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。

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) Pub Date : 2020-07-01 DOI: 10.1145/3311790.3396621

Danying Shao, Gretta Kellogg, Shaun Mahony, William Lai, B Franklin Pugh

{"title":"PEGR: a management platform for ChIP-based next generation sequencing pipelines.","authors":"Danying Shao, Gretta Kellogg, Shaun Mahony, William Lai, B Franklin Pugh","doi":"10.1145/3311790.3396621","DOIUrl":"https://doi.org/10.1145/3311790.3396621","url":null,"abstract":"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.","PeriodicalId":74406,"journal":{"name":"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)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3311790.3396621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9349845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform.","authors":"Subhashini Sivagnanam, Wyatt Gorman, Donald Doherty, Samuel A Neymotin, Stephan Fang, Hermine Hovhannisyan, William W Lytton, Salvador Dura-Bernal","doi":"10.1145/3311790.3399621","DOIUrl":"https://doi.org/10.1145/3311790.3399621","url":null,"abstract":"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.","PeriodicalId":74406,"journal":{"name":"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)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3311790.3399621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39873582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6