Bioinformatics in Neuroscience

FaculteitFaculty of Medical Sciences

NiveauMaster

Studiejaar2026-2027

Beschrijving

Course goals

To be familiar with the basics of the bioinformatics methods used most extensively across diverse types of neuroscience research
To gain experience in distilling the bioinformatics sections of neuroscience research papers down to key take home messages
To be familiar with big data resources relevant to Neuroscience and how to access them
To learn some basic programming skills (R, unix)
To gain experience with state-of-the art pipelines for bioinformatics data analysis
To recognize the importance of careful “data cleaning” in real research contexts
To identify real world scientific questions that can be answered through computational analyses of “omics” technologies
To differentiate strengths, limitations and computational methods associated with the most popular types of omics (SNP arrays, DNA sequencing, RNAseq)
To give examples of additive scientific gains that can be achieved by combining different bioinformatic workflows

Content

Period (from – till): 23 November - 04 December 2026 (BMS_P2_A).

Course coordinator: dr. K. Kenna and dr. K. van Eijk.

Faculty
Dr K. Kenna
Dr. K. van Eijk
Dr. O. Basak
Dr. K. Siletti
Dr. M. Bakker
Dr. J. Flores
Dr. G. van Haaften

Course description
This course will give you an insight into applying bioinformatics in neuroscience using different layers of omics data. It’s aimed to give you a broad view of different techniques and different kinds of data with accompanying challenges that are being faced. Every day, lectures are followed by hands-on computer practicals on your own laptop.
In addition, interesting keynote lectures will be given by experts from the field.
Assessment for this course is based on an assignment that you will work on in groups, followed by a final presentation.

Learning objectives per subject
Kenna: Overview of bioinformatics in neuroscience, web-resources, reading bioinformatic papers

Provide real world examples of how big data & bioinformatic analyses drive modern neuroscience research
Differentiate between the omics modalities & bioinformatic concepts used most extensively in neuroscience
Access online bioinformatic web-resources commonly used in neuroscience research
Recognize how bioinformatics can inform the design of wetlab experiments
Distill complex bioinformatics sections of real research papers down to key take home messages

Flores: Introduction to R

Understand and apply the basics of the R syntax
Able to work with Rstudio
Know about different data types and structures

Van Haaften:

Have a broad grasp of the prevalence and impact of orphan diseases (monogenic diseases)
Know how to use databases relevant for orphan disease diagnosis and research (OMIM, ClinVar and GnomAD) and apply the GnomAD constraint values in relation to different inheritance models of rare diseases.
Understand the principles of next generation sequencing and be able to detail the key differences between short and long-range sequencing technologies.
Have basic skills in visualizing next generation sequencing data in IGV for WES/WGS and RNAseq.

Van Eijk: GWAS

Understand and explain the concept of genome-wide association studies (GWAS), using terms as SNPs, linkage disequilibrium, allele frequency
Describe why applying quality control (QC) to data is important
Have knowledge of the different QC steps (both sample and SNP)
Explain the method for conducting a GWAS
Explain what a principle component analysis (PCA) is
Interpret a QQ-plot and Manhattan plot
Understand and explain the association between statistical power and effect size in a GWAS
Indicate non-genetic factors and their effect on GWAS results (confounding)

Bakker: post-GWAS analyses

Understand what GWAS summary statistics are and how they relate to a trait
Understand that most traits have a genetic predisposition
Understand the three main categories of post-GWAS analyses: correlation, causation and enrichment
Understand the concept of heritability, SNP-based heritability, and partitioning heritability
Understand that Mendelian Randomization (MR) identifies causality between traits
Understand that inferring causality in MR requires three main assumptions to be met
Run an MR analysis on CoCalc
Understand what gene set enrichment is and how it can help understand a disease
Know the limitations of available gene sets

Kenna: Rare variant association testing

Explain the rationale and methodological approaches behind rare variant association testing
Contrast the strengths and limitations of rare variant association testing with the methods used in GWAS and canonical monogenic disorders
Conduct rare variant association testing analyses of 10,507 ALS patients and 26,040 healthy controls and critically evaluate/interpret your results

van Dijk: Isoforms & long read RNAseq

Know what long-read RNA sequencing is and what type of biological questions can be answered using long-read RNA sequencing data.
Know the advantages and disadvantages of different sequencing platforms.
Know what the key steps are in the bioinformatic analysis pipeline and be able to interpret long-read RNA sequencing data.

Basak: scRNAseq

Know the state-of-the-art and major computational hurdles in the single cell genomic field
Be able to discuss the reasoning behind individual steps of the scRNAseq data analysis
Gain the capacity to run a standard scRNAseq data analysis pipeline independently
Have the ability to interpret the results of single cell data analysis

Literature/study material used

Course Modules (all web-based)

Expression
Networks
Genetics

Registration
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. Maximum participants 20.

Mandatory for students in Master’s programme
No.

Optional for students in other GSLS Master’s programme:
Yes.

Prerequisite knowledge:
Biomedical Sciences/Biology

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