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Metabolites
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Advances in Computational Metabolomics
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Advances in 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 (30 November 2021) | Viewed by 15802

Special Issue Editors

Dr. Alla Karnovsky

E-Mail Website
Guest Editor
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan
Interests: metabololomics; LC-MS; computational tools; knowledge-based and data-driven data analysis
Prof. George Michailidis

E-Mail Website
Guest Editor
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
Interests: network analysis; pathway enrichment analysis; integration of omics data; machine learning; high-dimensional statistics; optimization

Special Issue Information

Dear Colleagues,

Modern analytical methods allow simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. Analysis of metabolomics data is a multistep process that includes data processing, normalization, identification of differentially expressed metabolites, building statistical association or classification models, as well as bioinformatics analysis to aid biological data interpretation. Many commercial and open source computational tools have been built to help to perform these data analysis tasks; however, many challenges still remain. The focus of this Special Issue is to highlight the latest advances in computational methods and software tools for the analysis and modeling of metabolomics data.

Dr. Alla Karnovsky
Prof. George Michailidis
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

  • analytical platforms
  • data curation
  • QA/QC
  • processing
  • data integration
  • software tools
  • machine learning methods
  • knowledge-based tools
  • databases

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Published Papers (3 papers)

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Research

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16 pages, 3843 KiB  
Article
TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome
by Yao Lu, Jasmine Chong, Shiqian Shen, Joey-Bahige Chammas, Lorraine Chalifour and Jianguo Xia
Metabolites 2022, 12(1), 10; https://doi.org/10.3390/metabo12010010 - 23 Dec 2021
Cited by 19 | Viewed by 5831
Abstract
Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, [...] Read more.
Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods. Full article
(This article belongs to the Special Issue Advances in Computational Metabolomics)
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11 pages, 955 KiB  
Article
MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools
by Jonathan Dekermanjian, Wladimir Labeikovsky, Debashis Ghosh and Katerina Kechris
Metabolites 2021, 11(10), 678; https://doi.org/10.3390/metabo11100678 - 2 Oct 2021
Cited by 9 | Viewed by 3410
Abstract
The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many [...] Read more.
The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape. Full article
(This article belongs to the Special Issue Advances in Computational Metabolomics)
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Review

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18 pages, 1125 KiB  
Review
A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research
by Xinsong Du, Juan J. Aristizabal-Henao, Timothy J. Garrett, Mathias Brochhausen, William R. Hogan and Dominick J. Lemas
Metabolites 2022, 12(1), 87; https://doi.org/10.3390/metabo12010087 - 17 Jan 2022
Cited by 12 | Viewed by 5803
Abstract
Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists [...] Read more.
Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently. Full article
(This article belongs to the Special Issue Advances in Computational Metabolomics)
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