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 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 workshop aims to help researchers to visualize their data in different ways using R. This workshop will also aim to show researchers how they can make publication grade figures using R. A part of this workshop is also about making interactive plots that the researchers can view and share in a web-server to make interactive visualizations of the data.

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 intense one-week workshop provides an introduction to the analysis of next generation sequencing data.

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

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

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 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.

This course aims to help researchers who are already using R for basic bioinformatics analyses and who would like to start using R at a more advanced level.

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.

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!