We are looking for a PhD student in the field of translational bioinformatics. The project aims to detect chromosomal breakpoints with impact on colorectal cancer. Colorectal cancer (CRC) is caused by genomic alterations such as point mutations, DNA copy number aberrations and structural variants, the latter comprising deletions, insertions, inversions, and intra- and inter- chromosomal translocations, all of which involve chromosomal breaks. The impact of recurrent chromosomal breaks in CRC has been poorly characterized (van den Broek et al, 2015). This project will use advanced bioinformatics analyses and machine learning techniques to characterize recurrent breakpoint genes in more detail, in order to improve molecular DNA-based classification and thereby stratification of CRC patients for early diagnosis, prognosis and therapy prediction.
The computational challenge is to detect relevant breakpoints while filtering the experimental and biological background signal. The aim of this project is to use advanced machine learning techniques to characterize recurrent breakpoint genes in more detail, in order to detect those events with a direct impact on the tumor. By learning over multiple molecular and clinical data sources, we hope to improve molecular DNA-based classification and thereby stratification of CRC patients for early diagnosis, prognosis and therapy prediction. Our long-term goal is to implement detection of SVs with diagnostic, prognostic and/or predictive value into clinical care to improve disease management of CRC patients.
This is a collaborative project lead by Remond Fijneman (NKI, Translational Gastrointestinal Oncology group; head Gerrit Meijer) and Sanne Abeln (VU University, dept. of Computer Sciences, bioinformatics section; head, Jaap Heringa).
Your duties
- Analyzing large omics data sets
- Applying machine learning methods to heterogeneous datasets
- Developing methods to detect high resolution breakpoints
- Perform complex statistical analysis
- Communicate your work with both biomedical researchers and computer scientists
- Teaching computer practicals to MSc students (<5% fte)