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.

生物医学研究人员越来越有能力全面询问细胞和分子生物学， 例如，通过先进的成像和下一代测序。这些技术正在产生 数据集的大小和复杂性不断增加，生物医学需要高性能计算 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，我们的 疾病的分类，最终将使精准医学成为可能。 认识到数据科学在精确医学中的重要性，精密医学研究所(IPM) 匹兹堡大学投资了一个高吞吐量计算集群(HTC)，带有基础设施 适用于内存密集型和IO密集型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实现计算。HTC位于 研究计算(CRC)，一种安全的集中式企业设施，为 整个大学。CRC为研究计算提供了一个可扩展的模型，具有使用和 监控高级计算。2015年，HTC安装了16个节点(256个核心)。的用法 HTC增长迅速(2016年为50万核心小时，2019年为310万核心小时)，尽管增加了计算 2017年(4个节点)、2018年(4个节点)和2019年(16个节点)系统经常满负荷运行。此外， 这些节点已达到其可用寿命。在此应用程序中，我们申请资金以更换HTC 通过包含20个CPU节点(960核)和8个GPU卡的新群集实现群集并提高计算可用性， 2个CPU节点作为应用程序服务器，1.2 PB的BeeGFS存储，所有这些都通过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、 癌症、免疫学等领域。协助卫生科学研究人员、IPM、儿童基金会和 科学图书馆系统(HSLS)支持来自大量分析和生物信息学的机构范围的许可证 软件套件，其中几个安装在HTC服务器上。重要是，HSLS和CRC提供 通过命令行工具研讨会、基因组学R和软件套件进行教育推广。 使用CLC基因组学、Open OnDemand或命令行等软件套件的研究人员可以 透明迁移数据，对多核HTC进行分析。与儿童基金会的IPM协作 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 对进行NIH支持的研究的健康科学研究人员具有很高的价值。这一合作努力 大大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

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

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