IPGA: Characterization, Modeling, Prediction, and Visualization of the Plant Transcriptome

IPGA：植物转录组的表征、建模、预测和可视化

• 批准号: 1221984
• 负责人: Volker Brendel
• 金额: $ 138.49万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1221984&HistoricalAwards=false
• 关键词: IPGA; Characterization; Modeling; Prediction; Visualization

PI: Volker P. Brendel (Indiana University) CoPIs: Karin Dorman (Iowa State University), Shannon Schlueter (University of North Carolina - Charlotte) and Shailesh Lal (Oakland University) Senior Personnel: Jon Duvick and Yasser El-Manzalawy (Iowa State University) The premise of this project is that the scale of sequence and other data accumulation in plant genomics necessitates the development of novel, highly automated, scalable, comprehensive, and accurate approaches to genome annotation. The depth of transcript data accumulating for many plant species under numerous experimental conditions provide unprecedented evidence for the evaluation of all aspects of transcription, including precise mapping of transcription start sites as well as dominant and alternative splice sites. This project engages a team of experts in a wide range of fields, including genomics, molecular biology, bioinformatics, statistics, machine learning, high performance computing, and software engineering to jointly work toward a solution for accurately predicting the expressed protein-coding gene transcriptome from plant genome sequences. Successful completion of the project will result in the deployment of (1) software that implements the novel prediction algorithms, (2) visualization and data access portals, and (3) a cyberinfrastructure environment implementation of the developed tools for distributed computing, sharing of protocols, and analysis provenance recording. In the long run, the project seeks to explore the extent to which genomic biology can transition from a largely descriptive to a highly predictive science driven by quantitative measurements, with algorithms and computation as the domain-adapted language. The project will generate standardized, accurate protein-coding gene structure annotation for 25 plant genomes from a wide range of the phylogenetic spectrum. Initial emphasis will be on improved annotation of recently sequenced genomes, which will benefit the entire community of researchers working on these important crops. The anticipated algorithms for transcriptome prediction will be essential to the analysis of the thousands of complete plant genome sequences likely to become available within the next few years. Through the development of reliable gold standard annotations and the dissemination of training and test sets for algorithmic development, a larger community of computational data analysts, in particular from the machine learning community, will be engaged. All software developed and data generated in this research is freely available through project Web sites, in particular www.plantgdb.org. The project's plan for integration of research and education will train a new generation of scientists to work on genomics data with the broad range of interdisciplinary approaches represented by the project team.

主要研究者：沃尔克·布伦德尔（印第安纳州大学）CoPI：Karin Dorman（爱荷华州州立大学），香农·施米特（北卡罗来纳州-夏洛特大学）和Shailesh Lal（奥克兰大学）高级人员：Jon Duvick和Yasser El-Manzalawy（爱荷华州州立大学）该项目的前提是，植物基因组学中序列和其他数据积累的规模需要开发新颖的、高度自动化的、可扩展的，全面、准确的基因组注释方法。在许多实验条件下，许多植物物种的转录数据积累的深度提供了前所未有的证据，转录的各个方面的评估，包括转录起始位点以及显性和可变剪接位点的精确定位。该项目由基因组学、分子生物学、生物信息学、统计学、机器学习、高性能计算和软件工程等领域的专家组成，共同致力于从植物基因组序列中准确预测表达的蛋白质编码基因转录组的解决方案。该项目的成功完成将导致部署（1）实现新型预测算法的软件，（2）可视化和数据访问门户，以及（3）网络基础设施环境，用于分布式计算，共享协议和分析出处记录的开发工具的实施。从长远来看，该项目旨在探索基因组生物学在多大程度上可以从主要描述性转变为由定量测量驱动的高度预测性科学，并将算法和计算作为领域适应语言。该项目将从广泛的系统发育谱中为25个植物基因组生成标准化的、准确的蛋白质编码基因结构注释。最初的重点将是改进最近测序的基因组的注释，这将有利于研究这些重要作物的整个研究人员群体。预期的转录组预测算法将是必不可少的分析成千上万的完整的植物基因组序列可能成为在未来几年内。通过开发可靠的黄金标准注释和传播用于算法开发的训练和测试集，将吸引更大的计算数据分析师群体，特别是来自机器学习界的分析师。在这项研究中开发的所有软件和产生的数据都可通过项目网站，特别是www.plantgdb.org免费获得。该项目的研究与教育一体化计划将培训新一代科学家，以项目小组为代表，采用广泛的跨学科方法研究基因组学数据。