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: 1208685
• 负责人: Lisa Moore
• 金额: $ 29.69万
• 依托单位: University of Southern Maine
• 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 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208685&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百万个物种是相关的，这给生物学家和古生物学家带来了发现生命之树的分支模式的巨大挑战。应对这一巨大挑战的工作是绘制地球历史上物种相关性的地图，并回答诸如“物种的最近亲是什么？”等问题。以及“哪些物种可以生产重要的产品？”为此，科学家利用了所有可遗传的特征，包括基因型（例如 DNA）和表型（例如解剖学）。然而，表型研究仍然复杂而缓慢，因为它尚未被计算机科学和工程创新所彻底改变。 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调整计算机视觉、机器学习、众包和自然语言处理的方法，以实现大规模的生命之树表型的快速、自动化研究。三年目标是发布使用新方法构建的大型表组数据集，并为公众和科学界提供未来工作的工具。计划包括对教师和学生（幼儿园至博士后级别）的培训以及“公民科学家”的参与。大量的表型数据集（其中许多带有图像）将满足公众对生物多样性和化石记录的重要兴趣。