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

项目负责人：Volker P. Brendel（爱荷华州立大学）项目负责人：Karin Dorman（爱荷华州立大学）、Shannon Schlueter（北卡罗来纳大学夏洛特分校）、Shailesh Lal（奥克兰大学）项目负责人：Jon Duvick和Yasser El-Manzalawy（爱荷华州立大学）项目负责人：Jon Duvick和Yasser El-Manzalawy（爱荷华州立大学）本项目的前提是植物基因组学的序列规模和其他数据积累需要开发新颖、高度自动化、可扩展、全面和准确的基因组注释方法。在众多实验条件下积累的许多植物物种的转录数据深度为评估转录的各个方面提供了前所未有的证据，包括转录起始位点以及显性和替代剪接位点的精确定位。该项目汇集了包括基因组学、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在内的广泛领域的专家团队，共同致力于从植物基因组序列中准确预测表达蛋白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)用于分布式计算，协议共享和分析来源记录的开发工具的网络基础设施环境实施。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是描述性的科学过渡到高度预测性的科学，由定量测量驱动，算法和计算作为领域适应的语言。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、准确的蛋白质编码基因结构注释。最初的重点将放在对最近测序的基因组的改进注释上，这将使整个研究这些重要作物的研究人员受益。预期的转录组预测算法对于在未来几年内可能可用的数千个完整植物基因组序列的分析至关重要。通过开发可靠的金标准注释和传播用于算法开发的训练和测试集，将会有一个更大的计算数据分析师社区，特别是来自机器学习社区的计算数据分析师社区参与进来。在这项研究中开发的所有软件和产生的数据都可以通过项目网站免费获得，特别是www.plantgdb.org。该项目整合研究和教育的计划将训练新一代的科学家，用项目团队所代表的广泛的跨学科方法来研究基因组数据。

PI: Volker P. Brendel (Iowa State 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.