Bioinformatics tools for proteomics research

蛋白质组学关注复杂生物系统中所有蛋白质的表征，可以提供对疾病的重要见解，识别诊断和个性化医疗的生物标志物。今天，蛋白质表征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进程中的主要瓶颈。 为了应对这一挑战，我开发了创新的算法和分析软件，这些算法和软件经常被世界各地研究实验室的数千名研究人员使用，包括学术界和工业界。 这项研究的长期目标是为生物和健康研究人员提供一个强大的软件平台，用于用质谱法全面表征蛋白质。在新算法的支持下，该平台将在准确性方面得到大幅提高，并将具有内置的质量控制，以保证高质量的结果。这样的平台将在许多生物和健康应用中实现并加速发现过程。 在这项研究提案中，将追求四个短期目标和子任务： (1)更准确的从头测序：从头测序可识别任何数据库中都不存在的新肽。我们的研究将解决目前方法中准确性不足和缺乏质量控制的问题。 (2)具有低错误发现率（FDR）的新数据库搜索方法：对于数据库中存在的肽，我们将研究新方法以识别更多肽，同时降低FDR。 (3)翻译后修饰（PTM）：PTM及其数量变化是非常有前途的生物标志物。我们将开发有效的计算方法来识别和量化修饰的肽。 (4)人类蛋白质组谱：为了更好地表征重复出现的人肽，我们打算通过分析所有公开可用的人质谱数据来构建人蛋白质组谱。 需要创新的算法来实现这些目标。本提案中所述的研究活动将使有效的培训成为可能，将HQP置于跨学科和协作的环境中。

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.