Integrative omics data mining: Challenges and opportunities

Next-generation sequencing-based high-throughput data has opened novel opportunities to analyze and describe biological processes at a higher resolution. Nowadays, multiomics technologies are generating large amounts of heterogeneous genomics, proteomics, and metabolomics datasets. Integrative approaches enable us to study complex biological processes that combine the analysis of multiple omics datasets to highlight the interplay of the involved genes, transcripts, proteins, metabolites, etc., and their functions. Thus, data integration and data mining are imperative to exploring the mysteries of life and complex diseases in life sciences research. In the present scenario, integrating heterogeneous and huge amounts of genomics, proteomics, and metabolomics data poses conceptual and practical challenges, and encourages researchers to develop novel data integration methodologies, tools, and virtualization platforms. This chapter reviews the current efforts and state of the art about data integration and its mining in life sciences research. This chapter describes various tools and methods in detail that adopt an integrative approach to analyze multiomics data and data mining methods to address phenotype prediction, disease subtyping, a novel biomarker, novel pathways discovery, etc. This chapter provides an extensive overview in lucid style illustrating the methodologies, limitations of these tools, multiomics data repositories, and visualization platforms along with enumerating the challenges associated with multiomics data integration and mining making this chapter informative and reader friendly.