Abstract
Public repositories of metabolomics mass spectra encompass more than 1 billion entries. With open search, dot product or entropy similarity, comparisons of a single tandem mass spectrometry spectrum take more than 8 h. Flash entropy search speeds up calculations more than 10,000 times to query 1 billion spectra in less than 2 s, without loss in accuracy. It benefits from using multiple threads and GPU calculations. This algorithm can fully exploit large spectral libraries with little memory overhead for any mass spectrometry laboratory.
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Data availability
All spectra from MassBank.us (https://massbank.us/) and GNPS (https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.mgf) were downloaded on 3 March 2023. Additional MS/MS spectra from public repositories were downloaded from the MassIVE/GNPS (https://gnps.ucsd.edu/ProteoSAFe/datasets.jsp#%7B%22query%22%3A%7B%7D%2C%22title_input%22%3A%22GNPS%22%7D), MetabolomicWorkbench.org (https://www.metabolomicsworkbench.org/) and MetaboLights (https://www.ebi.ac.uk/metabolights/) in December 2022. In total, more than 939 million spectra were available (237,185,147 negative ESI and 701,996,947 positive ESI MS/MS spectra). All the spectra from those sources were used in this study. Source data are provided with this paper.
Code availability
The original source code and benchmark data for the Flash entropy search are available under the Apache License 2.0 on GitHub (https://github.com/YuanyueLi/FlashEntropySearch) and Zenodo (https://doi.org/10.5281/zenodo.7972082), as well as on CodeOcean (https://doi.org/10.24433/CO.8809500.v1). The GUI can be downloaded from the GitHub repository: https://github.com/YuanyueLi/EntropySearch. Flash entropy search is also integrated into the ‘MSEntropy’ package, available for download from https://github.com/YuanyueLi/MSEntropy. Comprehensive documentation for the ‘MSEntropy’ package can be found at https://msentropy.readthedocs.io.
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Acknowledgements
This study was funded by National Institutes of Health grants U2C ES030158 and R03 OD034497 (to O.F.).
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Authors and Affiliations
Contributions
Y.L. and O.F. conceptualized the study. Y.L. designed the algorithm and performed the benchmarking. O.F. supervised the project. Y.L. and O.F. wrote the manuscript.
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Nature Methods thanks Xusheng Wang and Jianguo Xia for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.
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Extended data
Extended Data Fig. 1 Examples for calculating Flash entropy similarity.
(a) Example when all ions match between query spectrum (top) and library spectrum (bottom). in the two spectra are matched. (b) Example when only one pair of ions matches between query and library spectra. Note that the sum intensities of ion abundances in each spectrum are normalized to equal 0.5 (see Supplementary Note 1 for equations). Hence, mismatched ions do not contribute themselves into the calculations, but are considered during the normalization process.
Extended Data Fig. 2 Distributions of spectral entropies when sampling spectra from different MS/MS repositories for benchmarking studies.
(a) MassBank.us, (b) GNPS for annotated compounds (library), (c) all combined experimental public MS/MS repositories including MassIVE/GNPS, MetaboLights, MetabolomicsWorkbench and West Coast Metabolomics Center.
Extended Data Fig. 3 Computation time required to perform ‘open search’ queries using entropy similarity for 100 positive ESI and 100 negative ESI mass spectra against spectral libraries of different sizes.
MS/MS spectra were sampled from (a) GNPS (b) public repositories. Box plots display medians as horizontal lines inside the boxes that delineate interquartile ranges (IQR). Whiskers extend to the lowest or highest data point within 1.5x IQR of the 25% and 75% quartiles. N = 200 independent MS/MS spectra randomly sampled from (a) GNPS (b) public.
Extended Data Fig. 4 Computation time required to perform ‘open search’ queries using dot product similarity for 100 positive ESI and 100 negative ESI mass spectra against spectral libraries of different sizes.
MS/MS spectra were sampled from (a) MassBank.us, (b) GNPS, (c) public repositories. Box plots display medians as horizontal lines inside the boxes that delineate interquartile ranges (IQR). Whiskers extend to the lowest or highest data point within 1.5x IQR of the 25% and 75% quartiles. N = 200 independent MS/MS spectra randomly sampled from (a) MassBank.us, (b) GNPS, (c) public repositories.
Extended Data Fig. 5 Computation time required to perform ‘neutral loss’ searches with entropy similarity for 100 positive ESI and 100 negative ESI mass spectra against spectral libraries of different sizes.
MS/MS spectra were sampled from (a) MassBank.us, (b) GNPS, (c) public repositories. Box plots display medians as horizontal lines inside the boxes that delineate interquartile ranges (IQR). Whiskers extend to the lowest or highest data point within 1.5x IQR of the 25% and 75% quartiles. N = 200 independent MS/MS spectra randomly sampled from (a) MassBank.us, (b) GNPS, (c) public repositories.
Extended Data Fig. 6 Computation time required to perform ‘hybrid searches’ with entropy similarity for 100 positive ESI and 100 negative ESI mass spectra against spectral libraries of different sizes.
MS/MS spectra were sampled from (a) MassBank.us, (b) GNPS, (c) public repositories. Box plots display medians as horizontal lines inside the boxes that delineate interquartile ranges (IQR). Whiskers extend to the lowest or highest data point within 1.5x IQR of the 25% and 75% quartiles. N = 200 independent MS/MS spectra randomly sampled from (a) MassBank.us, (b) GNPS, (c) public repositories.
Extended Data Fig. 7 Calculation time to open search 100 positive ESI and 100 negative ESI MS/MS spectra at different spectral entropy levels against randomly picked samples from the MassBank.us library.
Box plots display medians as horizontal lines inside the boxes that delineate interquartile ranges (IQR). Whiskers extend to the lowest or highest data point within 1.5x IQR of the 25% and 75% quartiles. N = 100 independent MS/MS spectra randomly sampled from MassBank.us.
Extended Data Fig. 8 Comparison of the accuracy of similarity query results between Flash entropy search and BLINK.
Each dot shows the maximum similarity difference between the fast algorithms and their classic algorithm counterparts. 100 positive ESI and 100 negative ESI MS/MS spectra were sampled from (a) MassBank.us, (b) GNPS, (c) public repositories.
Supplementary information
Supplementary Information
Supplementary Notes 1 and 2 and Tables 1 and 2.
Source data
Source Data Fig. 2
Statistical source data.
Source Data Extended Data Fig. 3
Statistical source data.
Source Data Extended Data Fig. 4
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Source Data Extended Data Fig. 5
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Source Data Extended Data Fig. 6
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Li, Y., Fiehn, O. Flash entropy search to query all mass spectral libraries in real time. Nat Methods 20, 1475–1478 (2023). https://doi.org/10.1038/s41592-023-02012-9
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DOI: https://doi.org/10.1038/s41592-023-02012-9
