Abstract
Abstract The analysis of omics data has revolutionized biology, leading to a better understanding of complex diseases and enabling the implementation of personalized medicine. Nevertheless, the scarcity of data caused by limited patient availability and privacy-related issues continues to be a significant bottleneck hindering further advancements in the field. In this context, generating synthetic data provides a promising tool for overcoming these limitations. In this review, we systematically review approaches for generating synthetic omics data derived from sequencing. We evaluate their strengths and limitations and discuss the validation metrics employed and their implications for reliable research outcomes.
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