Illuminating metabolomics dark matter - Reshaping how to mine and reuse big mass spectrometry data for small molecule discoveryProf. Mingxun Wang, Department of Computer Science and Engineering, UCR
High-throughput mass spectrometry has enabled unprecedented depth and versatility to observe the molecules in the world around us. Traditionally, a handful of molecules were detected in a typical measurement. Today, this has grown to thousands of molecules in a few minutes. The growth in data presents new opportunities for discovery but also challenges in data analysis. The development of new computational approaches for mass spectrometry data has already accelerated drug discovery, revealed the chemical dialog of the microbiome, and characterized the molecular dynamics of our oceans due human activity.
I will describe a few computational and data science approaches that transform mass spectrometry data analysis from a solitary activity to a community wide collaborative effort – crowd-sourcing mass spectrometry knowledge, reusing knowledge in an error-controlled fashion, and computationally amplifying knowledge to make new discoveries. I will discuss how these tools have transformed the community and how future computational work can further elucidate the mass spectrometry dark matter.