小柯机器人

澳大利亚麦考瑞大学Morten Thaysen-Andersen等研究人员揭示血清糖肽分析的高性能搜索策略。该研究于2021年11月1日在线发表于国际一流学术期刊《自然—方法学》。

通过HUPO人类糖蛋白组学计划，研究人员（包括糖蛋白组学软件的开发者和用户）共同评估了全系统糖肽分析的解决方案。与参与者共享相同的基于质谱的人类血清糖蛋白组学数据集，并通过正交性能测试全面确定N-和O-糖肽数据分析的相对团队性能。虽然结果各不相同，但还是确定了几个高性能的糖蛋白组学信息学策略。对数据的深入分析揭示了与性能相关的关键搜索参数，并提出了改进的"高覆盖率"和"高准确率"糖蛋白组学搜索解决方案的建议。这项研究的结论是，现存有用于综合糖肽数据分析的各种软件包，指出了几种高性能的搜索策略，并指明了指导未来软件发展和协助糖蛋白组学信息学决策的关键变量。

据了解，糖蛋白组学是一个强大但在分析上具有挑战性的研究工具。帮助解释复杂糖肽串联质谱的软件包已经出现，但它们的相对性能仍未得到检验。

附：英文原文

Title: Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Author: Kawahara, Rebeca, Chernykh, Anastasia, Alagesan, Kathirvel, Bern, Marshall, Cao, Weiqian, Chalkley, Robert J., Cheng, Kai, Choo, Matthew S., Edwards, Nathan, Goldman, Radoslav, Hoffmann, Marcus, Hu, Yingwei, Huang, Yifan, Kim, Jin Young, Kletter, Doron, Liquet, Benoit, Liu, Mingqi, Mechref, Yehia, Meng, Bo, Neelamegham, Sriram, Nguyen-Khuong, Terry, Nilsson, Jonas, Pap, Adam, Park, Gun Wook, Parker, Benjamin L., Pegg, Cassandra L., Penninger, Josef M., Phung, Toan K., Pioch, Markus, Rapp, Erdmann, Sakalli, Enes, Sanda, Miloslav, Schulz, Benjamin L., Scott, Nichollas E., Sofronov, Georgy, Stadlmann, Johannes, Vakhrushev, Sergey Y., Woo, Christina M., Wu, Hung-Yi, Yang, Pengyuan, Ying, Wantao, Zhang, Hui, Zhang, Yong, Zhao, Jingfu, Zaia, Joseph, Haslam, Stuart M., Palmisano, Giuseppe, Yoo, Jong Shin, Larson, Gran, Khoo, Kai-Hooi, Medzihradszky, Katalin F., Kolarich, Daniel, Packer, Nicolle H., Thaysen-Andersen, Morten

Issue&Volume: 2021-11-01

Abstract: Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

DOI: 10.1038/s41592-021-01309-x

Source: https://www.nature.com/articles/s41592-021-01309-x

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex