Guided extraction of genome-scale metabolic models for the integration and analysis of omics data
Omics 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 Cyp5...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021002476 |
| _version_ | 1818950484425179136 |
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| author | Andrew Walakira Damjana Rozman Tadeja Režen Miha Mraz Miha Moškon |
| author_facet | Andrew Walakira Damjana Rozman Tadeja Režen Miha Mraz Miha Moškon |
| author_sort | Andrew Walakira |
| collection | DOAJ |
| description | Omics 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 an 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 variability (>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. |
| first_indexed | 2024-12-20T09:19:19Z |
| format | Article |
| id | doaj.art-db4c20c2164a46089ee4723bddbac05a |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-20T09:19:19Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-db4c20c2164a46089ee4723bddbac05a2022-12-21T19:45:20ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011935213530Guided extraction of genome-scale metabolic models for the integration and analysis of omics dataAndrew Walakira0Damjana Rozman1Tadeja Režen2Miha Mraz3Miha Moškon4Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, SloveniaCentre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, SloveniaCentre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia; Corresponding author.Omics 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 an 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 variability (>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.http://www.sciencedirect.com/science/article/pii/S2001037021002476Genome-scale metabolic modelModel extraction methodsContext-specific metabolic modelOmics data integrationSubsystem enrichment analysisModel interpretability |
| spellingShingle | Andrew Walakira Damjana Rozman Tadeja Režen Miha Mraz Miha Moškon Guided extraction of genome-scale metabolic models for the integration and analysis of omics data Computational and Structural Biotechnology Journal Genome-scale metabolic model Model extraction methods Context-specific metabolic model Omics data integration Subsystem enrichment analysis Model interpretability |
| title | Guided extraction of genome-scale metabolic models for the integration and analysis of omics data |
| title_full | Guided extraction of genome-scale metabolic models for the integration and analysis of omics data |
| title_fullStr | Guided extraction of genome-scale metabolic models for the integration and analysis of omics data |
| title_full_unstemmed | Guided extraction of genome-scale metabolic models for the integration and analysis of omics data |
| title_short | Guided extraction of genome-scale metabolic models for the integration and analysis of omics data |
| title_sort | guided extraction of genome scale metabolic models for the integration and analysis of omics data |
| topic | Genome-scale metabolic model Model extraction methods Context-specific metabolic model Omics data integration Subsystem enrichment analysis Model interpretability |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021002476 |
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