乔亮 Liang Qiao

委员 Member

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主要从事蛋白质组学、微生物质谱分析、微流控质谱联用、质谱生物信息学等方面的基础和应用研究。先后承担和参与国家及省部级项目，如国家重点研发计划、国家自然科学基金重点项目、面上项目、上海市科委科技创新重点专项等21项。迄今在Nature Machine Intelligence、PNAS、Nature Communications、Chem、JACS、Angew. Chem. Int. Ed.、Anal. Chem.等权威期刊上发表论文160篇；申报及获得国际、国内发明专利21项。主要研究方向包括：1. 质谱和蛋白质组数据分析算法开发：(1) 基于质谱的细菌鉴定算法；(2) 基于深度学习的蛋白质组数据分析算法。2. 微生物宏蛋白质组。3. 基于多组学的细菌耐药机制研究。

Mainly engaged in basic and applied research in proteomics, microbial mass spectrometry analysis, microfluidic-mass spectrometry coupling, and mass spectrometry bioinformatics. Has undertaken and participated in 21 national and provincial-level projects, including the National Key R&D Program, National Natural Science Foundation Key Projects, General Programs, and Shanghai Science and Technology Commission Key Innovation Projects. To date, has published 160 papers in authoritative journals such as Nature Machine Intelligence, PNAS, Nature Communications, Chem, JACS, Angew. Chem. Int. Ed., and Anal. Chem.; has applied for and obtained 21 international and domestic invention patents. Main research directions include: 1. Development of mass spectrometry and proteomics data analysis algorithms: (1) Mass spectrometry-based bacterial identification algorithms; (2) Deep learning-based proteomics data analysis algorithms. 2. Microbial metaproteomics. 3. Multi-omics-based research on bacterial drug resistance mechanisms.

1. Y. Zong, Y. Wang, X. Qiu, X. Huang, L. Qiao*, Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics, Nature Machine Intelligence, 2024, DOI: 10.1038/s42256-024-00875-x
2. Y. Zong, Y. Wang, Y. Yang, D. Zhao, X. Wang, C. Shen, L. Qiao*, DeepFLR Facilitates False Localization Rate Control in Phosphoproteomics, Nature Communications, 2023, 14, 2269
3. Y. Yang#, G. Yan, S. Kong, M. Wu, P. Yang, W. Cao#,*, L. Qiao*, GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control, Nature Communications, 12(2021): 6073
4. Y. Yang, X. Liu, C. Shen, Y. Lin, P. Yang, L. Qiao*, In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics, Nature Communications, 11(2020): 146
5. E. Wu#, V. Mallawaarachchi#, J. Zhao, Y. Yang, H. Liu, X. Wang, C. Shen, Y. Lin, L. Qiao*, Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics, Microbiome, 2024, 12: 58