NBIS training

NBIS offers training and workshops within the area of Bioinformatics for Life Science community across Sweden. The Training events aims towards PhD students, postdocs, investigators and other researchers within all Swedish universities. The Training events are national and have a small fee for the participants.

Refer to nbis.se/training for upcoming workshop dates.

Workshops and programs

The Swedish Bioinformatics Advisory Program (2 Years)

PhD students are admitted to the Swedish Bioinformatics Advisory Program for a maximum of 2 years. During this time, the PhD student gets assigned a senior bioinformatician acting as a personal advisor. See program for more details.

Contact: Diana Ekman or Anna Johansson

RaukR: Advanced R for Bioinformatics, Summer workshop (2 weeks)

In Life Science and Bioinformatics, R is increasingly being used to transform and analyse data, perform statistical analysis and produce publication-ready visualisations. This Summer workshop will focus on advanced R functionality, to increase the participants skillset and understanding of what is possible to do today.

Contact: RaukR

Workshops

Biomarker Discovery: from theory to real world examples (3 days)

This workshop provides a good overview of biomarkers and biomarker studies and will explore recent research, showcasing diverse biomarker applications within clinical trials. Methods employed to identify biomarkers through various omics datasets will be reviewed. Participants will engage in discussion on biomarker studies and data analysis exercises that encompass approaches like machine learning methods and other integrative omics strategies used in biomarker discovery.

Contact: Biomarkers

Bring your own code - Snakemake (3 days)

Workshop open for PhD students (prioritized), postdocs, researchers and others who have previously participated in the NBIS Tools for reproducible research workshop.

Contact: BYOC-Snakemake

Data visualization in R (3 days)

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.

Contact: R-Plot

Epigenomics data analysis (5 days)

This workshop builds on our previous ChIPseq data analysis workshop to introduce best practice bioinformatics methods for processing, analyses and integration of epigenomics data. The online teaching includes lectures, programming tutorials and interactive group sessions.

Contact: Epigen-DA

R Foundations for Life Scientists (5 days)

This workshop covers fundamental concepts of programming and software design focusing on programming in R and targets life scientists with little or moderate experience in programming. After introductory lectures on good programming practices, basic software design theory and a brief overview of R, we will delve into programming. We start by learning how to use R as a simple calculator, what are variable types, how to use data structures, how to implement repeating actions with and without loops, and how to take actions based on certain conditions. We gradually proceed to loading data, importing data from common file formats, some basic matrix algebra and learning how to perform basic statistical tests and visualize results.

Contact: R-Foundations

Introduction to Bioinformatics using NGS data (5 days)

This workshop is aimed towards biologists, researchers, computer scientists or data analysts with limited experience in analysing NGS data.

Contact: Intro-NGS

Introduction to Biostatistics and ML (5 days)

National workshop open for PhD students, postdocs, researchers and other employees in need of biostatistical skills within all Swedish universities. The workshop is geared towards life scientists wanting to be able to understand and use basic statistical and machine learning methods. It would also suit those already applying biostatistical methods but have never got a chance to reflect on and truly grasp the basic statistical concepts, such as the commonly misinterpreted p-value.

Contact: ML-BioStat

Introduction to Data Management Practices (3 days)

National workshop open for PhD students, postdocs, researchers and other employees within all Swedish universities. The workshop is geared towards life scientists wanting to take the first steps towards a more systematic and reproducible approach to analysing and managing research data.

Contact: Intro-DM

Introduction to Python - with applications to bioinformatics (5 days)

Suitable for complete beginners and assumes no prior programming experience (beyond the ability to use a text editor).

Contact: Intro-Python

Neural networks and deep learning (5 days)

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

Contact: NN-DL

Omics Integration and Systems Biology (3 days)

The aim of this workshop is to provide an integrated view of data-driven hypothesis generation through biological network analysis, constraint-based modelling, and supervised and unsupervised integration methods. A general description of different methods for analysing different omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing key methods and pitfalls in their integration. The techniques will be discussed in terms of their rationale and applicability. The workshop will also include hands-on sessions and several seminars by invited speakers.

Contact: Omics-Integ

Population genomics in practice (5 days)

This workshop provides an introduction to commonly used methods in population genomics. As the focus of the course is on hands-on work, the topics have been designed to cover the fundamental analyses that are common in many population genomics studies. The course consists of lectures and exercises, with a focus on the practical aspects of analyses. Whereas lectures introduce some background theory, their primary aim is to set the stage for accompanying exercises.

Contact: Population-Genomics

RNA-Seq data analysis (5 days)

This workshop is aimed towards biologists, researchers, computer scientists or data analysts planning to run, analyse and interpret an RNA-Seq experiment. Basic knowledge of working on the Unix/Linux command line and in R is expected.

Contact: RNA-Seq

Single cell RNAseq analysis (5 days)

The workshop will cover the basic steps in single cell RNAseq (scRNAseq) processing and data analysis, with lectures and practical exercises, including: 1) Overview of the current scRNAseq technologies. 2) Basic overview of pipelines for processing raw reads into expression values. 3) Quality control and normalization. 4) Dimensionality reduction techniques. 5) Data integration and batch correction. 6) Differential gene expression. 7) Clustering techniques. 8) Celltype prediction. 9) Trajectory inference analysis. 10) Analysis of spatial transcriptomics datasets. 11) Comparison of different analysis pipelines such as Seurat, Scran and Scanpy.

Contact: Single-Cell

Spatial omics data analysis (5 days)

This workshop provides resources to advanced tools for analysis of spatial datasets including: 1) Hands-on experience with ST (Visium), ISS, scRNAseq data analysis; 2) Fluorescence-based image formats, standards and quality control. 3) Image alignment, registration and ISS decoding. 4) Nuclei-based and segmentation-free cell identification. 5) Data imputation using ISS and single cell datasets. 6) Analysis of Spatial Transcriptomics dataset. 7) Cell-type deconvolution (ST and single cell). 8) Cell-cell and ligand-receptor interaction analysis. 9) Mapping of multiple spatial data to a common reference. 10) High-resolution projection of gene expression to H&E images. 11) Interactive visualisation of spatial omics data.

Contact: Spatial-Omics

Tools for Reproducible Research (5 days)

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!

Contact: Tools-RR