Algorithms and software tools for analysis of mass spectrometry data

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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.