Genome-scale metabolic modelling is a widely used technique to research metabolic effects on organism properties. Additional omics data integration enables a more precise genotype-phenotype analysis for biotechnology, medicine and life sciences. Transcriptome data amounts rapidly increase each year.
Many genome-scale metabolic modelling tools with transcriptomics analysis have been proposed. However, these tools are mostly limited to specific functions or aims, and most of them are not compatible with the latest metabolic modelling tools, require advanced user skills and must be adjusted for metabolic models before use, making them hard to use by the scientific community.
IgemRNA was built to bypass existing compatibility and functionality limits and accessibility restrictions of previously published tools by standardising omics data input, implementing different pre-processing, non-optimisation and post-optimisation methods and additional validation functionality.
However, the novelty of IgemRNA is the Cobra Toolbox and Post-optimisation tasks and data analysis modules, which use a genome-scale metabolic model by considering the interconnectivity of a biochemical network, the steady state assumption and Gene – Protein – Reaction (GPR) associations. Modules enable transcriptome dataset processing, different phenotype data comparison, multiple dataset analysis and filtering built-in functionality and allow us to find and adjust inappropriate omics flux constraints. The Saccharomyces cerevisiae metabolic model was used as a test case.
Grausa, K, Mozga, I., Pleiko K., Pentjuss A., (2022) ‘Integrative Gene Expression and Metabolic Analysis Tool IgemRNA’, Biomolecules, 12(4), p. 586. https://doi: 10.3390/biom12040586