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Review on Multi-omics Data

Posted on
Tags: Multi-omics, Disease Subtyping

This note is based on Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights, 14, 1177932219899051.

To study complex biological processes holistically, take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions.

The review collected 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.

  • methodology, use-cases, and limitations of these tools
  • brief account of multi-omics data and visualization portals
  • challenges associated with multi-omics data integration

comprehensive understanding of human health and diseases requires interpretation of molecular intricacy and variations at multiple levels such as gnome, epigenome, transcriptome, proteome, and metabolome

biology has become increasingly dependent on data generated at these levels, which together is called as “multi-omics” data.

  • integrated approaches combine individual omics data, in a sequential or simultaneous manner, to understand the interplay of molecules.

Omics Data Types and Repositories

Leveraging Multi-omics Data to Derive Actionable Insights

  • Genes, transcripts, proteins, metabolites, and other macro/micro molecules systematically collaborate to perform compelx cellular processes.
  • Integration of multi-omics data sets can help in unraveling the underlying mechanisms at multiple omics levels.

Biological questions can be broadly categorized into 3 different case studies

  • disease subtyping and classification based on multi-omics profiles
  • prediction of biomarkers for various applications including diagnostics and driver genes for diseases
  • deriving insights into disease biology

Portals for Visualization and Interpretation of Multi-omics Data Sets


As the tools and methods are largely isolated, there is a need to have a uniform framework that can effectively process and analyze multi-omics data in an end-to-end manner along with easy and biologist-friendly visualization and interpretation.

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