Accelerated computation of bioinformatic data integration

随着下一代测序（NGS）成为癌症研究中高度利用的工具，围绕其计算分析的问题仍有待解决，特别是对大型数据集的分析速度。随着NGS技术的不断发展，可以在大型数据集中提供更深入的信息，目前的分析管道正在努力跟上，从而降低了有效挖掘这些数据集的能力。解决这一主要问题将使NGS成为精准癌症医学的有力候选者，作为在研究和临床环境中提供快速答案的可行工具。该项目的主要重点将是使用新型高性能计算（HPC）方法来实现大型RNA-Seq谱的快速并行计算，并在中央系统内与其他变量（如临床/病理、突变、甲基化标记等）进一步集成。新的并行计算选项将被开发，以满足分析问题的速度。这将增强数据和病例报告的可视化以及数字图像性能仓库，最终形成一个整体的“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。尽管该框架被设计用于部署，独立于癌症类型，但我们将重点关注前列腺癌和胶质瘤，以识别和验证可能代表放射耐药特征和临床结果恶化的分子标记。建立定义关键性能指标的最佳管道将使我们能够探索适合临床环境的周转时间。将开发和建立这些已定义的工作流程，以便在并行处理和报告中进一步使用。该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，这是一家专门从事基因组HPC的公司，它将提供从基因组环境中学习超现代计算技术的机会，并开发各种并行计算选项，以实现中央集成QUB/AE框架，为研究人员提供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实习来完成，以允许深度学习和尖端计算技术的实践经验。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。该项目还将增加发现生物标志物的可能性，并开发用于患者分层的诊断测试。这个跨学科项目提供了一个新的机会，将精密医学研究的各个方面的学科与计算机科学结合起来，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.