CAREER: Integrative Approaches to Uncovering Complex Genotype-Phenotype Relationships in High Dimensional Genomics Data

职业：揭示高维基因组数据中复杂基因型-表型关系的综合方法

• 批准号: 1750632
• 负责人: Xinghua Shi
• 金额: $ 59.43万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-15 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750632&HistoricalAwards=false
• 关键词: CAREER; Integrative; Approaches; Uncovering; Complex

The unprecedented accumulation of genomic data offers a unique opportunity to dive deep into the understanding of biology given appropriate tools to mine such data. This research will enable and accelerate the capabilities needed to realize the promise envisioned for big data genomics, and establish a new paradigm for genomics by fully exploiting the gamut of genomic datasets to better understand basis biology. Specifically, this project will combine robust statistical modeling and rigorous computational approaches toward predictive modeling of genomics data. Successful completion of the project will result in new knowledge, new tools, and most importantly long-lasting transformative enhancement of the usability and significance of genomic data. This project will have impact on education in genomics and bioinformatics at undergraduate and graduate levels and will outreach to K-12 students and underrepresented groups. To capitalize on the gamut of genomic data toward better understanding of biological systems, the community is in dire need of accurate, robust, scalable, and efficient methods to interpret such data toward predictive modeling of various phenotypes. Echoing the PI's overarching career goal of providing easy-to-use data analytics and software tools to computational and experimental scientists in life sciences, this research will result in a suite of tools that allow biologists to conduct novel scientific research in elucidating the landscape of genotype-phenotype relationships. The project will advance science through 1) novel Bayesian hierarchical models that incorporate domain knowledge to predict phenotypes from genotypes; 2) iterative pipelines to capitalize on the new models for uncovering the complex relationships between genotypes and phenotypes; and 3) new software modules integrated with existing data science infrastructure for scalable modeling and visualization of large-scale and high-dimensional genomic data. Further information may be found at https://shilab.uncc.edu.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基因组数据的空前积累为深入了解生物学提供了一个独特的机会，只要有适当的工具来挖掘这些数据。这项研究将实现并加速实现大数据基因组学所设想的承诺所需的能力，并通过充分利用基因组数据集的范围来更好地了解基础生物学，建立基因组学的新范式。具体而言，该项目将结合联合收割机强大的统计建模和严格的计算方法，对基因组数据的预测建模。该项目的成功完成将带来新的知识，新的工具，最重要的是基因组数据的可用性和重要性的长期变革性增强。该项目将对本科和研究生阶段的基因组学和生物信息学教育产生影响，并将向K-12学生和代表性不足的群体推广。为了利用基因组数据的范围来更好地理解生物系统，该社区迫切需要准确，强大，可扩展和有效的方法来解释这些数据，以预测各种表型的建模。与PI的总体职业目标相呼应，即为生命科学中的计算和实验科学家提供易于使用的数据分析和软件工具，这项研究将产生一套工具，使生物学家能够进行新的科学研究，以阐明基因型-表型关系的景观。该项目将通过以下方式推进科学：1）新型贝叶斯分层模型，该模型结合了领域知识，可从基因型中预测表型; 2）迭代管道，可利用新模型来揭示基因型和表型之间的复杂关系; 3）与现有数据科学基础设施集成的新软件模块，用于大规模和高维基因组数据的可扩展建模和可视化。更多信息可以在www.example.com上找到https://shilab.uncc.edu.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。