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Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library

Author

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  • Qiong Yang

    (Central South University)

  • Hongchao Ji

    (Chinese Academy of Agricultural Sciences)

  • Zhenbo Xu

    (Central South University)

  • Yiming Li

    (Central South University)

  • Pingshan Wang

    (Central South University)

  • Jinyu Sun

    (Central South University)

  • Xiaqiong Fan

    (Central South University)

  • Hailiang Zhang

    (Central South University)

  • Hongmei Lu

    (Central South University)

  • Zhimin Zhang

    (Central South University)

Abstract

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool.

Suggested Citation

  • Qiong Yang & Hongchao Ji & Zhenbo Xu & Yiming Li & Pingshan Wang & Jinyu Sun & Xiaqiong Fan & Hailiang Zhang & Hongmei Lu & Zhimin Zhang, 2023. "Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39279-7
    DOI: 10.1038/s41467-023-39279-7
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    References listed on IDEAS

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    1. Florian Huber & Lars Ridder & Stefan Verhoeven & Jurriaan H Spaaks & Faruk Diblen & Simon Rogers & Justin J J van der Hooft, 2021. "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-18, February.
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