Accelerated computation of bioinformatic data integration

随着下一代测序（NGS）成为癌症研究中高度使用的工具，围绕其计算分析的问题仍有待解决，特别是大型数据集的分析速度。随着NGS技术的不断发展，在大型数据集中提供更深入的信息，当前的分析管道正在努力跟上，降低了有效挖掘这些数据集的能力。解决这一重大问题将使NGS成为精准癌症医学的有力候选者，作为一种可行的工具，在研究和临床环境中提供快速答案。该项目的主要重点将是使用新型高性能计算（HPC）方法，以允许快速并行计算大型RNA-Seq图谱，并在中央系统中进一步整合其他变量，如临床/病理学，突变，甲基化标记等。将开发新的并行计算选项，以满足分析速度问题。这将导致增强数据和病例报告的可视化，沿着增加数字图像性能仓库，最终导致一个整体的“组学”和基于数字图像的系统，为研究人员提供强大的生物标志物分析工具。尽管该框架被设计为独立于癌症类型进行部署，我们将重点关注前列腺癌和神经胶质瘤，以鉴定和验证可能代表放射抵抗和临床恶化的分子标志物。结果。定义关键性能指标的最佳管道的建立将使我们能够探索适合临床环境的周转时间。这些定义的工作流程将被开发和建立，以进一步用于并行处理和报告。这个合作项目将在贝尔法斯特女王大学（QUB）和分析引擎有限公司（AE）之间举行，AE是一家专门从事基因组学HPC的公司，这将提供学习超-现代计算技术从基因组背景以及开发各种并行计算选项，着眼于中央集成QUB/AE框架为研究人员提供了生物标志物发现的高性能解决方案。这将通过在整个项目中持续的接触和安置来完成，以允许在尖端计算技术方面的深入学习和实践经验。该项目的影响将是为NGS社区设计定制架构，该社区将努力解决基因组处理瓶颈，为癌症研究机构，制药和临床研究组织带来好处。该项目还将导致生物标志物发现的可能性增加和用于患者分层的诊断测试的开发。这个跨学科项目提供了一个新颖的机会，将精确医学研究的各个方面的学科与计算机科学联合收割机结合起来，为生物数据的处理提供实用的见解。

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.