Integration of Multi-Omics Data and Approaches

Interpretation of molecular intricacy and variability at several levels, such as the genome, epigenome, transcriptome, proteome, and metabolome, is required for a comprehensive understanding of human health and disease. Biology has become more reliant on data collected at various levels, which is collectively referred to as "multi-omics" data, since the development of sequencing technologies. The availability of multi-omics data has transformed medicine and biology by allowing for integrated system-level techniques to be developed. The utilisation of multi-omics data in conjunction with clinical data has taken the lead in gaining useful insights into cellular functioning. Integration of multi-omics data containing information on biomolecules from several layers appears to be promising in terms of methodically and holistically understanding complicated biology. Individual omics data is combined in a sequential or simultaneous manner in integrated techniques to study the interplay of molecules. They aid in monitoring the flow of data from one omics level to the next, bridging the gap between genotype and phenotype. Integrative techniques have the potential to increase prognostics and predictive accuracy of disease phenotypes, and hence aid in improved treatment and prevention, due to their ability to explore biological phenomena holistically