STTR Phase I: Advanced Computational System for Assessing Genetic Provenance in Crop Plants and its Practical Applications

STTR 第一阶段：评估农作物遗传起源的先进计算系统及其实际应用

• 批准号: 1622840
• 负责人: Yuri Nikolsky
• 金额: $ 22.44万
• 依托单位: Prosapia Genetics LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622840&HistoricalAwards=false
• 关键词: STTR; Phase; Advanced; Computational; System

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project will be the development of a highly accurate genotype analysis technique and the computational pipeline for assessing genetic provenance in crop plants. The commercial potential of the proposed products and services is based on the need to increase crop yield and accelerate breeding programs. The proposed technology will revolutionize the field of plant selection by enabling breeders to analyze genotypes of individual plants and optimize crosses. Provided that most of ancestry data on major crops are not publicly available, the platform opens up a range of opportunities for researchers and practitioners. Since it is organism-independent, the software will be applied for analysis of biodiversity and adaptation of species to climate change. These species include, for example, fruit fly, Arabidopsis, Medicago, rat, mouse, as well as endangered plant and animal species such as pandas, bonobos, or whales. Organism-specific pipelines for predicting phenotype using a whole-genome prediction and modeling approach will be established. Free educational versions of the solution will be provided to academic institutions. This STTR Phase I project proposes to develop and test a novel software modeling solution that will be the first-in-class commercial "whole genome" tool in the booming agro-genomics segment, designed to deduce the exact percentages of genetic lines that went into given organism solely based on its genotype. This tool will help farmers to effectively select plants with required traits, and also will automatically identify possible genetic contamination that is a common problem in plant species. The solution will be cloud based with a friendly front end enabling breeders and biologists to easily operate and gain insights from complex plant datasets. This visualization tool will be customizable for various species and types of geographical/climate data. Visual integration of geographical, phenotypical, and genomic data will help breeders deducing relationship among individual plants, strains, and variants. The phenotypes will include yield, height, photosynthetic performances, metabolites concentrations, fitness, drought tolerance, cold tolerance, and quantitative disease resistance. The software will be a revolutionary discovery tool enabling agro-companies to significantly reduce number of breeding experiments, and make the breeding process predictable and efficient.

这个小企业技术转让（STTR）项目的更广泛的影响/商业潜力将是开发一种高度准确的基因型分析技术和用于评估作物遗传来源的计算管道。拟议产品和服务的商业潜力是基于增加作物产量和加速育种计划的需要。这项技术将使育种者能够分析单株植物的基因型并优化杂交，从而彻底改变植物选择领域。如果主要作物的大部分祖先数据不能公开，该平台为研究人员和从业人员提供了一系列机会。由于该软件独立于生物体，因此将用于分析生物多样性和物种对气候变化的适应。这些物种包括例如果蝇、拟南芥、苜蓿、大鼠、小鼠以及濒危植物和动物物种如熊猫、倭黑猩猩或鲸鱼。将建立使用全基因组预测和建模方法预测表型的生物体特异性管道。该解决方案的免费教育版本将提供给学术机构。这个STTR第一阶段项目提出开发和测试一种新的软件建模解决方案，该解决方案将成为蓬勃发展的农业基因组学领域的一流商业“全基因组”工具，旨在推断仅基于其基因型进入给定生物体的遗传系的确切百分比。该工具将帮助农民有效地选择具有所需性状的植物，并自动识别可能的遗传污染，这是植物物种中常见的问题。该解决方案将基于云，具有友好的前端，使育种者和生物学家能够轻松操作并从复杂的植物数据集中获得见解。这一可视化工具将可针对各种物种和地理/气候数据类型进行定制。地理、表型和基因组数据的可视化整合将有助于育种者推断个体植物、品系和变体之间的关系。表型将包括产量、高度、光合性能、代谢物浓度、适合度、耐旱性、耐寒性和数量抗病性。该软件将是一个革命性的发现工具，使农业公司能够大大减少育种实验的数量，并使育种过程可预测和高效。