Cargando…

Viime: Visualization and Integration of Metabolomics Experiments

Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological c...

Descripción completa

Detalles Bibliográficos
Autores principales: Choudhury, Roni, Beezley, Jon, Davis, Brandon, Tomeck, Jared, Gratzl, Samuel, Golzarri-Arroyo, Lilian, Wan, Jun, Raftery, Daniel, Baumes, Jeff, O’Connell, Thomas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990241/
https://www.ncbi.nlm.nih.gov/pubmed/33768193
http://dx.doi.org/10.21105/joss.02410
_version_ 1783669040208674816
author Choudhury, Roni
Beezley, Jon
Davis, Brandon
Tomeck, Jared
Gratzl, Samuel
Golzarri-Arroyo, Lilian
Wan, Jun
Raftery, Daniel
Baumes, Jeff
O’Connell, Thomas M.
author_facet Choudhury, Roni
Beezley, Jon
Davis, Brandon
Tomeck, Jared
Gratzl, Samuel
Golzarri-Arroyo, Lilian
Wan, Jun
Raftery, Daniel
Baumes, Jeff
O’Connell, Thomas M.
author_sort Choudhury, Roni
collection PubMed
description Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological condition of the organism (Beger et al., 2016; Wishart, 2016). The challenge for metabolomics is that no single analytical platform can provide a truly comprehensive coverage of the metabolome. The most commonly used platforms are based on mass-spectrometry (MS) and nuclear magnetic resonance (NMR). Investigators are increasingly using both methods to increase the metabolite coverage. The challenge for this type of multi-platform approach is that the data structure may be very different in these two platforms. For example, NMR data may be reported as a list of spectral features, e.g., bins or peaks with arbitrary intensity units or more directly with named metabolites reported in concentration units ranging from micromolar to millimolar. Some MS approaches can also provide data in the form of identified metabolite concentrations, but given the superior sensitivity of MS, the concentrations can be several orders of magnitude lower than for NMR. Other MS approaches yield data in the form of arbitrary response units where the dynamic range can be more than 6 orders of magnitude. Importantly, the variability and reproducibility of the data may differ across platforms. Given the diversity of data structures (i.e., magnitude and dynamic range) integrating the data from multiple platforms can be challenging. This often leads investigators to analyze the datasets separately, which prevents the observation of potentially interesting relationships and correlations between metabolites detected on different platforms. Viime (VIsualization and Integration of Metabolomics Experiments) is an open-source, web-based application designed to integrate metabolomics data from multiple platforms. The workflow of Viime for data integration and visualization is shown in Figure 1.
format Online
Article
Text
id pubmed-7990241
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-79902412021-03-24 Viime: Visualization and Integration of Metabolomics Experiments Choudhury, Roni Beezley, Jon Davis, Brandon Tomeck, Jared Gratzl, Samuel Golzarri-Arroyo, Lilian Wan, Jun Raftery, Daniel Baumes, Jeff O’Connell, Thomas M. J Open Source Softw Article Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological condition of the organism (Beger et al., 2016; Wishart, 2016). The challenge for metabolomics is that no single analytical platform can provide a truly comprehensive coverage of the metabolome. The most commonly used platforms are based on mass-spectrometry (MS) and nuclear magnetic resonance (NMR). Investigators are increasingly using both methods to increase the metabolite coverage. The challenge for this type of multi-platform approach is that the data structure may be very different in these two platforms. For example, NMR data may be reported as a list of spectral features, e.g., bins or peaks with arbitrary intensity units or more directly with named metabolites reported in concentration units ranging from micromolar to millimolar. Some MS approaches can also provide data in the form of identified metabolite concentrations, but given the superior sensitivity of MS, the concentrations can be several orders of magnitude lower than for NMR. Other MS approaches yield data in the form of arbitrary response units where the dynamic range can be more than 6 orders of magnitude. Importantly, the variability and reproducibility of the data may differ across platforms. Given the diversity of data structures (i.e., magnitude and dynamic range) integrating the data from multiple platforms can be challenging. This often leads investigators to analyze the datasets separately, which prevents the observation of potentially interesting relationships and correlations between metabolites detected on different platforms. Viime (VIsualization and Integration of Metabolomics Experiments) is an open-source, web-based application designed to integrate metabolomics data from multiple platforms. The workflow of Viime for data integration and visualization is shown in Figure 1. 2020-10-18 2020 /pmc/articles/PMC7990241/ /pubmed/33768193 http://dx.doi.org/10.21105/joss.02410 Text en License Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Choudhury, Roni
Beezley, Jon
Davis, Brandon
Tomeck, Jared
Gratzl, Samuel
Golzarri-Arroyo, Lilian
Wan, Jun
Raftery, Daniel
Baumes, Jeff
O’Connell, Thomas M.
Viime: Visualization and Integration of Metabolomics Experiments
title Viime: Visualization and Integration of Metabolomics Experiments
title_full Viime: Visualization and Integration of Metabolomics Experiments
title_fullStr Viime: Visualization and Integration of Metabolomics Experiments
title_full_unstemmed Viime: Visualization and Integration of Metabolomics Experiments
title_short Viime: Visualization and Integration of Metabolomics Experiments
title_sort viime: visualization and integration of metabolomics experiments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990241/
https://www.ncbi.nlm.nih.gov/pubmed/33768193
http://dx.doi.org/10.21105/joss.02410
work_keys_str_mv AT choudhuryroni viimevisualizationandintegrationofmetabolomicsexperiments
AT beezleyjon viimevisualizationandintegrationofmetabolomicsexperiments
AT davisbrandon viimevisualizationandintegrationofmetabolomicsexperiments
AT tomeckjared viimevisualizationandintegrationofmetabolomicsexperiments
AT gratzlsamuel viimevisualizationandintegrationofmetabolomicsexperiments
AT golzarriarroyolilian viimevisualizationandintegrationofmetabolomicsexperiments
AT wanjun viimevisualizationandintegrationofmetabolomicsexperiments
AT rafterydaniel viimevisualizationandintegrationofmetabolomicsexperiments
AT baumesjeff viimevisualizationandintegrationofmetabolomicsexperiments
AT oconnellthomasm viimevisualizationandintegrationofmetabolomicsexperiments