Abstract

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Keywords

OmicsData integrationData scienceComputer scienceSubtypingVisualizationData miningBioinformaticsBiology

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Year
2020
Type
review
Volume
14
Pages
117793221989905-117793221989905
Citations
1335
Access
Closed

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Indhupriya Subramanian, Srikant Verma, Shiva Kumar et al. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights , 14 , 117793221989905-117793221989905. https://doi.org/10.1177/1177932219899051

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DOI
10.1177/1177932219899051