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

拟议的研究计划将专注于开发算法和构建软件工具，用于肽、多糖和蛋白质的鉴定和利用质谱学和RNA结构分析进行分析。这项拟议研究的长期目标是为生物和健康研究人员提供用质谱学表征蛋白质和分析RNA结构的软件工具。我们将开发新的算法，以帮助构建精度和质量更高的软件工具。在这项研究提案中，将追求四个短期目标： (1)提高从头测序肽的识别率：从头测序可以识别任何数据库中不存在的新的多肽。我们的研究将解决与“混合物”光谱相关的低识别率问题，即两个或更多质量和保留时间相似的前体离子被MS/MS共洗脱。 (2)提高从头测序的准确性：我们将开发新的算法来解决现有方法中确定糖链结构的准确性不足的问题。 (3)利用MS/MS实现蛋白质全测序的自动化：我们将开发新的算法，实现无模板蛋白质的蛋白质全测序。 (4)RNA-RNA相互作用结构分析：比较和推理：研究RNA-RNA相互作用结构比较和推理问题的复杂性和设计算法。 这项申请中的拟议研究将使有效的跨学科培训成为可能，将总部置于协作环境中。

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.