Bioinformatics tools for proteomics research

用于蛋白质组学研究的生物信息学工具

• 批准号: 238748-2011
• 负责人: Ma, Bin
• 金额: $ 3.57万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570482
• 关键词: Bioinformatics; tools; proteomics; research

Proteomics concerns the characterization of all proteins in a complex biological system, and can provide important insight into diseases, identifying biomarkers for diagnosis and personalized medicine. Today, protein characterization is carried out by a mass spectrometry experiment followed by data analysis with software. However, the data analysis has remained a principle bottleneck in the process. To deal with this challenge, I have developed innovative algorithms and analytical software routinely used by thousands of researchers in research labs worldwide, from both academy and industry. The long-term goal of the proposed research is to provide biological and health researchers with a powerful software platform for fully characterizing proteins with mass spectrometry. Backed with novel algorithms, the platform will be much improved in terms of accuracy and will have built-in quality control that guarantees high-quality results. Such a platform will enable and hasten the discovery process in many biological and health applications. Within this research proposal four short-term goals and subtasks will be pursued: (1) More accurate de novo sequencing: De novo sequencing identifies novel peptides that do not exist in any database. Our research will address the inadequate accuracy and lack of quality control in current methods. (2) New database search method with low false discovery rate (FDR): For peptides existing in a database, we will research new methods to identify more peptides while reducing FDR. (3) Post-translational modifications (PTM): PTMs and their quantity changes are very promising biomarkers. We will develop efficient computational methods to identify and quantify the modified peptides. (4) Human proteome profile: In order to better characterize recurring human peptides, we intend to build a human proteome profile by analyzing all publicly available human mass spectrometry data. Innovative algorithms are needed to achieve these goals. The research activities described in this proposal will enable effective training, placing HQP in a cross-disciplinary and collaborative environment.

蛋白质组学涉及复杂生物系统中所有蛋白质的表征，可以为疾病提供重要的见解，识别诊断和个性化医疗的生物标志物。今天，蛋白质特性是通过质谱实验进行的，然后用软件进行数据分析。然而，数据分析仍然是这一过程中的主要瓶颈。为了应对这一挑战，我开发了创新的算法和分析软件，世界各地的研究实验室中有成千上万的研究人员经常使用这些算法和分析软件，包括学术界和工业界。