SBIR Phase II: Whole Genome Sequencing - Data to Insight in One Hour

这项小企业创新研究（SBIR）二期项目的更广泛影响/商业潜力是通过减少数据处理时间和成本来增强精准医疗，从而更快地获得结果，提高基因组数据处理吞吐量，并最终实现更好的患者治疗。许多疾病的影响都锁定在DNA数据中。为个人解锁数据可能会极大地促进人类医疗保健。与目前需要几天的标准相比，通过在一小时内提供全基因组测序（WGS）数据分析，而且成本只是目前的一小部分，DNA测试可以成为主流，研究可以加快。WGS数据正在成为确保对弱势群体（包括新生儿、癌症和罕见病患者以及孕妇）进行正确和及时治疗的关键组成部分。基于WGS数据的正确预测和治疗将通过加速精准医学的发展和广泛实施，延长人类生命，提高生活质量。提出的活动的智力价值是开发一个基于云的框架来加速全基因组测序数据分析。SBIR二期项目创建了一个软件框架，其中多个行业标准的基因组分析将透明地加速，运行速度将提高几个数量级，同时将计算成本降低4倍。提出的创新通过新颖的并行提取和映射技术解决了WGS计算的规模、性能和成本挑战。正在开发的加速基因组分析的框架已经实现，重点放在gpu和传统处理器（cpu）上。使用这个框架，辅助数据分析程序可以使用标准计算节点（CPU内核；gpu）中存在的所有计算资源，从而提高系统利用率和吞吐量。GPU加速软件的数据并行部分，而CPU主要负责CPU和GPU之间的数据编排，跨多个加速器进行负载平衡。通过提供在云和本地运行的框架，用户可以扩展以满足DNA分析行业爆炸式增长的需求。该软件生成与行业标准工具精确的结果，并且不会牺牲对用户至关重要的可配置性。该框架还将使研究人员和医学专业人员能够同时分析数千个基因组，从而获得更高质量的结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to enhance precision medicine by reducing data processing time and cost, leading to faster results, higher genomic data processing throughput and, ultimately leading to better treatment of patients. Many diseases have their impacts locked in DNA data. Unlocking the data for an individual may provide a significant boost to human healthcare. By providing Whole Genome Sequencing (WGS) data analysis in one hour, as compared to the current standard of several days, and at a fraction of today's cost, DNA tests can become mainstream and research can be accelerated. WGS data is becoming a key component in ensuring correct and timely treatment of vulnerable populations, including newborn babies, people with cancer and rare diseases, and pregnant women. Correct prediction and treatment based on WGS data will extend human lives and promote better quality of life by accelerating the development and broad implementation of precision medicine.The intellectual merit of the proposed activity is to develop a cloud-based framework to accelerate whole genome sequencing data analysis. This SBIR Phase II project creates a software framework where multiple industry-standard genomic analyses will be accelerated transparently to run orders of magnitude faster, while reducing computational costs by up to 4x. The proposed innovation solves the scaling, performance, and cost challenges of WGS computing through novel parallelism extraction and mapping techniques. The framework under development for accelerating genomic analysis has been implemented to focus on GPUs and traditional processors (CPUs). Using this framework, secondary data analysis programs can use all the computing resources present in a standard computing node (CPU cores; GPUs), leading to higher utilization of the system and higher throughput. The GPU accelerates the data parallel portion of the software, while the CPU is mainly responsible for the orchestration of data between CPUs and GPUs, load balancing across multiple accelerators. By providing this framework to run on the cloud and on-premise, users can scale to meet the exploding demands of the DNA analysis industry. The software generates exact results to industry standard tools and does not sacrifice configurability that is critical for users. This framework also will enable researchers and medical professionals to analyze several thousands of genomes simultaneously, leading to higher quality results.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.