Master/PhD courses

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 Introduction to Bioinformatics for Molecular Biologists. This course is open for Master and PhD students. Coordinator: Berend Snel. This course is given once a year. 

“Introduction to Bioinformatics for Molecular Biologists” is a joint course for the various life science Masters programs at the Utrecht University. This introductory course provides an overview of the importance of bioinformatics in various biological disciplines. While a biological background is required, no programming skills are needed. The course can be considered a general introduction to bioinformatics, with a focus on the research that is performed at Utrecht University. The theory and tools for bioinformatics provided are very useful for any life science researcher. The course will be partially theoretical with lectures taking up to 30% of the day while the major focus will be on working with various tools and datasets. These computer tasks are performed in groups of 2 students. The maximum number of participants for this course is 40 students. The course will be concluded on the last day with an exam that consists of normal pen-and-paper questions but also of tasks that should be completed on the computer. Active participation during the course provides sufficient preparation to complete the exam. Furthermore, follow-up courses are organized later in the year that will allow you to specialize in specific directions. Students are expected to be familiar with the subjects from this introductory course when participating in any of the follow-up courses.”  

 Advanced Bioinformatics: Data analysis and Data integration for biomedical research. This course is open for Master and PhD students. Coordinators: Joep de Light This course is given once a year.  See Osiris for more 


Course goals

At the end of the course, the student is able to;

  1. Integrate biological data
  2. Think critically about data storage and sharing
  3. Use alternatives to tabular data
  4. See the advantages and limitations of different data storage and sharing solutions
  5. Plot data in an interactive way


Period: 6 March – 10 March 2017, see www.CSnD.nl/courses

Joep de Ligt, Biomedical genetics/Genetics,
Pjotr Prins, Biomedical genetics/Genetics,
Edwin Cuppen, Biomedical genetics/Genetics,
Berend Snel, Theoretical Biology and Bioinformatics,

Invited speakers (different each year), 2016 participants listed below:
Jayne Hehir-Kwa, Radboud UMC,
Ruben van Boxtel, UMC Utrecht,
Victor Guryev, Groningen University/UMC,
Mark Wilkinson, Center for Biotechnology, Madrid.

Description of content
Effective mining of data and integrating data is one of the major challenges in biomedical research. Decennia of research have led to an accumulation of databases world-wide, including important resources, such as NCBI, KEGG, ENCODE, SWISS-PROT etc. Lately, new data acquisition technologies, especially next generation sequencing (NGS), are rapidly increasing the amount of information available online, from data published with papers all the way to large scale collaborations, such as The Genome Cancer Atlas (TCGA) involving a wide range of hospitals and research groups offering information on patients, diagnostics, treatments together with data on sequenced tumors, gene expression, methylation, etc. For an inspiring example see
http://www.cbioportal.org/public-portal/tumormap.do?case_id=TCGA-A2-A0CX&cancer_study_id=brca_tcga_pub.The challenge is to effectively mine resources, such as the TCGA, after performing an experiment or getting clinical results. For example, if you are sequencing cancer tumors of patients, the question is: how to mine this public data and compare the results against your own data and results. TCGA alone numbers over 50,000 files, there is no way to mine this data by hand. Likewise we have access to 1,000 public genomes and the genome of the Netherlands (GoNL). What are feasible strategies for using this data?In this course the morning is started with a lecture by a leading biomedical scientist. The topic can be in cancer research, for example, diagnostics or personalised medicine. The presenter will tell us about his/her research and the short term data mining and data integration issues he or she is facing. The lecture is followed by a discussion on possible approaches in solving one or more of these issues. Topics covered will include parsing tabular data, SQL databases, web services and the semantic web. The rest of the day the students will be tasked with finding a solution to a particular problem. Solving such problems can only be done through writing (small) computer programs. This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introductionary programming courses; having completed one of these is a hard prerequisite. The introduction to bioinformatics course is not a prerequisite but is highly recommended.The goal of this course is to outline current data integration challenges in biology and biomedical research and discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with NGS data.Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.Registration:
Please register online on the CS&D website: www.CSnD.nl/courses. CS&D students have priority in registration until 3 weeks before the start of the course. Thereafter, registration is on ‘first-come-first-serve’ basis until the maximum number of 25 participants is reached.



 Introduction into the statistical language [R], This course is open for Master and PhD students. Coordinators: Adrien Melquiond. This course is given once or twice a year.

“Due to technological advances in molecular biology (genomics, large-scale systems biology) research in the life sciences is becoming increasingly data rich. Currently, appropriate analyses of large-scale datasets (proteomics, genomes, Htseq, RNAseq, ChIPseq, etc) are a limiting factor. The aim of this course is to give a basic training in R for molecular biologists.
R is a widely used software environment for statistical computing and provides a wide variety of libraries for data manipulation, modeling and visualization. The course will give an introduction to R and Rstudio, an integrated development environment for R. Throughout the course you will use a single data set to take you through the basic functionality of R and Rstudio in an interactive fashion. Supplemented with several commonly used libraries, plotting and analyses routines and supporting data sets this will familiarize students with R and at the same time show the value of using R for analyzing and visualising large amounts of data. At the beginning of the course you will be given a programming assignment that has to be handed in at the end of the course, based upon which a grade will be given.”

 An advanced course in [R] for Molecular Biology. This course is open for Master and PhD students.  Coordinator: Henk van den Toorn. This course is given once a year. See Osiris

Course goals

We aim to provide knowledge into creating publishable R code and graphics.
At the end of the course, students should be able to have a deep understanding of: the data and package structures of R, ggplot graphics (out of the box and custom), and create an automatically generated document of a (simple) analysis.


Many researchers will need to apply statistical analysis in their work. Often, the R statistical language is chosen, since it is well established, free, and has many packages available for different tasks. If you want to be able to use the more powerful features of R, create visually attractive figures with ggplot, write concise and organized code that you can share with others, create automatically generated reports. This course gives you the knowledge to follow one of the subsequent courses of statistical analysis for omics technologies, and linear models with R.

Literature/study material used:
Provided during the course. Students are required to bring a laptop to work online via a web page.

Entry requirements

Prerequisite knowledge

The ‘Introduction to R’ course, or similar knowledge by an admittance test.

  Learn the Basics in Python. This course is open for Master and PhD students.  Coordinator: Adrien Melquiond and Marcel van der Verk. This course is given once a year.

 Learn advanced concepts and tools for reconstructing protein, genome and network evolution. This course is open for Master and PhD students. Coordinator:  Berend Snel. This course is given once every two years.
See the (potentially old) webpage of the course for more information