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

IF 5.4 Bioinformatics (Oxford, England) Pub Date : 2025-03-04 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 and implementation: All code, results, and notebooks supporting are freely available under the MIT license at https://github.com/pangeAI/simms/.

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用于串联质谱的gpu加速余弦相似度实现。

动机：非靶向代谢组学涉及大规模比较质谱的碎片模式与包含已知谱的数据库。考虑到所涉及的比较的数量，这一步可能会很耗时。在这项工作中，我们提出了一种用于串联质谱（MS）的gpu加速余弦相似度实现，与MatchMS参考实现相比，其速度提高了约1000倍，而精度没有任何损失。这一改进使储存库规模的光谱库匹配化合物鉴定，而不需要大型计算集群。这种影响扩展到任何基于光谱比较的方法，如分子网络方法和类似物搜索。可用性：在MIT许可下，所有代码、结果和支持的笔记本都可以在https://github.com/pangeAI/simms/.Supplementary上免费获得：补充数据可以在Bioinformatics在线上获得。

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Bioinformatics (Oxford, England)

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