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AMC Graduate School - Bioinformatics

This course is part of the PhD course program of the AMC Graduate School.

  • Location: Academic Medical Center, Amsterdam.
  • Course material: All course material is provided during the course, no need to print anything yourself.
  • ECTS: 1.1


Course objective

The aim of this course is to get acquainted with the basic principles and algorithms of commonly used bioinformatics methods. You will gain sufficient theoretical knowledge and practical skills to be able to apply bioinformatics adequately in your own work. Topics treated include use of biological databases, statistical concepts for omics data analysis, analysis of DNA microarray and metabolomic data, use of biological networks in your analysis, and genetical genomics. The course consists of a combination of lectures and computer labs that provide you with hands-on experience with the data and techniques used for data analysis. This course provides a good introduction for our more specialized courses Bioinformatics Sequence Analysis (March 2018) and Systems Medicine (Fall 2018).


Schedule and handouts

Schedule (updated: February 9, 2018)

Monday, February 12

Time Location Type Subject Teacher Material
09.30-10.30 HvA, D1-30 Lecture Introduction to Bioinformatics Perry Moerland Lecture (pptx)
10.30-12.30 HvA, D1-30 Lecture Possibilities and limitations of public biological databases Aldo Jongejan Lecture (pptx)
12.30-13.30   Break    
13.30-16.30 L0-227 Computer lab Tutorial 'genome browsers' Aldo Jongejan See below

Tuesday, February 13

Time Location Type Subject Teacher Material
09.30-11.30 K01-222-1 Lecture Statistical concepts for omics data analysis Perry Moerland Lecture (pptx)
11.30-12.30   Break      
12.30-14.00 K01-222-1 Lecture DNA microarray analysis Perry Moerland Lecture (pptx)
14.00-17.00 L0-227 Computer lab DNA microarray analysis Perry Moerland See below

Wednesday, February 14

Time Location Type Subject Teacher Material
10.30-12.30 K01-222-1 Lecture Metabolomics Antoine van Kampen Lecture (pptx)
12.30-13.30   Break      
13.30-16.30 L0-211 Computer lab Metabolomics Antoine van Kampen Computer lab

Thursday, February 15

Time Location Type Subject Teacher Material
10.00-12.00 HvA, C2-42 Lecture Pathways and networks Perry Moerland Lecture (pptx)
12.00-13.00   Break      
13.00-17.00 L0-227 Computer lab Pathways and networks Perry Moerland See below

Friday, February 16

Time Location Type Subject Teacher Material
10.00-12.00 G4-123 Lecture Genetical genomics Perry Moerland Lecture (pptx)
12.00-13.00   Break      
13.00-14.00 G4-123 Lecture Capita selecta Perry Moerland Lecture (pdf)
14.00-14.30 G4-123 Wrap-up Summary and question round Perry Moerland  

Computer exercises

In many of the computer exercises you will use the statistical software environment R. Although most of the exercises focus on the interpretation of the results and require nothing more than copying-pasting R code, you might want to have a short look at these before or during the course week:

  • A short introduction to R.
  • swirl: this package makes it fun and easy to learn R programming. Under Step 5, choose the module ‘R Programming’ to learn some of the basics of R.

For those really interested in R, twice a year we also teach the two-day Graduate School course Computing in R where we explain much of the basics of R.

Monday, February 12: The Ensembl and UCSC genome browsers

Web sites

Tuesday, February 13: Computer lab: DNA microarray analysis

Download the Rmd and HTML/PDF files and open the Rmd file containing the exercises in RStudio:

Thursday, February 15: Computer lab: Pathways and networks

  • Exercises: Rmd, HTML, PDF
  • Exercises & Answers: Rmd, HTML, PDF, R
  • The files referred to in the exercises can be downloaded here


  • Bioconductor workflows: a list of example workflows for the analysis of different types of omics data using Bioconductor packages.
  • F1000Research Bioconductor channel: This channel highlights Bioconductor package-based vignettes, cross-package workflows that guide users through common and important tasks in multi-omic data analysis and integrative bioinformatics, and other articles relating to the Bioconductor project.
  • Biomedical Data Science course of Rafael Irizarry et al.
Topic revision: r53 - 02 Mar 2018, PerryMoerland Search
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