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...
Autores principales: | , , , , , , , , , |
---|---|
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 |