Computational Metabolomics

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

Deadline for manuscript submissions: closed (21 January 2020) | Viewed by 19953

Special Issue Editor


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Guest Editor
Assistant Professor of Medicine, Director of Computational Metabolomics & Integrative Omics, Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, 615 Michael St, Rm 211 Atlanta, GA 30322, USA
Interests: bioinformatics; biomarker discovery machine learning; computaional metabolomics; integrative omics; text mining

Special Issue Information

Dear Colleagues,

Over the years, numerous studies have demonstrated the application of metabolomics for systems biology, biomarker discovery, precision medicine, and for assessing the impact of environmental exposures on human health. This special issue of “Computational Metabolomics” will be dedicated to review the advancements in the application of chemometrics, bioinformatics, and data science techniques for metabolomics data processing, annotation, pathway and network analysis, biomarker discovery, and integration of metabolomics data with other omics, experimental, and biomedical data. Articles focused on the current challenges and recommendations related to metabolomics/lipidomics data analysis, as well as on enhancing reproducibility and harmonization, are also highly desired.

Dr. Karan Uppal
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

  • computational metabolomics
  • peak detection, visualization, and alignment
  • data normalization
  • annotation
  • metabolite databases
  • MSn spectral matching
  • biomarker discovery
  • statistical methods and machine learning
  • pathway analysis
  • network analysis
  • integrative omics
  • data repositories

Published Papers (1 paper)

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Review

55 pages, 1854 KiB  
Review
The metaRbolomics Toolbox in Bioconductor and beyond
by Jan Stanstrup, Corey D. Broeckling, Rick Helmus, Nils Hoffmann, Ewy Mathé, Thomas Naake, Luca Nicolotti, Kristian Peters, Johannes Rainer, Reza M. Salek, Tobias Schulze, Emma L. Schymanski, Michael A. Stravs, Etienne A. Thévenot, Hendrik Treutler, Ralf J. M. Weber, Egon Willighagen, Michael Witting and Steffen Neumann
Metabolites 2019, 9(10), 200; https://doi.org/10.3390/metabo9100200 - 23 Sep 2019
Cited by 64 | Viewed by 19293
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing [...] Read more.
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub. Full article
(This article belongs to the Special Issue Computational Metabolomics)
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