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SIMILE enables alignment of tandem mass spectra with statistical significance

Author

Listed:
  • Daniel G. C. Treen

    (Environmental Genomics and Systems Biology Division & The Joint Genome Institute Lawrence Berkeley National Laboratory)

  • Mingxun Wang

    (University of California, San Diego)

  • Shipei Xing

    (University of British Columbia)

  • Katherine B. Louie

    (Environmental Genomics and Systems Biology Division & The Joint Genome Institute Lawrence Berkeley National Laboratory)

  • Tao Huan

    (University of British Columbia)

  • Pieter C. Dorrestein

    (University of California, San Diego)

  • Trent R. Northen

    (Environmental Genomics and Systems Biology Division & The Joint Genome Institute Lawrence Berkeley National Laboratory)

  • Benjamin P. Bowen

    (Environmental Genomics and Systems Biology Division & The Joint Genome Institute Lawrence Berkeley National Laboratory)

Abstract

Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

Suggested Citation

  • Daniel G. C. Treen & Mingxun Wang & Shipei Xing & Katherine B. Louie & Tao Huan & Pieter C. Dorrestein & Trent R. Northen & Benjamin P. Bowen, 2022. "SIMILE enables alignment of tandem mass spectra with statistical significance," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30118-9
    DOI: 10.1038/s41467-022-30118-9
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    References listed on IDEAS

    as
    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.
    2. Liu Cao & Mustafa Guler & Azat Tagirdzhanov & Yi-Yuan Lee & Alexey Gurevich & Hosein Mohimani, 2021. "MolDiscovery: learning mass spectrometry fragmentation of small molecules," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Kerstin Scheubert & Franziska Hufsky & Daniel Petras & Mingxun Wang & Louis-Félix Nothias & Kai Dührkop & Nuno Bandeira & Pieter C. Dorrestein & Sebastian Böcker, 2017. "Significance estimation for large scale metabolomics annotations by spectral matching," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

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