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: 1262327
• 负责人: Graham Coop
• 金额: $ 32.72万
• 依托单位: University of California-Davis
• 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 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262327&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（University of Southern California）和加州大学戴维斯分校（University of California Davis）获得了合作资助，以开发从基因组数据生成地图的方法和软件，并解决将来历不明的样本放在地图上的相关问题。生物之间的亲缘关系越密切，它们的基因组就越相似。通常，一种生物和它的近亲生活在相当近的地方，所以原则上，许多不同生物之间的基因组相似性可以用来构建当地的地理地图。在这张地图上，距离对应于生物体移动的容易程度；因此，地图将显示“生物所看到的”景观。具体来说，这里开发的方法将以一种允许几种不同标准优化的方式构建这些地图，并将使用随机算法进行模式识别，以便在大型基因组数据集上进行有效计算。另一套相关的方法将使用新兴的地质统计学工具，在严格量化不确定性的情况下，将其他个体置于这些地图上。从基因组数据中描述地理模式，以及未知样本的位置，将有许多应用。这些工具可用于确定新入侵寄生虫的来源，或确定划分濒危物种范围的关键障碍。它们将对推断祖先很有兴趣，并可能用于定位，例如，在100公里左右的范围内，一个人的祖父可能的家。它们还可以用来可视化现代人口之间的关系；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；或者其他我们对迁徙生态知之甚少的物种。作为该提案的一部分，开发的简单，交互式，动画图形将帮助研究人员提供一种显示信息的新方法，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结果，将在地方和国家会议上举办短期讲习班，由通过本提案聘用的博士后研究员和/或研究生担任主要角色。由此产生的工具将在http://ralphlab.usc.edu/和http://gcbias.org/上分发和讨论。