REGULATORY DATA ANALYSIS METHODS DRIVER

Multiple reviews are available that provide a summary of the multi-omics data integration methodologies categorized based on their underlying mathematical aspects.

There are various review articles that cover the importance of multi-omics approaches from different perspectives. Employment of multi-omics approach has resulted in the development of various tools, methods, and platforms provisioning multi-omics data analysis, visualization, and interpretation. These studies widely proved the importance of integrating multi-omics data over single omics analysis. Downstream of sphingosine, the impaired sphingosine-1-phosphate receptor 2 signaling represents a loss of tumor suppressor gene and a potential key oncogenic pathway for therapeutic targeting. The metabolite sphingosine demonstrated high specificity and sensitivity for distinguishing prostate cancer from benign prostatic hyperplasia, as reported in this study. 5 In another study, integrating metabolomics and transcriptomics yielded molecular perturbations underlying prostate cancer. Integration of proteomics data helped in the identification of potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34), and SRC (SRC proto-oncogene, nonreceptor tyrosine kinase). Their results showed that chromosome 20q amplicon was associated with the largest global changes at both messenger RNA (mRNA) and protein levels.

REGULATORY DATA ANALYSIS METHODS DRIVER

4 Zhang et al 5 showed the importance of integrating proteomics data along with genomic and transcriptomic data to prioritize driver genes in colon and rectal cancers. For instance, integrative analysis of ChIP-Seq and RNA-Seq data of head and neck squamous cell carcinoma (HNSCC) cell lines showed that cancer-specific histone marks, H3K4me3 and H3K27ac, are associated with transcriptional changes in HNSCC driver genes, epidermal growth factor receptor (EGFR), FGFR1, and FOXA1.

In recent times, various studies have shown that combining omics data sets yield better understanding and clearer picture of the system under study. Integrative approaches, by virtue of their ability to study the biological phenomenon holistically, have the ability to improve prognostics and predictive accuracy of disease phenotypes and hence can eventually aid in better treatment and prevention. 2 They help in assessing the flow of information from one omics level to the other and thus help in bridging the gap from genotype to phenotype. 1 Integrated approaches combine individual omics data, in a sequential or simultaneous manner, to understand the interplay of molecules.

Integration of multi-omics data providing information on biomolecules from different layers seems to be promising to understand the complex biology systematically and holistically. Availability of multi-omics data has revolutionized the field of medicine and biology by creating avenues for integrated system-level approaches.Р вЂќР вЂћnalysis of multi-omics data along with clinical information has taken the front seat in deriving useful insights into the cellular functions. With the advent of sequencing technology, biology has become increasingly dependent on data generated at these levels, which together is called as Р Р†Р вЂљРЎС™multi-omicsР Р†Р вЂљРЎСљ data. Comprehensive understanding of human health and diseases requires interpretation of molecular intricacy and variations at multiple levels such as genome, epigenome, transcriptome, proteome, and metabolome.

Regulatory data analysis methods