Parker, E.J., Billane, K.C., Austen, N. orcid.org/0000-0002-4873-5861 et al. (9 more authors) (2023) Untangling the complexities of processing and analysis for untargeted LC-MS data using open-source tools. Metabolites, 13 (4). 463. ISSN 2218-1989
Abstract
Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the ‘correct’ pipeline isn’t straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | metabolomics; untargeted; mass-spectrometry; open-source; bioinformatics |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Funding Information: | Funder Grant number BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/T010789/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Apr 2023 13:42 |
Last Modified: | 21 Apr 2023 13:42 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/metabo13040463 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198354 |