Fast mass spectrometry search and clustering of untargeted metabolomics data

Mihir Mongia; Tyler M Yasaka; Yudong Liu; Mustafa Guler; Liang Lu; Aditya Bhagwat; Bahar Behsaz; Mingxun Wang; Pieter C Dorrestein; Hosein Mohimani

doi:10.1038/s41587-023-01985-4

Fast mass spectrometry search and clustering of untargeted metabolomics data

Nat Biotechnol. 2024 Jan 2. doi: 10.1038/s41587-023-01985-4. Online ahead of print.

Authors

Mihir Mongia^#¹, Tyler M Yasaka^#¹, Yudong Liu^#¹, Mustafa Guler¹, Liang Lu¹, Aditya Bhagwat¹, Bahar Behsaz^{1

2}, Mingxun Wang³, Pieter C Dorrestein^{4

5}, Hosein Mohimani⁶

Affiliations

¹ Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
² Chemia Biosciences Inc., Pittsburgh, PA, USA.
³ Computer Science and Engineering, University of California Riverside, Riverside, CA, USA.
⁴ Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
⁵ Department of Pharmacology and Pediatrics, University of California San Diego, San Diego, CA, USA.
⁶ Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. hoseinm@andrew.cmu.edu.

^# Contributed equally.

PMID: 38168990
DOI: 10.1038/s41587-023-01985-4

Abstract

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.