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Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a r...

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Autores principales: Stanberry, Larissa, Mias, George I., Haynes, Winston, Higdon, Roger, Snyder, Michael, Kolker, Eugene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901289/
https://www.ncbi.nlm.nih.gov/pubmed/24958148
http://dx.doi.org/10.3390/metabo3030741
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author Stanberry, Larissa
Mias, George I.
Haynes, Winston
Higdon, Roger
Snyder, Michael
Kolker, Eugene
author_facet Stanberry, Larissa
Mias, George I.
Haynes, Winston
Higdon, Roger
Snyder, Michael
Kolker, Eugene
author_sort Stanberry, Larissa
collection PubMed
description The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.
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spelling pubmed-39012892014-05-27 Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile Stanberry, Larissa Mias, George I. Haynes, Winston Higdon, Roger Snyder, Michael Kolker, Eugene Metabolites Article The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. MDPI 2013-09-03 /pmc/articles/PMC3901289/ /pubmed/24958148 http://dx.doi.org/10.3390/metabo3030741 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Stanberry, Larissa
Mias, George I.
Haynes, Winston
Higdon, Roger
Snyder, Michael
Kolker, Eugene
Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title_full Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title_fullStr Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title_full_unstemmed Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title_short Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
title_sort integrative analysis of longitudinal metabolomics data from a personal multi-omics profile
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901289/
https://www.ncbi.nlm.nih.gov/pubmed/24958148
http://dx.doi.org/10.3390/metabo3030741
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