Nanopore long reads and cancer

PD Dr. med Franz-Josef Müller, Prof. Meissner, Dr. Kretzmer

November 04, 2021

The project will push the boundaries of what is considered technologically possible in the field of third generation sequencing technologies, specifically nanopore sequencing. The work will build upon our recent publications and work concerning the real time analysis of nanopore sequencing data from cancer biopsies.

For a conceptual perspective on our work, see this recording of a presentation of our project.
The position is offered initially for three years.

 

Responsibilities:

Successful candidates (m/f/d) will work in a high-profile environment and interact with a multidisciplinary team of experts in the fields electrical engineering, epigenetics, surgical oncology as well as industry partners in the field of applied clinical diagnostics. Alexander Meissner, Helene Kretzmer and Franz-Josef Müller will coordinate the project. The tasks will include the identification of sequence variants in nanopore datasets from tumor samples as well as the analysis of DNA methylation, with an emphasis on prediction of disease progression and integration/visualization of multiple datasets.

Qualifications:

The sequencing device has tiny nanopores that can determine both the DNA sequence and the epigenetic signature.

We are looking for highly motivated as well as skilled individuals in the area next-generation sequencing genome informatics. Eligible candidates should have a Master’s degree or a similar qualification in biological, chemical, physical or computational sciences. The candidates should have a strong background in statistics and computational biology. The position will be offered to individuals with prior experience in machine learning and its application to classification of cancer subtypes from functional genomics datasets. The early career researcher will have a firm grasp of the programming languages Python or R as well as working knowledge in Linux. The successful applicant will benefit from experience with random forest models and similar approaches to prognosis stratification in malignant disorders.

For more information visit the website of the Cellular Phenotyping Group.

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