Yuanyue Li
李渊越

Bioinformatics Scientist

University of California, Davis

👋 Hello

I am Yuanyue Li, a bioinformatics scientist with a passion for understanding the molecular mechanisms of life by combining experimental and computational methods. I hope my research can help to answer those questions: 1) How many different molecules are there in a cell? 2) How do they interact with each other? 3) How do they work together to make a cell functional?

Mass spectrometry is a powerful tool for measuring molecules with high sensitivity and accuracy. Therefore, I use mass spectrometry as my primary tool to measure molecules. I have developed serval methods to maximize the capabilities of mass spectrometry in life science research. Feel free to visit my GitHub page to try out the tools I’ve developed.

I grew up and received my doctoral degree in China, then enjoyed several years working in Germany and United States. Currently, I am working at Zhejiang University in China as an independent PI. In my spare time, I enjoy exploring the world with my wife and our two daughters.

I am actively seeking talented research assistants, graduate students, and postdoctoral researchers to join my lab. If you are interested in my research, please do not hesitate to send my an Email, I look forward to hearing from you!

Interests

Mass Spectrometry
Metabolomics
Proteomics
Molecular Biology

Education

Ph.D. in Biochemistry and Molecular Biology, 2014

Xiamen University
B.Sc. in Life Science, 2008

Xiamen University

Featured Projects

Flash Entropy Search

Flash Entropy Search is an ultra-fast algorithm capable of comparing billions of MS/MS spectra in seconds, and boosting the speed of library searches by five orders of magnitude. This method enables rapid comparison of MS/MS spectra against large spectral libraries, just like BLAST for sequence searches.
A video introduction to Flash Entropy Search can be found here.

Spectral Entropy & Entropy Similarity

By considering an MS/MS spectrum as a probability distribution, we introduced the concept of Spectral Entropy to evaluate the information within the spectrum. Expanding on this idea, we proposed Entropy Similarity as a metric to measure the similarity between two spectra. Utilizing this approach can lead to a reduction in the false positive rate for metabolite identification by up to 40%.
A video introduction to Spectral Entropy and Entropy Similarity can be found here.

Group-DIA

Group-DIA can analyze multiple DIA data files simultaneously, notably enhancing protein identification by grouping various mass spectrometry datasets.

Recent Publications

Yuanyue Li, Oliver Fiehn (2023). Flash entropy search to query all mass spectral libraries in real time. Nature Methods.

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Yuanyue Li, Tobias Kind, Jacob Folz, Arpana Vaniya, Sajjan Singh Mehta, Oliver Fiehn (2021). Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nature Methods.

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Yuanyue Li, Michael Kuhn, Joanna Zukowska-Kasprzyk, Marco L. Hennrich, Panagiotis L. Kastritis, Francis J. O’Reilly, Prasad Phapale, Martin Beck, Anne-Claude Gavin, Peer Bork (2021). Coupling proteomics and metabolomics for the unsupervised identification of protein–metabolite interactions in Chaetomium thermophilum. PLOS ONE.

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Yuanyue Li, Michael Kuhn, Anne-Claude Gavin, Peer Bork (2020). Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features. Bioinformatics.

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Yuanyue Li, Chuan-Qi Zhong, Xiaozheng Xu, Shaowei Cai, Xiurong Wu, Yingying Zhang, Jinan Chen, Jianghong Shi, Shengcai Lin, Jiahuai Han (2015). Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files. Nature Methods.

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