High-Throughput Computing for Genomics and Bioinformatics Research

用于基因组学和生物信息学研究的高通量计算

• 批准号: 10176848
• 负责人: Adrian V Lee
• 金额: $ 57.36万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10176848
• 关键词: Area; Bioinformatics; Biomedical Computing; CLC Gene; Cellular biology; Collaborations; Computer software; Data; Data Science; Data Set; Development; Disease; Education; Education and Outreach; Educational workshop; Ensure; Funding; Genomics; Health Science Library; Health Sciences; High Performance Computing; Hour; Infrastructure; Institutes; Institution; Licensing; Link; Longevity; Malignant Childhood Neoplasm; Medicine; Memory; Modeling; Molecular Biology; Monitor; Research; Research Personnel; Research Support; Running; Science; Secure; Support System; System; Taxonomy; Techniques; United States National Institutes of Health; Universities; cluster computing; computing resources; cost effective; image reconstruction; next generation sequencing; operation; precision medicine; programs; tool; tumor immunology

Biomedical researchers have an increasing ability to comprehensively interrogate cell and molecular biology, for example with advanced imaging and next-generation sequencing. These techniques are producing datasets of increasing size and complexity and necessitate high-performance computing for biomedical researchers. Results from these studies are rapidly changing our understanding of normal development, our taxonomy of disease, and ultimately will enable precision medicine. Recognizing the importance of data science in precision medicine, the Institute for Precision Medicine (IPM) and the University of Pittsburgh invested in a high-throughput computing cluster (HTC), with infrastructure suited for memory-intensive and IO-intensive genomic and bioinformatic operations, and an education program dedicated to enabling computing for health science researchers. The HTC is located in the Center for Research Computing (CRC), a secure and centralized enterprise facility providing computing resources to the whole of the University. The CRC provides a scalable model for research computing with expertise in use and monitoring of advanced computing. In 2015, the HTC was installed with 16 nodes (256 cores). Usage of the HTC grew rapidly (2016 - 0.5M core-hours to 2019 – 3.1M core-hours), and despite the addition of computing nodes in 2017 (4 nodes), 2018 (4 nodes) and 2019 (16 nodes) the system often runs at capacity. Furthermore, the nodes have reached their useable lifespan. In this application, we are requesting funds to replace the HTC cluster and increase computing availability with a new cluster of 20 CPU nodes (960 cores) and 8 GPU cards, 2 CPU nodes as application servers, and 1.2 PB of BeeGFS storage, all linked by Infiniband HDR networking. The new HTC cluster is dedicated to NIH-funded research in biomedical science including medicine, pediatrics, cancer, immunology, and other areas. To assist health science researchers, the IPM, CRC and the health science library system (HSLS) support institution-wide licenses from numerous analysis and bioinformatics software suites, several of which are installed on the HTC server. Importantly, HSLS and CRC provide education outreach through workshops for command line tools, R for genomics and software suites. Researchers using software suites such as CLC Genomics, Open OnDemand, or command line can transparently migrate data and perform analysis on the multi-core HTC. IPM collaboration with the CRC ensures that the new HTC cluster will be operated in the most cost-effective and efficient manner and will be of high value to health sciences researcher performing NIH-supported research. This collaborative effort significantly reduces barriers for biomedical researchers needing to perform computational research.

生物医学研究人员全面询问细胞和分子生物学的能力越来越强， 例如利用先进的成像和下一代测序。这些技术正在产生 数据集的规模和复杂性不断增加，需要高性能计算， 研究人员这些研究的结果正在迅速改变我们对正常发育的理解， 疾病的分类，并最终实现精准医疗。 认识到数据科学在精准医学中的重要性，精准医学研究所（IPM） 匹兹堡大学投资了一个高吞吐量计算集群（HTC）， 适用于内存密集型和IO密集型的基因组和生物信息学操作，以及教育计划 致力于为健康科学研究人员提供计算能力。HTC位于中心， 研究计算（CRC），一个安全和集中的企业设施提供计算资源， 整个大学。CRC为研究计算提供了一个可扩展的模型， 监控先进的计算技术。2015年，HTC安装了16个节点（256个核心）。的使用 HTC增长迅速（2016年- 0.5M核心小时到2019年- 3.1M核心小时），尽管增加了计算 在2017年（4个节点）、2018年（4个节点）和2019年（16个节点），系统通常满负荷运行。此外，委员会认为， 节点已达到其可用寿命。在这个应用程序中，我们要求资金，以取代宏达电 通过20个CPU节点（960个内核）和8个GPU卡的新群集进行群集并提高计算可用性， 2个CPU节点作为应用服务器，1.2 PB的BeeGFS存储，全部通过Infiniband HDR网络连接。 新的HTC集群致力于NIH资助的生物医学科学研究，包括医学，儿科学， 癌症、免疫学和其他领域。为了协助健康科学研究人员，IPM，CRC和健康 科学图书馆系统（HSLS）支持来自众多分析和生物信息学的机构范围的许可 软件套件，其中几个安装在HTC服务器上。重要的是，HSLS和CRC提供了 通过命令行工具讲习班、基因组学R和软件套件开展教育外联。 使用CLC Genomics、Open OnDemand或命令行等软件套件的研究人员可以 透明地迁移数据并在多核HTC上执行分析。IPM与CRC的合作 确保新的HTC集群将以最具成本效益和效率的方式运营，并将 高价值的健康科学研究人员进行NIH支持的研究。这一协作努力 显著降低了生物医学研究人员需要进行计算研究的障碍。

项目成果

ESR1 fusion proteins invoke breast cancer subtype-dependent enrichment of ligand independent pro-oncogenic signatures and phenotypes.

ESR1融合蛋白引起乳腺癌亚型依赖性配体独立的促癌特征和表型的富集。

• DOI: 10.1101/2023.09.18.558175
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Yates,MeganE;Li,Zheqi;Li,Yiting;Guzolik,Hannah;Wang,Xiaosong;Liu,Tiantong;Hooda,Jagmohan;Atkinson,JenniferM;Lee,AdrianV;Oesterreich,Steffi
• 通讯作者: Oesterreich,Steffi

Defining spatiotemporal gene modules in liver regeneration using Analytical Dynamic Visual Spatial Omics Representation (ADViSOR).

• DOI: 10.1097/hc9.0000000000000289
• 发表时间: 2023-11-01
• 期刊: Hepatology communications
• 影响因子: 5.1

Sin3a associated protein 130 kDa, sap130, plays an evolutionary conserved role in zebrafish heart development.

• DOI: 10.3389/fcell.2023.1197109
• 发表时间: 2023
• 期刊: Frontiers in cell and developmental biology
• 影响因子: 5.5

Preservation of ∼12-h ultradian rhythms of gene expression of mRNA and protein metabolism in the absence of canonical circadian clock.

• DOI: 10.3389/fphys.2023.1195001
• 发表时间: 2023
• 期刊: Frontiers in physiology
• 影响因子: 4

Loss of TAZ after YAP deletion severely impairs foregut development and worsens cholestatic hepatocellular injury.

• DOI: 10.1097/hc9.0000000000000220
• 发表时间: 2023-09-01
• 期刊: Hepatology communications
• 影响因子: 5.1