This Master project focuses on the real-time analysis of genome data and related information obtained from Xenograft experiment, such as cancer mutations, drug response results, and specific patient data. The project aims to apply in-memory technology for scientific data management. If you are interested in this project, you can download the detailed project description.
The vision of the human genome project was born in the early 1980s. One decade later, it was officially started in the U.S. in 1990. Another decade later, a first draft of the humane genome was announced in 2000. In the same period costs for computer hardware dropped and capacities of main memory and storage systems underwent an exponential growth. Today, sequencing and analysis of genome data turned into reality. Nowadays, researchers analyze genome data to understand diseases on the genome level. A proper understanding of genetic variations and their influence on drug effectiveness can pave the way for personalized treatments, especially in the field of cancer diagnostics. However, researchers trying to find correlations between genetic variations and drug responsiveness still miss proper tools for efficient analysis workflows. For example, researchers conduct Xenograft experiments with various drugs and investigate on their responsiveness for head and neck cancer tumors. For that, huge data sets are collected containing patient specifics, tumor sequence data, and process of tumor growth from the Xenograft experiments. Correlations between this data are currently searched for by hand by researchers themselves, primarily through the usage of spread sheets. This process can take up to several days.
Building on our long-standing experience in applying in-memory technology to selected enterprise challenges, we also focus on processing and analyzing of scientific data sets in real-time. In particular, the applicability of in-memory technology for analysis of genome data will be evaluated. Proof of concept prototypes will be engineered and shown to potential end users in the course of this project.