In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 2 ( 2021-2-16), p. e1008724-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008724
DOI:
10.1371/journal.pcbi.1008724.g001
DOI:
10.1371/journal.pcbi.1008724.g002
DOI:
10.1371/journal.pcbi.1008724.g003
DOI:
10.1371/journal.pcbi.1008724.g004
DOI:
10.1371/journal.pcbi.1008724.g005
DOI:
10.1371/journal.pcbi.1008724.g006
DOI:
10.1371/journal.pcbi.1008724.s001
DOI:
10.1371/journal.pcbi.1008724.s002
DOI:
10.1371/journal.pcbi.1008724.s003
DOI:
10.1371/journal.pcbi.1008724.s004
DOI:
10.1371/journal.pcbi.1008724.s005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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