Googling the cancer genome

Identification and prioritization of cancer-causing structural variations in whole genomes

Image: WallpaperUP

Cancer affects millions of people worldwide. With the advent of novel DNA sequencing technologies, genome sequencing has now started to become part of a routine workflow for cancer diagnostics and potentially enables fine-tuned treatment strategies tailored towards individual cancer patients. In spite of the massive genomic data production, systematic and comprehensive analysis of these data, in particular regarding the detection and interpretation of structural variation, are lagging behind due to computational and algorithmic limitations.

In this project, we will create novel analytical and computational frameworks that lead to fast, cost-efficient and comprehensive detection and annotation of structural variations in cancer genomes. We particularly focus on previously neglected variations occurring in unexplored regions of the cancer genome. Our methods will serve as an important component in future genome-first-based clinical-decision making for cancer patients and is essential to drive discovery of novel cancer genes and mechanism from modern-day whole genome sequencing data.

Participating organisations

Netherlands eScience Center
University Medical Center Utrecht

Impact

Output

Portable HPC workflows with Snakemake, Conda, and Xenon

Author(s): Jurriaan H. Spaaks
Published in 2018

Teaching machines to recognize cancer

Author(s): Netherlands eScience Center
Published in 2017

Team

Contact person

Lars Ridder

Lars Ridder

Netherlands eScience Center
Mail Lars
Arnold Kuzniar
Arnold Kuzniar
eScience Research Engineer
Netherlands eScience Center
JdR
Jeroen de Ridder
Principal investigator
University Medical Center Utrecht
Lars Ridder
Lars Ridder
eScience Coordinator
Netherlands eScience Center
LS
Luca Santuari
PhD student
University Medical Center Utrecht
Sonja Georgievska
Sonja Georgievska
eScience Research Engineer
Netherlands eScience Center

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