Agenda
Visual Communication for Researchers
Date: Wednesday 10 December 2025 9:00-11:00 Add to calendar
Room: Prolog (room 2465)
Description
The impact of research often depends on how clearly its ideas are communicated. In this hands-on workshop, participants learn how to use visual communication and design principles to make their science more engaging and easier to understand. The session focuses on the foundations of good design and how simple choices can transform a figure, slide, or graph into something that connects with an audience. Rather than focusing on a specific tool, the workshop builds skills that apply anywhere, from PowerPoint and Illustrator to R or Python. The goal is to help researchers think more like a designers: to plan visuals that are clear, intentional, and visually balanced. By the end of the session, participants will know how to create visuals that not only look professional but also tell a story, making complex ideas easier to follow and remember.
Learning outcomes
- After attending this workshop, learners will be able to apply core design principles to create clearer, more impactful visuals; refine slides and charts for effective communication; identify and correct common visual issues; and design visuals with intention to guide attention and strengthen their message.
Target audience
PhD and researchers who want to communicate their work with more confidence and impact.
Pre-requisites
- None.
Equipment to bring
none.
Instructor(s):
Elisa Pierfederici (main)
Basics of AlphaFold
Date: Thursday 11 December 2025 13:00-16:00 Add to calendar
Room: Java (room 2423)
Description
In this workshop you will learn how to use AlphaFold3 to predict the structure of proteins. The workshop provides examples of protein-protein complexes, proteins with PTMs, protein-DNA complexes and proteins in complex with simple ligands. You will learn how to interpret AlphaFold results in terms of confidence. If you have a Fox account, you will also learn how to use the AlphaFold2 package installed on the Fox server. We will also briefly explain how to access and use the software for processing experimental cryoEM data using the Fox server.
Learning outcomes
- After attending this workshop, learners are able to: run and interpret results of web based AlphaFold3 set-up and run AlphaFold2 installed on Fox server visualize, analyze, and compare predicted models using ChimeraX have knowledge on how to find and run Relion for cryoEM processing on Fox server.
Target audience
The course is suitable for all career levels.
Pre-requisites
- Programming experience is not required, but participants should be familiar with using the terminal to run Fox-based programs. The first part of the workshop will focus on the web-based AlphaFold, which does not require a Fox account and use of terminal. All participants should familiarize themselves with ChimeraX.
Equipment to bring
Participants should bring their own laptop with installed ChimeraX for protein visualization. A uio account is also required to access Fox.
Instructor(s):
Nikolina Sekulic (main)
Ahmad Ali-Ahmad
Developing Scientific Software of Industrial Quality
Date: Thursday 11 December 2025 13:00-17:00 Add to calendar
Room: Perl (room 2453)
Description
In this workshop, we will bridge the gap between writing code that "works for now" and developing robust, reliable, and maintainable software that meets industrial standards. Many scientists and researchers are self-taught programmers, focusing on rapid prototyping and results. However, as software becomes critical for analysis, simulation, and decision-making, its quality, reproducibility, and longevity become paramount. We will discuss the core principles and practical tools for building scientific software that is not only correct but also trustworthy and scalable. The session will cover foundational concepts like version control, testing, and documentation, framing them as essential components of the modern scientific workflow, not just "extra work." Finally, we will demonstrate how to structure a small scientific project from scratch, applying these best practices to create a solid foundation for future development.
Learning outcomes
After attending the workshop, participants will:
- Understand the key differences between exploratory code and production-quality software.
- Be aware of fundamental best practices including version control with Git, writing effective tests, and creating usable documentation.
- Know how to structure a project for clarity and reproducibility.
Target audience
Master students, PhD students, postdocs, and PIs in all scientific domains who write code for their research and want to make it more robust, reusable, and collaborative.
Pre-requisites
- Beginner-to-Intermediate knowledge of either Python, R or Julia is required. Participants should be comfortable writing basic scripts and functions. Familiarity with working from the command line/terminal is helpful but not mandatory.
Equipment to bring
A laptop is mandatory for this workshop.
Instructor(s):
Daniel Rivas (main)
Computational Structural Biology – How to Analyze and Visualize Molecules in 3D
Date: Tuesday 09 December 2025 13:00-16:00 Add to calendar
Room: Prolog (room 2465)
Description
This hands-on workshop introduces participants to the fundamentals of computational structural biology and the practical skills needed to visualize and analyze biomolecular structures in three dimensions. After a brief introduction to the principles and applications of structural biology, we will focus on protein structure visualization and analysis using PyMOL, a widely used molecular graphics tool. Participants will explore real examples, including full antibodies, single-domain antibodies (VHHs), and antibody–antigen complexes, gaining an understanding of how structural insights inform research in molecular biology, immunology, and drug design. The session will also include a concise overview of antibody architecture, helping attendees interpret structural features relevant to binding and function. By the end of the workshop, participants will be able to load, visualize, and manipulate molecular structures, identify key interaction sites, and generate publication-quality 3D representations. No prior experience in structural biology is required. Bring just curiosity and an interest in exploring how molecules look and behave in 3D space!
Learning outcomes
- After attending this workshop, learners will be able to: 1) Understand the basic concepts and applications of structural biology, 2) Describe the overall structure and components of antibodies and nanobodies (VHHs), 3) Use PyMOL to visualize, explore, and analyze 3D molecular structures, 4) Interpret structural features of antibodies and antibody–antigen complexes, 5) Apply molecular visualization skills to support research and communication in structural biology.
Target audience
This workshop is designed for students, early-career researchers, and professionals in the fields of biology, biochemistry, biotechnology, and bioinformatics who are interested in exploring molecular structures and learning how to visualize and analyze biomolecules in 3D. It is ideal for participants curious about the applications of computational tools in structural biology and antibody research.
Pre-requisites
- No prior experience in structural biology or programming is required. Basic understanding of molecular biology or biochemistry will be helpful but not essential. Participants should bring a laptop with PyMOL installed (a free educational version is available). Instructions for installation and test files will be provided before the workshop.
Equipment to bring
Participants should bring their own laptop with PyMOL installed (a free educational version is available). Installation instructions and example files will be shared before the workshop. No other software or accounts are required.
Instructor(s):
Eva Smorodina (main)
Manage your research smarter (Zotero)
Date: Thursday 11 December 2025 9:00-12:00 Add to calendar
Room: Perl (room 2453)
Description
Zotero is a research assistant tool that helps you not only manage your references, but collect and organize your research material, notes, and annotations. In this workshop, you will learn how to collect, organize, and share your bibliographies and research, including your notes and annotations.
Learning outcomes
- Bring your laptop and together we will explore the many ways Zotero can assist your research, importing digital copies of your research material, even scanning and importing your own research notes. With Zotero you can highlight your PDFs with a digital marker, tag text sections based on topics, write annotations in the margin, and write item attached or standalone notes. You can easily retrieve tagged sections and generate reports with your highlighted text sections, annotations, and notes in Zotero. Reports are a helpful research tool for gathering summaries of key points in your research material. The third course hour will provide optional time to practice and get help working with the tool.
Target audience
The workshop is open to all students and researchers, but the target group is PhDs and postdocs.
Pre-requisites
- Please bring your laptop with Zotero installed. This workshop requires some experience using Zotero to import references.
Equipment to bring
A laptop with Zotero application and Zotero browser plugin installed. See instructions for installation here: https://www.ub.uio.no/english/writing-publishing/referencing/zotero/get-started/index.html#install
Instructor(s):
Anne Sabo (main)
Spatial Data In Julia!
Date: Friday 12 December 2025 13:00-16:00 Add to calendar
Room: Perl (room 2453)
Description
Spatial biology is rapidly evolving, no one is really sure what the best approach or standard analysis will look like. With a fun and fast programming language like Julia, you can test out new solutions and approaches quickly in a reactive and reproducible notebook environment. Images are just big matrices, cell data just a big data frame. Why not wrangle them with a nicely designed language that makes vectorizing functions as easy as ".", and arrays are as easy as [1,2,3]?
Learning outcomes
- After attending this workshop, learners are able to/know how to use basic julia syntax, Pluto reactive notebooks and make plots with standard plotting library, Makie to analyze spatial single cell data. We'll use these basic building blocks to get you started on you journey with Julia and spatial biology.
Target audience
People who want to do spatial analysis but are frustrated with the tools that are out there. Bioinformaticians who are curious about what Julia looks like and whether it could be a useful language to learn. General nerds excited by new languages.
Pre-requisites
- The prerequisites are some programming experience, a good internet connection.
Equipment to bring
a laptop, preferably with julia installed using juliaup.
Instructor(s):
Colin LaMont (main)
Exploring Transcription Factor Binding with JASPAR: From PFMs to Deep Learning Models
Date: Monday 08 December 2025 13:00-16:00 Add to calendar
Room: Python (room 2269)
Description
JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over two decades. In this workshop, we will introduce participants to the latest updates to the CORE and UNVALIDATED collections, as well as the newly developed Deep Learning (DL) collection of JASPAR. The first part will cover the classical PFM-based collections, offering an overview of their structure, applications, and recent additions. The second part will focus on the Deep Learning collection, where participants will learn how to explore, download, and use DL models and derived motifs. Through guided hands-on exercises, we will demonstrate how to (i) scan sequences on the JASPAR website using DL-derived motifs, (ii) download DL models and related motif data, and (iii) perform model-based analyses using the tangermeme Python package (https://github.com/jmschrei/tangermeme). By the end of the session, attendees will gain both conceptual understanding and practical skills for integrating JASPAR resources into their bioinformatics research.
Learning outcomes
After this workshop, participants will be able to:
- Understand the structure and use of JASPAR’s PFM and deep learning collections
- Navigate and query the JASPAR website for TF binding profiles and models
- Use the tangermeme package to analyze DL models and motifs
Target audience
Researchers, students, and bioinformaticians interested in gene regulation, transcription factor binding, or computational motif analysis. Both beginners and intermediate users of JASPAR are welcome.
Pre-requisites
- Basic understanding of TF binding and gene regulation
- A foundational understanding of supervised learning and how neural networks function at a basic level
- Familiarity with Python and Jupyter notebooks (recommended, not mandatory)
Equipment to bring
A laptop
Instructor(s):
Damla Övek Baydar (main)
Ieva Rauluseviciute
Workflow management and sharing with Nextflow and nf-core
Date: Monday 08 December 2025 9:00-12:00 13:00-16:00 Add to calendar
Room: Prolog (room 2465)
Description
Managing bioinformatics workflows (e.g. sequencing analysis pipeline) is a central problem for many of us. Simple bash scripts are maybe easy to write, but hard to run and maintain. Snakemake is a good solution for reproducibility and automation, but it is hard to reuse its components. Nextflow is a workflow manager tool like Snakemake, but solves those problems too that make Snakemake sometimes difficult to run. The price to pay is that getting into Nextflow might be hard. Here, we would like to reduce the initial time investment and help you understand the main concepts while you try building a small pipeline.
Learning outcomes
- After attending the workshop you will be more confident in starting your next project in Nextflow, and keep learning more about its ways of solving various challenges. We will explain the logic and basics of Nextflow. We will guide you for browsing among the available collection of stable pipelines and modules provided by nf-core. Through an example project of processing paired-end sequencing reads for CAGE analysis, you will have hands-on experience about building a pipeline by lego-ing together modules published in nf-core and adding your own.
Target audience
Bioinformaticians of any stage, field or interest are welcome. This is a technical workshop which can benefit anyone.
Pre-requisites
- It will be easier for you to appreciate the solutions of Nextflow if you have experience with pipelines, so that you experienced the common challenges. We will also make some comparisons to Snakemake. Neither of these are strong requirements as we will explain the key points through an example.
Equipment to bring
Attendees should bring their own laptop.
Instructor(s):
Katalin Ferenc (main)
Villads Winton
Improving software quality in bioinformatics through testing
Date: Friday 12 December 2025 13:00-16:00 Add to calendar
Room: Prolog (room 2465)
Description
What is testing? What types of tests are we usually performing and what types are we not? We argue that bioinformaticians, especially tool developers, should integrate unit tests and automated tests into their development practices. In this workshop we show and practice how to write unit tests and make your code testable. Code testing is fundamental to achieve good quality software. Adding automated tests to a software base have several advantages. It increases confidence that the software performs as intended. Test coverage reduces the time required for ongoing development (eg. updating or adapting a tool or a pipeline) by ensuring that the new changes are not affecting the existing functionality in unexpected ways. Writing tests enforces thinking about cases where the code might fail, thus improving the usability of the software. This process also helps improve the modularization of the code, because complex, convoluted code is much harder to test. However, thinking about tests for the first time, especially thinking about good tests for testing scientific software is not easy. Through this workshop we aim to help you get started with unit testing. If time allows, we also spend some time on how to automate tests via GitHub actions.
Learning outcomes
- After attending this workshop, participants are more aware about the mindset and available tools to better test their software in an automated, detailed and thorough manner. We hope that participants will employ these techniques when they write their own tools and maybe even analysis pipelines, thus producing more robust and clean code.
Target audience
Bioinformaticians who write scripts, functions, or whole packages, but not quite (or not at all) get the hang of automated unit tests yet. Professionals of any career stages are welcome.
Pre-requisites
- Attendees should have some coding experience with Python or R.
Equipment to bring
Attendees should bring their laptops with set-up Python and/or R installations, so it is ready to be used. We encourage the participants to bring their own code (scripts or functions) that would be subjected to testing.
Instructor(s):
Katalin Ferenc (main)
Ladislav Hovan
Getting started with Python in VS Code
Date: Thursday 11 December 2025 9:00-12:00 Add to calendar
Room: Prolog (room 2465)
Description
This workshop is intended to get people started with Python development using VS Code, a lightweight extensible integrated development environment (IDE). It is not really intended to teach you Python - Python code will be used for demonstrations, as well as Jupyter notebooks, but the aim is to get you to the point where you can either follow Python tutorials easily, inspect and modify other people's Python code, or even start your own project! To this end, we will cover some useful aspects of VS Code such as integration with git, conda environments, and remote development.
Learning outcomes
- After attending this workshop, learners are able to use VS Code to: write and execute Python code, work on remote machines, create and manage conda environments, work within Jupyter notebooks, and sync their code remotely using git
Target audience
people at any career stage that would like to try out Python, may even have followed some tutorials online, but are wondering how to properly get started
Pre-requisites
- curiosity and interest in the topic :) some basic knowledge of shell commands and Python would also be useful, but the point is to help you get started on your journey
Equipment to bring
laptop; VS Code installed and a GitHub account would be a plus (but we can do it during the workshop)
Instructor(s):
Ladislav Hovan (main)
Introduction to neural oscillations analysis
Date: Friday 12 December 2025 9:00-12:00 Add to calendar
Room: Perl (room 2453)
Description
In the brain, vast networks of neurons are continuously active, exchanging information through electrical and chemical signals. The combined activity of these neurons generates rhythmic fluctuations in electrical fields, known as neural oscillations. Analysing these oscillations offers a window into ongoing neural computations and allows to distinguish between different functional brain states. In this workshop, we will explore different techniques to analyse the oscillations from electrocorticograms (ECoG) as well as Local Field Potential (LFP) recorded in the hippocampus of sleeping and behaving rats. We will start by working through specific examples to highlight the strengths and limitations of different methods, from basic filtering to more advanced approaches such as the Fourier transform and Morlet wavelet analysis. We will then explore how spectral features relate to different brain states, for example distinguishing sleep stages or identifying signatures of motor activity. Finally, we will address common pitfalls in time-frequency analysis, such as motion artifacts and electrical noise contamination, and present strategies to mitigate them.
Learning outcomes
- Basic understanding of neuronal oscillations
- Learn methods to extract and analyse oscillatory activity
- Use oscillatory features to classify sleep stages
- Recognise common pitfalls and ways to address them
Target audience
This workshop is designed for graduate, post-graduate students and researchers at any level who are interested in understanding and applying methods to analyse neuronal oscillations.
Pre-requisites
- Basic programming knowledge of Python is required. (numpy, matplotlib, scipy, …). Familiarity with electrophysiological recordings is helpful but not required.
Equipment to bring
A computer with Python and Jupyter notebook installed.
Instructor(s):
Cantin Ortiz (main)
Letizia Signorelli
Deep Learning for Drug-Target Interaction Prediction and Drug Repurposing
Date: Monday 08 December 2025 9:00-11:00 Add to calendar
Room: Python (room 2269)
Description
This workshop will explore deep learning applications in drug discovery, focusing on two key areas: drug-target interaction (DTI) prediction and drug repurposing. Participants will learn how deep learning models, such as Convolutional Neural Networks (CNNs), can be utilized to predict interactions between drugs and targets, as well as identify potential repurposing opportunities for existing drugs. The workshop will include hands-on coding sessions using publicly available datasets, such as the KIBA dataset, and practical model implementation.
Learning outcomes
- After attending this workshop, participants will gain an understanding of how to use deep learning in two significant aspects of drug discovery: drug-target interaction prediction and drug repurposing. They will gain practical experience in data preparation, model building, and evaluation, providing them with the skills needed to leverage AI techniques in drug discovery and development.
Target audience
Early-stage researchers, graduate students, and professionals in computational biology and bioinformatics with a basic understanding of machine learning and Python.
Pre-requisites
The workshop's prerequisites include:
- Basic understanding of Python programming, e.g., familiarity with core Python data structures, such as lists, dictionaries, and NumPy arrays and experience with basic data manipulation libraries like pandas 2.A foundational understanding of supervised learning and how neural networks function at a basic level (e.g., concepts like layers, weights, and activation functions). Although advanced familiarity with deep learning frameworks is not required, some previous exposure to Pytorch is beneficial 3.Some experience with Google Colab (or at least Jupyter Notebook)
Equipment to bring
A laptop
Instructor(s):
Damla Övek Baydar (main)
Using SiSaNA for the reconstruction and analysis of patient-specific gene regulatory networks
Date: Thursday 11 December 2025 13:00-16:00 Add to calendar
Room: Prolog (room 2465)
Description
Reconstructing gene regulatory networks is a common method in the field of bioinformatics for uncovering the transcriptional regulation driving patterns of disease. In these networks, some tools (such as PANDA and LIONESS) model the relationship between transcription factor and target genes. However, these tools are commonly not user-friendly, requiring a fair amount of prior programming experience to be able to reconstruct the networks and perform the downstream network. Therefore, a new tool called SiSaNA (Single-Sample Network Analysis) was developed. SiSaNA allows users to reconstruct and analyze single-sample gene regulatory networks all via the command line, eliminating the need for prior programming experience. Attendees will be worked through an example analysis that comes with the SiSaNA distribution, where we will preprocess input data, reconstruct networks, calculate critical network properties for identifying transcription factors and genes of interest, visualize these results, and identify biological pathways that are enriched in the sample groups. More information on SiSaNA can be found at the following GitHub repository: https://github.com/kuijjerlab/sisana/
Learning outcomes
- After attending this workshop, learners will be able to use SiSaNA to reconstruct and analyze single-sample (patient-specific) networks with RNA-Seq data from their own projects. Learners will know how to interpret the created networks and how to find top genes that are differentially regulated between sample groups, as well as identify pathways these genes are involved in.
Target audience
Researchers who have transcriptomic data (RNA-Seq or microarray data) or are using publicly available datasets such as The Cancer Genome Atlas (TCGA) can benefit from using SiSaNA. Users should be interested in better understanding the regulatory mechanisms that distinguish two sample groups from one another.
Pre-requisites
- Basic knowledge of navigating the command line in a Linux environment is necessary, but users do not need any prior programming expertise. However, users will need to install SiSaNA prior to the workshop. If users have issues with the installation, I will attempt to help troubleshoot the installation during the workshop, but I cannot guarantee we will have time to do that for everyone, so please complete the installation before-hand if possible. All installation steps can be found on the SiSaNA GitHub repo: https://github.com/kuijjerlab/sisana
Equipment to bring
Laptop with a linux environment and miniconda installed (https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html)
Instructor(s):
Nolan Newman (main)
Statistical principles in machine learning for small biomedical data
Date: Friday 12 December 2025 9:00-12:00 Add to calendar
Room: Prolog (room 2465)
Description
A central problem in machine learning is how to make an algorithm perform well not just on the training data, but also on new inputs. Many strategies in machine learning are explicitly designed to reduce this test error, possibly at the expense of increased training error. These strategies are collectively known as regularisation and they are instrumental for good performance of any kind of prediction or classification model, especially in the context of small data (many features, few samples). We will discuss basic connected concepts of generalisation, overfitting, bias-variance trade-off and regularisation and will illustrate the principles with penalised (generalised) linear regression models, with ridge, lasso and elastic net penalties as prominent examples. Finally, we will present the idea of structured penalties and priors, which can be tailored to account for structures present in the data, e.g. multi-modality or complex correlation structures. We will use examples from large-scale cancer pharmacogenomic screens, where penalised regression and alternative Bayesian approaches are used for predicting drug sensitivity and synergy based on the genomic characterisation of tumour samples. In the hands-on tutorial we will use R to perform an integrated analysis of multi-omics data with penalised regression.
Learning outcomes
- After attending this workshop, learners will understand key concepts for training machine learning models such as regularisation and how to incorporate data structure in the regularisation process.
Target audience
Graduate, post-graduate students and researchers at any level who are interested in applying machine learning methods to small data (i.e., few examples but potentially many features) or noisy data (e.g. biomedical data).
Pre-requisites
- Basic familiarity with R. Introductory level statistics including regression.
Equipment to bring
Laptop with a recent version of R and RStudio installed.
Instructor(s):
Manuela Zucknick (main)
Theophilus Asenso
Exploring Perturbation-Linked scRNA-Seq and GeneSetR for Functional Genomics Analysis
Date: Thursday 11 December 2025 13:00-15:00 Add to calendar
Room: Python (room 2269)
Description
This workshop offers a comprehensive introduction to perturbation-linked single-cell RNA sequencing (scRNA-seq), a cutting-edge technique that combines genetic perturbations with single-cell transcriptomics to unravel gene functions and regulatory networks at an unprecedented resolution. Participants will learn about the principles of perturbation-linked scRNA-seq, its experimental workflow, and the advantages it offers over traditional methods, such as high-throughput functional screening and the ability to dissect complex biological systems at the single-cell level. Building on this foundation, the workshop will introduce GeneSetR, a user-friendly tool designed to harness the power of Genome-wide Perturb-Seq (GWPS) data for in-depth analysis of user-defined gene lists. GeneSetR empowers researchers to interpret and cluster gene lists effectively, facilitating navigation through the recently available GWPS datasets.
Learning outcomes
- Participants will gain an understanding of perturbation-linked scRNA-sequencing, including its methodology, applications, and advantages in functional genomics research. The workshop will cover how to use GeneSetR to find genes that regulate or are regulated by a specific gene of interest, analyze RNA-seq results to identify significant biological pathways, and cluster gene lists to identify functional groups. Attendees will learn to create correlation maps based on knockdown profiles and gene expression upon perturbations, as well as generate heatmaps illustrating relationships between chosen genes and perturbations. Additionally, the workshop will demonstrate how to find perturbations that modulate a given gene signature, which is crucial for understanding gene function and regulation.
Target audience
Researchers in the fields of genomics, molecular biology, and functional genomics, Bioinformaticians and Computational Biologists. Students who are interested in recent developments in functional genomics.
Pre-requisites
- Access to web.
Equipment to bring
none
Instructor(s):
Omer Kuzu (main)
Victoria Therese Karlsen, Diana Domanska
Functional Meta-Omics: Bioinformatics in Action
Date: Monday 08 December 2025 9:00 - Wednesday 10 December 2025 16:00 9:00-12:00 13:00-16:00 Add to calendar
Room: Perl (room 2453)
Description
This hands-on workshop introduces participants to the bioinformatics of functional meta-omics, combining metagenomics and metaproteomics to explore microbial communities from genes to functions. We will begin with Oxford Nanopore long-read sequencing data, assembling metagenomes and generating metagenome-assembled genomes (MAGs). Participants will then apply state-of-the-art tools for taxonomic classification and functional annotation, gaining insights into community structure and metabolic potential. A central focus will be on using High-Performance Computing (HPC) to efficiently manage and parallelize large-scale analyses. Participants will learn how to run pipelines on HPC systems, handle job submissions, and optimize resources for data-intensive tasks. Moving to the protein level, the workshop will cover metaproteomic analysis using FragPipe for protein discovery and quantification. Functional interpretation will be expanded through MSstats for statistical visualization of protein expression and WGCNA for co-expression network analysis. Finally, we will integrate results using R-based visualization workflows, enabling participants to produce clear, interpretable figures that highlight taxonomic profiles, functional traits, and network-level interactions.
Learning outcomes
- By the end of the workshop, attendees will have hands-on experience across the full meta-omics pipeline—from raw sequencing reads to functional protein networks—equipped with the skills to analyze and interpret complex microbial community datasets using High-Performance Computing resources provided by Sigma2-NIRS.
Target audience
This course is designed for graduate students, postdocs, and researchers in microbial ecology, systems biology, and applied omics in health, agriculture, and environmental sciences.
Pre-requisites
- To benefit fully from this workshop, participants are expected to have: -Basic experience with Bash/Unix command line (navigating directo-ries, editing files, running commands). -Familiarity with HPC environments and job scheduling systems such as SLURM or PBS (submitting jobs, checking queues, handling resources). -Introductory knowledge of R (loading packages, importing data, running basic scripts). No prior experience with metagenomics or proteomics is required, but a general understanding of microbiology and bioinformatics concepts will be helpful
Equipment to bring
Participants should come prepared with: A laptop with SSH capability Unix/Linux or macOS: built-in terminal is sufficient Windows: SSH access via PowerShell or a client such as MobaXterm or PuTTY A working installation of R and RStudio
Instructor(s):
Arturo Vera Ponce de Leon (main)
smallRNA sequencing analysis, microRNA de novo annotation and expression analyses
Date: Tuesday 09 December 2025 9:00 - Wednesday 10 December 2025 16:00 9:00-12:00 13:00-16:00 Add to calendar
Room: Python (room 2269)
Description
We are suggesting a two day course on smallRNA sequencing analysis, microRNA de novo annotation and expression analyses.
Learning outcomes
- After attending this workshop learners will be able to 1) find and download publicly available smallRNA sequencing data 2) quality control and process the data for downstream analyses with MirTrace 3) know the ins & outs of microRNA annotation 4) correctly use microRNA annotation tools MirMachine and MirMiner 5) perform differential expression analysis 6) create publication-ready figures
Target audience
any stage. Students, PhD fellows, PostDocs and professors all welcome.
Pre-requisites
- computer with commandline (bash) & administrator rights
- previous (basic) experience with bash and R
Equipment to bring
laptop and a list of software we will provide
Instructor(s):
Bastian Fromm (main)
Oscar Walnoefer, Patrick Frühwirh, Eirik Høye
From Tidy Data to Tidy Models: Modeling Sensitive Health Data in R
Date: Friday 12 December 2025 9:00-12:00 13:00-16:00 Add to calendar
Room: Python (room 2269)
Description
Biomedical research increasingly relies on the ability to work responsibly with complex datasets while maintaining transparency and reproducibility. This hands-on workshop introduces participants to the powerful R ecosystems of tidyverse and tidymodels, with a focus on analyzing sensitive health care data. We will begin by exploring the tidyverse approach to data wrangling, visualization, and transformation—tools that make it easier to prepare real-world health data for analysis while ensuring reproducibility. Building on this foundation, we will turn to tidymodels, a cohesive framework for modeling and machine learning in R. Through guided examples, participants will learn how to manipulate data frames, clean data, specify models, create training and test splits, perform cross-validation, and evaluate predictive performance. By the end of the workshop, participants will have a practical workflow that takes them “from tidy data to tidy models,” equipping them with reproducible methods to analyze sensitive health data responsibly.
Learning outcomes
- By the end of the workshop, learners will be able to use tidyverse functions like select, filter, and mutate to manipulate a data frame. Learners will be able to perform data preprocessing for machine learning tasks, train and evaluate basic models using tidymodels, and interpret key model evaluation metrics.
Target audience
Graduate, post-graduate students and researchers at any level who are interested in programming in R for analyzing sensitive health data.
Pre-requisites
- No prior experience with modeling is required, but introductory level statistics would be helpful. Basic familiarity with R is required and some previous experience with tidyverse preferred.
Equipment to bring
A personal laptop with RStudio set up and tidymodels and tidyverse packages installed.
Instructor(s):
Brooke Wolford (main)
Galaxy project at UiO - combining the web interface and power of a high performance computer cluster FOX
Date: Tuesday 09 December 2025 9:00-11:00 Add to calendar
Room: Prolog (room 2465)
Description
At USIT (https://www.usit.uio.no/) we have a galaxy instance which is connected to the High Performance Computing cluster FOX (https://galaxy.educloud.no). This instance has more than 6500 tools for bioinformatics, it actually mounts the tool directory from the Galaxy project (by CVMFS). CVMFS (CernVM File System) is a service which deploys software and reference data on computing infrastructure. CernVM-FS is a read-only file system where the applications and reference data are hosted on servers outside the computing infrastructure (FOX cluster, in our case) and are mounted in the Galaxy server as any other directory. The galaxy-on-FOX instance is connected to an HPC cluster and allows for complex and heavy calculations. It also proposes a substantial storage of 1TB for each user. The datasets in this storage are manageable directly from the Galaxy by its file manager. Galaxy-on-FOX is free to use and provides a daily user support for all interested users. It is very convenient for courses and teamwork in groups as it allows for sharing of resources and for reproduction both of procedures and results. It can easily integrate partners from national and foreign institutions.
Learning outcomes
- The users will learn how to login into Galaxy-on-Fox, upload datasets, run jobs on them and store and manipulate the results.
Target audience
Bachelor, master and PhD students who want to test bioinformatics tools and run calculations using these tools. Professors who want to organize a course on some topic with the use of the tools in the Galaxy-on-Fox. Students who want to organize themselves in groups and share their work among the group members.
Pre-requisites
- The attendees shall be aware of the most common bioinformatics procedures and tools, but this is not a must. Galaxy-on-Fox offers access to more than 6500 tools and some of them will be used as an example during the workshop.
Equipment to bring
A laptop and internet connection is required.
Instructor(s):
Nikolay Vazov (main)
Torfinn Nome
Missing value imputation in methylation data
Date: Wednesday 10 December 2025 13:00-16:00 Add to calendar
Room: Prolog (room 2465)
Description
This workshop will teach you how to handle missing data values in your methylation data using R package methyLImp2. We will go over the types of missing values, consequences of doing (or not) missing value imputation and performance under different missing value patterns. We will also try out methyLImp2 package in action for some missing value imputation.
Learning outcomes
- After this workshop, learners understand why missing value imputation is necessary and can confidently perform it with a suggested package.
Target audience
This workshop will be most beneficial for those who have already worked on methylation analysis in R and want to deepen their knowledge of one particular element of the pipeline.
Pre-requisites
- You can attend even if you have never worked with methylation data but there won't be much explanation of its concepts, we will focus on missing data. You need to be comfortable in R: change directory, load the data, understand concept of functions and matrices and vectors etc.
Equipment to bring
Laptop with R (>= 4.3.0) installed
Instructor(s):
Anna Plaksienko (main)