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

合作研究：创新：开创大规模探索泛基因组空间的新方法

• 批准号: 1759522
• 负责人: Brendan Mumey
• 金额: $ 37.92万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759522&HistoricalAwards=false
• 关键词: Collaborative; Research; Innovation; Pioneering; New

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.

该项目开发了用于pangenomic分析的新软件工具，这是一个相对较新的基因组研究领域，研究了来自多种生物体的大量基因组序列，以了解生物如何使其基因组适应其环境。 随着DNA测序的成本继续降低，现在每个物种都可以进行分析的多个基因组是常规的，从而提供了更多有关该物种的信息。该方法利用基于图的pangenome表示，并利用此表示，以有效地找到跨基因组的共享和独特的感兴趣区域。每个个体的基因组序列都对应于称为de bruijn图的图数据结构中的路径。这些图很大，可以具有数百万个节点和边缘。 所开发的工具基于在De Bruijn图中找到频繁的区域（FRS）。这些区域是热点，通常代表一个或多个基因组中感兴趣的特征。将向更大的科学界提供算法和软件工具，以促进新的Pangenomics研究。 该项目将为蒙大拿州立大学的博士后和即将到来的博士生提供支持和培训。它还将在过去两年中在国家基因组资源中心的暑期实习生提供支持。 该项目的各个方面将纳入MSU的本科和研究生课程，并将其纳入NCGR的几项外展和培训活动。此外，MSU还制定了几个计划，可以为美洲印第安学生提供服务，而PI将积极招募并参与该社区。当前的下一代测序改进的轨迹，包括成本下降和增加的阅读长度和吞吐量，确保在下一十年中每个物种将是常规的多个基因组。该项目启动了下一代生物信息学软件的工作，该软件可以利用来自多个加入可用的增加信息内容，并智能地使用数据进行无偏见的，全物种的分析。 拟议的工作将完善算法并开发软件，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。 通过自动鉴定pangenome感兴趣的候选区域，pangenomic生物学将得到推进。将开发方法来发现在进化空间，新颖的区域以及由于积极选择而差异的区域。 机器学习技术将用于搜索有趣的基因组区域。最后，这项工作将补充模型工厂Medicago truncatula所做的工作，从而有助于研究其共生关系。 该项目的结果可以在以下网址找到：www.cs.montana.edu/pangenomics.this Award反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Maximal Perfect Haplotype Blocks with Wildcards

• DOI: 10.1016/j.isci.2020.101149
• 发表时间: 2020-06-26
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Williams, Lucia;Mumey, Brendan
• 通讯作者: Mumey, Brendan

Flow decomposition with subpath constraints

• DOI: 10.4230/lipics.wabi.2021.16
• 发表时间: 1000-01-01
• 期刊: 21 INT WORKSHOP ALGO

Sparsifying, Shrinking and Splicing for Minimum Path Cover in Parameterized Linear Time

• DOI: 10.1137/1.9781611977073.18
• 发表时间: 2021-07
• 作者: Manuel O. C'aceres;Massimo Cairo;B. Mumey;Romeo Rizzi;Alexandru I. Tomescu

Extending Maximal Perfect Haplotype Blocks to the Realm of Pangenomics

• DOI: 10.1007/978-3-030-42266-0_4
• 发表时间: 2020-02-01
• 期刊: Algorithms for Computational Biology
• 作者: Williams L;Mumey B
• 通讯作者: Mumey B

Safety in Multi-Assembly via Paths Appearing in All Path Covers of a DAG

• DOI: 10.1109/tcbb.2021.3131203
• 发表时间: 2022-11-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Caceres, Manuel;Mumey, Brendan;Tomescu, Alexandru I.
• 通讯作者: Tomescu, Alexandru I.