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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2013
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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. |
format | Online Article Text |
id | pubmed-3901289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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