报告时间：2018年12月26日（星期三）上午9:00

报告地点：生物楼学术报告厅

报告人：迟浩副研究员，中国科学院计算技术研究所

　　报告人简介：

　　迟浩，中国科学院计算技术研究所副研究员，2013年毕业于中科院计算所并获得工学博士学位，曾获2013年度中国科学院院长特别奖。自2006年以来，致力于蛋白质组海量质谱数据的深度解析研究，是蛋白质鉴定搜索引擎pFind系统的主要设计者和开发者，在数据库搜索领域提出新一代开放式数据库搜索引擎Open-pFind，在从头测序领域提出并发展了pNovo系列算法，速度和精度均达到当时国际最佳水平。相关研究工作发表于Nature Biotechnology、Journal of Proteome Research、Journal of Proteomics等期刊。pFind软件目前在国内外注册下载达2000余套，支持领域内200余项研究工作发表，在蛋白质组学科研和教学中发挥着积极作用。

　　报告摘要：

　　Shotgun proteomics has grown rapidly in recent decades, especially for peptide and protein identification. However, more than 50% of MS/MS data acquired in shotgun proteomics have not been successfully identified. As shown in a number of studies, unexpected modifications is a major reason underlying the low identification rate, and several other factors also hinder precise peptide identification, e.g., semi- and non-specific digestion, in-source fragmentation and co-eluting peptides in mixed spectra. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi- or non-specific digestion and co-eluting peptides. We re-analyzed an entire human proteome dataset consisting of ~25 million spectra. It took Open-pFind ~5 hours on a 64-core workstation to search all of these spectra. More than one million peptides were identified, which were 86.7% more than those reported previously. The results obtained with Open-pFind demonstrated that the characteristics of MS/MS data vary according to different methods for sample preparation and LC-MS/MS. Open search strategies, as made practical by Open-pFind, will most likely be the preferred tools for large-scale MS/MS data analyses in the future.

　　联系人：1810组 张丽华

　　联系电话：89720