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Metabolites
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Metabolomics–Integration of Technology and Bioinformatics
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Metabolomics–Integration of Technology and Bioinformatics

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 37607

Special Issue Editors

Dr. H. Paul Benton

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Guest Editor
Scripps Center for Metabolomics, Scripps Research, 10550 North Torrey Pines, La Jolla, CA 92037, USA
Interests: bioinformatics; data processing; ML and NLP; automation
Dr. Michael E. Kurczy

E-Mail
Guest Editor
DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
Interests: mass spec imaging; isotopes; drug metabolism

Special Issue Information

Dear Colleagues,

Metabolomics, by its very nature, is a complex multifaceted discipline. Compounding this complexity has been the dramatic rise in the number of researchers and groups approaching metabolomic technologies with their own unique perspectives, as well as the interest to answer their specific questions or explore their special hypotheses. This increase in population and diversity has prompted an increase in the publications of new methods and software tools.

In this Special Issue of Metabolites, we invite authors to demonstrate their tools and explore the integration of other tools with their technology developments. It is well known that some of the best software integrates many aspects of technology into its design and interface. We feel that this is a feature of some of the most interesting areas of development, and invite developers and users to exhibit these multifaceted developments. We hope to explore a range of different metabolomic technologies in LC–MS, alterative ionization sources, MALDI, and SIMS. Tools that cover later aspects of processing, such as machine learning, but include latent effects of these technology within the model or processing are also encouraged.

Dr. H. Paul Benton
Dr. Michael E. Kurczy
Guest Editors

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

  • bioinformatics
  • technology integration
  • stable isotopes
  • temporal data
  • mass spec imaging
  • automation
  • big data

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Published Papers (4 papers)

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Research

15 pages, 3119 KiB  
Article
Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
by Marten H. P. M. Kerkhofs, Hanneke A. Haijes, A. Marcel Willemsen, Koen L. I. van Gassen, Maria van der Ham, Johan Gerrits, Monique G. M. de Sain-van der Velden, Hubertus C. M. T. Prinsen, Hanneke W. M. van Deutekom, Peter M. van Hasselt, Nanda M. Verhoeven-Duif and Judith J. M. Jans
Metabolites 2020, 10(5), 206; https://doi.org/10.3390/metabo10050206 - 18 May 2020
Cited by 22 | Viewed by 3720
Abstract
Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a [...] Read more.
Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction. Full article
(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)
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16 pages, 1926 KiB  
Article
JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics
by Xusheng Wang, Ji-Hoon Cho, Suresh Poudel, Yuxin Li, Drew R. Jones, Timothy I. Shaw, Haiyan Tan, Boer Xie and Junmin Peng
Metabolites 2020, 10(5), 190; https://doi.org/10.3390/metabo10050190 - 12 May 2020
Cited by 8 | Viewed by 4862
Abstract
Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During [...] Read more.
Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics. Full article
(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)
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14 pages, 7327 KiB  
Article
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics
by Zhiqiang Pang, Jasmine Chong, Shuzhao Li and Jianguo Xia
Metabolites 2020, 10(5), 186; https://doi.org/10.3390/metabo10050186 - 7 May 2020
Cited by 357 | Viewed by 23586
Abstract
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain [...] Read more.
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment. Full article
(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)
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9 pages, 2885 KiB  
Communication
The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics
by Christoph W. Turck, Tytus D Mak, Maryam Goudarzi, Reza M Salek and Amrita K Cheema
Metabolites 2020, 10(4), 128; https://doi.org/10.3390/metabo10040128 - 27 Mar 2020
Cited by 6 | Viewed by 4399
Abstract
Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is [...] Read more.
Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies. Full article
(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)
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