Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools
-
Published:2022-12-05
Issue:12
Volume:18
Page:
-
ISSN:1573-3890
-
Container-title:Metabolomics
-
language:en
-
Short-container-title:Metabolomics
Author:
de Jonge Niek F.ORCID, Mildau KevinORCID, Meijer DavidORCID, Louwen Joris J. R.ORCID, Bueschl ChristophORCID, Huber FlorianORCID, van der Hooft Justin J. J.ORCID
Abstract
Abstract
Background
Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.
Aim of review
We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.
Key scientific concepts of review
This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.
Funder
Netherlands eScience Center
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
Reference90 articles.
1. Aisporna, A., Benton, H. P., Chen, A., Derks, R. J. E., Galano, J. M., Giera, M., & Siuzdak, G. (2022). Neutral loss mass spectral data enhances molecular similarity analysis in METLIN. Journal of the American Society for Mass Spectrometry, 33, 530–534. 2. Alseekh, S., Aharoni, A., Brotman, Y., Contrepois, K., D’Auria, J., Ewald, J., Fraser, P. D., Giavalisco, P., Hall, R. D., Heinemann, M., Link, H., Luo, J., Neumann, S., Nielsen, J., Perez de Souza, L., Saito, K., Sauer, U., Schroeder, F. C., Schuster, S., et al. (2021). Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Natural Methods, 18, 747–756. 3. Aron, A. T., Gentry, E. C., McPhail, K. L., Nothias, L.-F., Nothias-Esposito, M., Bouslimani, A., Petras, D., Gauglitz, J. M., Sikora, N., Vargas, F., van der Hooft, J. J. J., Ernst, M., Kang, K. B., Aceves, C. M., Caraballo-Rodríguez, A. M., Koester, I., Weldon, K. C., Bertrand, S., Roullier, C., et al. (2020). Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nature Protocols, 15, 1954–1991. 4. Bach, E., Schymanski, E. L., & Rousu, J. (2022) Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. bioRxiv. 5. Baraniuk, R., Donoho, D., & Gavish, M. (2020). The science of deep learning. Proceedings of the National Academy of Sciences USA, 117, 30029–30032.
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|