Accelerated computation of bioinformatic data integration

With Next-Generation Sequencing (NGS) becoming a highly utilised tool within cancer research, the issues surrounding its computational analysis remains to be addressed, particularly speed of analysis of large data sets. With NGS technology ever evolving to provide more in depth information in large data sets, current analytical pipelines are struggling to keep up, reducing the ability to mine these data sets effectively. Addressing this major issue would allow NGS to become a powerful candidate for precision cancer medicine as a feasible tool to provide fast answers in both a research and clinical setting.
The main focus of this project will be to use novel high performance computing (HPC) methods to allow for fast, parallel computation of large RNA-Seq profiles, to be further integrated with other variables such as clinical/pathological, mutational, methylation markers etc, within a central system. Novel parallel compute options will be developed to meet the speed of analysis issues. This will lead to enhancing visualization of data and case reporting along with digital image performance warehousing, ultimately leading to an overall 'omics' and digital image based system providing researchers with a powerful biomarker analysis tool.
Although the framework is designed to be deployed, independent of cancer type, we will have a focus within prostate cancer and glioma for the identification and validation of molecular markers that may represent the hallmarks of radiation resistance and a worsening clinical outcome. The establishment of optimal pipelines defining key performance metrics will allow us to explore turn-around-times amenable for a clinical context. These defined workflows will be developed and established to be further used in parallel processing and reporting.
This collaborative project will be held between Queen's University Belfast (QUB) and Analytics Engines Ltd (AE), a company specializing in genomics HPC, which will provide opportunities to learn ultra-modern compute techniques from a genomic context as well as developing various parallel compute options with a view to a central integrated QUB/AE framework providing a high performance solution in biomarker discovery for researchers. This will be done though continual contact and placements throughout the project to allow for in depth learning and practical experience in cutting edge computing techniques.
The impact of this project will be in the design of bespoke architecture for the NGS community that will strive to address genomic processing bottlenecks bringing benefit to cancer research institutes, pharma and clinical research organisations. This project will also lead to an increase in likelihood of biomarker discovery and the development of diagnostic tests for patient stratification.
This interdisciplinary project provides a novel opportunity to combine the disciplines of various aspects of precision medicine research with computer science to provide practical insights into the processing of biological data.