SimMS：一种用于串联质谱的GPU加速余弦相似度实现方法。
SimMS: a GPU-accelerated cosine similarity implementation for tandem mass spectrometry.
作者信息
Onoprishvili Tornike, Yuan Jui-Hung, Petrov Kamen, Ingalalli Vijay, Khederlarian Lila, Leuchtenmuller Niklas, Chandra Sona, Duarte Aurelien, Bender Andreas, Gloaguen Yoann
机构信息
Independent Consultant.
Pangea Botanica Germany GmbH, Berlin 10623, Germany.
出版信息
Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf081.
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/.
动机
非靶向代谢组学涉及将质谱的碎片模式与包含已知光谱的数据库进行大规模比较。鉴于所涉及的比较数量，这一步可能很耗时。
结果
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方法，与MatchMS参考实现相比，速度提高了约1000倍，且没有任何精度损失。这种改进使得无需大型计算集群即可进行库规模的光谱库匹配以进行化合物鉴定。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。
可用性和实现
支持的所有代码、结果和笔记本都在https://github.com/pangeAI/simms/上根据MIT许可免费提供。
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