Bioinformatics for Translational Medicine

Course code:
Period 5
Language of tuition:
Faculty of Science
B. Stringer MSc
dr. S. Abeln
prof. dr. J. Heringa
Teaching method(s):
Lecture, Practical

Course objective

Observations from biological high-throughput experiments will allow us
to improve diagnosis and give a personalised treatment plan for
patients. However, integrating data from several sources and using this
data for predictions is non-trivial.

This is a theoretical and practical Bioinformatics course on
computational methods for Translational Medicine; we will focus on
Bioinformatics methods that are used to predict the clinical outcome
for patients and analysis methods to obtain deeper understanding of
complex diseases, by combining data from various high-throughput
experiments such as proteomics, microarrays and next-generation
sequencing as well as existing biological databases.

• At the end of the course, students will be aware of
Bioinformatics methods that are applicable to the area of Translational
• Students should be able to combine these methods to come to a
creative solution to get new insights from large scale biological
• At the end of the course, students will have hands-on experience
in handling large biological datasets, and will understand the
complexity of the biological data both from high-throughput experiments
and existing biological databases.
• The student will become familiar with a few in depth research
topics that lie within the expertise area of several (Bioinformatics)
researchers at the VU, UvA,AMC, NKI and VUMC.

Course content

• Computational analysis of molecular profiling techniques, such as:
WGS, WES, proteomics, RNA sequencing, exome sequencing, arrayCGH.
• Computational methods include: machine learning, normalisation,
feature selection,
classification, read mapping, clustering.
• All data analysis is relevant in a clinical setting, for diagnosis,
treatment decisions or biomarker discovery.


• Classification Assessment of Tumor Subtypes (CATS): This is a large
assignment for which you have to build a classifier that can
discriminate different tumor subtypes based on arrayCGH profiles. You
need to hand in predictions (class contest), write a paper and give a
presentation. Note that this is a group project.

Small data analysis practicals are also given on:
• DNA/ RNA sequence analysis
• Proteomics

Form of tuition

• 13 Lectures (2 two-hour lectures per week)
• 12 computer practicals (2 two-hour sessions per week)

Type of assessment

The final grade for this course will consist of 50% practical work (see
above) and 50% theoretical assessment.

Practical assessment (50%):
• CATS assignment (50%)
• 2/3 data analysis assignments (pass/fail)

Theoretical assessment: (50%)
• Oral or written exam (depending on number of course students).
• The exam is based on a selection of 8-10 scientific papers in the
field of Bioinformatics & Translational Medicine.

Course reading

• course material on
• 8-10 scientific papers are provided, and make up the course syllabus.

Entry requirements

Some basic programming skills, in either R or python are required, as
well as some basic knowledge on molecular biology.
If you are not following the MSc Bioinformatics, it is advisable to
first follow the MSc course "Fundamentals of Bioinformatics".

Recommended background knowledge

If you are not following the MSc Bioinformatics, it is advisable to
first follow the MSc course "Fundamentals of Bioinformatics".

Target audience

mAI, mBio, mCS


The course is taught in English.

• Compulsory course for students in MSc of Bioinformatics.
• Optional course for students with a Bachelor in Physics, Chemistry,
Mathematics, Computer Science, Biology, or Biomedical Sciences (see
requirements below).

© Copyright VU University Amsterdam
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