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Assistant Professor
@UCR_CSE. Computational Mass Spectrometry, Bioinformatics. Loves collaborative science!

https://www.cs.ucr.edu/~mingxunw/

#massspec #teammassspec #molecularnetworking #GNPS #MassQL #Bioinformatics #datascience

Websitehttps://www.cs.ucr.edu/~mingxunw/
Wrapping up my first quarter as a professor at UC Riverside, the lab had its first outing together. One of my favorite parts of the job is the opportunity to work with these energetic and talented students. Excited to see what the next year holds!

Excited to share a new perspective that looks back on the role of spectral libraries in compound ID. Wout Bittremieux was the real driver in putting this all together, so huge kudos to him.

One takeaway is how the whole community has been able to help grow the size and annotation rates of data throughout the last 10 years.

Read it here!

https://link.springer.com/epdf/10.1007/s11306-022-01947-y?sharing_token=_uaFZb0oBKsrCQSF98bFhfe4RwlQNchNByi7wbcMAY51DaNPFalP6NMEj8bKkTsRyGwu2zVjbtu-BxFCgK4pw2yDdtQ-ifHPU0229TpP7EUVyeAbZdFQJ6b0EKtrtZw6mnJpGTglxaMLlLpw6V_l-jJFuiA3i7GGgA7IZVU-B0Y=

#massspec #metabolomics #naturalproducts #bioinformatics #teammassspec

The critical role that spectral libraries play in capturing the metabolomics community knowledge - Metabolomics

Background Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. Aim of review We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. Key scientific concepts of review This review focuses on the current state of spectral libraries for untargeted LC–MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.

SpringerLink