Multiomics is a new approach where the data sets of different omic groups are combined during analysis. The different omic strategies employed during multiomics are genome, proteome, transcriptome, epigenome, and microbiome. Multiomics, multi-omics, integrative omics, “panomics” or ‘pan-omics’ is a biological analysis approach in which the data sets are multiple above omics. In other words, the use of multiple omics technologies to study life in a concerted way. By combining these “omes”, scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association.
Multiomics strategy
With the progress in all the different omics fields, it is being increasingly recognized that the answer to a research question cannot be answered by one form of omics. The microbiome influences the gene and protein expression which in turn influence the metabolome, and all these processes crosstalk and regulate each other. Studying these processes in their entirety is critical to find strategies to treat diseases. This is where the multiomics field is coming in. This field encompasses all the omics fields and trues to understand the native and altered state of an organism by the analysis of the data from different omics experiments.
Multiomics analysis
Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification or physical separation of RNA and genomic DNA. Modern sequencing technology has led to many comprehensive assays being routinely available to experimenters, giving us different ways to peek at the internal doings of the cells, each experiment revealing a different part of some underlying processes. If we treat the DNA with bisulfite prior to sequencing, cytosine residues are converted to uracil, but 5-methylcytosine residues are unaffected. This allows us to probe the methylation patterns of the genome, or its methylome. By sequencing the mRNA molecules in a cell, we can calculate the abundance, in different samples, of different mRNA transcripts, or uncover its transcriptome. Performing different experiments on the same samples, for instance RNA-seq, DNA-seq, and BS-seq, results in multi-dimensional omics datasets, which enable the study of relationships between different biological processes, e.g. DNA methylation, mutations, and gene expression, and the leveraging of multiple data types to draw inferences about biological systems.
