权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Bioinformatic algorithm and infrastructure development for post-translational modification analysis

职业：翻译后修饰分析的生物信息学算法和基础设施开发

基本信息

批准号：
2046122
负责人：
So Young Ryu
金额：
$ 60万
依托单位：
Board of Regents, NSHE, obo University of Nevada, Reno
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046122&HistoricalAwards=false
关键词：
CAREER Bioinformatic algorithm infrastructure development

项目摘要

Protein post translational modifications (PTMs) play important roles in many aspects of cell biology, including cell growth, differentiation, and survival. Due to the complex nature of PTMs (e.g., various PTM types and sites), there have to date been limitations in the technology used for PTM identification/quantification. This project will overcome such challenges by developing bioinformatic methods that incorporate unique characteristics of data and fully utilize publicly available data, providing comprehensive characterization and quantification of PTMs. This development will enhance fundamental understanding of functional proteomics in many biological areas, including plant science, biomedicine, and microbiology. Bioinformatic tools generated from the research component of this project will be re-designed as an interactive educational app. Through the associated outreach activities using this app, the project will provide valuable interdisciplinary training to both graduate and undergraduate students, including first-generation college, low-income, and minority students, and inspire high school students to pursue STEM majors. The research goal of this project is to develop statistical and computational methods to advance the field of mass spectrometry-based PTM analysis. Specifically, this project will establish PTM algorithms and infrastructures that 1) utilize publicly available data to improve PTM identification in biological samples of interest; 2) make use of instrumental technique-specific characteristics of data; 3) provide biologically- meaningful information by properly measuring site occupancy rates and stoichiometry; 4) develop a mixed-model framework to perform differential PTM analysis; and 5) suggest an optimal experimental design (e.g., instrumental technique and sample preparation, sample size calculation) for future studies. This development will not only advance knowledge across various biological fields, but also help scientists generate critical hypotheses using millions of publicly available data generated previously, without PTM enrichment. The resulting bioinformatic tools and infrastructure will accelerate scientific discoveries using mass spectrometry-based proteomic data. The results of the project can be found at https://github.com/soyoungryu.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.

蛋白质翻译后修饰（PTMs）在细胞生物学的许多方面发挥重要作用，包括细胞生长、分化和存活。由于PTM的复杂性质（例如，各种PTM类型和位点），迄今为止，用于PTM鉴定/定量的技术存在局限性。该项目将通过开发生物信息学方法来克服这些挑战，这些方法将数据的独特特征结合起来，并充分利用公开的数据，提供对PTM的全面表征和量化。这一发展将增强对许多生物学领域的功能蛋白质组学的基本理解，包括植物科学，生物医学和微生物学。该项目的研究部分产生的生物信息学工具将被重新设计为互动教育应用程序，通过使用该应用程序的相关外展活动，该项目将为研究生和本科生（包括第一代大学生、低收入学生和少数民族学生）提供有价值的跨学科培训，并激励高中生攻读STEM专业。该项目的研究目标是开发统计和计算方法，以推进基于质谱的PTM分析领域。具体而言，该项目将建立PTM算法和基础设施，1）利用公开可用的数据来改进感兴趣的生物样品中的PTM鉴定; 2）利用仪器技术特定的数据特征; 3）通过适当测量位点占用率和化学计量提供生物学意义的信息; 4）开发混合模型框架来执行差异PTM分析;和5）建议最佳实验设计（例如，仪器技术和样品制备，样品量计算）用于未来研究。这一发展不仅将推进各个生物领域的知识，还将帮助科学家利用以前生成的数百万公开数据生成关键假设，而无需PTM富集。由此产生的生物信息学工具和基础设施将加速使用基于质谱的蛋白质组数据的科学发现。该项目的结果可以在www.example.com上找到https://github.com/soyoungryu.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。