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Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize tra...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920133/ https://www.ncbi.nlm.nih.gov/pubmed/29731765 http://dx.doi.org/10.3389/fpls.2018.00538 |
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author | Schwahn, Kevin Nikoloski, Zoran |
author_facet | Schwahn, Kevin Nikoloski, Zoran |
author_sort | Schwahn, Kevin |
collection | PubMed |
description | The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana. |
format | Online Article Text |
id | pubmed-5920133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59201332018-05-04 Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism Schwahn, Kevin Nikoloski, Zoran Front Plant Sci Plant Science The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana. Frontiers Media S.A. 2018-04-20 /pmc/articles/PMC5920133/ /pubmed/29731765 http://dx.doi.org/10.3389/fpls.2018.00538 Text en Copyright © 2018 Schwahn and Nikoloski. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Schwahn, Kevin Nikoloski, Zoran Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title | Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title_full | Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title_fullStr | Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title_full_unstemmed | Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title_short | Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism |
title_sort | data reduction approaches for dissecting transcriptional effects on metabolism |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920133/ https://www.ncbi.nlm.nih.gov/pubmed/29731765 http://dx.doi.org/10.3389/fpls.2018.00538 |
work_keys_str_mv | AT schwahnkevin datareductionapproachesfordissectingtranscriptionaleffectsonmetabolism AT nikoloskizoran datareductionapproachesfordissectingtranscriptionaleffectsonmetabolism |