SimMS: a GPU-accelerated cosine similarity implementation for tandem mass spectrometry.

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