RT Journal Article
SR Electronic
T1 Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.11.245928
DO 10.1101/2020.08.11.245928
A1 Florian Huber
A1 Lars Ridder
A1 Stefan Verhoeven
A1 Jurriaan H. Spaaks
A1 Faruk Diblen
A1 Simon Rogers
A1 Justin J.J. van der Hooft
YR 2020
UL http://biorxiv.org/content/early/2020/09/25/2020.08.11.245928.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.