Medical Bioinformatics and e-Bioscience


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Courses and training programmes Main teachers
Medical Informatics (AMC)
MIK1.2 Biomedische basis principes MIK2.1 Databases en computernetwerken MIK3 DGO-Bioinformatics
MIK Master, Current issues in medical informatics I MIK GRID Summer school: Bridging Health Care with IT
MIK Bachelor Seminars 2011 MIK Bachelor Seminars 2012  
UvA Faculty of Science: SILS
Biomedical Sciences/Biology (SILS; BW02K) Medical Molecular Systems Biology (SILS 051BMS) Communicatie in de biologie (1003B)
Miniscriptie (SILS 290BMW) Genomics of Disease  
UvA Faculty of Science: Informatics
Bioinformatics-I (Computational Sciences) Bioinformatics-II Microarrays (Computational Sciences) Bioinformatics-II NGS (Computational Sciences)
Amsterdam Graduate School of Sciences (AGSS)
Translational Medicine    
AMC Graduate School
Introduction to Bioinformatics DNA Technology Proteomics, Mass Spectrometry and Protein Research
Statistical Computing in R    
Other
Introduction to Unix Pattern Recognition (NBIC PhD School) Optimization (NBIC PhD School)
NBICTutorial NBIC RNA-seq Bioinformatics/e-Bioscience Show Cases
Antoine van Kampen
Perry Moerland
Silvia Olabarriaga
Barbera van Schaik
Marcel Willemsen


Information for trainees
The Bioinformatics Laboratory offers students the possibility to do a bioinformatics project ('stage') to further specialize in this scientific field. The minimum period for a practical period is three months but depends on the background and skills of the student. The student is assumed to have programming and other skills (e.g., statistics, relational databases, bioinformatics) to successfully engage in a project. During the practical period the student is allowed to participate in bioinformatics courses or meetings that are organized by the Bioinformatics Laboratory.

If you are interested then contact Antoine van Kampen (a.h.vankampen@amc.uva.nl) to discuss interests and possibilities.

See also Research projects for master students (Biological, Biomedical, and life sciences)

Trainee projects

  • Bioinformatics analysis of LC-MS metabolomics data
    • Supervisor: Antoine van Kampen, a.h.vankampen@amc.uva.nl, 020-5667096
    • Description: Liquid chromatography coupled to mass spectrometry (LC/MS) is used in metabolomics research. In this context, the technology has been increasingly used for e.g, the discovery of biomarkers. One of the challenges in this domain remains development of better approaches for the bioinformatics analysis of LC/MS data. In this project we aim to develop a processing pipeline for pre-processing and statistical analysis of metabolomics data.
    • Technical skills: The students should have programming skills and interest in bioinformatics. Knowledge and interest in statistical analysis is important. The methods will be developed in the R-statistical package and we will explore the use of Taverna for workflow management.

  • An in-silico approach for the detection of contaminated tumor cell lines
    • Supervisor: Perry Moerland, p.d.moerland@amc.uva.nl, 020-5664660
    • Description: For decades, hundreds of different human tumor type–specific cell lines have been used in experimental cancer research as models for their respective tumors. However, sometimes cell lines that have been used for years turn out to be contaminated (see here for a recent example). In this project you will work on an approach that uses publicly available microarray datasets of human cancer cell lines, primary tumors, and tissues to detect contaminated cell lines. The approach is based on the intuitive idea that often contamination can be traced back to different tumor types or tissues. We expect that these signatures of contamination can also be found in the cell line's gene expression profile. Our lab already has most tools in place to integrate multiple microarray datasets in a single database. Focus of this project would be on developing the methods for comparing cell line gene expression data with other datasets. The ultimate goal would be a tool to which a researcher can submit his cell line gene expression data and that scores the likelihood of the cell line being contaminated.
    • Technical skills: knowledge of bionformatics in general and specifically the statistical programming language R would be an advantage. Following our MSc level course at the University of Amsterdam on the analysis of genome-wide experiments provides most of the required skills.

Topic revision: r48 - 2012-01-22 - AntoineVanKampen
 
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