Multi-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to

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... form data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study. Results: The interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features. Conclusions: MOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.