Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Li, Yuanyue; Kind, Tobias; Folz, Jacob; Vaniya, Arpana; Mehta, Sajjan S.; Fiehn, Oliver

doi:10.1038/s41592-021-01331-z

Cited by 120 publications

(146 citation statements)

References 28 publications

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“…A high score of spectral database matching often indicates high confidence in structural annotations. Recently, a method called entropy similarity that outperformed dot product similarity was proposed [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example

Wang

Yao

et al. 2022

Molecules

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The complexity of metabolites in traditional Chinese medicine (TCM) hinders the comprehensive profiling and accurate identification of metabolites. In this study, an approach that integrates enhanced column separation, mass spectrometry post-processing and result verification was proposed and applied in the identification of flavonoids in Dalbergia odorifera. Firstly, column chromatography fractionation, followed by liquid chromatography–tandem mass spectrometry was used for systematic separation and detection. Secondly, a three-level data post-processing method was applied to the identification of flavonoids. Finally, fragmentation rules were used to verify the flavonoid compounds. As a result, a total of 197 flavonoids were characterized in D. odorifera, among which seven compounds were unambiguously identified in level 1, 80 compounds were tentatively identified by MS-DIAL and Compound Discoverer in level 2a, 95 compounds were annotated by Compound discoverer and Peogenesis QI in level 2b, and 15 compounds were exclusively annotated by using SIRIUS software in level 3. This study provides an approach for the rapid and efficient identification of the majority of components in herbal medicines.

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Section: Resultsmentioning

confidence: 99%

Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example

Wang

Yao

et al. 2022

Molecules

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“…Efforts have been made to control FDR on MS/MS spectral matching-based metabolite identification 27, 35, 46 , yet the FDR estimation for unknown annotation 10 remains elusive. In our approach, Platt scaling monotonically maps MLR prediction scores onto a probability scale.…”

Section: Discussionmentioning

confidence: 99%

Molecular formula discovery via bottom-up MS/MS interrogation

Xing

Shen

et al. 2022

Preprint

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A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics. Here we present bottom-up tandem MS (MS/MS) interrogation to illuminate the unidentified features via accurate molecular formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking, and offers false discovery rate estimation. Compared to the existing MS1-initiated formula annotation, our approach shrinks the formula candidate space by 42.8% on average. The superior annotation accuracy of our bottom-up interrogation was demonstrated on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 annotated recurrent unidentified spectra (ARUS), our approach confidently annotated >5,000 novel molecular formulae unarchived in chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global peak annotation. This approach reveals peak interrelationships, allowing the systematic annotation of 37 fatty acid amide molecules in human fecal data, among other applications. All bioinformatics pipelines are available in a standalone software, BUDDY (https://github.com/HuanLab/BUDDY/).

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“…Recently, the similar conclusion was also reported by the Hofft group 34 . We believe the addition of newly developed scoring approaches for MS/MS similarity, like CSS score 35 , Spec2Vec 34 , spectral entropy score 36 , would further enhance the performance of KGMN. Second, we need to be aware of the challenge of discriminating high structurally similar isomers of unknown metabolites.…”

Section: Discussionmentioning

confidence: 99%

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network

Zhou

Luo

Zhang

et al. 2022

Preprint

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Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, we developed an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrated three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we applied KGMN in an in-vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites were validated with in-silico MS/MS tools. Finally, we successfully validated 5 unknown metabolites through the repository-mining and the syntheses of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites towards deciphering dark matters in untargeted metabolomics.

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Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Cited by 120 publications

References 28 publications

Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example

Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example

Molecular formula discovery via bottom-up MS/MS interrogation

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network

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