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Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities hav...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909622/ https://www.ncbi.nlm.nih.gov/pubmed/33591968 http://dx.doi.org/10.1371/journal.pcbi.1008724 |
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author | Huber, Florian Ridder, Lars Verhoeven, Stefan Spaaks, Jurriaan H. Diblen, Faruk Rogers, Simon van der Hooft, Justin J. J. |
author_facet | Huber, Florian Ridder, Lars Verhoeven, Stefan Spaaks, Jurriaan H. Diblen, Faruk Rogers, Simon van der Hooft, Justin J. J. |
author_sort | Huber, Florian |
collection | PubMed |
description | Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds. |
format | Online Article Text |
id | pubmed-7909622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79096222021-03-05 Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships Huber, Florian Ridder, Lars Verhoeven, Stefan Spaaks, Jurriaan H. Diblen, Faruk Rogers, Simon van der Hooft, Justin J. J. PLoS Comput Biol Research Article Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds. Public Library of Science 2021-02-16 /pmc/articles/PMC7909622/ /pubmed/33591968 http://dx.doi.org/10.1371/journal.pcbi.1008724 Text en © 2021 Huber et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Huber, Florian Ridder, Lars Verhoeven, Stefan Spaaks, Jurriaan H. Diblen, Faruk Rogers, Simon van der Hooft, Justin J. J. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title | Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title_full | Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title_fullStr | Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title_full_unstemmed | Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title_short | Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships |
title_sort | spec2vec: improved mass spectral similarity scoring through learning of structural relationships |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909622/ https://www.ncbi.nlm.nih.gov/pubmed/33591968 http://dx.doi.org/10.1371/journal.pcbi.1008724 |
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