Bioinformatics tools for proteomics research

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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.