Open-Source Software in Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: 25 June 2024 | Viewed by 6524

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Guest Editor
Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
Interests: clinical trial design; survival analysis; PK/PD; metabolomics
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Special Issue Information

Dear Colleagues,

Over the past 20 years, the field of metabolomics has expanded significantly, highlighting the need for open-source software for experimental design, analysis, visualization, database creation, and more. For this Special Issue, we encourage data scientists to share open-source software with the metabolomics community. We welcome all papers on open-source software, including open-source web tools, developed in any computer language, such as R, Python, MATLAB, JAVA, C++, etc. The software must be publicly and freely available to non-commercial users. We also welcome original research comparing the performances of existing open-source software and/or developing new methodologies for experimental design, analysis, visualization, database creation, and more, and review articles exploring existing open-source software in metabolomics. There is no restriction on paper length, but articles not including original research or review articles should ideally fall within approximately 2000 words.

Prof. Dr. Seongho Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GC-MS
  • LC-MS
  • mass spectrometry
  • metabolomics
  • open-source software

Published Papers (4 papers)

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13 pages, 2106 KiB  
Article
metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics
by Hani Habra, Jennifer L. Meijer, Tong Shen, Oliver Fiehn, David A. Gaul, Facundo M. Fernández, Kaitlin R. Rempfert, Thomas O. Metz, Karen E. Peterson, Charles R. Evans and Alla Karnovsky
Metabolites 2024, 14(2), 125; https://doi.org/10.3390/metabo14020125 - 15 Feb 2024
Viewed by 1211
Abstract
Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified [...] Read more.
Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App. Full article
(This article belongs to the Special Issue Open-Source Software in Metabolomics)
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13 pages, 2070 KiB  
Article
Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data
by Vadim V. Yanshole, Arsenty D. Melnikov, Lyudmila V. Yanshole, Ekaterina A. Zelentsova, Olga A. Snytnikova, Nataliya A. Osik, Maxim V. Fomenko, Ekaterina D. Savina, Anastasia V. Kalinina, Kirill A. Sharshov, Nikita A. Dubovitskiy, Mikhail S. Kobtsev, Anatolii A. Zaikovskii, Sofia S. Mariasina and Yuri P. Tsentalovich
Metabolites 2023, 13(10), 1088; https://doi.org/10.3390/metabo13101088 - 17 Oct 2023
Cited by 2 | Viewed by 1085
Abstract
The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations [...] Read more.
The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60–90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies. Full article
(This article belongs to the Special Issue Open-Source Software in Metabolomics)
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11 pages, 1472 KiB  
Article
Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools
by Elizabeth J. Parker, Kathryn C. Billane, Nichola Austen, Anne Cotton, Rachel M. George, David Hopkins, Janice A. Lake, James K. Pitman, James N. Prout, Heather J. Walker, Alex Williams and Duncan D. Cameron
Metabolites 2023, 13(4), 463; https://doi.org/10.3390/metabo13040463 - 23 Mar 2023
Cited by 1 | Viewed by 2020
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Open-Source Software in Metabolomics)
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16 pages, 1418 KiB  
Technical Note
Implementation of FAIR Practices in Computational Metabolomics Workflows—A Case Study
by Mahnoor Zulfiqar, Michael R. Crusoe, Birgitta König-Ries, Christoph Steinbeck, Kristian Peters and Luiz Gadelha
Metabolites 2024, 14(2), 118; https://doi.org/10.3390/metabo14020118 - 10 Feb 2024
Viewed by 1190
Abstract
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR [...] Read more.
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area. Full article
(This article belongs to the Special Issue Open-Source Software in Metabolomics)
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