Algorithms and software tools for mass spectrometry data analysis and RNA structure analysis

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The proposed research program will focus on the development of algorithms and the construction of software tools for peptide, glycan, and protein identification and analysis with mass spectrometry and RNA structure analysis. The long-term objective of the proposed research is to provide biological and health researchers with software tools for characterizing proteins with mass spectrometry and analyzing RNA structures. We will develop novel algorithms to help constructing software tools with improved accuracy and quality. Within this research proposal four short-term goals will be pursued: (1) Increase de novo sequencing peptide identification rate: De novo sequencing can identify novel peptides that do not exist in any database. Our research will address the low identification rate problem associated with the ''mixture'' spectra, where two or more precursor ions with similar mass and retention time are co-eluted by MS/MS. (2) Improve accuracy for de novo glycan sequencing: We will develop new algorithms to address the inadequate accuracy in current methods for the determination of glycan structure. (3) Automate complete protein sequencing with MS/MS: We will develop new algorithms for complete protein sequencing without template proteins. (4) Analyze RNA-RNA interaction structure: comparison and inference: We will study the complexity and design algorithms for RNA-RNA joint structure comparison and inference problems. The proposed research in this application will enable effective cross-disciplinary training, placing HQP in a collaborative environment.