Training at NBIS

This workshop will guide participants through the process of using the Berzelius compute cluster for cryo-EM data processing. We will focus on processing the beta-galactosidase dataset, a familiar example from Relion tutorials. However, we will utilize cryoSPARC on Berzelius, leveraging the implementation provided by the PReSTO team.

This course is designed to help researchers in visualizing their data through various techniques using R and Quarto. Participants will learn how to create publication-ready visualisations with R.

A seminar series showcasing the advances in Bioinformatics and Genomics (aka “BiG Talks”). The event is open to the public via Zoom, and recorded videos will be available at the NBIS YouTube channel.

This workshop explores the latest research in biomarker discovery and application within clinical trials. Participants will delve into methods for identifying biomarkers using diverse omics datasets, discuss the design and analysis of biomarker studies, and learn about advanced techniques such as machine learning and integrative omics approaches.

This course offers guidance, ideas and tips for designing training/teaching, development and delivery on training activities, all based on research-driven educational principles.

This workshop aims to introduce the best practice bioinformatics methods for processing, analyses, visualisation and integration of epigenomics and functional genomics data.

This 2 days course is based on the FAIR training handbook and 10 simple rules to make material FAIR publication. All sessions are structured in a way that complement each other aiming to introduce participants with a theoretical & hands-on approach of how to design FAIR training material.

Activities are intended to increase the technical skills and critical thinking during the development of a new training course. From A to Z participants are going to be challenged to work in a group to build a FAIR lesson on a topic to be presented at the end of the course. Come inspired!

This intense one-week workshop provides an introduction to the analysis of next generation sequencing data. We cover working on the UNIX/LINUX command line, Bioinformatic/NGS data formats and QC, the essentials of DNA variant calling workflow and RNA sequence analysis workflow.

This course will introduce important aspects of Research Data Management through a series of lectures and hands-on computer exercises.

A course for those who are in need of metabolomics data analysis skills. We will go through the principles of LCMS, GCMS and NMR experiments, pre-process the raw data acquired from analytical instruments and perform downstream data analysis and interpretation using multivariate methods.

This course is aimed for those who seek to deepen their biostatistical and machine learning skills. Building on the Introduction to Biostatistics and Machine Learning course, this course expands on common life science data analysis methods, including dimensionality reduction techniques beyond PCA, mixed-effects models for analysis of repeated measures, and survival analysis. We will also dive deeper into machine learning, covering more classification algorithms, ensemble techniques, optimization strategies and PLS methods for single and multi-omics data analysis.

National workshop open for PhD students, postdocs, researchers and other employees in need of Neural networks and Deep Learning skills within all Swedish universities.

The aim of this course is to provide an integrated view of data-driven hypothesis generation through machine learning integration methods, biological graph / network analysis and genome-scale metabolic models. A general description of different methods for analyzing different omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing key methods and pitfalls in their integration.

This course is a collaboration between SciLifeLab Training Hub, SND and NBIS. It is designed for participants to:

1. Get acquainted with Open Science principles and how they will be implemented into Swedish academia within the coming years
2. Be able to execute every step of a research project according to Open Science principles

This workshop introduces commonly used methods in population genomics. It focuses on hands-on exercises to cover fundamental analyses used in many population genomics studies. Lectures will provide background theory, but the emphasis is on practical application.

This course aims to give life scientists, bioinformaticians, and other scientists with some experience in programming and scripting an understanding of the underlying principles of software development, design, and programming. The course aims to strengthen the understanding of more advanced programming concepts, ability to produce more reusable scripts through modular programming and to enable a better understanding of how to evaluate a script or programs performance.

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are led through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs from scratch and to customize more complex code to fit their needs.

This course is designed for life scientists with limited programming experience who want to learn R for efficient data analysis. The course covers fundamental programming concepts, data manipulation, statistical analysis, and data visualization in R. Participants will gain hands-on experience through practical exercises and learn how to utilize R for their specific research needs.

For users who are familiar with R, this workshop goes beyond the basics, offering participants a comprehensive understanding of the R ecosystem. We explore best coding practices, code profiling, data wrangling, generating reports from notebooks and development of web apps and more.

This workshop will introduce the best practice bioinformatics methods for processing and analyses of RNA-seq data via a series of lectures and computer practicals.

This workshop will introduce the best practice bioinformatics methods for processing and analyses of single cell RNA-seq data via a series of online lectures and computer practicals. The total course duration is 5 days, including the lectures and practical exercises.

The NBIS Tools for reproducible research course is held twice a year and provides an introduction into Snakemake and a tutorial to learn how to use Snakemake. This Snakemake BYOC (bring-your-own-code) workshop provides a follow-up to that introduction.

This workshop provides resources to advanced tools for analysis of spatial datasets . It is aimed towards biologists, researchers, computer scientists or data analysts planning to run, analyse and interpret single cells RNA-seq experiments independently.

The course is geared towards life scientists wanting to be able to understand and use basic statistical methods.

Using real research examples, the course introduces core ideas of statistical inference and guides participants through widely used methods such as hypothesis testing, regression, mixed-effects models for repeated or hierarchical data, and survival analysis. The course is suitable both for those with limited prior training and for researchers already applying statistical methods who want a stronger conceptual foundation to confidently choose, apply, and interpret statistical analyses in their own work.

In this workshop you will learn how to make your bioinformatic data analyses reproducible using state-of-the-art tools. Reproducible research not only leads to proper scientific conduct, but also enables other researchers to build upon previous work. Most importantly, the person who organizes their work with reproducibility in mind will quickly realize the immediate personal benefits: an organized and structured way of working. The person that most often has to reproduce your own analysis is your future self!

This course introduces you to using AI in lifescience. It covers a number of topics that you should be familiar with if you want to use AI consciously and responsibly in your work.