Collaborative Research: ABI Innovation: Visualization and Statistics for Spatial Population Genomic Analysis

合作研究：ABI 创新：空间群体基因组分析的可视化和统计

• 批准号: 1262645
• 负责人: Peter Ralph
• 金额: $ 31.43万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262645&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Visualization

The University of Southern California and the University of California Davis are awarded collaborative grants to create methods and software to generate maps from genomic data, as well as to solve the related problem of placing samples of unknown origin on the map. Organisms that are more closely related to each other have more similar genomes. Often, an organism and its relatives live fairly nearby, so in principle genome similarity between many different organisms can be used to construct a map of the local geography. In this map, distances correspond to how easy it is for the organism to travel; so the map will display the landscape "as the organism sees it". Specifically, the methods developed here will construct these maps in a way to allow optimization of several different criteria, and will use randomized algorithms for pattern identification to allow efficient computation on large genomic datasets. The other, related suite of methods will use emerging tools from geostatistics to place other individuals on these maps, with rigorously quantified uncertainty. The description of geographic patterns from genomic data, and location of unknown samples, will have many applications. These tools could be used to identify the origin of a newly invading parasite, or to identify key barriers dividing the range of an endangered species. They will be of interest for inferring ancestry, and might be used to locate, for instance, the likely home of a person's grandfather to within 100km or so. They can also be used to visualize the relationships between modern populations; for instance, of humans, whose modern distribution reflects a complex history of large- and small-scale migrations; or of other species for which we often know little about their migration ecology. The simple, interactive, animated graphics developed as part of this proposal will help give researchers a new way of displaying informationand contribute to the field of next-generation data visualization. To further enable effective communication of genetic results, short workshops will be offered both locally and at national meetings, with the post-doctoral fellow and/or graduate students hired through this proposal taking a leading role. The resulting tools will be distributed and discussed at http://ralphlab.usc.edu/ and http://gcbias.org/.

南加州大学和加州大学戴维斯分校获得合作赠款，以创建从基因组数据生成地图的方法和软件，以及解决将未知来源的样本放置在地图上的相关问题。彼此关系越密切的有机体拥有更相似的基因组。通常，一个生物体及其近亲居住在相当近的地方，因此原则上许多不同生物体之间的基因组相似性可以用来构建当地地理地图。在这张地图中，距离与生物体移动的容易程度相对应；因此，地图将显示“生物体所看到的”风景。具体地说，这里开发的方法将以一种方式构建这些图谱，以允许优化几个不同的标准，并将使用随机算法进行模式识别，以允许在大型基因组数据集上进行高效计算。另一套相关的方法将使用地质统计学的新兴工具，将其他人放在这些地图上，具有严格量化的不确定性。从基因组数据描述地理模式，以及未知样本的定位，将有许多应用。这些工具可以用来识别新入侵的寄生虫的来源，或者识别划分濒危物种范围的关键障碍。它们将被用于推断祖先，并可能被用来定位，例如，一个人的祖父可能在100公里以内的家。它们还可以用来可视化现代种群之间的关系；例如，人类的现代分布反映了大规模和小规模迁徙的复杂历史；或者我们通常对其迁徙生态知之甚少的其他物种的关系。作为这一提议的一部分而开发的简单、交互的动画图形将有助于为研究人员提供一种显示信息的新方式，并为下一代数据可视化领域做出贡献。为了进一步有效地交流遗传成果，将在当地和国家会议上举办短期讲习班，由通过这项提议聘用的博士后研究员和/或研究生发挥主导作用。生成的工具将在http://ralphlab.usc.edu/和http://gcbias.org/.上发布和讨论