Bioinformatics analysis of gene expression data from bulk RNAseq (BAGED-bulk)
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a specialised course. This means that 80% of the seats are reserved to PhD students enrolled at the Faculty of Health and Medical Sciences at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS).
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member faculties. All other participants must pay the course fee.
Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member faculties. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.
Learning objectives
A student who has met the objectives of the course will be able to:
1. Analyze RNA sequencing data by applying relevant bioinformatic tools
2. Assess the quality of sequence data
3. Assess the results of bioinformatic tools
4. Interpret the implications of a data analysis in broader context than the specific analyzed data
5. Apply the statistical software R for data manipulation and analysis
Content
Driven by the technological progress, biology continuously undergoes a revolution that started in the late 1990s with the sequencing of the human genome. This revolution is on a scale that constantly redefines our basic understanding of biology and reaches deep into human and animal science, for example with respect to diseases and nutrition. Understanding the opportunities that come with the technological development is crucial for strategy planning, experiment design, and interpretation of large scale-omics datasets.
The course will focus on bulk RNA sequencing of large pools of cells such as tissues, cell populations or blood. We will introduce tools to analyze the high-throughput sequencing data generated from contemporary RNA-seq technologies with high relevance for characterizing gene expression, regulatory dynamics, and transcriptional responses. To this end, basic concepts of sequence alignment, quality control, gene expression quantification, detection of differentially expressed genes, identification of novel transcripts, etc., will be introduced. Furthermore, the functional context of genes will be addressed through the studies of biochemical pathways and biological networks.
Major software tools introduced and applied during the course are the web-based platforms Galaxy and UCloud for running data intensive bioinformatic analysis pipelines, Snakemake for building reproducible and scalable data analysis workflows, the statistical software R for statistical computing (including packages for expression analysis and visualization), and Cytoscape for network visualization and biological interpretation. Emphasis will be placed on reproducibility, documentation, and best practices in computational biology.
Participants
Students should have a background in genetics or bioinformatics. The target group are students that are exposed to high-throughput sequencing data during their PhD study and want to learn how to analyse the data. A basic knowledge of statistics and previous experience with the software R are recommended.
Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
Biostatistics and Bioinformatics
Public Health and Epidemiology
Veterinary, Animal Health and Microbiological Sciences
Language
English
Form
The course combines theoretical lectures, tutorials, and student projects. Each participant will define a data analysis project based on their own PhD project. The raw data for the project should be either RNA-seq data generated during the PhD project or relevant publicly available sequencing data. Students with related data and/or scientific questions are encouraged to work together in small groups. Each participant will give an oral presentation of the project work at the last course day and deliver a short report after the course. Students are expected to work on their project during the course hours and six additional hours per week.
Course director
Ernst Stefan Seemann, Assoc. Professor, IFSV, sse@sund.ku.dk
Teachers
Ernst Stefan Seemann, Assoc. Professor, IFSV
Ali Altintas, Assistant Professor, CBMR
Nadezhda Tsankova Doncheva, Assistant Professor, IFSV
Jan Gorodkin, Professor, IFSV
Dates
1 – 18 March 2027
1st and 2nd week: Monday 9-12, Tuesday 9-16, Thursday 9-16
3rd week: Tuesday 9-16, Thursday 9-16
Course location
Department of Public Health (IFSV), Univ. of Copenhagen
Registration
Please register before 31 January 2027
Expected frequency
Annual
Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules.
Applications from other participants will be considered after the last day of enrolment.
Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.