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 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 coil, SaccharomyciesThe 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 coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.…
Author details: | Kevin Schwahn, Zoran NikoloskiORCiDGND |
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DOI: | https://doi.org/10.3389/fpls.2018.00538 |
ISSN: | 1664-462X |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/29731765 |
Title of parent work (English): | Frontiers in plant science |
Publisher: | Frontiers Research Foundation |
Place of publishing: | Lausanne |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/04/20 |
Publication year: | 2018 |
Release date: | 2021/12/08 |
Tag: | A. thaliana; E. coil; S. cerevisiae; data reduction; metabolomics; partial correlation; principal component analysis; regulation |
Volume: | 9 |
Number of pages: | 12 |
Funding institution: | International Max Planck Research School on Plant Growth at the Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany; Max Planck SocietyMax Planck Society; PlantaSYST - EU |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
DOAJ gelistet | |
License (German): | CC-BY - Namensnennung 4.0 International |