Collaborative Research: AVATOL - Next Generation Phenomics for the Tree of Life

合作研究：AVATOL - 生命之树的下一代表型组学

• 批准号: 1208619
• 负责人: Edward Theriot
• 金额: $ 10.32万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208619&HistoricalAwards=false
• 关键词: Collaborative; Research; AVATOL; Next; Generation

Darwin's theory of evolution explained that millions of species are related, and dealt biologists and paleontologists the enormous challenge of discovering the branching pattern of the Tree of Life. Work on this great challenge is producing a map of species-relatedness through Earth history, and answering questions such as "what is the closest relative of a species?" and "what species make important products?" To do this scientists draw on all heritable features, including genotypes (e.g., DNA) and phenotypes (e.g., anatomy). Studying phenotypes, however, has remained complicated and slow, because it has not been revolutionized by computer science and engineering innovations. A team of biologists, computer scientists, and paleontologists will extend and adapt methods from computer vision, machine learning, crowd-sourcing and natural language processing to enable rapid and automated study of phenotypes for the Tree of Life on a vast scale. The three-year goal is to release large phenomic datasets built using new methods, and to provide the public and scientific community with tools for future work. Planned is the training of teachers and students (kindergarten - postdoctoral levels) and the engagement of "citizen scien-tists." Enormous phenomic datasets, many with images, will fill an important public interest in biodiversity and the fossil record.

达尔文的进化论解释了数以百万计的物种是相互关联的，并向生物学家和古生物学家提出了发现生命之树分支模式的巨大挑战。这一巨大挑战的工作是绘制一幅地球历史上物种亲缘关系的地图，并回答诸如“一个物种最近的亲戚是什么？”和“什么物种生产重要产品？”为了做到这一点，科学家们利用了所有可遗传的特征，包括基因型（如DNA）和表型（如解剖结构）。然而，研究表现型仍然是复杂而缓慢的，因为它没有被计算机科学和工程创新彻底改变。一个由生物学家、计算机科学家和古生物学家组成的团队将扩展和适应计算机视觉、机器学习、众包和自然语言处理的方法，以实现对生命之树大规模表型的快速和自动化研究。这个为期三年的目标是发布使用新方法构建的大型现象数据集，并为公众和科学界提供未来工作的工具。计划培训教师和学生（幼儿园-博士后水平）和“公民科学家”的参与。庞大的现象数据集，其中许多带有图像，将填补公众对生物多样性和化石记录的重要兴趣。