Data modelling for animal phenomics

动物表型组学的数据建模

• 批准号: RGPIN-2022-03452
• 负责人: Tulpan, Dan
• 金额: $ 2.11万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743566
• 关键词: Data; modelling; animal; phenomics

One of the major limiting factors impacting multiple areas of animal science such as livestock breeding, production and genomics is related to collecting, managing and effectively modeling abundant, real-time, high-throughput and high-quality phenotypic data. Significant advancements in closing the gap between phenotypes and genotypes and developing a better understanding of complex interactions between phenotypes and production systems can be achieved by standardizing and automating phenotype collection protocols, improving the underlining collection technologies and augmenting the collected phenotypic information with environmental, geographic, genotypic, physical and physiological data 1,2. Significant efforts are being made by both, researchers and private businesses, to develop systems and technologies that harvest, store, model and analyze animal-related information synchronously within periodical and pre-established time frames relevant to specific phenotypic features and various steps of multiple production value chains. Given the large number of components of an animal phenotype such as morphological, developmental, biochemical, physiological and behavioural, this research proposal will focus on the study of phenotypes related to animal growth. The short-term goals of my research project include: (S1) developing efficient and accurate semi-automatic and automatic image acquisition and processing protocols for pigs, dairy and beef cattle using affordable sensors and reference objects, (S2) extracting and analyzing morphometric information (e.g. body dimensions, posture, body condition score and motion patterns) from images using data mining, computer vision and machine/deep learning methods, (S3) investigating the level of correlation among various morphometric measurements, animal physiological states and animal development/growth and their putative use in models capable to estimate or predict growth, and (S4) developing species-specific regression and classification phenotypic models for estimation of growth represented by live body weights and body condition scores (and other relevant existing and potentially new traits) based on digital images that include reference objects with known dimensions. The long-term goals include: (L1) researching methods that increase the accuracy and usability of semi-automatic and automatic phenotypic data collection, which can be further integrated with complementary livestock information to produce animals more efficiently and more sustainably, and to improve economic outcomes on the farm, (L2) collecting and integrating phenotypic results into models that can be used to develop predictive decision support systems for the Canadian industry, and (L3) recruiting and training highly qualified personnel in livestock phenomics.

影响动物科学多个领域的主要限制因素之一，例如牲畜育种，生产和基因组学，与收集，管理和有效建模丰富，实时，高通量和高质量的表型数据有关。可以通过标准化和自动化表型收集协议，改善下划线收集技术并增强收集到的表型信息与环境，地理，基因型，物理和物理学数据1,2。研究人员和私人企业都在做出了重大努力，以开发系统和技术，这些系统和技术在周期性和预先建立的时间范围内同步地收获，存储和分析与特定表型特征以及多个生产价值链的各种步骤有关的动物相关信息。鉴于动物表型的大量组成部分，例如形态，发育，生化，生理和行为，该研究建议将集中在与动物生长相关的表型的研究上。我的研究项目的短期目标包括：（S1）使用负担得起的传感器和参考对象（（S2）提取和分析形态尺寸（例如，象征性的计算和动作模式），使用负担得起的传感器和参考对象，使用负担得起的传感器和参考对象来开发有效，准确的半自动和自动图像获取和处理协议，用于猪，乳制品和牛奶，（S2）提取和分析形态学的方法（例如）各种形态测量，动物生理状态和动物发育/生长及其在能够估计或预测增长的模型中使用的相关性水平，以及（S4）（S4）（S4）开发物种特异性的回归和分类表型模型，用于估计由活体重和身体状况斑点（以及其他相关的现有特征）基于数字图像的估算代表的生长，并包括对象的其他相关特征。长期目标包括：（L1）研究方法，以提高半自动和自动表型数据收集的准确性和可用性，可以进一步与互补的牲畜信息相结合，以更有效，更可持续地产生动物，以更有效，更可持续性，以改善农场上的经济成果，并改善农场的经济成果，（L2）将模型和整合的模型用于模型，以使现象型的模型（L2）成为现实的模型（L2和训练牲畜现象学高素质的人员。