GPF-PG: Genome Structure and Diversity of Wheat and Its Wild Relatives

小麦（Triticum aestivum L.）是世界上最重要的粮食作物之一，也是全世界数十亿人的主食。 隐藏在面包中的是重要植物物种中最大和最复杂的基因组之一。 面包小麦基因组的大小和复杂性是人类基因组的五倍多，阻碍了可作为遗传研究和育种应用资源的参考框架的开发。 对小麦基因组的更好理解也将使我们深入了解复杂的多倍体植物基因组的结构、功能和进化。 用于组装植物基因组的基于群体的测序方法的进步将在整个植物基因组学社区中产生广泛的影响，特别是对于基因组资源有限且基因组庞大且难以处理的物种。 通过该项目开发的用户友好的群体测序工具将使这种方法能够在更广泛的植物基因组社区中应用。 通过综合教育和推广活动，该项目将招募和培训新一代的计算生物学家，以解决复杂和具有挑战性的基因组学问题。 与堪萨斯农业课堂基金会（KFAC）合作，将在全州的初中和高中课堂上开发和实施关于植物基因组和驯化的多层教育材料。 通过KFAC，学生将获得针对下一代科学标准的先进材料，包括植物科学职业的资源材料。 在本科阶段，将开发实习机会，以吸引来自计算密集型领域的非生物学学生。 具有不同社会和学术背景的实习生将通过美国实验室的课程和研究，接受前沿生物信息学的挑战，然后与欧洲项目合作者一起进行暑期实习。 在博士后阶段，从事该项目的学者将获得尖端基因组学的技能，沿着指导和国际研究经验。 为了传播通过该项目开发的生物信息学方法，将每年举办生物信息学培训讲习班，培训研究生和博士后。作为开发大基因组作物序列资源的方法，下一代测序（NGS）可以用于快速生成易于组装成短重叠群的全基因组序列。 这些重叠群主要包含复杂基因组的基因丰富的非重复部分。 然而，WGS组装具有有限的实用性，因为这些富含基因的重叠群在染色体上保持未锚定和无序。 需要新颖的、具有成本效益的方法来锚定和排序大型复杂的基因组。 通过该项目，将开发组装和排序复杂植物基因组的新方法，并将其应用于理解小麦及其野生亲缘植物的基因组。 NGS将应用于二倍体、四倍体和六倍体小麦物种的分离群体，利用连锁信息为每个物种开发有序的全基因组组装。 这种方法（POPSEQ）使用数百万个遗传标记将组装的基因空间排列到物理框架上。 该项目将针对多个二倍体（AA和DD基因组）、四倍体（AABB）和六倍体（AABBDD）种群的1x全基因组序列覆盖率，以开发高密度遗传图谱。 这些高密度遗传图谱可用于锚序列重叠群和支架，为基因组组装提供物理背景。 有序组装体将通过MIPS（http：//mips.helmholtz-muenchen.de/plant/genomes.jsp）、T3（http：//triticeaetoolbox.org/）和GrainGenes（http：//wheat.tag.usda.gov/）数据库进行迭代改进并提供给小麦社区。 遗传库存将通过KSU小麦遗传资源中心和USDA-ARS国家小谷物收藏提供。

Wheat (Triticum aestivum L.) is one of the world's most important food crops and a staple for billions throughout the world. Hidden within loaves of bread is one of largest and most complex genomes of important plant species. The size and complexity of the bread wheat genome, which is more than five times larger than the human genome, have hindered development of a reference framework that can serve as a resource for genetic studies and breeding applications. A better understanding of the wheat genome will also give insight into the structure, function and evolution of complex, polyploid plant genomes. Advancement of population-based sequencing methods for assembling plant genomes will have broad implications throughout the plant genomics community, particularly for species with limited genomic resources and large, intractable genomes. User-friendly tools for population sequencing developed through this project will enable applications of this approach in the broader plant genome community. Through integrated education and outreach activities, this project will recruit and train new generations of computational biologists to address complex and challenging genomics questions. In partnership with the Kansas Foundation for Ag in the Classroom (KFAC), multi-tiered educational materials on plant genomes and domestication will be developed and implemented in middle and high school classrooms across the state. Through KFAC, students will be given access to advanced material targeting Next Generation Science Standards, including resource materials on careers in plant science. At the undergraduate level, internships will be developed to attract non-biology students from computationally-intensive fields. Interns with diverse social and academic backgrounds will be challenged with cutting-edge bioinformatics through coursework and research in U.S. labs followed by summer internships working with European project collaborators. At the postdoctoral level, scholars working on this project will gain skills in cutting-edge genomics along with mentoring and international research experience. To disseminate bioinformatics approaches developed through this project, annual workshops for bioinformatics training in population sequencing and genotyping-by-sequencing will be held for graduate and postdoctoral training.As an approach for developing sequence resources for crop plants with large genomes, next-generation sequencing (NGS) can be applied to rapidly generate a whole genome sequence that is readily assembled into short contigs. These contigs primarily contain the gene-rich, non-repetitive portions of complex genomes. However, WGS assemblies have limited utility because these gene-rich contigs remain unanchored and unordered on the chromosomes. Novel, cost-effective approaches are needed for anchoring and ordering large, complex genomes. Through this project, new methods for assembling and ordering complex plant genomes will be developed and applied to understanding the genomes of wheat and its wild relatives. NGS will be applied to segregating populations of diploid, tetraploid and hexaploid wheat species, leveraging linkage information to develop an ordered whole-genome assembly for each species. This approach (POPSEQ) uses millions of genetic markers to order the assembled gene space onto a physical framework. The project will target 1x whole-genome sequence coverage across multiple diploid (AA and DD genomes), tetraploid (AABB) and hexaploid (AABBDD) populations to develop high-density genetic maps. These high-density genetic maps can then be used to anchor sequence contigs and scaffolds, giving physical context to genome assemblies. Ordered assemblies will be iteratively improved and made available to the wheat community through MIPS (http://mips.helmholtz-muenchen.de/plant/genomes.jsp), T3 (http://triticeaetoolbox.org/) and GrainGenes (http://wheat.pw.usda.gov/) databases. Genetic stocks will be made available through the KSU Wheat Genetics Resource Center and the USDA-ARS National Small Grains Collection.