PEGR: a management platform for ChIP-based next generation sequencing pipelines.

Danying Shao, Gretta Kellogg, Shaun Mahony, William Lai, B Franklin Pugh
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引用次数: 1

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.

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PEGR:基于芯片的下一代测序流水线管理平台。
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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PEGR: a management platform for ChIP-based next generation sequencing pipelines. Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform.
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