Hamburg Transcriptomics Winter School 2027

The BNITM's Data Science Center warmly invites to the Hamburg Transcriptomics Winter School 2027.

Transcriptomics has become an essential tool for understanding gene expression patterns and biological processes. It is increasingly important for experimental scientists to gain the bioinformatics and programming skills required to process, analyse and interpret large volumes of RNA-sequencing data. During this winter school, participants will have the opportunity to learn about transcriptomics data analysis in R.

Target Audience

This winter school targets those with a specific interest in transcriptomics and R programming, and is particularly relevant for PhD students and postdocs from the life sciences. If you are unsure whether you can participate as an undergraduate or Master student, Principal Investigator, or in another capacity, please don't hesitate to contact us.

Programme

This intensive 5-day winter school provides experimental biologists with hands-on training in transcriptomics data analysis using R and state-of-the-art bioinformatics tools. This course emphasises hands-on practical skills (80%) rather than theoretical/mathematical foundations (20%). Participants will gain practical experience with RNA-Seq data analysis, from raw sequencing reads to publication-ready results, while building competency in transcriptomics terminology and underlying concepts.

Topics Include

• Introduction to R: data manipulation & visualisation
• Introduction to Galaxy for raw data processing
• Bulk RNA-Seq analysis with DESeq2
• Differential expression and GO enrichment analysis
• Single-cell RNA-Seq basics and integration
• Introduction to spatial omics
• Group task on different datasets

Learning Objectives

After attending this course, participants should be able to:

• Comfortably work with R and bioinformatics software for transcriptomics analysis
• Have a critical understanding of transcriptomics bioinformatics methods
• Understand and interpret transcriptomics methods in scientific papers
• Process NGS (transcriptomics) data from raw reads to publication-ready plots
• Perform differential expression, enrichment and integration analyses
• Apply transcriptomics analysis to your own data independently
• Continue working on transcriptomics analysis on their own computer after the course

Full Description

Transcriptomics has become an essential tool for understanding gene expression and identifying biological processes. With the ongoing expansion of omics initiatives, we aim to provide PhD students and postdocs the opportunity to learn about transcriptomics data analysis using R.

This week-long course provides experimental biologists working with RNA-Seq data with: (i) hands-on experience in transcriptomics data analysis, (ii) a comprehension of transcriptomics language and terminology, and (iii) an understanding of the underlying concepts.

Key content areas:

• R Programming: Learn R syntax and data manipulation for working with transcriptomics datasets. Create publication-ready figures using ggplot2 and other visualization tools.
• Galaxy: Introduction to Galaxy for processing raw sequencing data, including quality control and read mapping.
• Bulk RNA-Seq: Analyse RNA-Seq datasets using DESeq2. Learn differential expression analysis, visualisation, and interpretation.
• Functional Analysis: Perform enrichment analysis to gain biological insights.
• Explore the usefulness (and limitations) of Large Language Models in the interpretation
• Single-cell RNA-Seq: Basic introduction to scRNA-Seq analysis and data integration using R packages such as Seurat.
• Spatial Omics: Overview and basic introduction to spatial transcriptomics analysis methods.

Data used during the workshop will be derived from various organisms, including parasites and human samples, but the learning outcomes can be applied to any organism.

At the end of the course, participants will work on a group task with real datasets to independently apply their newly acquired skills. Participants are encouraged to bring their own data if desired and can discuss suitability with the course organisers in advance.

Practical Information

• Prior R or programming experience is helpful but not required. The course starts with R basics.
• Participants must bring their own laptop with at least 8GB of RAM. We will use the browser for most of the work
• No installation (apart of a browser) is required. We can help to install R locally, but all of the work will be done on servers. 
• Registration includes course materials, refreshments, and one social dinner. Please note: lunch is not provided; there are several options (from supermarkets, bakeries & restaurants within a 5-minute walk).
• The course will be cancelled should the minimum of 15 participants not be reached. Participants will be informed immediately and the course fee fully refunded.
• We have 20 places available. 

Key Information

Dates:01–05 February 2027

Mode: In person on site at the Bernhard Nocht Institute for Tropical Medicine, Hamburg

Timetable: 9:00 am – 18:00 pm each day. Thursday evening is a an optional group task day.

Language: English

Course tuition: €1,000 for academics / €1,300 for commercial participants
Course materials, refreshments, one social dinner. (Note: lunch not provided)

Cancellation policy: A participant who withdraws from this short course:

• Less than 6 weeks prior to start: 25% of fee charged
• Less than 4 weeks prior to start: 50% of fee charged
• Less than 2 weeks prior to start: 100% of fee charged

Selection: Places assigned on a first-come, first-served basis upon receipt of the course fee.

Certificate: Available for those completing at least 80% of the course sessions.

Online application: Please complete this online application form 

Application deadline: 15 January 2027, 23:59 CET
However, it is likely that the course will fillup quickly. A registration is complete, once the fee is paid.

For further information please contact us at: