Algorithms and software tools for analysis of mass spectrometry data

用于分析质谱数据的算法和软件工具

• 批准号: RGPIN-2018-05939
• 负责人: Zhang, Kaizhong
• 金额: $ 3.5万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713954
• 关键词: Algorithms; software; tools; analysis; mass

This research proposal focuses on the general area of Bioinformatics, and in particular, on the computational analysis of mass spectrometry based proteomics data. The proposed research program will focus on algorithms and software tools for peptide, glycan, and protein identification and analysis with mass spectrometry. The long-term goal is to provide biological and health researchers with software tools for characterizing proteins with mass spectrometry. We will develop novel algorithms to help constructing software tools with improved accuracy and quality. Within this research proposal four short-term objectives will be pursued: (1) Increase the accuracy of antibody sequencing and validate antibody intact mass: Our research will improve the quality of antibody sequencing by improved disulfide bond linkage analysis and overlapping spectra information analysis. We will also develop new method to address antibody intact mass validation. (2) Increase peptide identification rate by improving multiple spectra sequencing: We will develop new algorithms to increase the efficiency of spectra similarity detection. We will also consider new methods for the information merge from multiple spectra. (3) Improve peptide identification rate for complex spectra: With spectra involving more than one peptides, we will extend our work on disulfide bond linkage analysis to include two disulfide bonds with possibly three peptides. We will design new techniques for the mixture spectra sequencing. (4) Follow the development of new mass spectrometry equipment, new experimental methods, and new computational approaches. Design computational solutions based on new technology. The proposed research in this application will enable effective cross-disciplinary training, placing HQP in a collaborative environment.

该研究计划侧重于生物信息学的一般领域，特别是基于质谱的蛋白质组学数据的计算分析。拟议的研究计划将集中在算法和软件工具的肽，聚糖和蛋白质的鉴定和分析与质谱。长期目标是提供 生物学和健康研究人员使用软件工具，用质谱法表征蛋白质。我们将开发新的算法，以帮助构建具有更高精度和质量的软件工具。在这项研究提案中，将追求四个短期目标： (1)提高抗体测序的准确性和验证抗体完整质量：我们的研究将通过改进的二硫键连接分析和重叠光谱信息分析来提高抗体测序的质量。我们还将开发新方法来解决抗体完整质量验证。 (2)通过改进多光谱测序提高肽识别率：我们将开发新的算法来提高光谱相似性检测的效率。我们还将考虑新的方法，从多个光谱的信息合并。 (3)提高复杂光谱的肽识别率：对于涉及多个肽的光谱，我们将扩展二硫键连接分析的工作，以包括可能有三个肽的两个二硫键。我们将设计新的混合光谱测序技术。 (4)跟踪新质谱设备、新实验方法和新计算方法的发展。基于新技术设计计算解决方案。 本申请中的拟议研究将实现有效的跨学科培训，将HQP置于协作环境中。