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

Pi：Volker P.Brendel(爱荷华州立大学)Copis：Karin Dorman(爱荷华州立大学)，Shannon Schlueter(北卡罗来纳大学夏洛特分校)和Shailesh Lal(奥克兰大学)高级人员：Jon Duvick和Yasser El-Manzalawy(爱荷华州立大学)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要求开发新的、高度自动化的、可扩展的、全面的和准确的基因组注释方法。许多植物物种在众多实验条件下积累的转录数据的深度为转录的所有方面的评估提供了前所未有的证据，包括转录起始位点以及主要和替代剪接位点的精确定位。该项目涉及广泛领域的专家团队，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，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)实施新的预测算法的软件，(2)可视化和数据访问门户，以及(3)为分布式计算、共享协议和分析来源记录开发的工具的网络基础设施环境的实施。从长远来看，该项目寻求探索基因组生物学可以在多大程度上从一门主要是描述性的科学过渡到一门由定量测量驱动、以算法和计算为适应领域的语言的高度预测的科学。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。最初的重点将是改进最近测序的基因组的注释，这将使从事这些重要作物工作的整个研究人员社区受益。预期的转录组预测算法对于分析可能在未来几年内出现的数千个完整的植物基因组序列将是必不可少的。通过开发可靠的黄金标准注释以及分发用于算法开发的培训和测试集，将雇用更多的计算数据分析员，特别是来自机器学习界的分析员。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免费获得，特别是www.PLANGDB.org。该项目的研究和教育一体化计划将培训新一代科学家，利用该项目团队所代表的广泛的跨学科方法研究基因组数据。

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.