串联质谱数据的蛋白鉴定是蛋白质组学研究的重要内容，数据库搜索方法是蛋白质质谱数据分析的主要方法。研究者长期通过挖掘图谱本身特征信息来提高肽段鉴定量，而融合多组学信息提高蛋白质鉴定量的报道相对较少。生物蛋白质表达需要经历基因转录和翻译过程，因此转录组和翻译组（多组学）的丰度信息可作为蛋白表达的先验信息加以利用。目前有研究证实利用多组学先验信息在数据库搜索方法中约束蛋白搜索库能提高肽段鉴定量，然而将多组学先验信息融入数据库搜索方法的打分模型还有待进一步研究。本项目拟将多组学丰度信息定量化为相应的蛋白存在概率，整合蛋白存在概率和基于图谱特征的打分算法构建新肽段打分模型，从而建立一种在打分模型中融入多组学先验信息的数据库搜索方法，并将该方法应用于肺癌蛋白质组学分析。新方法能有效增加实验串联质谱数据的信息利用率和提高蛋白质样品的肽段鉴定量，为蛋白质组学研究提供方法参考和技术支持。

Protein identification of tandem mass spectrometry (MS) data is an important content of proteomics, and database search algorithm (DSA) is the main method of MS data analysis. Researchers have tried for a long time to improve the identified peptide numbers of samples by digging into MS characteristic information, however integration of multi-omics information to improve the protein identification is rarely reported. Gene transcription and translation is the precondition of biological protein expression, so the abundance information of the transcriptomics and translatomics (multi-omics) can be used as prior information for protein expression. At present, some studies have confirmed that using multi-omics prior information constraint protein-searching database in DSA can increase the identified peptide numbers. Nevertheless, incorporating the multi-omics prior abundance information into the the scoring model remains to be further researched. This project intends to quantify the abundance information of multi-omics into the corresponding protein existing probability, and incorporates above probability and MS characteristic scoring algorithm to establish a new scoring model of DSA. Eventually this new scoring model will be applied to proteomics analysis of lung cancer. The new algorithm this project proposes can effectively increase the utilization of MS characteristic information and improve the identified peptide number of samples, and providing a new method for reference and technical support for proteomic study.