Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

Simple item page
dc.contributor.authorWalakira, Andrew
dc.contributor.authorRozman, Damjana
dc.contributor.authorRežen, Tadeja
dc.contributor.authorMraz, Miha
dc.contributor.authorMoškon, Miha
dc.date.accessioned2023-05-08T17:56:57Z
dc.date.available2023-05-08T17:56:57Z
dc.date.issued2021
dc.description.abstractOmics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT and tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the vari- ability (> 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.en_US
dc.identifier.citationA. Walakira, D. Rozman, T. Režen, M. Mraz, M. Moškon, Guided extraction of genome-scale metabolic models for the integration and analysis of omics data, Computational and Structural Biotechnology Journal (2021), doi: https://doi.org/10.1016/j.csbj.2021.06.009en_US
dc.identifier.urihttps://doi.org/10.1016/j.csbj.2021.06.009
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/8659
dc.language.isoenen_US
dc.publisherComputational and Structural Biotechnology Journalen_US
dc.subjectGenome-scale metabolic modelen_US
dc.subjectModel extraction methodsen_US
dc.subjectContext-specific metabolic modelen_US
dc.subjectOmics data integrationen_US
dc.subjectsubsystem enrichment analysisen_US
dc.subjectModel interpretabilityen_US
dc.titleGuided extraction of genome-scale metabolic models for the integration and analysis of omics dataen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Guided extraction of genome-scale metabolic models for the integration and.pdf
Size:
1.08 MB
Format:
Adobe Portable Document Format
Description:
Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering and Technology