Integration Of Transcriptomic And Proteomic Data For Disease Insights

Omics technologies have dominated molecular analysis of the biological processes in normal and disease contexts in the post-genomic era, and large data is continuously accumulating. Data repositories, new databases, data mining, and analysis approaches are being developed. One of the approaches that is emerging in recent years is integration of gene expression data generated at different levels of regulation. Transcriptomic and proteomic data available at present and metabolic data in the future hold strong promise to understand molecular changes associated with the disease processes. Integration of these data would offer an interconnected view of these changes to add further value to the single ome datasets. We have been interested to explore known methods or develop new ways for integrating transcriptomic and proteomics data and have developed strategies that appear promising. The chapter describes these methods with examples in the context of glioblastomas-the major primary tumors of the central nervous system (CNS). The pipelines shown are however not rigid but indicative of the feasibility of approaches for the purpose of transcriptomic and proteomic data integration and can be adapted suitably for use in other tumor types as well as any clinical conditions in general based on the specific end objectives.