Guided extraction of genome-scale metabolic models for the integration and analysis of omics data
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Computational and Structural Biotechnology Journal
Abstract
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 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.
Description
Keywords
Genome-scale metabolic model, Model extraction methods, Context-specific metabolic model, Omics data integration, subsystem enrichment analysis, Model interpretability
Citation
A. 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.2021.06.009