diff --git a/access/UCloud.qmd b/access/UCloud.qmd index a4408043..b264702d 100644 --- a/access/UCloud.qmd +++ b/access/UCloud.qmd @@ -17,6 +17,10 @@ Each Danish university has its usage relationship with UCloud as governed by the Extensive documentation on the general use of UCloud (how to use apps and run jobs, etc.) is available in the UCloud [user guide](https://docs.cloud.sdu.dk/). +:::{.callout-tip} +Click on the images to view them in full size. +::: + ## Example: how to open a Sandbox app ### **Step 1** @@ -30,7 +34,7 @@ When you are logged in, choose the project from the dashboard (highlighted in re For this example, we select Sandbox_workshop. -![Dashboard: your workspace](../assets/images/workspace.png){fig-alt="Screenshot of your dashboard"} +![Dashboard: your workspace](../images/workspace.png){fig-alt="Screenshot of your dashboard" .black-box .lightbox} On the left side, you can see the structure of the project (content changes when you select a different project): @@ -55,7 +59,7 @@ In the dashboard, you will also find news, your favorite apps, recent runs, reso Then click on Apps in the left panel to investigate what tools and environments you can use (green circle). The easiest way to find Sandbox resources is to search via the toolbar (red circle). In this example, we'll select the Genomics Sandbox (which will bring you to the submission screen). -![Dashboard: all apps](../assets/images/apps.png){fig-alt="Screenshot of apps dashboard"} +![Dashboard: all apps](../images/apps.png){fig-alt="Screenshot of apps dashboard" .black-box .lightbox} :::{.callout-tip} Mark them as favorites so they appear on your dashboard. @@ -65,7 +69,7 @@ Mark them as favorites so they appear on your dashboard. Click on the app button to get into the settings window. First, we recommend reading the documentation of the app (highlighted in green). Then, you can configure the app as shown below, or be provided with a configuration file made available in a workshop's project folders (*import parameters*) which will take care of everything for you. -![Dashboard jobs: configuration step](../assets/images/configure_NGS.png){fig-alt="Screenshot of how to configure your job"} +![Dashboard jobs: configuration step](../images/configure_NGS.png){fig-alt="Screenshot of how to configure your job" .black-box .lightbox} In this example, we configure our session by: @@ -88,7 +92,7 @@ There are different types of apps, and therefore, interfaces. Some, like RStudio Wait to go through the queue. When the session starts, the timer begins to count down. In a couple of minutes, you should be able to open the interface through the button (green circle) in a new window (refresh the window if needed). -![Dashboard jobs: running the app](../assets/images/running_NGS.png){fig-alt="Screenshot of dashboard running app"} +![Dashboard jobs: running the app](../images/running_NGS.png){fig-alt="Screenshot of dashboard running app" .black-box .lightbox} This page will remain open while you work (or you can return to it via 'Runs' in the left panel). You can end your session early by pressing and holding 'Stop application' (pink circle), you can see how much time you have left (red circle) and you can add hours to your session as you go (buttons in blue square). @@ -96,11 +100,11 @@ This page will remain open while you work (or you can return to it via 'Runs' in If you are testing the genomic app, your interface should look like in the image below. Different apps might use other development environments. In this case, you will be working from [JupyterLab](https://jupyter.org/). You can open Jupyter Notebooks (yellow square), R studio (blue square) or a terminal (black square) among others. In this case, #1 and #2 have all the software and packages that you will need pre-installed (this is not the case with Python 3 to the left). -![JupyterLab interface: running the app](../assets/images/interface_jupyterlab.png){fig-alt="Screenshot of JupyterLab interface"} +![JupyterLab interface: running the app](../images/interface_jupyterlab.png){fig-alt="Screenshot of JupyterLab interface" .black-box .lightbox} You can navigate through the different folders and start running the Python notebooks (pink arrow). -![JupyterLab interface: openning notebook](../assets/images/openning_notebook.png){fig-alt="Screenshot of Jupyter Notebook"} +![JupyterLab interface: openning notebook](../images/openning_notebook.png){fig-alt="Screenshot of Jupyter Notebook" .black-box .lightbox} If you are an advanced user, you can also create your own Python files and select the kernel *NGS (python)* to use the pre-installed software. Learn how to manage (upload and download new data) and share files that you have created/developed with collaborators [here](https://docs.cloud.sdu.dk/tutorials/tutorial1.html). diff --git a/css/materialight.scss b/css/materialight.scss index 35ceb79f..f92c7427 100644 --- a/css/materialight.scss +++ b/css/materialight.scss @@ -6,6 +6,18 @@ $navbar-bg: #4266A1; $sidebar-bg: white; $footer-bg: #4266A1; +// Center h1 titles .centered h1 { text-align: center; - } \ No newline at end of file + } + +// embedded image in titles h2 + +.embedded h2 { + margin-bottom: -100px; +} + +// frames for images +.black-box { + border: 1px solid rgb(169, 169, 169); +} diff --git a/modules/index.qmd b/modules/index.qmd index dffda603..51e60148 100644 --- a/modules/index.qmd +++ b/modules/index.qmd @@ -16,10 +16,14 @@ You can **access our training modules** through: + Independently accessible **Sandbox apps on [GenomeDK](https://genome.au.dk)**, the bioinformatics high-throughput HPC at University of Aarhus + Virtual machines deployed on the **[Course Platform](https://www.computerome.dk/solutions/course-platform) at Computerome**, the academic HPC at the Technical University of Denmark (Sandbox rollout still under development!) **tutorials and guides** and **popular tools for analysis and visualization**. [Email us](mailto:nhds_sandbox@sund.ku.dk) with any questions, comments or suggestions for new workshops! + +  -## Genomics +## Genomics {.embedded} -![](../images/genomics2.png){fig-align="right" width="20%"} +
+![](../images/genomics2.png){fig-align="right" width="18%"} +
Genomics is the study of genomes, the complete set of an organism's DNA. Genomics research now encompasses functional and structural studies, epigenomics, and metagenomics, and genomic medicine is under active implementation and extension in the health sector. @@ -29,9 +33,13 @@ Genomics is the study of genomes, the complete set of an organism's DNA. Genomic - [Introduction to Population Genomics](https://hds-sandbox.github.io/PopulationGenomicsCourse/) (implementation of a course by Prof. Kasper Munch of Aarhus University) (last update: March 2023) - [Introduction to GWAS](https://hds-sandbox.github.io/GWAS_course/) (last update: March 2023) -## Transcriptomics +  + +## Transcriptomics {.embedded} -![](../images/transcriptomics.png){fig-align="right" width="20%"} +
+![](../images/transcriptomics.png){fig-align="right" width="18%"} +
Transcriptomics is the study of transcriptomes, which investigates RNA transcripts within a cell or tissue to determine what genes are being expressed and in what proportion. These RNA transcripts include mRNAs, tRNA, rRNA, and other non-coding RNA present in a cell. @@ -42,10 +50,13 @@ Transcriptomics is the study of transcriptomes, which investigates RNA transcrip - Cirrocumulus (a popular tool for visualizing different types of RNA-seq data and downstream analysis) - RNAseq in RStudio (RStudio session with pre-installed RNAseq analysis packages for exploring with your own uploaded data) +  -## Proteomics +## Proteomics {.embedded} -![](../images/proteomics.png){fig-align="right" width="20%"} +
+![](../images/proteomics.png){fig-align="right" width="18%"} +
Proteomics is the study of proteins that are produced by an organism. Proteomics allows us to analyze protein composition and structure, which have great importance in determining their function. @@ -58,10 +69,13 @@ We also offer a tutorial on UCloud's [ColabFold app](https://cloud.sdu.dk/app/jo - [ColabFold Intro](https://hds-sandbox.github.io/proteomics-sandbox/colabfold.html) (last update: October 2022) +  -## Electronic Health Records +## Electronic Health Records {.embedded} -![](../images/EHRs.png){fig-align="right" width="20%"} +
+![](../images/EHRs.png){fig-align="right" width="18%"} +
Electronic health records (EHRs) are digital records kept in the public health sector that record the medical histories of individuals, and access is normally highly restricted to preserve patient privacy. This data is sometimes also shared (partly or in full) in secondary patient registries that support research on a specific disease or condition (such as breast cancer or cystic fibrosis). These datasets are extraordinarily valuable in the development of predictive models used in precision medicine. @@ -70,9 +84,13 @@ The chronic lymphocytic leukemia synthetic dataset listed below is generated sol - Chronic Lymphocytic Leukemia synthetic dataset created for use in "Fra realworld data til personlig medicin", a course from the University of Copenhagen's [MS in Personlig Medicin](https://personligmedicin.ku.dk/) (last update: January 2023) - Intro to EHR analysis (workshop under development) -## Data Carpentry and management +  + +## Data Carpentry and management {.embedded} -![](../images/HPC.png){fig-align="right" width="20%"} +
+![](../images/HPC.png){fig-align="right" width="18%"} +
Computing skills are an important foundation for health data science (and using the above training modules), but formal training is often lacking as biomedical researchers navigate increasingly difficult computational tasks in their projects. These skills range from programming to the use of high-performance computers (HPC) to proper research data management.