Analyzing Next-Generation Sequencing Data

分析下一代测序数据

• 批准号: 8150859
• 负责人: C. Titus BROWN
• 金额: $ 5.4万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-23 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8150859
• 关键词: Address; Allelotyping; Automation; Bioinformatics; Biological; Biomedical Research; Computer software; DNA Resequencing; Data; Data Analyses; Data Set; Educational process of instructing; Experimental Designs; Future; Gene Expression Profile; Genome; Genomics; Goals; Graph; Individual; Institution; Internet; Learning; Maps; Memory; Messenger RNA; Operating System; Participant; Paste substance; Procedures; Reading; Recipe; Research; Research Personnel; Running; Sampling; Scientist; Sequence Analysis; Source; Students; System; Techniques; Technology; Thinking; Training; Work; base; computerized tools; computing resources; data mining; data reduction; design; experience; genome sequencing; next generation; novel; programs; research study; skills; tool; transcriptomics; web services

DESCRIPTION (provided by applicant): Modern biomedical research is increasingly making use of genome-scale from next-generation sequencing platforms, including Roche 454, Illumina GA2, and ABI SOLiD. These platforms make it possible for individual labs to quickly and cheaply generate vast amounts of genomic and transcriptomic data from de novo sequencing, resequencing, ChIP-seq, mRNA-seq, and allelotyping experiments. Despite this ability to generate large data sets, biomedical researchers are rarely trained in the computational and statistical techniques necessary to make sense of this data. Thus, many researchers must rely on others - often computational scientists with little biological training - to design and implement appropriate data reduction and data mining techniques. Moreover, most institutions do not have access to computational resources necessary to run these analyses. Our specific aims are to help bridge this gap in a short, two-week course, by teaching biomedical researchers to (1) run analyses on remote UNIX servers hosted in the Amazon Web Services "cloud"; (2) perform mapping and assembly on large short-read data sets; (3) tackle specific biological problems with existing short-read data; and (4) design computational pipelines capable of addressing their own research questions. All specific aims will be accompanied by in-depth hands-on practical training in the relevant techniques. Our experience is that this practical training leads to a substantial improvement in the basic computational sophistication of participants. This short course will help train the current and next generation of independent biomedical re- searchers in basic computational thinking and procedure, as well as teaching them how to make use of scalable Internet computing resources for their own research. Our end goal is increase the efficiency and sophistication with which biomedical researchers make use of novel sequencing technologies. PUBLIC HEALTH RELEVANCE: Many biomedical researchers are not well trained in computational tools that would help them make use of genomic and other bioinformatics data. We propose a two- week short course for advanced researchers that will help train to take advantage of sequence data in their research.

描述（由申请人提供）：现代生物医学研究越来越多地利用下一代测序平台的基因组规模，包括Roche 454，Illumina GA 2和ABI SOLiD。这些平台使各个实验室能够快速、廉价地从从头测序、重测序、ChIP-seq、mRNA-seq和等位基因分型实验中生成大量的基因组和转录组数据。尽管有这种生成大型数据集的能力，但生物医学研究人员很少接受必要的计算和统计技术培训，以理解这些数据。因此，许多研究人员必须依靠其他人-通常是几乎没有生物学训练的计算科学家-来设计和实施适当的数据简化和数据挖掘技术。此外，大多数机构无法获得运行这些分析所需的计算资源。我们的具体目标是在短短两周的课程中帮助弥合这一差距，通过教授生物医学研究人员（1）在托管在Amazon Web Services“云”上的远程UNIX服务器上运行分析;（2）对大型短读数据集进行映射和组装;（3）利用现有的短读数据解决特定的生物学问题;（4）在现有的短读数据上进行分析。以及（4）设计能够解决他们自己的研究问题的计算管道。所有具体目标都将伴随着相关技术的深入实践培训。我们的经验是，这种实际的培训导致参与者的基本计算复杂性的实质性改善。这个短期课程将有助于培训当前和下一代独立的生物医学研究人员在基本的计算思维和程序，以及教他们如何利用可扩展的互联网计算资源为自己的研究。我们的最终目标是提高生物医学研究人员利用新型测序技术的效率和复杂性。 公共卫生相关性：许多生物医学研究人员没有受过良好的计算工具培训，这将有助于他们利用基因组和其他生物信息学数据。 我们建议为高级研究人员提供为期两周的短期课程，这将有助于培训他们在研究中利用序列数据。