Collaborative Research: Innovation: Pioneering New Approaches to Explore Pangenomic Space at Scale

该项目为泛基因组分析开发了新的软件工具，泛基因组分析是基因组研究的一个相对较新的领域，它研究来自多种生物体的大量基因组序列，以了解生物体如何使其基因组适应其环境。 随着DNA测序成本的不断降低，现在每个物种的多个基因组都可以用于分析，从而提供有关该物种的更多信息。该方法利用泛基因组的基于图形的表示，并利用这种表示来有效地找到跨基因组的共享和独特的感兴趣区域。每个人？的基因组序列对应于称为De Bruijn图的图数据结构中的路径;这些图很大，可以有数百万个节点和边。 正在开发的工具是基于在De Bruijn图中寻找频繁区域（FR）;这些区域是热点，通常代表一个或多个基因组中的感兴趣特征。算法和软件工具将提供给更大的科学界，以促进新的泛基因组学研究。 该项目将为蒙大拿州立大学的一名博士后和一名即将入学的博士生提供支持和培训。它还将在过去两年中支持国家基因组资源中心的暑期实习生。 该项目的各个方面将纳入密歇根州立大学的本科生和研究生课程，并纳入NCGR的几项推广和培训活动。此外，密歇根州立大学有几个项目为美国印第安学生服务，PI将积极招募并参与这个社区。当前下一代测序改进的轨迹，包括降低成本和增加读取长度和吞吐量，确保每个物种的多个基因组将在未来十年内成为常规。该项目启动了下一代生物信息学软件的工作，该软件可以利用多个加入物中增加的信息内容，并智能地使用数据进行无偏见的全物种分析。 拟议的工作将改进算法并开发软件，以解决每个确定领域的重要问题。研究团队拥有各种互补的专业知识，包括分子生物学，算法，机器学习和基因组学研究。 泛基因组生物学将通过自动识别泛基因组中感兴趣的候选区域来推进。将开发方法来发现在进化空间中保守的区域，新的区域以及由于正选择而产生分歧的区域。 机器学习技术将用于搜索感兴趣的基因组区域。最后，这项工作将补充对模式植物蒺藜苜蓿所做的工作，有助于研究其共生关系。 该项目的结果可以在以下网站找到：www.cs.montana.edu/pangenomics.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This project develops new software tools for pangenomic analysis, which is a relatively new area of genomic research that studies large numbers of genome sequences from multiple organisms to understand how organisms adapt their genomes to their environments. As the cost of DNA sequencing continues to decrease, it is now routine for multiple genomes per species to be available for analysis, giving much more information about the species. The approach makes use of a graph-based representation of a pangenome and exploits this representation to efficiently find both shared and unique regions of interest across genomes. Each individual?s genomic sequence corresponds to path in a graph data structure called a De Bruijn graph; these graphs are large and can have millions of nodes and edges. The tools being developed are based on finding frequented regions (FRs) in De Bruijn graphs; these regions are hotspots that often represent features of interest in one or more genomes. Algorithms and software tools will be made available to the greater scientific community to facilitate new pangenomics research. The project will provide support and training for a postdoc and an incoming PhD student at Montana State University. It will also support a summer intern in the last two years at the National Center for Genome Resources. Aspects of the project will be incorporated into undergraduate and graduate courses at MSU, as well as integrated into several outreach and training activities at NCGR. In addition, MSU has several programs in place to serve American Indian students and the PIs will actively recruit from and engage this community.The current trajectory of next generation sequencing improvements, including falling costs and increased read lengths and throughput, ensure that multiple genomes per species will be routine within the next decade. This project initiates work on a next generation of bioinformatics software that can exploit the increased information content available from multiple accessions and intelligently use the data for unbiased, species-wide analyses. The proposed work will refine algorithms and develop software to address important problems in each of the identified areas. The research team has a variety of complementary expertise ranging from molecular biology, algorithms, machine learning and genomics research. Pangenomic biology will be advanced through automatic identification of candidate regions of interest in a pangenome. Methods will be developed to discover regions that are conserved across evolutionary space, regions that are novel, and regions that have diverged due to positive selection. Machine learning techniques will be used to search for interesting genomic regions. Lastly, this work will complement the work being done on the model plant, Medicago truncatula, contributing to research on its symbiotic relationships. Results of the project can be found at: www.cs.montana.edu/pangenomics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.