bdpgypQpitYJZ2dn9OzZE23atMFPP/2ksU5DjrWwsDBMnz5daUG3yZMna+wnpc+FXc7Qa5Pc5cuX0atXL6SkpODFixeoV68e1q5dq/aWjbZOwHJZWyd0XdgP0Jy8ZmRkYP369di1axfi4uIgk8ng5eWFb7/9Fr1799Y50VGJNb8kIgsXLlS7/dWrVzh//jzCwsJw6NAhxYQtmuRkuuDseha/evUKR48ezTYR2bhxI1asWIHY2FicPn0a7u7uWLBgATw9PdG+fXvFflL3EclJrFnp05PaWGtuZJWSkoIlS5Zg9OjRKq9ZWFjg3r17il74tra2OHfunM7NvzVr1sTdu3fx4cMHuLu7q3xGdb8kVq1ahUOHDsHCwgLDhw/Hl19+iX///Rc///wzbt26hd69e6tdfgBQvpVkYWGB8ePHw8/PD4GBgZgzZw4qV66s8VaSNtoSCn3jff/+PYKCghRlx44diw4dOmDdunWYNGkSZDIZ/Pz8MGHChBzFmhPGSoQBYPPmzfjzzz9VEpWs0tPTsWfPHsV5pVKlSmjfvj3Mzc1V9jUzM0OVKlWyjUnbbRJnZ2eEhYWhZs2aqFmzJkaOHIk+ffogJiYG1atXz/FaKtqOl8zevn2LwYMHY/v27cjIyMC0adMwZswYrRcfe3t7REVFoUyZMihUqBBOnDiBypUr4/Lly2jfvr3WX9n6HGv60ufCLmesa9Pr168xaNAg7Ny5EwCwevXqXPnxqCshBNq1a4eDBw+ievXqqFixIoQQuHHjBq5cuYJvvvkGe/bs0eu9882tGXXT8mY2depUBAYGavyy9ZkuOLsWBkdHR7XDfjNbvnw5Jk+ejBEjRiA4OFiRtDg5OWHBggVKF3e9s0k9yxkSq1zWntT//POPTrezDF1z49mzZzh79iwsLCzQvHlzmJub48OHD1i2bBlmzJiBtLQ0tSfWjIwMpU645ubmORp61r59+xz9vefMmYOJEyeiWrVquHHjBvbu3YtJkyZh3rx58Pf3x9ChQ7X+vbTdSjp48KDWW0m6JBTGjDcwMBBLly5Fy5YtcfLkSXz33Xfo378/IiIiMGPGDPTo0UOlA7SxmGJI4ZdffolBgwZp3efu3bv46quvcP/+fVSoUAFCCNy+fRtubm44cOAAypQpo1Im6y3irLL799WyZUsMHDgQNWvWxO3btxV9Ra5du6axE6G+x0tmt27dwvnz51GqVCk8ePAAN2/eRHJysta/c8GCBRW3xlxdXRETE6PoN/Ps2TOt9eX0WMvqwoULSslh7dq1Ne6b3e2V+Ph4BAYGqn3N0GsTAJw8eRK9evVCkSJFEB0djZMnT8Lf3x8HDhzAypUrUahQIaX9z549ixcvXijNyBsSEoKAgAC8ffsWHTp0wOLFi1WmqzDE+vXrcfz4cYSHh6t8ln///RcdOnRASEhIttdEtfSegSSPuX79uihSpIjG17t06SJq166tNHvdtWvXRJ06dUS3bt1MFlelSpXE7t27hRAfJ+iRT65z5coVlXilntAsJ7FmrtvW1laMHDlSZbIibRMX1atXTwwbNky8fv1azJ07V8hkMlG+fHlx9OjRbOM8efKkcHJyUkwUVLduXXHt2jVRrlw5UaZMGbF48WLx9u1bjfF+9dVXomPHjqJjx46iQIEColWrVorn8oexVKxYUaxZs0YIIcSRI0eETCYTzZs3Fy9fvtSpfOnSpRUT78XExAiZTCaGDx+ebbnZs2cLCwsLUbt2bWFraytsbW1FcHCwKFKkiAgMDNQ4AZIh8ZYpU0Yxa21UVJSQyWSiW7duBk10lB1TTeyUnJwshg8fLsqXL691v7Zt24o2bdqI58+fK7Y9e/ZMtGnTRnz11Vcq+2d3rMbHx4vvv/9ea50vX74UQ4cOFd988404ePCgYvvkyZPFtGnTVPY35HiRmzFjhrC0tBR+fn7i3bt34urVq6JGjRrCy8tLnDp1SmO59u3bi99//10IIcSYMWNE2bJlxbRp00StWrV0nnwtpxISEkTDhg2FTCYThQoVEoUKFRIymUz4+PiI+Ph4vd4zKipKpwkz1cnu2iSEEJaWlmLcuHHi/fv3im13794V9evXFyVLllTZv3Xr1mLmzJmK59HR0aJAgQJi4MCBYu7cuaJ48eIiICBAqYyTk5Pi75HdQ52WLVuKGTNmaPwMwcHBolWrVlo/pyafTSJijOmC9WFtbS3i4uKEEMoX99u3bwtra2uT1asPfWJ1d3cXHh4eWh/qpkx3dHRUzHr54cMHYW5uLv7++2+d4mzWrJno2rWruHLlihg5cqSQyWTC09NTbNiwQWRkZGgt269fP50emnh6eopnz56pbH/58qXaz2ljYyPu3buneG5paSnOnDmj0+cUQv+pz/VNKAyJ19LSUmkWVSsrK7UzYxqTvolwZllP0PJlIOzt7cXevXu1lrW1tVU7m2pUVJQoWLCgyvbslmMw5IKniSHHi1zx4sVVjs/379+L0aNHC0tLS43lYmJiFDPFvn37Vvz000+iatWqomPHjopzjSY5PdbkWrZsKb788ktx8+ZNxbabN2+KBg0aiJYtW2qtUxNTJyIRERFqt6enp4spU6aobC9evLg4f/684vnEiROFj4+P4vm2bdtEpUqVlMqsX79e54c6Li4uWo/nyMhI4eLiou1javTJJCJTpkwRTZo00fi6vtMFG6pSpUpiz549ihjkF/eFCxeKWrVqmaxefeRmrIasuVGkSBFx9epVIcTHk5uZmZnYtm2bUePTRNMv2kePHgkLC4ts98/8d9WFvlOf65tQGBKvurI5naY9p/RNhDPLehIOCQkRBw8e1Lo2jlyhQoXEyZMnVbafOHFC7S/L7FpEdL3gvXz5UsyZM0cMGDBA8StY0zIMxjhetLU0abqIGiqnx5qctbW1iIyMVNl+8eJFvX/8GZKIaLs2tW3bVul7mzZtmtKPhWfPnqkkFEJ8TPIzt+74+PiIqVOnKp7HxsYKOzs7veLVxMLCQjx48EDj6/fv39ealGqTb/qIaOrVnJiYiPPnz+PgwYMICwvTWL5Zs2YYPny4ynTBI0eORPPmzU0SMwCMGTMGQ4cORUpKCoQQOHfuHLZs2YIZM2Zg9erVJqtXH7kd6/Xr1xUjKIQQuHXrFt6+fau0j7qJfF68eKHo02NrawtbW1utnY6NYd++fYr/DwsLU+o/lJ6ejvDwcI0d3lavXq3oE5CWlob169er9LPQNAmQEAL9+vVT3OtNSUnB4MGDs536PCUlRWmEmKWlpc4jNQyJd/LkyYrhue/fv8e0adNU+lppmjtCH8YYUmhIh8B27drhhx9+wJo1a1C3bl0AH+/fDx48WO0MssaYEuDChQto3bo1bGxsULduXQghMH/+fEyfPh2HDh1SGRZrjOOlaNGiePXqFXbs2IGYmBiMGTMGhQsXRmRkpNGXhDDkWAOA0qVLK/qeZZaWlqZxxmRDGHJtCgsLUxpe/ttvv6F79+5wcnJSxHzr1i2Vci4uLoiNjYWbmxvev3+PyMhIpVExr1+/zrY/VkxMDNatW4eYmBgsXLgQzs7OCA0NhZubm9plAtLT07V2sjY3N1e7grMu8s2oGU3/8BwcHFCxYkWMGjUKX375pcby+k4XbAyrVq3CtGnTkJCQAAAoWbIkAgMDjTos0FhyGqu+w94MWXPD3Nwct2/fRrFixSCEgJubG06cOKHSUc/BwUGn2HQhnyBMXcwWFhbw8PDA3LlzVTrYGjoJkL5Tn5uZmWHatGmKhGLcuHEYM2ZMtgmFIfH6+vrqVPbff//Vuk9OGWNI4fHjx7Fy5Ur8999/2L59O0qWLImNGzfC09MTDRs21Fju1atX6Nu3L/bv36848aelpeGbb77B+vXrc7x2ki5TAjRq1Ahly5bFqlWrFBeGtLQ0DBw4EP/99x+OHTumtL8xjpfo6Gi0aNECjo6OiIuLw61bt+Dl5YVff/0V9+7dQ0hIiGLfQoUK6dzJVN3cRvoea3J79+7F9OnTsXTpUtSuXRsymQwXLlyAv78/xo0bp5hbKjNDRkgacm3KOuIx8wRswMcZV11dXVXq/fHHH3HlyhX89ttv2LNnDzZs2IAHDx4o5nj5448/sGDBApw/f15tvUePHkXbtm3h4+ODY8eO4caNG/Dy8sKsWbNw7tw57NixQ22smdeDyio1NRWhoaF6TWeRbxIRY8npdMHG9OzZM2RkZBhlmK2p6RqrvsPeMs/gmZ6erjLUMSMjA2ZmZmon2Mo6AZU8acn63BRTiXt6euL8+fNGW2PBVOQJhaZkD9B/FsS8RBhhSOHOnTvRu3dv9OzZExs3bsT169fh5eWFZcuW4a+//sLff/+dbRx37txROq9oaiUwxpQANjY2uHTpkspMqdevX0edOnWQnJystN0Yx0uLFi1Qq1YtzJo1S+lieerUKfTo0UOpZUrXWZMB7a1R+h5rhQoVQnJyMtLS0pQSNfmSEpnJEyFTrXWUHX0TkadPn6JTp044efIk7OzssGHDBnTs2FHxevPmzVGvXj0EBwerrbd+/fr47rvvMGrUKKU6z58/jw4dOqidJNGUf6N8m4i8fPkSGzZswJ07d+Dq6oo+ffponFSH8i59FiM8evSoTu+tbWjr5+D9+/do1aoVVq5ciQoVKkgdjkmsW7cOw4cPx969ezUOKVyyZInWIYWZ5+LIfFKOiopCmzZtjDoBmzFO5i4uLti4caPK5FxhYWHo06cPHj9+rLTdGMeLo6MjIiMjUaZMGaW/0b1791ChQgWkpKToVEduMFYilBM3btzAmTNn0KBBA1SoUAE3b97EwoULkZqail69eqFZs2Zqy5mbm+PRo0eKW2f29vaIjo5W/MDTlIjIJSYmws7OTuWH3IsXL2BnZ6doIcnKzs4OV65cgaenp9L3GRcXh4oVK+b695lv+oi4urriypUrKFKkCGJjYxWre1atWhX79u3D7NmzcebMGa3rKZw7dw4RERF48uQJMjIylF4z5n3rzDRNqyuTyWBtbY2yZcuiX79+2U52kxv0idXQ8exZf53JvXnzRuMsuFInGOHh4QgPD1f770jTSszDhg1TzOKa2ZIlS3D37l2dZ3bNCUtLS1y7dk3txFq6CA8Px/z58xVzMVSsWBEjRozIthUxPT0d69ev1/g3MuatmS1btmDixIlqj59mzZph/Pjx+OOPP7QmIrdu3ULjxo1Vtjs4OODVq1da68/pZzXGL+quXbtiwIABmDNnDho0aACZTIYTJ05gzJgxaie2M8bxYm1tjaSkJJXtt27d0tr3KDIyEhYWFqhatSqAj7dN1q1bB29vbwQGBmq8UMrpc6zl9iRgoaGhaN++Pezs7JCcnIzdu3ejT58+qF69OoQQaN26NcLCwtQmI9n1ActueQJNt/6ym4nWyckJDx8+VGnRvnTpkkn60WQn++Uy84hHjx4pssKJEyeiYsWKiImJwaFDh3D37l00atQIv/76q8by06dPR7169bBu3TpcuHABly5dUjyioqJMFnebNm3w33//oWDBgmjatCl8fX1hZ2eHmJgYfPHFF3j48CFatGiR7eyNuUGfWAMCApRm57xy5QoGDBiAFi1aYPz48di/fz9mzJihUteoUaMwatQoyGQy/Prrr4rno0aNwvDhw9G1a1e164hkPhkmJSVpfZhCUFAQWrVqhfDwcDx79gwvX75Uemiyc+dO+Pj4qGxv0KCB2vuxxtKnTx+9OhovWbIEbdq0gb29PYYPH45hw4bBwcEBX331VbbT7g8fPhzDhw9Heno6qlSpgurVqys9jCk6Ohpt2rTR+Hrbtm1x+fJlre9RokQJ3L17V2X7iRMnFE3kmuTmZ5WbM2cOOnXqhD59+sDDwwPu7u7o168fOnfujN9++01jub///lttp8mwsDAcPHhQa53t27fHlClTFJ1A5X3sxo8fr3GtLuBjX4bbt28DAP777z907doVtra22L59O8aOHau1Tn2PNeBjR8xffvkF3bt3x5MnTwB8TBiuXbumtZw+pkyZgjFjxuD58+dYt24devTogUGDBuHw4cP4559/MHbsWMycOVNt2b59+8LZ2RmOjo5wdHREr1694Orqqnju7Oys3wRh2ejRowfGjRuHR48eQSaTISMjAydPnsTo0aNNUl+29BprI4HMQ7k8PT1FeHi40utnzpwRpUqV0lje2dlZrFu3zpQhqjVw4EC148CnTp0qBg4cKIT4OBFR7dq1czs0FfrEqs94diGE8PX1Fb6+vkImk4kGDRoonvv6+opWrVqJH374Qdy+fVulXOZ5GOQTNGV9yLebQvHixUVISEiOy1lZWYk7d+6obL9z546wsrIyRmhq+fn5CQcHB1GrVi3xww8/iJEjRyo9NHF1dRWLFy9W2b5kyRJRokQJrXUWKVJEHDhwwODYdWGMIYW//fab8Pb2FmfOnBH29vbi+PHjYtOmTaJYsWJq/waZ5eZnzert27ciOjpaXL58OdsJyYQQomrVqmpjPXjwoKhWrZrWsomJicLHx0cxx4qbm5uwsLAQjRs3Fm/evNFYzsHBQTEsf+bMmYoJr06cOKH1fC2E/sdaRESEsLGxES1atBCWlpaK4ee//fab+Pbbb3P8ftlxcHBQHNvp6emiQIEC4uLFi4rXr1y5ovf8Gqby/v170aNHD8X50sLCQpiZmYlevXqJtLS0XI8nXyUi8vkUXF1dFePi5WJjY7We0IsXL672wmZqmf+RZnbnzh3h4OAghBDixo0bRh/zrQ99YjV0PHu/fv1EYmKizjFGREQoZuqMiIjQ+jCFwoUL6zzfSWaVK1dWe1FbtGiR2kTNWDIneFkfTZs21VjOzs5O7b+F27dvq52oK7MSJUooJqsztazzrGT16NEjnZLSiRMnChsbGyGTyYRMJhPW1tbil19+ybZcbn5WdeLj45UmkdPG2tpaxMbGqmyPjY0Vtra2Or1HeHi4mD17tvjtt98UM/5qY29vrzjvtmjRQixYsEAIIcS9e/eyndND32OtXr16Yu7cuUII5Xlwzp07J1xdXXP8ftnJet7MOvdOXFxcnpu8Ui4mJkZs375d/Pnnn5JcH+XyTR8R4GNP4AIFCiApKQm3b99WGuscHx+vtXf1yJEjsXTpUpPci9fG2toap06dUulFf+rUKUUfiIyMDKOuCaAvfWI1dDx7Tu+ZZ77f3aRJE6SkpCA6OlrtPWRTGDhwIDZv3qz1NqA6o0aNgp+fH54+faq4VxweHo45c+ZoXDTLGI4cOaJXuW+++Qa7d+/GmDFjlLbv3bsX//vf/7SW/fnnn7Fw4UIsWbLEaOsgaSKy3GPPKrt77HLBwcGYNGkSrl+/joyMDHh7e2tdD0YuNz+rXFpaGoKCgrBo0SLFAnd2dnbw9/dHQECAxuPN0dER//33n8qw3bt37+q8Lk+zZs00drxUp06dOpg2bRpatGiBo0ePYvny5QA+zqeiblXZzPQ91q5cuYLNmzerbC9WrBieP3+eo/fShYeHB+7evas4b54+fRqlS5dWvJ6QkIASJUoYvV5DTJkyBaNHj4aXl5fS7cd3795h9uzZmDx5cq7Gk28SkazLEssnTZLbv38/GjVqpLH86NGj8fXXX6NMmTLw9vZWOVizTghlLP7+/hg8eDAuXryIL774AjKZDOfOncPq1asxceJEAFCspCk1fWJt06YNxo8frxjPbmtrq/Q9REdHq134yxhCQ0PRp08ftYtnmWr4bkpKCn7//Xf8888/qFatmsq/I02dnvv374/U1FQEBwdj6tSpAD4OT1yxYoU092SzUalSJQQHByMiIgL169cHAJw5cwYnT57Ezz//rDR/TNYOuCdOnMCRI0dw8OBBVK5c2aTHmi4dE3X9+9ra2qJOnTpISkrCP//8gwoVKqBSpUpay+TmZ5Xz8/PD7t27MWvWLMV3c/r0aQQGBuLZs2dYsWKF2nLffPMNRowYgd27dyuOybt37+Lnn39WO/marnMEAZonuFuwYAF69uyJPXv2YNKkSYqL9Y4dOxQDDjTR91jL7Y6YP/30k9K5JutK3gcPHsxR8pYbgoKCMHjwYJXraHJyMoKCgnI9Ecm3w3dzaujQoVizZg2aNm0KFxcXlV8vxh4fntkff/yBJUuWKGbIq1ChAvz9/dGjRw8AH7NQ+cgUqeU0VkPHsxuibNmyaN26NSZPnpztrytjyW50k6YWiHfv3kEIAVtbWzx9+hSPHz/G4cOH4e3tjdatW5siVIPoMj8MoH4ukuyGqJryWNNHly5d0LhxY/j5+eHdu3eoUaMGYmNjIYTA1q1btXbGlOKzOjo6YuvWrUoj1YCPF7xu3bohMTFRbbnExES0adMGFy5cUEzgmJCQgMaNG2PXrl2K2TzlDPk3kJ2UlBSYm5trbS3V91gbO3YsTp8+je3bt6N8+fKIjIzE48eP0adPH/Tp00flR+3nyMzMDI8fP1YZ8fTvv/+ia9euePr0ae4GJNlNoVxmZ2cn/vrrr1yt88OHDyIwMFDvFR9zk6Gxvnr1Sm0np+fPn4vU1FRDw1PL3t5er3vIUmjZsqVYvny5EOLjOiEuLi6iVKlSwtraWixbtkzi6IwrOTlZqQNjbGysmD9/vggNDZUwKs1cXFxEVFSUEEKIP/74Q5QtW1a8fftWLFu2TNSoUUPi6FQ5OzsrrSIud/36dVG0aFGtZTMyMsShQ4fErFmzxOLFi3Va7Tq/yWsdMfMS+QKPZmZmKos9Ojg4CDMzMzFkyJBcj+uzaRFxd3dHWFiY1nlGTMHOzg5Xr15VuS+bF+WnWIGPtzt8fHxyZar87GbEBD7+Mty5c6fa14oWLYqjR4+icuXKWL16NRYvXoxLly5h586dmDx5Mm7cuGHskPX24cMHVKhQAX/99Re8vb1zXL5Vq1bo1KkTBg8ejFevXqFixYqwsLDAs2fPMG/ePPz0008miFp/NjY2uH37Ntzc3NCnTx+4urpi5syZiI+Ph7e3t6IfhjqbNm1Cr1691L42ZswYzJ492+jxTpkyBTdv3sS6desUfWNSU1MxYMAAlCtXTusvfn3m5chKfsnQ1idG12ne1U3xbsixJoRAfHw8ihUrhkePHiEyMhIZGRmoWbMmypUrl+37fuo2bNgAIQT69++PBQsWKM1DYmlpCQ8PD8XtvtyUb/qIGCowMBABAQFYt26dyn0xU2rRogUiIiLQr1+/XKtTX/kpVuDjXBffffcdjh8/jqpVq6o082q6b62PnK4ZklVycjLs7e0BAIcOHUKnTp1gZmaGevXqKU13nxdYWFggNTVV786XkZGRmD9/PoCPfQFcXFyUkq68loi4ubnh9OnTKFy4MEJDQ7F161YAH2dvzu52qZ+fH5ycnFTWPRk5ciS2bt1qkkTk0qVLCA8PR6lSpRRzlVy+fBnv379H8+bNlS7kmfuoBAUFYcqUKahTpw5KlCiR4+93zZo1mD9/Pu7cuQMAKFeuHEaMGIGBAweq7GvIoABDjjUhBMqVK4dr166hXLly2c4D87mR96ny9PREgwYNsl0YL7d8NonIokWLEBMTAxcXF3h4eKh8AZGRkSapt23btpgwYQKuXr2K2rVrq/ROV9dJTCr5KVYA2Lx5M8LCwmBjY4OIiAilE6tMJjNqImLovf6yZctiz5496NixI8LCwjBy5EgAwJMnT4y6OJ+x+Pv747fffsPq1au1rripTn5KugBgxIgR6NmzJ+zs7FC6dGn4+voCAI4dO6aYEVSTrVu3olu3bti3b59idlZ/f3/s2rVL7xFL2XFyclLpt6LL8hYrVqzA+vXr0bt37xzX+euvv2L+/Pnw9/dX6iA7cuRIxMXFYdq0aUr7GzK7qSHHmpmZGcqVK4fnz5+zBUSLzKMP3717p7JacW6fkz6bWzOZh5SqY6oOTPKVJNUx1cgOfeWnWAGgePHiGDZsGMaPH6819rxgx44d6NGjB9LT09G8eXMcOnQIADBjxgwcO3Ys25ktc1vHjh0RHh4OOzs7VK1aVSUp1TYapFq1ahg4cCA6duyIKlWqIDQ0FPXr18fFixfx9ddfG3XtFmO5ePEi4uPj0aJFC9jZ2UEmk+HAgQNwcnJSOyNuZlu3bsWQIUNw6NAhrF27Fnv37sWRI0dQvnz5XIpeN0WKFMG5c+f0GsVWtGhRLF68WGUK+S1btsDf31/tyLWshgwZgilTpph8wcgDBw5g5syZWL58ucoIFvooOTkZY8eOxbZt29QOac7tc/1nk4jQp6dw4cI4f/68yYYHG9ujR4/w8OFDVK9eXZE4nTt3TrFceF5iyGiQ/JZ0ATm77aDO8uXLMXLkSBQrVgxHjhzRuPquMT19+hS3bt2CTCZD+fLlta75AgDjxo2DnZ1djuflAD72+Th37pxKK8Pt27dRt27dbNfkAT7+yo6KijL57ZLMq+9aWlrCxsZG6XV1/VI+N0OHDsWRI0cwZcoU9OnTB0uXLsX9+/excuVKzJw5Ez179szVeD67ROTixYuKRby8vb1zdf6OlJSUPDFEVxf5IVb5iV8+xwkZz7t375CRkaFoCYmLi8OePXtQqVIlnYYb56ekS9NthyVLlmD48OEqtx1GjRql9n127NiBmjVrKiXGplhM8+3bt/D390dISIiiw6m5uTn69OmDxYsXa+wDN3z4cISEhKBatWo5mpcD+Hi7ycLCQmWf0aNH4927d1i6dGm2cWdd4t5Uslt9N7cXxcuLSpcujZCQEPj6+sLBwQGRkZEoW7YsNm7ciC1btuDvv//O1Xg+m0TkyZMn6NatGyIiIuDk5AQhBBITE9G0aVNs3bo1218T+kpPT8f06dOxYsUKPH78GLdv34aXlxd+/fVXeHh45MqID13lp1iBj51RQ0JCUL169RyfWEm7/DbyxRA5ve2g60rZMpnMqCsNy/3444/4559/sGTJEsVtoxMnTmDYsGFo2bKlYvbSrLTFnV2s8sTHzc0N9erVA/BxgruEhAT06dNH6djTdNzlViKiq5kzZ2Lw4MEq86d8Duzs7HDt2jW4u7ujVKlS2LVrF+rWrYvY2FhUrVpV60gxk8j1AcMS6dKli6hdu7bS+Ptr166JOnXqiG7dupms3qCgIOHl5SU2bdokbGxsFGsQ/Pnnn6JevXomq1cf+SlWIfRfR4WyV6RIEcV6TqtWrRLVqlUT6enpYtu2baJixYoSR2dcTk5OatfZuHXrlnB0dMz9gLJRpEgRceTIEZXt//77b7bziOhL27GWX487e3t7pTVhPidVq1ZVrMfVsmVL8fPPPwshhFi4cKEoWbJkrsfz2SQiDg4O4ty5cyrbz549a9KTTZkyZcQ///wjhFBeDOnGjRvCycnJZPXqIz/FSqZlY2Mj7t27J4QQ4rvvvhOBgYFCiI+LrNnY2EgZmtH5+fmpXYn4559/lmRyp+zY2NiondDs6tWrOi9el9uuXr0qLl++rHhkXbRUClkXp/uczJs3TyxcuFAI8TGBtbGxEZaWlsLMzEyxMGFu+myG72ZkZKgdM21hYWHSxdLu37+vtuNaRkaGypApqeWnWMm08ttwY0OtWbMGhw4dUnvbIXOfkKy3Hd6+fYuZM2dqnCQsp1Of66J+/foICAhASEiIoh/Xu3fvEBQUJMlkVOocP34co0aNwvnz5wEA9erVQ3JystJkaGFhYWjRooWUYX625Mcz8PGW3c2bN3HhwgWUKVNGMTdNbvpsEpFmzZph+PDh2LJlC1xdXQF8vPCOHDkSzZs3N1m9lStXxvHjx+Hu7q60ffv27XliobvM8lOsZFqTJ09Gjx49FMeH/AJ36NChT+7fwtWrV1GrVi0AQExMDICPK7UWK1YMV69eVeynbgKwgQMH4ujRo+jdu7dek4TpY8GCBWjbtq1iQjOZTIaoqChYW1sjLCzMJHWmpKRg8eLFOHLkiNqEK+s8TMuWLVOZr+TIkSNwd3eHEAKLFi3C8uXLmYjksrNnz+LFixdK6xSFhIQgICAAb9++RYcOHbB48eJcXw3+s0lElixZgvbt28PDwwNubm6QyWSIj49H1apVsWnTJpPVGxAQgN69e+P+/fvIyMjArl27cOvWLYSEhOCvv/4yWb36yE+xkml17twZDRs2VIx8kWvevLnSooafAkMmHjt48CAOHDiQ7VwjxlS1alXcuXMHmzZtws2bNyGEQLdu3dCzZ0+VoarG0r9/fxw+fBidO3dG3bp1s024zp8/j+HDhyttK1WqlOJHTu/evfH111+bJFbSLDAwEL6+vopE5MqVKxgwYAD69esHb29vzJo1C66urggMDMzdwHL9ZpDEDh06JBYtWiQWLlwoDh8+nCt1hoaGisaNG4uCBQsKGxsb4ePjI8LCwnKl7pzKT7ESSc3Dw0Ntfw1Tef/+vfD09BTXrl3LtTqF+NjH7sSJEzrvb21tLeLi4hTPd+7cKd6+fat4HhcXJywtLY0aY059jn1EihcvLs6fP694PnHiROHj46N4vm3bNlGpUqVcj+uzaBFJS0uDtbU1oqKi0LJlS7Rs2TJX62/dunWeXOpdnfwUK5HUpk6dismTJ2PDhg25soaVoesA6atkyZKKaft1YW9vj9jYWEULSNaF7GJjYyXva9SoUSOTtSDlVS9fvoSLi4vi+dGjR9GmTRvF8y+++AIJCQm5HlfenhfbSAoUKAB3d3dJpij38vJSO4Xuq1ev8sx4ern8FCtRXjB37lyEhYXBxcUFVatWRa1atZQepiBfBygtLc0k76/O3LlzMW7cOJ3XCvryyy8REhKi8fX169fjyy+/NFZ4KmJiYvDLL7+ge/fuePLkCQAgNDQU165dU+zz999/o0SJEiaLIS9ycXFBbGwsAOD9+/eIjIxU6uD8+vVrSRbC+yxaRADgl19+wYQJE7Bp0yYULlw41+qNi4tTmwClpqbi/v37uRaHLvJTrER5Qfv27XO9deLs2bMIDw/HoUOHcrwOkL7q1KmDlJQUeHl5wdbWVuVilXXa9FGjRqFFixYoUqQIxowZA2dnZwAfR1399ttv2LRpk2Lqf2M7evQo2rZtCx8fHxw7dgzBwcFwdnZGdHQ0Vq9ejR07dpik3vygTZs2GD9+PH777Tfs2bMHtra2aNSokeL16OhoSZbM+GwSkUWLFuHu3btwdXWFu7u7ysFr7NV39+3bp/j/sLAwpaWt09PTER4eDg8PD6PWqa/8FCtRXpLrnfqgfvVdU+vevTvu37+P6dOnw8XFJdvkq2nTpli8eDFGjhyJefPmwcHBATKZDImJiShQoAAWLFiAZs2amSTW8ePHY9q0aRg1apTS7aSmTZti4cKFJqkzv5g2bRo6deqEJk2awM7ODhs2bIClpaXi9bVr16JVq1a5HtdnM8V7UFAQZDIZNH1cY6++K19fQ12dFhYW8PDwwNy5c9GuXTuj1quP/BQrUV5gZmam9mLs4OCAChUqYOzYsSr9IvIzW1tbnD59OsdzTCQkJGDHjh1Kiwl27twZbm5upggTwMfpy69cuQJPT0+laeXj4uJQsWJFpKSkmKzu/CIxMRF2dnYwNzdX2v7ixQvY2dkpJSe54ZNvEUlOTsaYMWOwZ88efPjwAc2bN8fixYtNvhS1fJy9p6cnzp8/b/L6DJGfYiXKC3bv3q12+6tXr3Du3Dn06tULGzZswHfffWf0ups1a4Zdu3aprJGSlJSEDh06mGR9m4oVK+Ldu3c5Lufm5qY0eVZucHJywsOHD+Hp6am0/dKlSyhZsmSuxpJXZW71ziw3uy1k9sm3iIwZMwbLli1TjLHfvHkzfH19sX37dqlDI6JP1NKlSxESEoKzZ88a/b3NzMzw6NEjRb8LuSdPnqBkyZImmQX50KFDCAoKQnBwMKpWrarSR0TbCJhbt25h8eLFilXPK1asCD8/P5Otwjx27FicPn0a27dvR/ny5REZGYnHjx+jT58+6NOnj9Fbv8lwn3wiUqZMGQQHB6Nbt24APi5F7uPjg5SUFJVmKVMJDw/XOAX02rVrcyUGXeWnWInyqjt37qBu3bp4+fKl0d4zOjoaAFCjRg38+++/Sr9e09PTERoaipUrVyIuLs5odcplvn2bmRACMplM44jEHTt2oHv37qhTp45idMaZM2dw/vx5bN682SQtRh8+fEC/fv2wdetWCCFQoEABpKeno0ePHli/fn2unfdJd598ImJpaYnY2FilJjkbGxvcvn3bpPcp5YKCgjBlyhTUqVNH7RTQmpp4pZCfYiXKy6Kjo9G6dWs8fPjQaO+ZuV+KutO2jY0NFi9ejP79+xutTrmjR49qfb1JkyZqt3t5eaFXr16YMmWK0vaAgABs3LjRJGvxyMXExODSpUvIyMhAzZo1Ua5cOZPVRYb55BMRc3NzPHr0CMWKFVNss7e3R3R0tMo9RFMoUaIEZs2apbLuQl6Un2Ilysv8/f0RExODv//+22jvee/ePQgh4OXlhXPnzimd0ywtLeHs7Jznfu3b2toiOjpaZTHNO3fuoHr16khOTpYoMspLPvnOqkII9OvXT2kRn5SUFAwePFhpCK8pxt4DHyeNadCggUne29jyU6xEUsq8Im9miYmJuHDhAmJiYnD8+HGj1imfpdSUq4Vr8+rVK6xZs0bR18Pb2xv9+/fX2PERAHx9fXH8+HGVROTEiRNK81cYStP3oU7WFZRJep98i8j333+v037r1q0zSf3jxo2DnZ0dfv31V5O8vzHlp1iJpNS0aVO12x0cHFCxYkUMGTJEZRVrY9q4cSNWrFiB2NhYnD59Gu7u7pg/fz68vLzQvn17o9d34cIFtG7dGjY2Nqhbty6EELhw4QLevXuHQ4cOaZxFdsWKFZg8eTK6dOmCevXqAfjYR2T79u0ICgpSrIQOAN98843e8Wn6PrKSyWQmGVVEhvnkExGpDR8+HCEhIahWrRqqVaum0ts8L2Xn+SlWos/V8uXLMXnyZIwYMQLBwcG4evUqvLy8sH79emzYsMGg1YQ1adSoEcqWLYtVq1ahQIGPDelpaWkYOHAg/vvvPxw7dkxtOXkn1+xo6/BKnz4mIiaWXaZuipOGvvJTrESfK29vb0yfPh0dOnRQmrDr6tWr8PX1xbNnz4xep42NDS5duqQy5Pb69euoU6dOnu3rkZCQAJlMhlKlSkkdCmnxyfcRkVp+unjnp1iJPlexsbGoWbOmynYrKyu8ffvWJHU6ODggPj5eJRFJSEhQuyrvv//+Cz8/P5w5c0ZljpHExEQ0aNAAK1asMGo/Ebm0tDQEBQVh0aJFePPmDYCPs636+/sjICBAkkXdSDsmIiaiy/TOMpkMO3fuzIVotMtPsRJ97jw9PREVFaXSB+XgwYOoVKmSSers2rUrBgwYgDlz5qBBgwaQyWQ4ceIExowZg+7du6vsv2DBAgwaNEjtRGeOjo748ccfMW/ePJMkIn5+fti9ezdmzZqlmLvk9OnTCAwMxLNnz7BixQqj10mGYSJiItp6kuc1+SlWos/dmDFjMHToUKSkpEAIgXPnzmHLli2YPn061qxZY5I658yZA5lMhj59+iAtLQ3Ax3WofvrpJ8ycOVNl/8uXL+O3337T+H6tWrXCnDlzTBLrli1bsHXrVrRt21axrVq1aihdujS6devGRCQPYh8RIqJ8ZtWqVZg2bRoSEhIAACVLlkRQUBBat25t0vVUkpOTERMTAyEEypYtC1tbW7X7WVtb4+rVqyrDduXu3r2LqlWr6rV+TXZcXFwQERGh0jp048YNNG7cGE+fPjV6nWQY3bo0ExFRnjFo0CDcu3cPT548waNHj3Du3DlcunRJ44XfUImJiXjx4gVsbW1RtWpVVKtWDba2tnjx4gWSkpJU9i9ZsiSuXLmi8f2io6NRokQJk8Q6dOhQTJ06FampqYptqampCA4Ohp+fn0nqJMMwESEiygdevXqFnj17olixYnB1dcWiRYtQuHBhLF26FGXLlsWZM2dMth5Ut27dsHXrVpXt27ZtU6zjldlXX32FyZMnIyUlReW1d+/eISAgAO3atTNJrJcuXcJff/2FUqVKoUWLFmjRogVKlSqF/fv34/Lly+jUqZPiQXkDb80QEeUDQ4YMwf79+9G1a1eEhobixo0baN26NVJSUhAQEKBxvRdjKFy4ME6ePKlyu+PmzZvw8fHB8+fPlbY/fvwYtWrVgrm5Ofz8/FChQgXIZDLcuHEDS5cuRXp6OiIjI+Hi4mL0WHWdxBIw3USWlDNMRIiI8gF3d3esWbMGLVq0wH///YeyZcti2LBhWLBggcnrLliwIM6cOYOqVasqbb9y5Qq+/PJLtfOI3Lt3Dz/99BPCwsIUi/TJZDK0bt0ay5Ytg4eHh8njpvyBiQgRUT5gYWGBe/fuKaZFt7W1xblz51ClShWT1+3r64uqVati8eLFStuHDh2K6OhorevqvHz5Enfv3oUQAuXKlUOhQoVMHS7lMxy+S0SUD2RkZChNxmVubq60cKcpBQcHo0WLFrh8+TKaN28OAAgPD8f58+dx6NAhrWULFSqEL774IjfCBAA8f/4ckydPxpEjR/DkyROVRQJfvHiRa7GQbpiIEBHlA1lXEle3ijhgmpXEfXx8cPr0acyePRvbtm2DjY0NqlWrhjVr1qBcuXJGr88QvXr1QkxMDAYMGAAXFxfIZDKpQ6Js8NYMEVE+IPVK4rqYOXMmBg8eDCcnJ8lisLe3x4kTJ1C9enXJYqCcYSJCRERG4eDggKioKHh5eUkWwxdffIHFixejXr16ksVAOcN5RIiIyCjywu/aZcuWYdKkSTh69CieP3+OpKQkpQflPewjQkREnwwnJyckJiaiWbNmStuFEJDJZEhPT5coMtKEiQgREX0yevbsCUtLS2zevJmdVfMJJiJERPTJuHr1Ki5duoQKFSpIHQrpiH1EiIjok1GnTh3FqsSUP7BFhIiIjKJRo0awsbGRNAZ/f38MHz4cY8aMQdWqVZUmgQOAatWqSRQZacLhu0REpFXPnj3RpEkT+Pr6onz58lKHo5WZmWpDv0wmY2fVPIwtIkREpJWdnR3mzZuHwYMHo3jx4mjSpIkiMalYsaLU4SmJjY2VOgTKIbaIEBGRTh49eoSIiAhERETg6NGjuH37NpydnfHw4UOpQ6N8jC0iRESkE3t7exQqVAiFChWCk5MTChQogOLFi0sdFvbt24e2bdvCwsIC+/bt07rvN998k0tRka7YIkJERFqNGzcOR48exeXLl1GlShU0btwYTZo0QePGjSVdV0bOzMwMjx49grOzs9o+InLsI5I3MREhIiKtzMzMUKxYMYwcORLt27dHpUqVpA6JPiGcR4SIiLS6dOkSJk2ahHPnzqFx48YoXrw4unbtiuXLl+PGjRtShwcAOHv2LA4ePKi0LSQkBJ6ennB2dsYPP/yA1NRUiaIjbdgiQkREOXL58mUsWLAAmzZtQkZGRp643dG2bVv4+vpi3LhxAIArV66gVq1a6NevHypVqoTZs2fjxx9/RGBgoLSBkgp2ViUiomxdunRJMWLm+PHjSEpKQo0aNdC0aVOpQwMAREVFYerUqYrnW7duxZdffolVq1YBANzc3BAQEMBEJA9iIkJERFoVKlQIb968QfXq1eHr64tBgwahcePGcHBwkDo0hZcvX8LFxUXx/OjRo2jTpo3i+RdffMGp3/MoJiJERKTVxo0b81zikZWLiwtiY2Ph5uaG9+/fIzIyEkFBQYrXX79+rTLdO+UN7KxKRERatWvXTpGE/N///R/u378vcUSq2rRpg/Hjx+P48eOYMGECbG1t0ahRI8Xr0dHRKFOmjIQRkiZMRIiISKuMjAxMmTIFjo6OcHd3R+nSpeHk5ISpU6ciIyND6vAAANOmTYO5uTmaNGmCVatWYdWqVbC0tFS8vnbtWrRq1UrCCEkTjpohIiKtJkyYgDVr1iAoKAg+Pj4QQuDkyZMIDAzEoEGDEBwcLHWIComJibCzs4O5ubnS9hcvXsDOzk4pOaG8gYkIERFp5erqihUrVqhMj753714MGTIkT96qofyDt2aIiEirFy9eqF1lt2LFinjx4oUEEdGnhIkIERFpVb16dSxZskRl+5IlS1CtWjUJIqJPCW/NEBGRVkePHsXXX3+N0qVLo379+pDJZDh16hQSEhLw999/K41OIcoptogQEZFWTZo0we3bt9GxY0e8evUKL168QKdOnXDt2jWsW7dO6vAon2OLCBER6eXy5cuoVatWnlhrhvIvtogQERGRZJiIEBERkWSYiBAREZFkuOgdERGp1alTJ62vv3r1KncCoU8aExEiIlLL0dEx29f79OmTS9HQp4qjZoiIiEgy7CNCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkT5WmBgIGrUqCF1GESkJyYiRJSr4uLiIJPJEBUVleOyMpkMe/bsUdo2evRohIeHGyc4Isp1nEeEiPI1Ozs72NnZSR0GEemJLSJEn5HQ0FA0bNgQTk5OKFKkCNq1a4eYmBgA/7+lYteuXWjatClsbW1RvXp1nD59WlF+/fr1cHJyQlhYGCpVqgQ7Ozu0adMGDx8+VOyTkZGBKVOmoFSpUrCyskKNGjUQGhqqeN3T0xMAULNmTchkMvj6+gIAzp8/j5YtW6Jo0aJwdHREkyZNEBkZqSjn4eEBAOjYsSNkMpniedZbM9nVr8vnJKLcw0SE6DPy9u1bjBo1CufPn0d4eDjMzMzQsWNHZGRkKPaZNGkSRo8ejaioKJQvXx7du3dHWlqa4vXk5GTMmTMHGzduxLFjxxAfH4/Ro0crXl+4cCHmzp2LOXPmIDo6Gq1bt8Y333yDO3fuAADOnTsHAPjnn3/w8OFD7Nq1CwDw+vVr9O3bF8ePH8eZM2dQrlw5fPXVV3j9+jWAj4kKAKxbtw4PHz5UPM8qu/p1/ZxElEsEEX22njx5IgCIK1euiNjYWAFArF69WvH6tWvXBABx48YNIYQQ69atEwDE3bt3FfssXbpUuLi4KJ67urqK4OBgpXq++OILMWTIECGEUNRz6dIlrbGlpaUJe3t7sX//fsU2AGL37t1K+wUEBIjq1avnuH5tn5OIcg9bRIg+IzExMejRowe8vLzg4OCguE0SHx+v2KdatWqK/y9RogQA4MmTJ4pttra2KFOmjNI+8teTkpLw4MED+Pj4KNXr4+ODGzduaI3tyZMnGDx4MMqXLw9HR0c4OjrizZs3SrFlJyf1Z/c5iSh3sLMq0Wfkf//7H9zc3LBq1Sq4uroiIyMDVapUwfv37xX7WFhYKP5fJpMBgNKtm8yvy/cRWZaskpeTE0KobMuqX79+ePr0KRYsWAB3d3dYWVmhfv36SrHpSpf6s/ucRJQ72CJC9Jl4/vw5bty4gV9++QXNmzdHpUqV8PLlS6PW4eDgAFdXV5w4cUJp+6lTp1CpUiUAgKWlJQAgPT1daZ/jx49j2LBh+Oqrr1C5cmVYWVnh2bNnSvtYWFiolMtp/USUt7BFhOgzUahQIRQpUgS///47SpQogfj4eIwfP97o9YwZMwYBAQEoU6YMatSogXXr1iEqKgp//PEHAMDZ2Rk2NjYIDQ1FqVKlYG1tDUdHR5QtWxYbN25EnTp1kJSUhDFjxsDGxkbpvT08PBAeHg4fHx9YWVmhUKFCOa6fiPIWtogQfSbMzMywdetWXLx4EVWqVMHIkSMxe/Zso9czbNgw/Pzzz/j5559RtWpVhIaGYt++fShXrhwAoECBAli0aBFWrlwJV1dXtG/fHgCwdu1avHz5EjVr1kTv3r0xbNgwODs7K7333LlzcfjwYbi5uaFmzZp61U9EeYtMZL25S0RERJRL2CJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESSYSJCREREkmEiQkRERJJhIkJERESS+X+YJkee7VJu+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annstats_sorted.plot.bar(x=\"annotation\", y=\"foldenrichment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd988cb-5409-4734-bcf7-5a740a9e9a96",
   "metadata": {},
   "source": [
    "Homer also outputs the nearest annotation for each peak. Let's look at the first few lines of our annotation file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bff56b0d-72a8-4532-b1f7-fa4f064f2c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeakID (cmd=annotatePeaks.pl Tutorial3/GenomeAnnotation/CTL_specific_peaks.bed hg38 -go Tutorial3/GenomeAnnotation/CTL_GO -annStats Tutorial3/GenomeAnnotation/CTL_annStats.txt)\tChr\tStart\tEnd\tStrand\tPeak Score\tFocus Ratio/Region Size\tAnnotation\tDetailed Annotation\tDistance to TSS\tNearest PromoterID\tEntrez ID\tNearest Unigene\tNearest Refseq\tNearest Ensembl\tGene Name\tGene Alias\tGene Description\tGene Type\n",
      "CTL_unique_56\tchr4\t64144792\t64145494\t+\t3.01611\tNA\tIntergenic\tHERVK11-int|LTR|ERVK\t264307\tNM_001010874\t253017\tHs.227752\tNM_001010874\tENSG00000205678\tTECRL\tCPVT3|GPSN2L|SRD5A2L2|TERL\ttrans-2,3-enoyl-CoA reductase like\tprotein-coding\n",
      "merged_common_90\tchr4\t54545647\t54546360\t+\t2.38644\tNA\tIntergenic\tIntergenic\t61128\tNR_134657\t339978\t\tNR_134657\tENSG00000250456\tLINC02260\t-\tlong intergenic non-protein coding RNA 2260\tncRNA\n",
      "CTL_unique_51\tchr4\t62835674\t62836120\t+\t2.34453\tNA\tIntergenic\tIntergenic\t-674132\tNR_110595\t101927186\tHs.723269\tNR_110595\t\tADGRL3-AS1\tLPHN3-AS1\tadhesion G protein-coupled receptor L3 antisense RNA 1\tncRNA\n"
     ]
    }
   ],
   "source": [
    "!head -4 Tutorial3/GenomeAnnotation/CTL_specific_Annotated.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7193cee9-f1cc-40b5-84be-a1aae44bd217",
   "metadata": {},
   "source": [
    "Lastly, let's take a look at the gene ontology results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "59b16ae4-3d55-4363-8d27-4d812a98b115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "biocyc.txt\t\tinteractions.txt\t  prints.txt\n",
      "biological_process.txt\tinterpro.txt\t\t  prosite.txt\n",
      "cellular_component.txt\tkegg.txt\t\t  reactome.txt\n",
      "chromosome.txt\t\tlipidmaps.txt\t\t  smart.txt\n",
      "cosmic.txt\t\tmolecular_function.txt\t  smpdb.txt\n",
      "gene3d.txt\t\tmsigdb.txt\t\t  wikipathways.txt\n",
      "geneOntology.html\tpathwayInteractionDB.txt\n",
      "gwas.txt\t\tpfam.txt\n"
     ]
    }
   ],
   "source": [
    "#list the files in our GO directory\n",
    "!ls Tutorial3/GenomeAnnotation/CTL_GO/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ea0509-a6a4-4df0-8d69-dec674e7e3f1",
   "metadata": {},
   "source": [
    "Let's view the top terms in the biological_process category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "98cdaf7b-a83b-40de-bff4-9587642d60fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TermID</th>\n",
       "      <th>Term</th>\n",
       "      <th>Enrichment</th>\n",
       "      <th>logP</th>\n",
       "      <th>Genes in Term</th>\n",
       "      <th>Target Genes in Term</th>\n",
       "      <th>Fraction of Targets in Term</th>\n",
       "      <th>Total Target Genes</th>\n",
       "      <th>Total Genes</th>\n",
       "      <th>Entrez Gene IDs</th>\n",
       "      <th>Gene Symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0052695</td>\n",
       "      <td>cellular glucuronidation</td>\n",
       "      <td>8.476896e-07</td>\n",
       "      <td>-13.980751</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>10941,7364,79799</td>\n",
       "      <td>UGT2A1,UGT2B7,UGT2A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0006063</td>\n",
       "      <td>uronic acid metabolic process</td>\n",
       "      <td>1.833506e-06</td>\n",
       "      <td>-13.209280</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>10941,7364,79799</td>\n",
       "      <td>UGT2A1,UGT2B7,UGT2A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GO:0019585</td>\n",
       "      <td>glucuronate metabolic process</td>\n",
       "      <td>1.833506e-06</td>\n",
       "      <td>-13.209280</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>79799,7364,10941</td>\n",
       "      <td>UGT2A3,UGT2B7,UGT2A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO:0010817</td>\n",
       "      <td>regulation of hormone levels</td>\n",
       "      <td>1.370570e-05</td>\n",
       "      <td>-11.197699</td>\n",
       "      <td>528</td>\n",
       "      <td>6</td>\n",
       "      <td>0.30</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>7364,2044,6783,27284,9575,5978</td>\n",
       "      <td>UGT2B7,EPHA5,SULT1E1,SULT1B1,CLOCK,REST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO:0006068</td>\n",
       "      <td>ethanol catabolic process</td>\n",
       "      <td>7.141780e-05</td>\n",
       "      <td>-9.546963</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>6783,27284</td>\n",
       "      <td>SULT1E1,SULT1B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GO:0034310</td>\n",
       "      <td>primary alcohol catabolic process</td>\n",
       "      <td>1.134005e-04</td>\n",
       "      <td>-9.084585</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>27284,6783</td>\n",
       "      <td>SULT1B1,SULT1E1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GO:0051923</td>\n",
       "      <td>sulfation</td>\n",
       "      <td>1.295173e-04</td>\n",
       "      <td>-8.951696</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>27284,6783</td>\n",
       "      <td>SULT1B1,SULT1E1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GO:0006067</td>\n",
       "      <td>ethanol metabolic process</td>\n",
       "      <td>2.259286e-04</td>\n",
       "      <td>-8.395291</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>27284,6783</td>\n",
       "      <td>SULT1B1,SULT1E1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GO:0050427</td>\n",
       "      <td>3'-phosphoadenosine 5'-phosphosulfate metaboli...</td>\n",
       "      <td>2.963633e-04</td>\n",
       "      <td>-8.123925</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>27284,6783</td>\n",
       "      <td>SULT1B1,SULT1E1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GO:0034035</td>\n",
       "      <td>purine ribonucleoside bisphosphate metabolic p...</td>\n",
       "      <td>3.219272e-04</td>\n",
       "      <td>-8.041185</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>20</td>\n",
       "      <td>18680</td>\n",
       "      <td>27284,6783</td>\n",
       "      <td>SULT1B1,SULT1E1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TermID                                               Term  \\\n",
       "0  GO:0052695                           cellular glucuronidation   \n",
       "1  GO:0006063                      uronic acid metabolic process   \n",
       "2  GO:0019585                      glucuronate metabolic process   \n",
       "3  GO:0010817                       regulation of hormone levels   \n",
       "4  GO:0006068                          ethanol catabolic process   \n",
       "5  GO:0034310                  primary alcohol catabolic process   \n",
       "6  GO:0051923                                          sulfation   \n",
       "7  GO:0006067                          ethanol metabolic process   \n",
       "8  GO:0050427  3'-phosphoadenosine 5'-phosphosulfate metaboli...   \n",
       "9  GO:0034035  purine ribonucleoside bisphosphate metabolic p...   \n",
       "\n",
       "     Enrichment       logP  Genes in Term  Target Genes in Term  \\\n",
       "0  8.476896e-07 -13.980751             18                     3   \n",
       "1  1.833506e-06 -13.209280             23                     3   \n",
       "2  1.833506e-06 -13.209280             23                     3   \n",
       "3  1.370570e-05 -11.197699            528                     6   \n",
       "4  7.141780e-05  -9.546963             12                     2   \n",
       "5  1.134005e-04  -9.084585             15                     2   \n",
       "6  1.295173e-04  -8.951696             16                     2   \n",
       "7  2.259286e-04  -8.395291             21                     2   \n",
       "8  2.963633e-04  -8.123925             24                     2   \n",
       "9  3.219272e-04  -8.041185             25                     2   \n",
       "\n",
       "   Fraction of Targets in Term  Total Target Genes  Total Genes  \\\n",
       "0                         0.15                  20        18680   \n",
       "1                         0.15                  20        18680   \n",
       "2                         0.15                  20        18680   \n",
       "3                         0.30                  20        18680   \n",
       "4                         0.10                  20        18680   \n",
       "5                         0.10                  20        18680   \n",
       "6                         0.10                  20        18680   \n",
       "7                         0.10                  20        18680   \n",
       "8                         0.10                  20        18680   \n",
       "9                         0.10                  20        18680   \n",
       "\n",
       "                  Entrez Gene IDs                             Gene Symbols  \n",
       "0                10941,7364,79799                     UGT2A1,UGT2B7,UGT2A3  \n",
       "1                10941,7364,79799                     UGT2A1,UGT2B7,UGT2A3  \n",
       "2                79799,7364,10941                     UGT2A3,UGT2B7,UGT2A1  \n",
       "3  7364,2044,6783,27284,9575,5978  UGT2B7,EPHA5,SULT1E1,SULT1B1,CLOCK,REST  \n",
       "4                      6783,27284                          SULT1E1,SULT1B1  \n",
       "5                      27284,6783                          SULT1B1,SULT1E1  \n",
       "6                      27284,6783                          SULT1B1,SULT1E1  \n",
       "7                      27284,6783                          SULT1B1,SULT1E1  \n",
       "8                      27284,6783                          SULT1B1,SULT1E1  \n",
       "9                      27284,6783                          SULT1B1,SULT1E1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bp_GO = pd.read_csv(\"Tutorial3/GenomeAnnotation/CTL_GO/biological_process.txt\", sep='\\t')\n",
    "\n",
    "#keep most significant\n",
    "bp_GO_top10 = bp_GO.nsmallest(10, \"logP\")\n",
    "display(bp_GO_top10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86df868-6af0-4bf9-880f-62142230bdc4",
   "metadata": {},
   "source": [
    "We can also plot the enrichment scores\n",
    "\n",
    "Note that our results may look a little odd because we have severely downsampled the data to run quickly and focus on a single region of chr4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0aec57a7-412d-41fd-8aea-d8b164faa49b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Term'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp_GO_top10.plot.bar(x=\"Term\", y=\"Enrichment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dcf583-f555-48b6-b330-b9bdfdd295da",
   "metadata": {},
   "source": [
    "Homer also saves an html file where you can navigate through the various categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "deac4a6f-882c-42eb-acb5-f74712bbec2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"600\"\n",
       "            src=\"Tutorial3/GenomeAnnotation/CTL_GO/geneOntology.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f929c9ed9d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the html results\n",
    "IFrame(src='Tutorial3/GenomeAnnotation/CTL_GO/geneOntology.html', width=900, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e519a4-f4b8-4641-b03d-2e8d51ad9e6f",
   "metadata": {},
   "source": [
    "In the above html you can click throught the different ontology categories to view enriched terms and scores for genes near our differential peaks. Note that there are links to motifs, but these lead to \"pages not found\" because we have yet to do this analysis. We will run motif analysis in the next section using TOBIAS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff448764-630e-4898-8321-19bb6f6d357e",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\" style=\"font-size:200%\">\n",
    "Motif Footprinting\n",
    "</div>\n",
    "\n",
    "### ATAC-seq can be used to identify accessibility at transcription factor (TF) binding sites. We'll use [tobias](https://anaconda.org/bioconda/tobias).\n",
    "\n",
    "<img src=\"Tutorial3/LessonImages/TobiasFigure.jpg\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
    "\n",
    "From: [Bentsen et al., Nat. Comm. 2020](https://www.nature.com/articles/s41467-020-18035-1)\n",
    "\n",
    "Tn5 insertion during ATAC-seq has a sequence bias. In our first step, let's correct for that bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bd4b3b34-2e1a-40f3-9f41-ad0bd6ae20f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# TOBIAS 0.13.3 ATACorrect (run started 2022-10-31 18:14:08.348624)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS ATACorrect --bam Tutorial3/InputFiles/CTL_dedup.bam --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting --prefix CTL --cores 3 --verbosity 1\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# bam:\tTutorial3/InputFiles/CTL_dedup.bam\n",
      "# genome:\tTutorial3/InputFiles/chr4.fa\n",
      "# peaks:\tTutorial3/MotifFootprinting/MasterPeakList.bed\n",
      "# regions_in:\tNone\n",
      "# regions_out:\tNone\n",
      "# blacklist:\tNone\n",
      "# extend:\t100\n",
      "# split_strands:\tFalse\n",
      "# norm_off:\tFalse\n",
      "# track_off:\t[]\n",
      "# k_flank:\t12\n",
      "# read_shift:\t[4, -5]\n",
      "# bg_shift:\t100\n",
      "# window:\t100\n",
      "# score_mat:\tDWM\n",
      "# prefix:\tCTL\n",
      "# outdir:\t/home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting\n",
      "# cores:\t3\n",
      "# split:\t100\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/CTL_uncorrected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/CTL_bias.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/CTL_expected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/CTL_corrected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/CTL_atacorrect.pdf\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# TOBIAS 0.13.3 ATACorrect (run started 2022-10-31 18:16:15.297465)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS ATACorrect --bam Tutorial3/InputFiles/Mutant_dedup.bam --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting --prefix Mutant --cores 3 --verbosity 1\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# bam:\tTutorial3/InputFiles/Mutant_dedup.bam\n",
      "# genome:\tTutorial3/InputFiles/chr4.fa\n",
      "# peaks:\tTutorial3/MotifFootprinting/MasterPeakList.bed\n",
      "# regions_in:\tNone\n",
      "# regions_out:\tNone\n",
      "# blacklist:\tNone\n",
      "# extend:\t100\n",
      "# split_strands:\tFalse\n",
      "# norm_off:\tFalse\n",
      "# track_off:\t[]\n",
      "# k_flank:\t12\n",
      "# read_shift:\t[4, -5]\n",
      "# bg_shift:\t100\n",
      "# window:\t100\n",
      "# score_mat:\tDWM\n",
      "# prefix:\tMutant\n",
      "# outdir:\t/home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting\n",
      "# cores:\t3\n",
      "# split:\t100\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/Mutant_uncorrected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/Mutant_bias.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/Mutant_expected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/Mutant_corrected.bw\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/Mutant_atacorrect.pdf\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#Index the bam  \n",
    "!samtools index Tutorial3/InputFiles/CTL_dedup.bam\n",
    "!samtools index Tutorial3/InputFiles/Mutant_dedup.bam\n",
    "#Tn5 has an insertion sequence bias, which Tobias can correct for. Let's use the master list of peaks provided by manorm, but we need to first remove the header and extra columns.\n",
    "!cat Tutorial3/DiffPeaks/CTL_vs_Mutant_all_MAvalues.xls | cut -f 1-3 | grep -v start > Tutorial3/MotifFootprinting/MasterPeakList.bed\n",
    "\n",
    "#Now let's do the signal correction\n",
    "!TOBIAS ATACorrect --bam Tutorial3/InputFiles/CTL_dedup.bam --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting --prefix CTL --cores $numthreadsint --verbosity 1\n",
    "#Let's also do this for the mutant\n",
    "!TOBIAS ATACorrect --bam Tutorial3/InputFiles/Mutant_dedup.bam --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting --prefix Mutant --cores $numthreadsint --verbosity 1\n",
    "\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21da1001-529f-4e62-8e44-2f475bfd35d4",
   "metadata": {},
   "source": [
    "Now let's use the bias-corrected bigwig files to calculate footprint scores around peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6ca0ae04-b409-4c5d-b318-0887edc0f3c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# TOBIAS 0.13.3 ScoreBigwig (run started 2022-10-31 18:17:36.595658)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS ScoreBigwig -s Tutorial3/MotifFootprinting/CTL_corrected.bw -r Tutorial3/MotifFootprinting/MasterPeakList.bed -o Tutorial3/MotifFootprinting/CTL_footprintscores.bw --cores 3 --verbosity 1\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# signal:\tTutorial3/MotifFootprinting/CTL_corrected.bw\n",
      "# output:\tTutorial3/MotifFootprinting/CTL_footprintscores.bw\n",
      "# regions:\tTutorial3/MotifFootprinting/MasterPeakList.bed\n",
      "# score:\tfootprint\n",
      "# absolute:\tFalse\n",
      "# extend:\t100\n",
      "# smooth:\t1\n",
      "# min_limit:\tNone\n",
      "# max_limit:\tNone\n",
      "# fp_min:\t20\n",
      "# fp_max:\t50\n",
      "# flank_min:\t10\n",
      "# flank_max:\t30\n",
      "# window:\t100\n",
      "# cores:\t3\n",
      "# split:\t100\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# Tutorial3/MotifFootprinting/CTL_footprintscores.bw\n",
      "\n",
      "\n",
      "\n",
      "# TOBIAS 0.13.3 ScoreBigwig (run started 2022-10-31 18:18:14.533729)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS ScoreBigwig -s Tutorial3/MotifFootprinting/Mutant_corrected.bw -r Tutorial3/MotifFootprinting/MasterPeakList.bed -o Tutorial3/MotifFootprinting/Mutant_footprintscores.bw --cores 3 --verbosity 1\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# signal:\tTutorial3/MotifFootprinting/Mutant_corrected.bw\n",
      "# output:\tTutorial3/MotifFootprinting/Mutant_footprintscores.bw\n",
      "# regions:\tTutorial3/MotifFootprinting/MasterPeakList.bed\n",
      "# score:\tfootprint\n",
      "# absolute:\tFalse\n",
      "# extend:\t100\n",
      "# smooth:\t1\n",
      "# min_limit:\tNone\n",
      "# max_limit:\tNone\n",
      "# fp_min:\t20\n",
      "# fp_max:\t50\n",
      "# flank_min:\t10\n",
      "# flank_max:\t30\n",
      "# window:\t100\n",
      "# cores:\t3\n",
      "# split:\t100\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# Tutorial3/MotifFootprinting/Mutant_footprintscores.bw\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!TOBIAS ScoreBigwig -s Tutorial3/MotifFootprinting/CTL_corrected.bw -r Tutorial3/MotifFootprinting/MasterPeakList.bed -o Tutorial3/MotifFootprinting/CTL_footprintscores.bw --cores $numthreadsint --verbosity 1\n",
    "\n",
    "#Let's do the same for our mutant sample\n",
    "!TOBIAS ScoreBigwig -s Tutorial3/MotifFootprinting/Mutant_corrected.bw -r Tutorial3/MotifFootprinting/MasterPeakList.bed -o Tutorial3/MotifFootprinting/Mutant_footprintscores.bw --cores $numthreadsint --verbosity 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affb7253-ed1b-4b21-8377-47f20f868eab",
   "metadata": {},
   "source": [
    "Now that we have our corrected signal and footprint scores, let's do TF binding site prediciton as well as differential footprinting.\n",
    "\n",
    "Caution: this step searches throug the signal at every signal location corresponding to motifs in your jaspar file. Here we use all the motifs in the jaspar database. This can take several minutes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "688f95c8-2649-4f7b-aa91-43ac8f4cd163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-10-31 18:18:50--  https://jaspar.genereg.net/download/data/2022/CORE/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt\n",
      "Resolving jaspar.genereg.net (jaspar.genereg.net)... 193.60.222.202\n",
      "Connecting to jaspar.genereg.net (jaspar.genereg.net)|193.60.222.202|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 327864 (320K) [text/plain]\n",
      "Saving to: ‘Tutorial3/MotifFootprinting/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt.1’\n",
      "\n",
      "JASPAR2022_CORE_ver 100%[===================>] 320.18K   823KB/s    in 0.4s    \n",
      "\n",
      "2022-10-31 18:18:51 (823 KB/s) - ‘Tutorial3/MotifFootprinting/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt.1’ saved [327864/327864]\n",
      "\n",
      "# TOBIAS 0.13.3 BINDetect (run started 2022-10-31 18:18:58.427557)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS BINDetect --motifs Tutorial3/MotifFootprinting/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt --signals Tutorial3/MotifFootprinting/CTL_footprintscores.bw Tutorial3/MotifFootprinting/Mutant_footprintscores.bw --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting/DiffMotifs --cond_names CTL Mutant --cores 3 --verbosity 1\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# signals:\t['Tutorial3/MotifFootprinting/CTL_footprintscores.bw', 'Tutorial3/MotifFootprinting/Mutant_footprintscores.bw']\n",
      "# peaks:\tTutorial3/MotifFootprinting/MasterPeakList.bed\n",
      "# motifs:\t['Tutorial3/MotifFootprinting/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt']\n",
      "# genome:\tTutorial3/InputFiles/chr4.fa\n",
      "# cond_names:\t['CTL', 'Mutant']\n",
      "# peak_header:\tNone\n",
      "# naming:\tname_id\n",
      "# motif_pvalue:\t0.0001\n",
      "# bound_pvalue:\t0.001\n",
      "# pseudo:\tNone\n",
      "# time_series:\tFalse\n",
      "# skip_excel:\tFalse\n",
      "# output_peaks:\tNone\n",
      "# norm_off:\tFalse\n",
      "# prefix:\tbindetect\n",
      "# outdir:\t/home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs\n",
      "# cores:\t3\n",
      "# split:\t100\n",
      "# debug:\tFalse\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/beds/*_CTL_bound.bed\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/beds/*_CTL_unbound.bed\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/beds/*_Mutant_bound.bed\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/beds/*_Mutant_unbound.bed\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/beds/*_all.bed\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/plots/*_log2fcs.pdf\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/*_overview.txt\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/*/*_overview.xlsx\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/bindetect_distances.txt\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/bindetect_results.txt\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/bindetect_results.xlsx\n",
      "# /home/jupyter/nosi-unmc-seq/Tutorial3/MotifFootprinting/DiffMotifs/bindetect_figures.pdf\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#First, we'll download the current jaspar motifs\n",
    "!wget https://jaspar.genereg.net/download/data/2022/CORE/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt -P Tutorial3/MotifFootprinting/\n",
    "\n",
    "#Next we can calculate statistics for each motif represented in our jaspar motif file. If we list both our CTL and Mutant sample, it will calculate the differential footprint score for us as well.\n",
    "!TOBIAS BINDetect --motifs Tutorial3/MotifFootprinting/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt --signals Tutorial3/MotifFootprinting/CTL_footprintscores.bw Tutorial3/MotifFootprinting/Mutant_footprintscores.bw --genome Tutorial3/InputFiles/chr4.fa --peaks Tutorial3/MotifFootprinting/MasterPeakList.bed --outdir Tutorial3/MotifFootprinting/DiffMotifs --cond_names CTL Mutant --cores $numthreadsint --verbosity 1\n",
    "\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "880bab0f-ee58-4f1b-8e62-e1e6e1811809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"600\"\n",
       "            src=\"Tutorial3/MotifFootprinting/DiffMotifs/bindetect_CTL_Mutant.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f929ca3f410>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the html results\n",
    "IFrame(src='Tutorial3/MotifFootprinting/DiffMotifs/bindetect_CTL_Mutant.html', width=900, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5180387-42ea-4d87-b1f2-2d320640af2b",
   "metadata": {},
   "source": [
    "In the above html file you can hover over each point to see the motif name and the sequence. This type of plot is a volcano plot showing the differntial signal on the x-axis and the significance values on the y-axis.\n",
    "\n",
    "For example, the original paper focused on TP63, which is one of our differential dots in the html file. \n",
    "\n",
    "<img src=\"Tutorial3/LessonImages/TP63_volcano.jpg\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
    "\n",
    "Let's visualize the averge footprint at TP63 motifs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f9683a4e-48ba-426b-91cb-5befa06b1d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# TOBIAS 0.13.3 PlotAggregate (run started 2022-10-31 18:26:51.751953)\n",
      "# Working directory: /home/jupyter/nosi-unmc-seq\n",
      "# Command line call: TOBIAS PlotAggregate --TFBS Tutorial3/MotifFootprinting/DiffMotifs/TP63_MA0525.2/beds/TP63_MA0525.2_all.bed --signals Tutorial3/MotifFootprinting/CTL_corrected.bw Tutorial3/MotifFootprinting/Mutant_corrected.bw --output Tutorial3/MotifFootprinting/TP63_footprint_compare.png --share_y both --verbosity 1 --plot_boundaries --flank 60 --smooth 2 --signal-on-x\n",
      "\n",
      "# ----- Input parameters -----\n",
      "# TFBS:\t['Tutorial3/MotifFootprinting/DiffMotifs/TP63_MA0525.2/beds/TP63_MA0525.2_all.bed']\n",
      "# signals:\t['Tutorial3/MotifFootprinting/CTL_corrected.bw', 'Tutorial3/MotifFootprinting/Mutant_corrected.bw']\n",
      "# regions:\t[]\n",
      "# whitelist:\t[]\n",
      "# blacklist:\t[]\n",
      "# output:\tTutorial3/MotifFootprinting/TP63_footprint_compare.png\n",
      "# output_txt:\tNone\n",
      "# title:\tAggregated signals\n",
      "# flank:\t60\n",
      "# TFBS_labels:\tNone\n",
      "# signal_labels:\tNone\n",
      "# region_labels:\tNone\n",
      "# share_y:\tboth\n",
      "# normalize:\tFalse\n",
      "# negate:\tFalse\n",
      "# smooth:\t2\n",
      "# log_transform:\tFalse\n",
      "# plot_boundaries:\tTrue\n",
      "# signal_on_x:\tTrue\n",
      "# remove_outliers:\t1\n",
      "# verbosity:\t1\n",
      "\n",
      "\n",
      "# ----- Output files -----\n",
      "# Tutorial3/MotifFootprinting/TP63_footprint_compare.png\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#IFrame(src='Tutorial2/MotifFootprinting/MYBL1_MA0776.1/plots/MYBL1_MA0776.1_log2fcs.pdf', width=900, height=600) \n",
    "\n",
    "#note change to Tutorial3\n",
    "#!TOBIAS PlotAggregate --TFBS Tutorial3/MotifFootprinting/DiffMotifs/TP63_MA0525.2/beds/TP63_MA0525.2_all.bed --signals Tutorial3/MotifFootprinting/CTL_corrected.bw Tutorial3/MotifFootprinting/Mutant_corrected.bw --output Tutorial3/MotifFootprinting/TP63_footprint_compare.png --share_y both --verbosity 1 --plot_boundaries --flank 60 --smooth 2\n",
    "!TOBIAS PlotAggregate --TFBS Tutorial3/MotifFootprinting/DiffMotifs/TP63_MA0525.2/beds/TP63_MA0525.2_all.bed --signals Tutorial3/MotifFootprinting/CTL_corrected.bw Tutorial3/MotifFootprinting/Mutant_corrected.bw --output Tutorial3/MotifFootprinting/TP63_footprint_compare.png --share_y both --verbosity 1 --plot_boundaries --flank 60 --smooth 2 --signal-on-x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ad9b80da-1111-4079-a308-d7cb6ddc2a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"400\"\n",
       "            src=\"Tutorial3/MotifFootprinting/TP63_footprint_compare.png\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f929cc47510>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='Tutorial3/MotifFootprinting/TP63_footprint_compare.png', width=600, height=400) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b72adc-4b71-4234-9549-6381a56bfb39",
   "metadata": {},
   "source": [
    "We can also get all the motifs that have differential footprints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5a06ef84-9a27-4d55-be1d-fed572711daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output_prefix</th>\n",
       "      <th>name</th>\n",
       "      <th>motif_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>total_tfbs</th>\n",
       "      <th>CTL_mean_score</th>\n",
       "      <th>CTL_bound</th>\n",
       "      <th>Mutant_mean_score</th>\n",
       "      <th>Mutant_bound</th>\n",
       "      <th>CTL_Mutant_change</th>\n",
       "      <th>CTL_Mutant_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arnt_MA0004.1</td>\n",
       "      <td>Arnt</td>\n",
       "      <td>MA0004.1</td>\n",
       "      <td>C_MYC</td>\n",
       "      <td>38</td>\n",
       "      <td>85.96989</td>\n",
       "      <td>16</td>\n",
       "      <td>108.49836</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.38044</td>\n",
       "      <td>6.770010e-46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AhrArnt_MA0006.1</td>\n",
       "      <td>Ahr::Arnt</td>\n",
       "      <td>MA0006.1</td>\n",
       "      <td>C_Ahr::Arnt</td>\n",
       "      <td>48</td>\n",
       "      <td>122.76261</td>\n",
       "      <td>24</td>\n",
       "      <td>115.51752</td>\n",
       "      <td>22</td>\n",
       "      <td>0.16911</td>\n",
       "      <td>1.449010e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ddit3Cebpa_MA0019.1</td>\n",
       "      <td>Ddit3::Cebpa</td>\n",
       "      <td>MA0019.1</td>\n",
       "      <td>C_Ddit3::Cebpa</td>\n",
       "      <td>62</td>\n",
       "      <td>80.02984</td>\n",
       "      <td>24</td>\n",
       "      <td>80.33066</td>\n",
       "      <td>23</td>\n",
       "      <td>0.01484</td>\n",
       "      <td>3.154620e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mecom_MA0029.1</td>\n",
       "      <td>Mecom</td>\n",
       "      <td>MA0029.1</td>\n",
       "      <td>C_Mecom</td>\n",
       "      <td>74</td>\n",
       "      <td>61.73375</td>\n",
       "      <td>20</td>\n",
       "      <td>58.21405</td>\n",
       "      <td>21</td>\n",
       "      <td>0.04212</td>\n",
       "      <td>6.585970e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOXF2_MA0030.1</td>\n",
       "      <td>FOXF2</td>\n",
       "      <td>MA0030.1</td>\n",
       "      <td>C_FOXD1</td>\n",
       "      <td>70</td>\n",
       "      <td>54.91816</td>\n",
       "      <td>16</td>\n",
       "      <td>57.50246</td>\n",
       "      <td>12</td>\n",
       "      <td>0.02761</td>\n",
       "      <td>8.515360e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>ZNF281_MA1630.2</td>\n",
       "      <td>ZNF281</td>\n",
       "      <td>MA1630.2</td>\n",
       "      <td>C_ZNF281</td>\n",
       "      <td>442</td>\n",
       "      <td>87.47939</td>\n",
       "      <td>177</td>\n",
       "      <td>93.39031</td>\n",
       "      <td>192</td>\n",
       "      <td>-0.09821</td>\n",
       "      <td>5.581870e-38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>BACH1_MA1633.2</td>\n",
       "      <td>BACH1</td>\n",
       "      <td>MA1633.2</td>\n",
       "      <td>C_JUNB</td>\n",
       "      <td>292</td>\n",
       "      <td>107.34839</td>\n",
       "      <td>180</td>\n",
       "      <td>93.78244</td>\n",
       "      <td>132</td>\n",
       "      <td>0.44431</td>\n",
       "      <td>1.568740e-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Prdm4_MA1647.2</td>\n",
       "      <td>Prdm4</td>\n",
       "      <td>MA1647.2</td>\n",
       "      <td>C_Prdm4</td>\n",
       "      <td>90</td>\n",
       "      <td>65.87505</td>\n",
       "      <td>28</td>\n",
       "      <td>60.36949</td>\n",
       "      <td>24</td>\n",
       "      <td>0.21506</td>\n",
       "      <td>1.012680e-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>THAP1_MA0597.2</td>\n",
       "      <td>THAP1</td>\n",
       "      <td>MA0597.2</td>\n",
       "      <td>C_THAP1</td>\n",
       "      <td>236</td>\n",
       "      <td>90.56928</td>\n",
       "      <td>101</td>\n",
       "      <td>82.79266</td>\n",
       "      <td>92</td>\n",
       "      <td>0.16360</td>\n",
       "      <td>6.101670e-48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>NR5A1_MA1540.2</td>\n",
       "      <td>NR5A1</td>\n",
       "      <td>MA1540.2</td>\n",
       "      <td>C_NR5A1</td>\n",
       "      <td>88</td>\n",
       "      <td>60.87309</td>\n",
       "      <td>20</td>\n",
       "      <td>63.31646</td>\n",
       "      <td>24</td>\n",
       "      <td>0.00384</td>\n",
       "      <td>2.234170e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           output_prefix          name  motif_id         cluster  total_tfbs  \\\n",
       "0          Arnt_MA0004.1          Arnt  MA0004.1           C_MYC          38   \n",
       "1       AhrArnt_MA0006.1     Ahr::Arnt  MA0006.1     C_Ahr::Arnt          48   \n",
       "2    Ddit3Cebpa_MA0019.1  Ddit3::Cebpa  MA0019.1  C_Ddit3::Cebpa          62   \n",
       "3         Mecom_MA0029.1         Mecom  MA0029.1         C_Mecom          74   \n",
       "4         FOXF2_MA0030.1         FOXF2  MA0030.1         C_FOXD1          70   \n",
       "..                   ...           ...       ...             ...         ...   \n",
       "836      ZNF281_MA1630.2        ZNF281  MA1630.2        C_ZNF281         442   \n",
       "837       BACH1_MA1633.2         BACH1  MA1633.2          C_JUNB         292   \n",
       "838       Prdm4_MA1647.2         Prdm4  MA1647.2         C_Prdm4          90   \n",
       "839       THAP1_MA0597.2         THAP1  MA0597.2         C_THAP1         236   \n",
       "840       NR5A1_MA1540.2         NR5A1  MA1540.2         C_NR5A1          88   \n",
       "\n",
       "     CTL_mean_score  CTL_bound  Mutant_mean_score  Mutant_bound  \\\n",
       "0          85.96989         16          108.49836            17   \n",
       "1         122.76261         24          115.51752            22   \n",
       "2          80.02984         24           80.33066            23   \n",
       "3          61.73375         20           58.21405            21   \n",
       "4          54.91816         16           57.50246            12   \n",
       "..              ...        ...                ...           ...   \n",
       "836        87.47939        177           93.39031           192   \n",
       "837       107.34839        180           93.78244           132   \n",
       "838        65.87505         28           60.36949            24   \n",
       "839        90.56928        101           82.79266            92   \n",
       "840        60.87309         20           63.31646            24   \n",
       "\n",
       "     CTL_Mutant_change  CTL_Mutant_pvalue  \n",
       "0             -0.38044       6.770010e-46  \n",
       "1              0.16911       1.449010e-19  \n",
       "2              0.01484       3.154620e-01  \n",
       "3              0.04212       6.585970e-03  \n",
       "4              0.02761       8.515360e-02  \n",
       "..                 ...                ...  \n",
       "836           -0.09821       5.581870e-38  \n",
       "837            0.44431       1.568740e-86  \n",
       "838            0.21506       1.012680e-39  \n",
       "839            0.16360       6.101670e-48  \n",
       "840            0.00384       2.234170e-01  \n",
       "\n",
       "[841 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!load the results as a pandas table Tutorial2/MotifFootprinting/bindetect_results.txt\n",
    "dframe = pd.read_csv(\"Tutorial3/MotifFootprinting/DiffMotifs/bindetect_results.txt\", sep='\\t')\n",
    "display(dframe)\n",
    "DiffMotifs = dframe[dframe['CTL_Mutant_pvalue'] < .05]\n",
    "#Write out to a tab separated file\n",
    "DiffMotifs.to_csv('Tutorial3/MotifFootprinting/DiffMotifs_p05.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12439dc4-b9ad-49fa-9ccb-4383a272bcaf",
   "metadata": {},
   "source": [
    "<div class=\"alert-success\" style=\"font-size:200%\">\n",
    "Great job! \n",
    "</div>\n",
    "Thank you for completing these tutorials. Feel free to download these notebooks, customize, and use them to process your own data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15583c22-1c70-43e8-a949-34f54f21aada",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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