Difference between revisions of "In the driver’s seat: Identification of significant mutations in cancer"

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|Master name=Bioinformatics
 
|Master name=Bioinformatics
 
|Student name=Stefan Lelieveld
 
|Student name=Stefan Lelieveld
|Project start date=2013/04/01
+
|Project start date=2013/04/23
|Project end date=2013/08/01
+
|Project end date=2013/08/31
 
|Supervisor=Lodewyk Wessels
 
|Supervisor=Lodewyk Wessels
 
|Second supervisor=Anton Feenstra
 
|Second supervisor=Anton Feenstra
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|Poster=Posternaam.pdf
 
|Poster=Posternaam.pdf
 
}}
 
}}
 
 
Performing high-throughput experiments on a set of tumor samples or cell lines, one typically wants to identify which genes are recurrently mutated in many samples and how significant this is in the context of tumorigenesis. A few methods have been proposed based on various statistics and different ways to compute a background mutation rate (Jeoblom et al., 2007; Getz et al., 2007, Comment; Rubin and Green, 2007, Comment; Dees et al., 2012).
 
Performing high-throughput experiments on a set of tumor samples or cell lines, one typically wants to identify which genes are recurrently mutated in many samples and how significant this is in the context of tumorigenesis. A few methods have been proposed based on various statistics and different ways to compute a background mutation rate (Jeoblom et al., 2007; Getz et al., 2007, Comment; Rubin and Green, 2007, Comment; Dees et al., 2012).
  

Revision as of 12:13, 21 May 2013


In the driver’s seat: Identification of significant mutations in cancer
status: ongoing
Master: project within::Bioinformatics
Student name: student name::Stefan Lelieveld
Dates
Start start date:=2013/04/23
End end date:=2013/08/31
Supervision
Supervisor: Lodewyk Wessels
Second supervisor: Anton Feenstra
Second reader: has second reader::Magali Michaut
Company: has company::NKI
Thesis: has thesis::Media:Thesis.pdf
Poster: has poster::Media:Posternaam.pdf

Signature supervisor



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Abstract

Performing high-throughput experiments on a set of tumor samples or cell lines, one typically wants to identify which genes are recurrently mutated in many samples and how significant this is in the context of tumorigenesis. A few methods have been proposed based on various statistics and different ways to compute a background mutation rate (Jeoblom et al., 2007; Getz et al., 2007, Comment; Rubin and Green, 2007, Comment; Dees et al., 2012).

However, there is no standard method and researchers use ad-hoc methods combined with manual curation. We have developed a method, which compares the observed mutations with a constant mutation rate. We would like to investigate alternative ways to define the background mutation rate. In addition, most genes have low mutation frequency. But if several genes from the same pathway are frequently mutated as a group, we want to able to detect it. Finally, investigating the position of the mutations in the context of functional domains in the associated proteins can give some functional information about the consequences of the mutations.

What needs to be done

We propose to develop methods to identify significant mutations at the domain, gene and pathway levels. We want to compare these methods with the few existing ones and make them easily available for the community. Finally we want to apply the best methods to our data on colorectal and breast cancer.

Requirements

We are looking for a motivated student with a strong background in computational biology, statistics and the R programming language to work on this project. There will be ample opportunity to bring forward your own ideas. During the project a report has to be written about the work performed.

The research group

The project will be carried out in Amsterdam at the Netherlands Cancer Institute (NKI-AVL), a dynamic and inter-disciplinary research institute. The Bioinformatics and Statistics group headed by Prof. Dr. Lodewyk Wessels consists of about 15 scientists including postdocs, PhD students, bioinformaticians and MSc students. The research is focused on the development of novel computational approaches exploiting a wide variety of data sources in order to improve cancer diagnosis and treatment. Supervision will be performed by Magali Michaut and Lodewyk Wessels.

Additional information

For further information contact, you can contact: Magali Michaut (m.michaut@nki.nl) or Lodewyk Wessels (l.wessels@nki.nl)

NKI Student Project page: bioinformatics.nki.nl/student_projects.php