SimMS: A GPU-Accelerated Cosine Similarity implementation for Tandem Mass Spectrometry.

Bioinformatics (Oxford, England) Pub Date : 2025-02-20 DOI:10.1093/bioinformatics/btaf081

Tornike Onoprishvili, Jui-Hung Yuan, Kamen Petrov, Vijay Ingalalli, Lila Khederlarian, Niklas Leuchtenmuller, Sona Chandra, Aurelien Duarte, Andreas Bender, Yoann Gloaguen

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Abstract

Motivation: Untargeted metabolomics involves a large-scale comparison of the fragmentation pattern of a mass spectrum against a database containing known spectra. Given the number of comparisons involved, this step can be time-consuming.

Results: In this work, we present a GPU-accelerated cosine similarity implementation for Tandem Mass Spectrometry (MS), with an approximately 1000-fold speedup compared to the MatchMS reference implementation, without any loss of accuracy. This improvement enables repository-scale spectral library matching for compound identification without the need for large compute clusters. This impact extends to any spectral comparison-based methods such as molecular networking approaches and analogue search.

Availability: All code, results, and notebooks supporting are freely available under the MIT license at https://github.com/pangeAI/simms/.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Bioinformatics (Oxford, England)

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