Literature DB >> 32144942

Current status of retention time prediction in metabolite identification.

Michael Witting1,2, Sebastian Böcker3.   

Abstract

Metabolite identification is a crucial step in nontargeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem mass spectrometry analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem mass spectrometry data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010.
© 2020 The Authors. Journal of Separation Science published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  liquid chromatography, mass spectrometry; metabolite identification; metabolomics; retention time prediction

Year:  2020        PMID: 32144942     DOI: 10.1002/jssc.202000060

Source DB:  PubMed          Journal:  J Sep Sci        ISSN: 1615-9306            Impact factor:   3.645


  13 in total

1.  Probabilistic metabolite annotation using retention time prediction and meta-learned projections.

Authors:  Constantino A García; Alberto Gil-de-la-Fuente; Coral Barbas; Abraham Otero
Journal:  J Cheminform       Date:  2022-06-07       Impact factor: 8.489

2.  MetNC: Predicting Metabolites in vivo for Natural Compounds.

Authors:  Zikun Chen; Deyu Yan; Mou Zhang; Wenhao Han; Yuan Wang; Shudi Xu; Kailin Tang; Jian Gao; Zhiwei Cao
Journal:  Front Chem       Date:  2022-05-12       Impact factor: 5.545

Review 3.  New software tools, databases, and resources in metabolomics: updates from 2020.

Authors:  Biswapriya B Misra
Journal:  Metabolomics       Date:  2021-05-11       Impact factor: 4.290

4.  Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis.

Authors:  Roman Szucs; Roland Brown; Claudio Brunelli; James C Heaton; Jasna Hradski
Journal:  Int J Mol Sci       Date:  2021-04-08       Impact factor: 5.923

Review 5.  Natural product drug discovery in the artificial intelligence era.

Authors:  F I Saldívar-González; V D Aldas-Bulos; J L Medina-Franco; F Plisson
Journal:  Chem Sci       Date:  2021-12-13       Impact factor: 9.825

6.  AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.

Authors:  Lauren M Petrick; Noam Shomron
Journal:  Cell Rep Phys Sci       Date:  2022-07-20

7.  N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics.

Authors:  Rainer Stoffel; Michael A Quilliam; Normand Hardt; Anders Fridstrom; Michael Witting
Journal:  Anal Bioanal Chem       Date:  2021-12-15       Impact factor: 4.478

8.  Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification.

Authors:  Eric Bach; Simon Rogers; John Williamson; Juho Rousu
Journal:  Bioinformatics       Date:  2021-07-19       Impact factor: 6.937

Review 9.  Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity.

Authors:  Marina Creydt; Markus Fischer
Journal:  Molecules       Date:  2020-08-31       Impact factor: 4.411

10.  Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.

Authors:  Evelyn Rampler; Yasin El Abiead; Harald Schoeny; Mate Rusz; Felina Hildebrand; Veronika Fitz; Gunda Koellensperger
Journal:  Anal Chem       Date:  2020-11-28       Impact factor: 6.986

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.