MetChem: a new pipeline to explore structural similarity across metabolite modules
File(s)vbad053.pdf (616.68 KB)
Published version
Author(s)
Type
Journal Article
Abstract
Summary
Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information.
Availability
MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).
Supplementary information
Supplementary data are available at Bioinformatics online.
Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information.
Availability
MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).
Supplementary information
Supplementary data are available at Bioinformatics online.
Editor(s)
Gromiha, Michael
Date Issued
2023-04-21
Date Acceptance
2023-04-19
Citation
Bioinformatics Advances, 2023, 3 (1), pp.1-4
ISSN
2635-0041
Publisher
Oxford University Press
Start Page
1
End Page
4
Journal / Book Title
Bioinformatics Advances
Volume
3
Issue
1
Copyright Statement
© The Author(s) 2023. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
http://dx.doi.org/10.1093/bioadv/vbad053
Publication Status
Published
Date Publish Online
2023-04-21