From 7f0d8573891dcbf95f5c92eff3187ecad8cbc899 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Thu, 14 Nov 2024 13:59:19 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                              |   2 +-
 develop/04c_preprocessing_nf3112.html  | 108 ++++++++++++-------------
 develop/04c_preprocessing_nf3170.html  |  42 +++++-----
 develop/05a_data_analysis_setup.html   |   2 +-
 develop/05b_count_matrix.html          |   2 +-
 develop/08b_FA_overrepresentation.html | 106 ++++++++++++------------
 develop/index.html                     |   8 +-
 search.json                            |  10 +--
 sitemap.xml                            |  78 +++++++++---------
 9 files changed, 179 insertions(+), 179 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 196a048..5b37aa7 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-bbd8ecdc
\ No newline at end of file
+12c2d7b8
\ No newline at end of file
diff --git a/develop/04c_preprocessing_nf3112.html b/develop/04c_preprocessing_nf3112.html
index 990430f..480f247 100644
--- a/develop/04c_preprocessing_nf3112.html
+++ b/develop/04c_preprocessing_nf3112.html
@@ -321,23 +321,23 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
 <p>The <code>--input</code> parameter points to the <code>samplesheet.csv</code> file that contains all the info regarding our samples. The file looks like this:</p>
 <div class="cell">
 <div class="cell-output-display">
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@@ -363,12 +363,12 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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@@ -404,13 +404,13 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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@@ -445,7 +445,7 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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@@ -457,15 +457,15 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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@@ -613,7 +613,7 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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@@ -623,7 +623,7 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
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diff --git a/develop/04c_preprocessing_nf3170.html b/develop/04c_preprocessing_nf3170.html
index ce015a8..fb9e4f7 100644
--- a/develop/04c_preprocessing_nf3170.html
+++ b/develop/04c_preprocessing_nf3170.html
@@ -285,15 +285,15 @@ <h1 class="title">nf-core/rnaseq 3.17.0</h1>
 </div>
 <section id="submit-a-job" class="level2">
 <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
-<p>Access <a href="https://cloud.sdu.dk">Ucloud</a> with your account and choose the project <code>Sandbox RNASeq Workshop</code> where you have been invited (<a href="https://hds-sandbox.github.io/contact/contact.html">contact</a> the team if you haven’t).</p>
+<p>Access <a href="https://cloud.sdu.dk">Ucloud</a> with your account and choose the project <code>Sandbox RNASeq Workshop</code> to which you have been invited (<a href="https://hds-sandbox.github.io/contact/contact.html">contact</a> the team if you haven’t).</p>
 <p><a href="./img/04c_preprocessing_setup/chooseProject.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1"><img src="./img/04c_preprocessing_setup/chooseProject.png" class="img-fluid"></a></p>
-<p>Click on <code>Apps</code> on the left-side menu, and search for the application <code>nf-core rnaseq</code> and click on it.</p>
+<p>Click on <code>Apps</code> on the left-side menu, search for the application <code>nf-core rnaseq</code>, and click on it.</p>
 <p><a href="./img/04c_preprocessing_setup/chooseRNAseq.png" class="lightbox" data-gallery="quarto-lightbox-gallery-2"><img src="./img/04c_preprocessing_setup/chooseRNAseq.png" class="img-fluid"></a></p>
 <p>Next, let’s set up the parameters to run the pipeline. Your screen should resemble the image below—please verify it does.</p>
 <ol type="1">
 <li>Assign a job title using your name and the task (e.g., “preprocessing salmon”).</li>
 <li>Select 8 cores and a runtime of 4 hours.</li>
-<li>Choose the <strong>sequencing data</strong> drive containing the sequencing reads and scripts needed for the pipeline.</li>
+<li>Choose the <strong>sequencing data</strong> drive containing the sequencing reads and scripts needed for the pipeline. Please, also add your personal drive—the one with your name (e.g.<code>Yourname#xxxx</code>).</li>
 <li>Enable <strong>interactive mode</strong> to start a session through the terminal app, allowing us to work with the command line and monitor the progress of the pipeline.</li>
 </ol>
 <div class="callout callout-style-default callout-warning callout-titled">
@@ -307,7 +307,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 </div>
 <div class="callout-body-container callout-body">
 <p><strong>Ensure that the hard-drive icon is labeled “sequencing_data” and you are in the “sandbox_bulkRNAseq” workspace.</strong></p>
-<p>You can select the drive by clicking on the down arrow (<strong>∨</strong>) icon to open a dropdown menu.</p>
+<p>You can select the drive by clicking the down arrow (<strong>∨</strong>) icon to open a dropdown menu.</p>
 </div>
 </div>
 <p><a href="./img/04c_preprocessing_setup/select_options_rnaseq.png" class="lightbox" data-gallery="quarto-lightbox-gallery-3"><img src="./img/04c_preprocessing_setup/select_options_rnaseq.png" class="img-fluid"></a></p>
@@ -316,7 +316,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 <p>Now, click the button on the top-right of the screen (<code>submit</code>) to start the job.</p>
 <p>Next, wait until the screen looks like the figure below. This process usually takes a few minutes. You can always come back to this screen via the <code>Runs</code> button in the left menu on UCloud. From there, you can add extra time or stop the app if you no longer need it.</p>
 <p><a href="./img/04c_preprocessing_setup/startApp.png" class="lightbox" data-gallery="quarto-lightbox-gallery-5"><img src="./img/04c_preprocessing_setup/startApp.png" class="img-fluid"></a></p>
-<p>Now, click on <code>Open terminal</code> on the top right-hand side of the screen. You will start terminal session through your browser! Once inside the terminal, you will need to do one last thing before starting the pipeline:</p>
+<p>Now, click on <code>Open terminal</code> on the top right-hand side of the screen. You will start the terminal session through your browser! Once inside the terminal, you will need to do one last thing before starting the pipeline:</p>
 <div class="code-with-filename">
 <div class="code-with-filename-file">
 <pre><strong>Terminal</strong></pre>
@@ -340,7 +340,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 </div>
 </div>
 <div class="callout-body-container callout-body">
-<p>If you want to leave the tmux session (<em>and not kill it</em>), you can do so by pressing simultaneously <code>Ctrl</code> and <code>B</code> keys, and then press <code>D</code>. Then, you can reconnect to the session using the command:</p>
+<p>If you want to leave the tmux session (<em>and not kill it</em>), you can do so by pressing simultaneously <code>Ctrl</code> and <code>B</code> keys, and then pressing <code>D</code>. Then, you can reconnect to the session using the command:</p>
 <div class="code-with-filename">
 <div class="code-with-filename-file">
 <pre><strong>Terminal</strong></pre>
@@ -359,7 +359,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="fu">mkdir</span> nfcore-results</span>
 <span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Make the new dir your current working dir!</span></span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> nfcore-results <span class="co"># you might have named it differently </span></span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="bu">cd</span> nfcore-results <span class="co"># You might have named it differently </span></span>
 <span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Copy our bash script to your directory </span></span>
 <span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="fu">cp</span> /work/sequencing_data/scripts_v3.17.0/preprocessing_salmon.sh .</span>
@@ -368,7 +368,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 <span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ls</span> . </span>
 <span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a><span class="co"># ls is a Linux shell command that lists directory contents of files and directories.</span></span>
 <span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a><span class="co"># You will run the pipeline from here as follows BUT not yet! (that's why is commented-out)</span></span>
+<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a><span class="co"># You will run the pipeline from here as follows BUT not yet! (that's why is commented out)</span></span>
 <span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="co"># bash ./preprocessing_salmon.sh</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Once copied, review the bash script:</p>
@@ -376,7 +376,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 <div class="code-with-filename-file">
 <pre><strong>preprocessing_salmon.sh</strong></pre>
 </div>
-<div class="sourceCode" id="cb4" data-filename="preprocessing_salmon.sh"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">#!/bin/bash  # Specifies the script should be run in bash shell</span></span>
+<div class="sourceCode" id="cb4" data-filename="preprocessing_salmon.sh"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">#!/bin/bash  # Specifies the script that should be run in the bash shell</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="va">VRNASEQ</span><span class="op">=</span><span class="st">"3.17.0"</span>    <span class="co"># Defines the version of the rnaseq pipeline to use</span></span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="va">PARAMSF</span><span class="op">=</span><span class="st">"/work/sequencing_data/scripts_v3.17.0/nf-params_salmon.json"</span>   <span class="co"># Set path to configuration pipeline parameters (e.g. aligner software)</span></span>
@@ -384,7 +384,7 @@ <h2 class="anchored" data-anchor-id="submit-a-job">Submit a job</h2>
 <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a><span class="ex">nextflow</span> run nf-core/rnaseq <span class="at">-r</span> <span class="va">$VRNASEQ</span> <span class="at">-params-file</span> <span class="va">$PARAMSF</span> <span class="at">-profile</span> conda <span class="at">-c</span> <span class="va">$CONFIGF</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<p>This small bash script initiates the <code>nf-core/rnaseq</code> pipeline. It’s a good practice to save the command used to run the pipeline, and now you’ve copied it into your results folder! You’ll notice extra parameters and configuration settings defined in the script, which we’ll explore shortly. The final line of the script runs the RNA-seq pipeline with the specified version, parameters, Conda profile, and configuration file. <strong>We will inspect each part of the nextflow command below in the section <a href="#understanding-pipeline-options">Understanding the pipeline arguments</a> but we will first look at the different input files.</strong></p>
+<p>This small bash script initiates the <code>nf-core/rnaseq</code> pipeline. It’s a good practice to save the command used to run the pipeline, and now you’ve copied it into your results folder! You’ll notice extra parameters and configuration settings defined in the script, which we’ll explore shortly. The final line of the script runs the RNA-seq pipeline with the specified version, parameters, Conda profile, and configuration file. <strong>We will inspect each part of the Nextflow command below in the section <a href="#understanding-pipeline-options">Understanding the pipeline arguments</a> but we will first look at the different input files.</strong></p>
 <section id="parameters-file" class="level4">
 <h4 class="anchored" data-anchor-id="parameters-file">Parameters file</h4>
 <p>The <strong>parameters JSON file</strong> is used in nf-core pipelines to define various arguments and software options that can be utilized within the pipeline. You can find a full list of all parameters on the official page: https://nf-co.re/rnaseq/3.17.0/parameters/. Let’s take a look at the different configuration files we’re using.</p>
@@ -501,19 +501,19 @@ <h2 class="anchored" data-anchor-id="understanding-the-pipeline-options">Underst
 <div class="sourceCode" id="cb9"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="ex">nextflow</span> run nf-core/rnaseq <span class="at">-r</span> 3.17.0</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>While usually one would run an nf-core pipeline using <code>nextflow run nf-core/rnaseq</code> and fetch the pipeline remotely, UCloud has installed the pipelines locally. Specifically, we are using the latest version by using the argument <code>-r 3.17.0</code>.</p>
 <hr>
-<p>The <code>-params-file</code> argument is another nextflow core argument that allows us to give the nf-core rnaseq pipeline arguments in a <a href="https://www.json.org/json-en.html">json file</a>, instead of creating an excessively long command. Writing the parameters this way allows for better reproducibility, since you can reuse the file in the future.</p>
+<p>The <code>-params-file</code> argument is another nextflow core argument that allows us to give the nf-core rnaseq pipeline arguments in a <a href="https://www.json.org/json-en.html">json file</a>, instead of creating an excessively long command. Writing the parameters this way allows for better reproducibility since you can reuse the file in the future.</p>
 <div class="sourceCode" id="cb10"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Set path to configuration pipeline parameters (e.g. aligner software)</span></span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="va">PARAMSF</span><span class="op">=</span><span class="st">"/path/to/params.json"</span>   </span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="ex">-params-file</span> <span class="va">$PARAMSF</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <hr>
-<p>The next argument defines the <code>-profile</code>. Profiles can give configuration presets for different compute environments. Unfortunately, the UCloud implementation of the nf-core pipelines do not currently allow the use of docker or singularity, which are the recommended profile options. However, UCloud has made sure that there is a working conda environment ready to use!</p>
+<p>The next argument defines the <code>-profile</code>. Profiles can give configuration presets for different compute environments. Unfortunately, the UCloud implementation of the nf-core pipelines does not currently allow the use of docker or singularity, which are the recommended profile options. However, UCloud has made sure that there is a working conda environment ready to use!</p>
 <div class="sourceCode" id="cb11"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="ex">-profile</span> conda</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <hr>
 <p>Finally, we include the argument to specify the config file containing the resource settings:</p>
 <div class="sourceCode" id="cb12"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Set path to maximum cores and memory usage (e.g. what you requested on UCloud)</span></span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="va">CONFIGF</span><span class="op">=</span><span class="st">"/path/to/config.json"</span>   </span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="ex">-c</span> <span class="va">$CONFIGF</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>These are nf-core specific arguments that indicates nextflow to only use as maximum the number of CPUs and RAM we have requested when we submitted the job. We are using 8 cores since it is what we requested in the submission page (e.g.&nbsp;if you submitted a job with 4 CPUs, this will be equal to 4). We are using slightly less RAM than we requested (48Gb) just in case there is a problem with memory overflow.</p>
+<p>These are nf-core specific arguments that indicate Nextflow to only use as maximum the number of CPUs and RAM we requested when we submitted the job. We are using 8 cores since it is what we requested on the submission page (e.g.&nbsp;if you submitted a job with 4 CPUs, this will be equal to 4). We are using slightly less RAM than we requested (48Gb) just in case there is a problem with memory overflow.</p>
 <div class="callout callout-style-default callout-warning callout-titled">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
@@ -546,7 +546,7 @@ <h2 class="anchored" data-anchor-id="running-the-pipeline">Running the pipeline<
 </section>
 <section id="restarting-a-failed-run" class="level2">
 <h2 class="anchored" data-anchor-id="restarting-a-failed-run">Restarting a failed run</h2>
-<p>When running a nf-core pipelines for the first time, you might encounter some errors, for example, one of your files has an incorrect path, or a program failed to do its job. <strong>It is very important to read the error message carefully (try Googling it, as chances are others have encountered the same issue)!</strong></p>
+<p>When running a nf-core pipeline for the first time, you might encounter some errors, for example, one of your files has an incorrect path, or a program failed to do its job. <strong>It is very important to read the error message carefully (try Googling it, as chances are others have encountered the same issue)!</strong></p>
 <div class="callout callout-style-default callout-warning callout-titled" title="Failure">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
@@ -564,7 +564,7 @@ <h2 class="anchored" data-anchor-id="restarting-a-failed-run">Restarting a faile
 </div>
 </div>
 <p><a href="./img/04c_preprocessing_setup/conda_error.png" class="lightbox" data-gallery="quarto-lightbox-gallery-9"><img src="./img/04c_preprocessing_setup/conda_error.png" class="img-fluid"></a></p>
-<p>Once you fix the error, it is possible to <em>resume</em> a pipeline instead of restarting the whole workflow. You can do this by adding the <code>-resume</code> argument to the nextflow command:</p>
+<p>Once you fix the error, it is possible to <em>resume</em> a pipeline instead of restarting the whole workflow. You can do this by adding the <code>-resume</code> argument to the Nextflow command:</p>
 <div class="sourceCode" id="cb15"><pre class="sourceCode bash code-overflow-wrap code-with-copy"><code class="sourceCode bash"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="ex">nextflow</span> run nf-core/rnaseq <span class="at">-r</span> <span class="va">$VRNASEQ</span> <span class="at">-params-file</span> <span class="va">$PARAMSF</span> <span class="at">-profile</span> conda <span class="at">-c</span> <span class="va">$CONFIGF</span> <span class="at">-resume</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
 <section id="stopping-the-app" class="level2">
@@ -576,7 +576,7 @@ <h2 class="anchored" data-anchor-id="stopping-the-app">Stopping the app</h2>
 <h2 class="anchored" data-anchor-id="saved-results">Saved results</h2>
 <p>After finishing the job, everything that the pipeline has created will be saved in the directory you created in your personal drive.</p>
 <ol type="1">
-<li>All generated files will be saved in a volume with your username, that you should be able to find under the menu <code>Files</code> &gt; <code>Drives</code> &gt; <code>Member Files:YourName#numbers</code></li>
+<li>All generated files will be saved in a volume with your username, which you should be able to find under the menu <code>Files</code> &gt; <code>Drives</code> &gt; <code>Member Files:YourName#numbers</code></li>
 </ol>
 <p><a href="./img/04c_preprocessing_setup/fileDrives.png" class="lightbox" data-gallery="quarto-lightbox-gallery-11"><img src="./img/04c_preprocessing_setup/fileDrives.png" class="black-box img-fluid"></a></p>
 <ol start="2" type="1">
@@ -608,7 +608,7 @@ <h2 class="anchored" data-anchor-id="saved-results">Saved results</h2>
 </div>
 <div id="callout-9" class="callout-9-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<p>You can always find past generated files by looking into the <code>Jobs</code> folder! You will need to remember which app and job name you provided before subting the job to UCloud. For example,</p>
+<p>You can always find past generated files by looking into the <code>Jobs</code> folder! You will need to remember which app and job name you provided before submitting the job to UCloud. For example,</p>
 <ul>
 <li>Go to <code>Jobs</code> -&gt; <code>nf-core: rnaseq</code> -&gt; <code>job_name (RNAseq preprocessing ...)</code> -&gt; <code>preprocessing_results_salmon</code> <img src="./img/04c_preprocessing_setup/savedWork2.png" class="img-fluid"></li>
 </ul>
@@ -619,7 +619,7 @@ <h2 class="anchored" data-anchor-id="saved-results">Saved results</h2>
 </section>
 <section id="downstream-analysis-using-your-results" class="level2">
 <h2 class="anchored" data-anchor-id="downstream-analysis-using-your-results">Downstream analysis using your results</h2>
-<p><strong>Optional</strong>: If you have successfully run nf-core RNA jobs and want to use the files you generated from your own preprocessing results, don’t forget to choose use folder when submitting a new job. How?</p>
+<p><strong>Optional</strong>: If you have successfully run nf-core RNA jobs and want to use the files you generated from your own preprocessing results, don’t forget to choose <code>Use folder</code> when submitting a new job. How?</p>
 <blockquote class="blockquote">
 <p>Go to <code>Select folders to use</code> and <code>Add folder</code> by selecting the one containing the pipeline results like shown in the image above. They will be located in <code>Member Files:YourName#numbers</code> -&gt; <code>nfcore-results</code> -&gt; <code>preprocessing_results_salmon</code> (unless you have moved them elsewhere).</p>
 </blockquote>
@@ -629,12 +629,12 @@ <h2 class="anchored" data-anchor-id="downstream-analysis-using-your-results">Dow
 IF (AND ONLY IF) you have **correctly imported parameters but you do not find the Data folder** follow the instructions below! If you still run into problems, please contact us.
 
 ### "Mount the backup data drive"
-If the data isn't present, you will need to mount the data drive by selecting one of our backup folders that contains the data. Click on `add folder` and then choose `/sandbox_bulkRNAseq(82207)/Intro_to_bulkRNAseq/`. Change the drive by clicking on the down arrow (**&or;**) icon to open a dropdown menu. Afterwards, add your home folder so you can write the results to your own drive. **You will not have write permissions on the `sandbox_bulRNAseq` drive**. 
+If the data isn't present, you will need to mount the data drive by selecting one of our backup folders that contains the data. Click on `add folder` and then choose `/sandbox_bulkRNAseq(82207)/Intro_to_bulkRNAseq/`. Change the drive by clicking the down arrow (**&or;**) icon to open a dropdown menu. Afterward, add your home folder so you can write the results to your own drive. **You will not have write permissions on the `sandbox_bulRNAseq` drive**. 
 
 ![back-up data](./img/05a_data_analysis_setup/data_select.png)
 
 
-Alternatively, you can also select the hard-drive icon is labeled "sequencing_data". 
+Alternatively, you can also select the hard-drive icon labeled "sequencing_data". 
 -->
 
 
@@ -1215,7 +1215,7 @@ <h2 class="anchored" data-anchor-id="downstream-analysis-using-your-results">Dow
   </div>
 </nav>
 </div> <!-- /content -->
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diff --git a/develop/05a_data_analysis_setup.html b/develop/05a_data_analysis_setup.html
index 301604e..4be4df7 100644
--- a/develop/05a_data_analysis_setup.html
+++ b/develop/05a_data_analysis_setup.html
@@ -1023,7 +1023,7 @@ <h3 class="anchored" data-anchor-id="reusing-old-job-parameters">Reusing old job
   </div>
 </nav>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","openEffect":"zoom","descPosition":"bottom","loop":false,"selector":".lightbox"});
+<script>var lightboxQuarto = GLightbox({"descPosition":"bottom","loop":false,"closeEffect":"zoom","openEffect":"zoom","selector":".lightbox"});
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diff --git a/develop/05b_count_matrix.html b/develop/05b_count_matrix.html
index 666de11..584bd05 100644
--- a/develop/05b_count_matrix.html
+++ b/develop/05b_count_matrix.html
@@ -1187,7 +1187,7 @@ <h2 class="anchored" data-anchor-id="differential-expression-analysis-workflow">
   </div>
 </nav>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","loop":false,"selector":".lightbox","openEffect":"zoom","descPosition":"bottom"});
+<script>var lightboxQuarto = GLightbox({"openEffect":"zoom","descPosition":"bottom","closeEffect":"zoom","selector":".lightbox","loop":false});
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diff --git a/develop/08b_FA_overrepresentation.html b/develop/08b_FA_overrepresentation.html
index 1b487b7..b2f2a5e 100644
--- a/develop/08b_FA_overrepresentation.html
+++ b/develop/08b_FA_overrepresentation.html
@@ -556,23 +556,23 @@ <h3 class="anchored" data-anchor-id="running-clusterprofiler">Running clusterPro
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="fu">write.csv</span>(cluster_summary, <span class="st">"../Results/clusterProfiler_Cont-Vamp.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell">
 <div class="cell-output-display">
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diff --git a/develop/index.html b/develop/index.html
index ec7d68b..56488d6 100644
--- a/develop/index.html
+++ b/develop/index.html
@@ -383,7 +383,7 @@ <h4>Authors</h4>
 
 <div class="quarto-listing quarto-listing-container-grid" id="listing-listing">
 <div class="list grid quarto-listing-cols-5">
-<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1731590912013" data-listing-reading-time-sort="1" data-listing-word-count-sort="23">
+<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1731592625998" data-listing-reading-time-sort="1" data-listing-word-count-sort="23">
 <a href="../develop/cards/AdrijaKalvisa.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top">
@@ -400,7 +400,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
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-<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1731590912013" data-listing-reading-time-sort="1" data-listing-word-count-sort="47">
+<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1731592625998" data-listing-reading-time-sort="1" data-listing-word-count-sort="47">
 <a href="../develop/cards/AlbaMartinez.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="cards/alba.jpg" style="height: 200px;"  class="thumbnail-image card-img"/></p>
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 </div>
 </a>
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+<div class="g-col-1" data-index="2" data-listing-file-modified-sort="1731592625998" data-listing-reading-time-sort="1" data-listing-word-count-sort="4">
 <a href="../develop/cards/HenrikeZschach.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="img/cards/henrike.jpeg" style="height: 200px;"  class="thumbnail-image card-img"/></p>
@@ -430,7 +430,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="3" data-listing-file-modified-sort="1731590912017" data-listing-reading-time-sort="1" data-listing-word-count-sort="11">
+<div class="g-col-1" data-index="3" data-listing-file-modified-sort="1731592625998" data-listing-reading-time-sort="1" data-listing-word-count-sort="11">
 <a href="../develop/cards/thildeTerkelsen.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="img/cards/thilde.JPG" style="height: 200px;"  class="thumbnail-image card-img"/></p>
diff --git a/search.json b/search.json
index d7d7bc6..d55165e 100644
--- a/search.json
+++ b/search.json
@@ -120,7 +120,7 @@
     "href": "develop/04c_preprocessing_nf3170.html#submit-a-job",
     "title": "nf-core/rnaseq 3.17.0",
     "section": "Submit a job",
-    "text": "Submit a job\nAccess Ucloud with your account and choose the project Sandbox RNASeq Workshop where you have been invited (contact the team if you haven’t).\n\nClick on Apps on the left-side menu, and search for the application nf-core rnaseq and click on it.\n\nNext, let’s set up the parameters to run the pipeline. Your screen should resemble the image below—please verify it does.\n\nAssign a job title using your name and the task (e.g., “preprocessing salmon”).\nSelect 8 cores and a runtime of 4 hours.\nChoose the sequencing data drive containing the sequencing reads and scripts needed for the pipeline.\nEnable interactive mode to start a session through the terminal app, allowing us to work with the command line and monitor the progress of the pipeline.\n\n\n\n\n\n\n\nWarning\n\n\n\nEnsure that the hard-drive icon is labeled “sequencing_data” and you are in the “sandbox_bulkRNAseq” workspace.\nYou can select the drive by clicking on the down arrow (∨) icon to open a dropdown menu.\n\n\n\nYou are almost ready to run the app! But first, make sure your screen looks like the one shown here. Review the arguments used to run the job: we have specified a Job name, Hours, Machine type, and an additional parameter, Interactive mode.\n\nNow, click the button on the top-right of the screen (submit) to start the job.\nNext, wait until the screen looks like the figure below. This process usually takes a few minutes. You can always come back to this screen via the Runs button in the left menu on UCloud. From there, you can add extra time or stop the app if you no longer need it.\n\nNow, click on Open terminal on the top right-hand side of the screen. You will start terminal session through your browser! Once inside the terminal, you will need to do one last thing before starting the pipeline:\n\n\nTerminal\n\n # This will create a session with an automatically assigned numeric name, starting from 0\ntmux \n\n# Alternatively, \ntmux new -s session_name\n\n\nThe tmux command initiates a persistent virtual command line session. This is crucial because, once the pipeline starts, you won’t be able to monitor its progress if your computer disconnects or enters sleep mode. With tmux, however, you can close your computer, and the session will still be active when you reconnect—unless your requested job time has run out. You’ll know you’re inside the tmux session by checking the bottom of the screen.\n\n\n\n\n\n\n\nReconnect to tmux session\n\n\n\nIf you want to leave the tmux session (and not kill it), you can do so by pressing simultaneously Ctrl and B keys, and then press D. Then, you can reconnect to the session using the command:\n\n\nTerminal\n\ntmux attach -t 0 # abbreviated as tmux a -t0\n\n\n\nLet’s prepare to run the pipeline. First, navigate to your personal drive—the one with your name (e.g.Yourname#xxxx)—and create a directory to serve as your working directory where all pipeline results will be saved (e.g., nfcore-results). Make sure you’re now located within this new directory. Finally, copy the bash script that runs the Nextflow command into this directory. Check the commands you need to run below:\n\n\nTerminal\n\n# Change location to your personal drive and create a new directory\n# cd /work/&lt;YourName#xxxx&gt;\nmkdir nfcore-results\n\n# Make the new dir your current working dir!\ncd nfcore-results # you might have named it differently \n\n# Copy our bash script to your directory \ncp /work/sequencing_data/scripts_v3.17.0/preprocessing_salmon.sh .\n\n# Make sure you have copied the bash script using the ls command \nls . \n# ls is a Linux shell command that lists directory contents of files and directories.\n\n# You will run the pipeline from here as follows BUT not yet! (that's why is commented-out)\n# bash ./preprocessing_salmon.sh\n\nOnce copied, review the bash script:\n\n\npreprocessing_salmon.sh\n\n#!/bin/bash  # Specifies the script should be run in bash shell\n\nVRNASEQ=\"3.17.0\"    # Defines the version of the rnaseq pipeline to use\nPARAMSF=\"/work/sequencing_data/scripts_v3.17.0/nf-params_salmon.json\"   # Set path to configuration pipeline parameters (e.g. aligner software)\nCONFIGF=\"/work/sequencing_data/scripts_v3.17.0/maxcores.config\"     # Set path to maximum cores and memory usage (e.g. what you requested on UCloud)\n\nnextflow run nf-core/rnaseq -r $VRNASEQ -params-file $PARAMSF -profile conda -c $CONFIGF\n\nThis small bash script initiates the nf-core/rnaseq pipeline. It’s a good practice to save the command used to run the pipeline, and now you’ve copied it into your results folder! You’ll notice extra parameters and configuration settings defined in the script, which we’ll explore shortly. The final line of the script runs the RNA-seq pipeline with the specified version, parameters, Conda profile, and configuration file. We will inspect each part of the nextflow command below in the section Understanding the pipeline arguments but we will first look at the different input files.\n\nParameters file\nThe parameters JSON file is used in nf-core pipelines to define various arguments and software options that can be utilized within the pipeline. You can find a full list of all parameters on the official page: https://nf-co.re/rnaseq/3.17.0/parameters/. Let’s take a look at the different configuration files we’re using.\nIn the parameters file, we specify that we want to use the pseudoaligner Salmon for this pipeline. The key elements we need to define are the location of the samplesheet, the output directory where Nextflow will save the results, and the paths to the FASTA and GTF files. It is important to use full paths for these locations to ensure that Nextflow can correctly access the required files, regardless of the working directory.\n\n\nnf-params.json\n\n{\n    \"input\": \"/work/sequencing_data/raw_reads/samplesheet_v3.17.0.csv\",\n    \"outdir\": \"preprocessing_results_salmon\",\n    \"fasta\": \"/work/sequencing_data/genomic_resources/homo_sapiens/GRCh38/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz\",\n    \"gtf\": \"/work/sequencing_data/genomic_resources/homo_sapiens/GRCh38/Homo_sapiens.GRCh38.109.MODIFIED.gtf.gz\",\n    \"pseudo_aligner\": \"salmon\",\n    \"skip_stringtie\": true,\n    \"skip_rseqc\": true,\n    \"skip_preseq\": true,\n    \"skip_qualimap\": true,\n    \"skip_biotype_qc\": true,\n    \"skip_bigwig\": true,\n    \"skip_deseq2_qc\": true,\n    \"skip_bbsplit\": true,\n    \"skip_alignment\": true,\n    \"extra_salmon_quant_args\": \"--gcBias\"\n}\n\n\n\n\n\n\n\nMore info on the parameters file\n\n\n\n--input parameter\nThe --input parameter points to the samplesheet.csv file that contains all the info regarding our samples.\n--outdir parameter\nThe --outdir parameter indicates where the results of the pipeline will be saved.\n--fasta parameter\nPath to FASTA reference genome file.\n--gtf parameter\nPath to GTF annotation file that contains genomic region information.\n--pseudo_aligner argument\nThe --pseudo_aligner argument indicates that we want to use salmon to quantify transcription levels.\nFinally, we are skipping several QC and extra steps that we did not explain in the previous lesson. Do not worry if you cannot manage to run the pipeline or you do not have the time, we have prepared a backup folder that contains the results from a traditional alignment + pseudoquantification for you to freely explore! (More about that below).\n\n\n\n\nSamples’ sheet\nLet’s quickly explore our samplesheet.csv file (we named it samplesheet_v3.17.0.csv to differentiate it from other formats, as the file may have slightly changed with Nextflow updates—don’t worry about this). Additional details, including descriptions of the columns here\n\n\nsamplesheet_v3.17.0.csv\n\nsample,fastq_1,fastq_2,strandedness\ncontrol_3,/work/sequencing_data/raw_reads/Control_3.fastq.gz,,unstranded\ncontrol_2,/work/sequencing_data/raw_reads/Control_2.fastq.gz,,unstranded\ncontrol_1,/work/sequencing_data/raw_reads/Control_1.fastq.gz,,unstranded\nvampirium_3,/work/sequencing_data/raw_reads/Vampirium_3.fastq.gz,,unstranded\nvampirium_2,/work/sequencing_data/raw_reads/Vampirium_2.fastq.gz,,unstranded\nvampirium_1,/work/sequencing_data/raw_reads/Vampirium_1.fastq.gz,,unstranded\ngarlicum_3,/work/sequencing_data/raw_reads/Garlicum_3.fastq.gz,,unstranded\ngarlicum_2,/work/sequencing_data/raw_reads/Garlicum_2.fastq.gz,,unstranded\n\nIt is crucial that the FASTQ paths inside this file match the paths in your job exactly! A good practice is to use the full paths to avoid any discrepancies.\n\n\nConfiguration file\nThe configuration file ensures that, regardless of how nf-core defines the resources required for each task, the jobs will run with the resources we allocate when submitting the job. You can learn more about configuration parameters here. One of Nextflow’s key strengths is its ability to run on virtually any computational infrastructure, so these settings might differ if you run the workflow on a different machine.\n\n\nmaxcores.config\n\nprocess {\n  resourceLimits = [\n    cpus: 8,\n    memory: 40.GB,\n  ]\n}",
+    "text": "Submit a job\nAccess Ucloud with your account and choose the project Sandbox RNASeq Workshop to which you have been invited (contact the team if you haven’t).\n\nClick on Apps on the left-side menu, search for the application nf-core rnaseq, and click on it.\n\nNext, let’s set up the parameters to run the pipeline. Your screen should resemble the image below—please verify it does.\n\nAssign a job title using your name and the task (e.g., “preprocessing salmon”).\nSelect 8 cores and a runtime of 4 hours.\nChoose the sequencing data drive containing the sequencing reads and scripts needed for the pipeline. Please, also add your personal drive—the one with your name (e.g.Yourname#xxxx).\nEnable interactive mode to start a session through the terminal app, allowing us to work with the command line and monitor the progress of the pipeline.\n\n\n\n\n\n\n\nWarning\n\n\n\nEnsure that the hard-drive icon is labeled “sequencing_data” and you are in the “sandbox_bulkRNAseq” workspace.\nYou can select the drive by clicking the down arrow (∨) icon to open a dropdown menu.\n\n\n\nYou are almost ready to run the app! But first, make sure your screen looks like the one shown here. Review the arguments used to run the job: we have specified a Job name, Hours, Machine type, and an additional parameter, Interactive mode.\n\nNow, click the button on the top-right of the screen (submit) to start the job.\nNext, wait until the screen looks like the figure below. This process usually takes a few minutes. You can always come back to this screen via the Runs button in the left menu on UCloud. From there, you can add extra time or stop the app if you no longer need it.\n\nNow, click on Open terminal on the top right-hand side of the screen. You will start the terminal session through your browser! Once inside the terminal, you will need to do one last thing before starting the pipeline:\n\n\nTerminal\n\n # This will create a session with an automatically assigned numeric name, starting from 0\ntmux \n\n# Alternatively, \ntmux new -s session_name\n\n\nThe tmux command initiates a persistent virtual command line session. This is crucial because, once the pipeline starts, you won’t be able to monitor its progress if your computer disconnects or enters sleep mode. With tmux, however, you can close your computer, and the session will still be active when you reconnect—unless your requested job time has run out. You’ll know you’re inside the tmux session by checking the bottom of the screen.\n\n\n\n\n\n\n\nReconnect to tmux session\n\n\n\nIf you want to leave the tmux session (and not kill it), you can do so by pressing simultaneously Ctrl and B keys, and then pressing D. Then, you can reconnect to the session using the command:\n\n\nTerminal\n\ntmux attach -t 0 # abbreviated as tmux a -t0\n\n\n\nLet’s prepare to run the pipeline. First, navigate to your personal drive—the one with your name (e.g.Yourname#xxxx)—and create a directory to serve as your working directory where all pipeline results will be saved (e.g., nfcore-results). Make sure you’re now located within this new directory. Finally, copy the bash script that runs the Nextflow command into this directory. Check the commands you need to run below:\n\n\nTerminal\n\n# Change location to your personal drive and create a new directory\n# cd /work/&lt;YourName#xxxx&gt;\nmkdir nfcore-results\n\n# Make the new dir your current working dir!\ncd nfcore-results # You might have named it differently \n\n# Copy our bash script to your directory \ncp /work/sequencing_data/scripts_v3.17.0/preprocessing_salmon.sh .\n\n# Make sure you have copied the bash script using the ls command \nls . \n# ls is a Linux shell command that lists directory contents of files and directories.\n\n# You will run the pipeline from here as follows BUT not yet! (that's why is commented out)\n# bash ./preprocessing_salmon.sh\n\nOnce copied, review the bash script:\n\n\npreprocessing_salmon.sh\n\n#!/bin/bash  # Specifies the script that should be run in the bash shell\n\nVRNASEQ=\"3.17.0\"    # Defines the version of the rnaseq pipeline to use\nPARAMSF=\"/work/sequencing_data/scripts_v3.17.0/nf-params_salmon.json\"   # Set path to configuration pipeline parameters (e.g. aligner software)\nCONFIGF=\"/work/sequencing_data/scripts_v3.17.0/maxcores.config\"     # Set path to maximum cores and memory usage (e.g. what you requested on UCloud)\n\nnextflow run nf-core/rnaseq -r $VRNASEQ -params-file $PARAMSF -profile conda -c $CONFIGF\n\nThis small bash script initiates the nf-core/rnaseq pipeline. It’s a good practice to save the command used to run the pipeline, and now you’ve copied it into your results folder! You’ll notice extra parameters and configuration settings defined in the script, which we’ll explore shortly. The final line of the script runs the RNA-seq pipeline with the specified version, parameters, Conda profile, and configuration file. We will inspect each part of the Nextflow command below in the section Understanding the pipeline arguments but we will first look at the different input files.\n\nParameters file\nThe parameters JSON file is used in nf-core pipelines to define various arguments and software options that can be utilized within the pipeline. You can find a full list of all parameters on the official page: https://nf-co.re/rnaseq/3.17.0/parameters/. Let’s take a look at the different configuration files we’re using.\nIn the parameters file, we specify that we want to use the pseudoaligner Salmon for this pipeline. The key elements we need to define are the location of the samplesheet, the output directory where Nextflow will save the results, and the paths to the FASTA and GTF files. It is important to use full paths for these locations to ensure that Nextflow can correctly access the required files, regardless of the working directory.\n\n\nnf-params.json\n\n{\n    \"input\": \"/work/sequencing_data/raw_reads/samplesheet_v3.17.0.csv\",\n    \"outdir\": \"preprocessing_results_salmon\",\n    \"fasta\": \"/work/sequencing_data/genomic_resources/homo_sapiens/GRCh38/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz\",\n    \"gtf\": \"/work/sequencing_data/genomic_resources/homo_sapiens/GRCh38/Homo_sapiens.GRCh38.109.MODIFIED.gtf.gz\",\n    \"pseudo_aligner\": \"salmon\",\n    \"skip_stringtie\": true,\n    \"skip_rseqc\": true,\n    \"skip_preseq\": true,\n    \"skip_qualimap\": true,\n    \"skip_biotype_qc\": true,\n    \"skip_bigwig\": true,\n    \"skip_deseq2_qc\": true,\n    \"skip_bbsplit\": true,\n    \"skip_alignment\": true,\n    \"extra_salmon_quant_args\": \"--gcBias\"\n}\n\n\n\n\n\n\n\nMore info on the parameters file\n\n\n\n--input parameter\nThe --input parameter points to the samplesheet.csv file that contains all the info regarding our samples.\n--outdir parameter\nThe --outdir parameter indicates where the results of the pipeline will be saved.\n--fasta parameter\nPath to FASTA reference genome file.\n--gtf parameter\nPath to GTF annotation file that contains genomic region information.\n--pseudo_aligner argument\nThe --pseudo_aligner argument indicates that we want to use salmon to quantify transcription levels.\nFinally, we are skipping several QC and extra steps that we did not explain in the previous lesson. Do not worry if you cannot manage to run the pipeline or you do not have the time, we have prepared a backup folder that contains the results from a traditional alignment + pseudoquantification for you to freely explore! (More about that below).\n\n\n\n\nSamples’ sheet\nLet’s quickly explore our samplesheet.csv file (we named it samplesheet_v3.17.0.csv to differentiate it from other formats, as the file may have slightly changed with Nextflow updates—don’t worry about this). Additional details, including descriptions of the columns here\n\n\nsamplesheet_v3.17.0.csv\n\nsample,fastq_1,fastq_2,strandedness\ncontrol_3,/work/sequencing_data/raw_reads/Control_3.fastq.gz,,unstranded\ncontrol_2,/work/sequencing_data/raw_reads/Control_2.fastq.gz,,unstranded\ncontrol_1,/work/sequencing_data/raw_reads/Control_1.fastq.gz,,unstranded\nvampirium_3,/work/sequencing_data/raw_reads/Vampirium_3.fastq.gz,,unstranded\nvampirium_2,/work/sequencing_data/raw_reads/Vampirium_2.fastq.gz,,unstranded\nvampirium_1,/work/sequencing_data/raw_reads/Vampirium_1.fastq.gz,,unstranded\ngarlicum_3,/work/sequencing_data/raw_reads/Garlicum_3.fastq.gz,,unstranded\ngarlicum_2,/work/sequencing_data/raw_reads/Garlicum_2.fastq.gz,,unstranded\n\nIt is crucial that the FASTQ paths inside this file match the paths in your job exactly! A good practice is to use the full paths to avoid any discrepancies.\n\n\nConfiguration file\nThe configuration file ensures that, regardless of how nf-core defines the resources required for each task, the jobs will run with the resources we allocate when submitting the job. You can learn more about configuration parameters here. One of Nextflow’s key strengths is its ability to run on virtually any computational infrastructure, so these settings might differ if you run the workflow on a different machine.\n\n\nmaxcores.config\n\nprocess {\n  resourceLimits = [\n    cpus: 8,\n    memory: 40.GB,\n  ]\n}",
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@@ -132,7 +132,7 @@
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     "title": "nf-core/rnaseq 3.17.0",
     "section": "Understanding the pipeline arguments",
-    "text": "Understanding the pipeline arguments\nLet’s divide the command into different sections.\n\n\nNextflow command\n\nnextflow run \\\n    nf-core/rnaseq -r &lt;VERSION&gt;\\\n    -params-file &lt;PARAMS_FILE&gt; \\\n    -profile &lt;conda,docker,arm&gt;  \\\n    -c &lt;CONFIG_FILE&gt;  \\\n\n\n\n\n\n\n\nBash scripting\n\n\n\nThe notation used in the command to run Nextflow is a common practice in bash scripting. It involves defining a variable for the file path (e.g., PARAMSF=\"/work/sequencing_data/scripts_v3.17.0/nf-params_salmon.json\") and then referencing the variable in the command (e.g., -params-file $PARAMSF). This approach enhances the readability and manageability of the script.\nYou can easily define other paths and reuse the script!\n\n\nFirst we have:\nnextflow run nf-core/rnaseq -r 3.17.0\nWhile usually one would run an nf-core pipeline using nextflow run nf-core/rnaseq and fetch the pipeline remotely, UCloud has installed the pipelines locally. Specifically, we are using the latest version by using the argument -r 3.17.0.\n\nThe -params-file argument is another nextflow core argument that allows us to give the nf-core rnaseq pipeline arguments in a json file, instead of creating an excessively long command. Writing the parameters this way allows for better reproducibility, since you can reuse the file in the future.\n# Set path to configuration pipeline parameters (e.g. aligner software)\nPARAMSF=\"/path/to/params.json\"   \n-params-file $PARAMSF\n\nThe next argument defines the -profile. Profiles can give configuration presets for different compute environments. Unfortunately, the UCloud implementation of the nf-core pipelines do not currently allow the use of docker or singularity, which are the recommended profile options. However, UCloud has made sure that there is a working conda environment ready to use!\n-profile conda\n\nFinally, we include the argument to specify the config file containing the resource settings:\n# Set path to maximum cores and memory usage (e.g. what you requested on UCloud)\nCONFIGF=\"/path/to/config.json\"   \n-c $CONFIGF\nThese are nf-core specific arguments that indicates nextflow to only use as maximum the number of CPUs and RAM we have requested when we submitted the job. We are using 8 cores since it is what we requested in the submission page (e.g. if you submitted a job with 4 CPUs, this will be equal to 4). We are using slightly less RAM than we requested (48Gb) just in case there is a problem with memory overflow.\n\n\n\n\n\n\nNeed more help?\n\n\n\n\nAsk your instructors if you have any questions before we start running it.\nYou can also check nf-core usage documentation here.",
+    "text": "Understanding the pipeline arguments\nLet’s divide the command into different sections.\n\n\nNextflow command\n\nnextflow run \\\n    nf-core/rnaseq -r &lt;VERSION&gt;\\\n    -params-file &lt;PARAMS_FILE&gt; \\\n    -profile &lt;conda,docker,arm&gt;  \\\n    -c &lt;CONFIG_FILE&gt;  \\\n\n\n\n\n\n\n\nBash scripting\n\n\n\nThe notation used in the command to run Nextflow is a common practice in bash scripting. It involves defining a variable for the file path (e.g., PARAMSF=\"/work/sequencing_data/scripts_v3.17.0/nf-params_salmon.json\") and then referencing the variable in the command (e.g., -params-file $PARAMSF). This approach enhances the readability and manageability of the script.\nYou can easily define other paths and reuse the script!\n\n\nFirst we have:\nnextflow run nf-core/rnaseq -r 3.17.0\nWhile usually one would run an nf-core pipeline using nextflow run nf-core/rnaseq and fetch the pipeline remotely, UCloud has installed the pipelines locally. Specifically, we are using the latest version by using the argument -r 3.17.0.\n\nThe -params-file argument is another nextflow core argument that allows us to give the nf-core rnaseq pipeline arguments in a json file, instead of creating an excessively long command. Writing the parameters this way allows for better reproducibility since you can reuse the file in the future.\n# Set path to configuration pipeline parameters (e.g. aligner software)\nPARAMSF=\"/path/to/params.json\"   \n-params-file $PARAMSF\n\nThe next argument defines the -profile. Profiles can give configuration presets for different compute environments. Unfortunately, the UCloud implementation of the nf-core pipelines does not currently allow the use of docker or singularity, which are the recommended profile options. However, UCloud has made sure that there is a working conda environment ready to use!\n-profile conda\n\nFinally, we include the argument to specify the config file containing the resource settings:\n# Set path to maximum cores and memory usage (e.g. what you requested on UCloud)\nCONFIGF=\"/path/to/config.json\"   \n-c $CONFIGF\nThese are nf-core specific arguments that indicate Nextflow to only use as maximum the number of CPUs and RAM we requested when we submitted the job. We are using 8 cores since it is what we requested on the submission page (e.g. if you submitted a job with 4 CPUs, this will be equal to 4). We are using slightly less RAM than we requested (48Gb) just in case there is a problem with memory overflow.\n\n\n\n\n\n\nNeed more help?\n\n\n\n\nAsk your instructors if you have any questions before we start running it.\nYou can also check nf-core usage documentation here.",
     "crumbs": [
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@@ -156,7 +156,7 @@
     "href": "develop/04c_preprocessing_nf3170.html#restarting-a-failed-run",
     "title": "nf-core/rnaseq 3.17.0",
     "section": "Restarting a failed run",
-    "text": "Restarting a failed run\nWhen running a nf-core pipelines for the first time, you might encounter some errors, for example, one of your files has an incorrect path, or a program failed to do its job. It is very important to read the error message carefully (try Googling it, as chances are others have encountered the same issue)!\n\n\n\n\n\n\nFailure\n\n\n\n# Error in terminal\nError executing process &gt;\nCaused by:\n    Failed to create create conda environment\n\n\n\nOnce you fix the error, it is possible to resume a pipeline instead of restarting the whole workflow. You can do this by adding the -resume argument to the nextflow command:\nnextflow run nf-core/rnaseq -r $VRNASEQ -params-file $PARAMSF -profile conda -c $CONFIGF -resume",
+    "text": "Restarting a failed run\nWhen running a nf-core pipeline for the first time, you might encounter some errors, for example, one of your files has an incorrect path, or a program failed to do its job. It is very important to read the error message carefully (try Googling it, as chances are others have encountered the same issue)!\n\n\n\n\n\n\nFailure\n\n\n\n# Error in terminal\nError executing process &gt;\nCaused by:\n    Failed to create create conda environment\n\n\n\nOnce you fix the error, it is possible to resume a pipeline instead of restarting the whole workflow. You can do this by adding the -resume argument to the Nextflow command:\nnextflow run nf-core/rnaseq -r $VRNASEQ -params-file $PARAMSF -profile conda -c $CONFIGF -resume",
     "crumbs": [
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@@ -180,7 +180,7 @@
     "href": "develop/04c_preprocessing_nf3170.html#saved-results",
     "title": "nf-core/rnaseq 3.17.0",
     "section": "Saved results",
-    "text": "Saved results\nAfter finishing the job, everything that the pipeline has created will be saved in the directory you created in your personal drive.\n\nAll generated files will be saved in a volume with your username, that you should be able to find under the menu Files &gt; Drives &gt; Member Files:YourName#numbers\n\n\n\nYou will find the results under the preprocessing_results_salmon which you used as --outdir\n\n\n\n\n\n\n\n\nNextflow work directory\n\n\n\nA common practice is to remove the work directory once the pipeline completes successfully. This helps save storage space in the long run, as the work directory can accumulate over multiple runs. Feel free to explore the contents to see more details on how the jobs were run, and if there are any failures, you can find additional information there.\n\n\n\n\n\n\n\n\nDid you not save the results in a specific folder?\n\n\n\n\n\nYou can always find past generated files by looking into the Jobs folder! You will need to remember which app and job name you provided before subting the job to UCloud. For example,\n\nGo to Jobs -&gt; nf-core: rnaseq -&gt; job_name (RNAseq preprocessing ...) -&gt; preprocessing_results_salmon \n\n\n\n\nNow you have access to the full results of your pipeline! As explained in the previous lesson, the nf-core rnaseq workflow will create a MultiQC report summarizing several steps into a single html file that is interactive and explorable. In addition, there will be a folder with the results of the individual QC steps as well as the alignment and quantification results. Take your time and check it all out!",
+    "text": "Saved results\nAfter finishing the job, everything that the pipeline has created will be saved in the directory you created in your personal drive.\n\nAll generated files will be saved in a volume with your username, which you should be able to find under the menu Files &gt; Drives &gt; Member Files:YourName#numbers\n\n\n\nYou will find the results under the preprocessing_results_salmon which you used as --outdir\n\n\n\n\n\n\n\n\nNextflow work directory\n\n\n\nA common practice is to remove the work directory once the pipeline completes successfully. This helps save storage space in the long run, as the work directory can accumulate over multiple runs. Feel free to explore the contents to see more details on how the jobs were run, and if there are any failures, you can find additional information there.\n\n\n\n\n\n\n\n\nDid you not save the results in a specific folder?\n\n\n\n\n\nYou can always find past generated files by looking into the Jobs folder! You will need to remember which app and job name you provided before submitting the job to UCloud. For example,\n\nGo to Jobs -&gt; nf-core: rnaseq -&gt; job_name (RNAseq preprocessing ...) -&gt; preprocessing_results_salmon \n\n\n\n\nNow you have access to the full results of your pipeline! As explained in the previous lesson, the nf-core rnaseq workflow will create a MultiQC report summarizing several steps into a single html file that is interactive and explorable. In addition, there will be a folder with the results of the individual QC steps as well as the alignment and quantification results. Take your time and check it all out!",
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@@ -192,7 +192,7 @@
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     "title": "nf-core/rnaseq 3.17.0",
     "section": "Downstream analysis using your results",
-    "text": "Downstream analysis using your results\nOptional: If you have successfully run nf-core RNA jobs and want to use the files you generated from your own preprocessing results, don’t forget to choose use folder when submitting a new job. How?\n\nGo to Select folders to use and Add folder by selecting the one containing the pipeline results like shown in the image above. They will be located in Member Files:YourName#numbers -&gt; nfcore-results -&gt; preprocessing_results_salmon (unless you have moved them elsewhere).",
+    "text": "Downstream analysis using your results\nOptional: If you have successfully run nf-core RNA jobs and want to use the files you generated from your own preprocessing results, don’t forget to choose Use folder when submitting a new job. How?\n\nGo to Select folders to use and Add folder by selecting the one containing the pipeline results like shown in the image above. They will be located in Member Files:YourName#numbers -&gt; nfcore-results -&gt; preprocessing_results_salmon (unless you have moved them elsewhere).",
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     <loc>https://hds-sandbox.github.io/bulk_RNAseq_course/develop/05c_count_normalization.html</loc>
-    <lastmod>2024-11-14T13:28:32.009Z</lastmod>
+    <lastmod>2024-11-14T13:57:05.990Z</lastmod>
   </url>
 </urlset>