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PEARC20 : Practice and Experience in Advanced Research Computing 2020 : Catch the wave : July 27-31, 2020, Portland, Or Virtual Conference. Practice and Experience in Advanced Research Computing (Conference) (2020 : Online)最新文献

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PEGR: a management platform for ChIP-based next generation sequencing pipelines. PEGR:基于芯片的下一代测序流水线管理平台。
Danying Shao, Gretta Kellogg, Shaun Mahony, William Lai, B Franklin Pugh

There has been a rapid development in genome sequencing, including high-throughput next generation sequencing (NGS) technologies, automation in biological experiments, new bioinformatics tools and utilization of high-performance computing and cloud computing. ChIP-based NGS technologies, e.g. ChIP-seq and ChIP-exo, are widely used to detect the binding sites of DNA-interacting proteins in the genome and help us to have a deeper mechanistic understanding of genomic regulation. As sequencing data is generated at an unprecedented pace from the ChIP-based NGS pipelines, there is an urgent need for a metadata management system. To meet this need, we developed the Platform for Eukaryotic Genomic Regulation (PEGR), a web service platform that logs metadata for samples and sequencing experiments, manages the data processing workflows, and provides reporting and visualization. PEGR links together people, samples, protocols, DNA sequencers and bioinformatics computation. With the help of PEGR, scientists can have a more integrated understanding of the sequencing data and better understand the scientific mechanisms of genomic regulation. In this paper, we present the architecture and the major functionalities of PEGR. We also share our experience in developing this application and discuss the future directions.

基因组测序技术发展迅速,包括高通量下一代测序技术、生物实验自动化、新型生物信息学工具以及高性能计算和云计算的应用。基于芯片的NGS技术,如ChIP-seq和ChIP-exo,被广泛用于检测基因组中dna相互作用蛋白的结合位点,帮助我们对基因组调控有更深层次的机制理解。由于基于芯片的NGS管道以前所未有的速度生成测序数据,因此迫切需要元数据管理系统。为了满足这一需求,我们开发了真核生物基因组调控平台(PEGR),这是一个web服务平台,记录样本和测序实验的元数据,管理数据处理工作流程,并提供报告和可视化。PEGR将人、样品、协议、DNA测序仪和生物信息学计算联系在一起。借助PEGR,科学家可以更全面地了解测序数据,更好地了解基因组调控的科学机制。在本文中,我们介绍了PEGR的体系结构和主要功能。我们还分享了开发该应用程序的经验,并讨论了未来的发展方向。
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引用次数: 1
Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. 利用谷歌云平台模拟大脑神经元回路的大规模模型。
Subhashini Sivagnanam, Wyatt Gorman, Donald Doherty, Samuel A Neymotin, Stephan Fang, Hermine Hovhannisyan, William W Lytton, Salvador Dura-Bernal

Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resultin brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations requiring significant computational resources. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.

生物物理详细建模提供了一种无与伦比的方法来整合来自许多不同实验研究的数据,并以高精度操作和探索结果脑回路模拟。我们开发了一个大脑运动皮层回路的详细模型,模拟了超过10,000个生物物理上详细的神经元和3000万个突触连接。通过使用网格搜索、参数扫描和进化算法进行参数探索,实现了皮质模型参数和响应的优化和评估。这需要运行数以万计的模拟,需要大量的计算资源。本文介绍了我们建立和使用Google Compute Platform (GCP)和Slurm来运行这些大规模模拟的经验。我们描述了在此过程中出现的问题的最佳实践和解决方案,并介绍了在GCP上运行模拟的初步结果。
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引用次数: 6
期刊
PEARC20 : Practice and Experience in Advanced Research Computing 2020 : Catch the wave : July 27-31, 2020, Portland, Or Virtual Conference. Practice and Experience in Advanced Research Computing (Conference) (2020 : Online)
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