Multi-Omics: a Revolutionary Approach to Data Analysis

With the advent of advanced high-throughput technologies, researchers can now quantify the cellular changes at different molecular levels. Global examination of molecular levels comprises of analysing data integrated from multiple “omes” like genome, transcriptome, proteome, interactome, epigenome, metabolome, lipidome ,  and microbiome, referred to as Multi-omics. Multi-omics approach is revolutionary in comparison to single omics approach because it gathers information from multiple “omes” that allows better understanding of complex diseases like cancer. The outcomes of this approach can further impact in designing better diagnostic tools and therapies for the treatment of a disease.

What is Multi-omics data analysis approach?

Single “omics” technologies provide a comprehensive view of the molecules that makes up the cell, tissue and organism. However the view is usually limited only to single levels like genomic, transcriptomic, proteomic, metabolomic etc. levels. Integrating the single levels to generate a global view is often referred to as multi-omics approach.

Why Multi-omics data analysis approach is important?

The single omics analyses approach can provide information about the biological processes which are active among the disease group in comparison to normal group. But, these analyses are often limited to correlations and may generally end up identifying the consequential changes rather than causative ones. The multi-omics approach provides more evidence for biological mechanisms as it utilises the information derived independently from several omic levels.

How are Multi-omics data used in research studies ?

The multi-omics approach is extremely useful in understanding complex diseases like cancer where the disease etiology is influenced by several genetic and environmental factors. The multi-omics approach can be broadly classified into genetic, phenotypic and environmental factors based approaches [1]. The genotype based multi-omic approach aims to use genome wide association studies to identify loci that are associated with the risk of the disease. Further examination of the locus regions can help in identifying the candidate genes which may play role in the disease initiation. Further validation for the associated genes is done by exploring the mutational or expression changes at genomic and transcriptomic levels. Secondly, the phenotype based multi-omics approach undermines the knowledge from the correlations between disease, clinical factors and omics-based data. This approach can provide useful biological insights about the disease development. Thirdly, the environment based multi-omics approach combines the information from omics data such as microbiome, genome or metabolome levels and estimates the association with environmental factors such as smoking and diet.

What are the major challenges in analysing Multi-Omics data?

The multi-omics data analysis poses several challenges [2]. The challenges are majorly involved with multi-omics data integration. Integrating omics data through a computational pipeline often requires a huge storage space. The underlying preprocessing steps vary for different omics data because the data were generated using different technical platforms. Hence,integrating multi-omics requires creating a pipeline that integrates data generated from different platforms. It is important to ensure that the differences observed in the samples before integration are due to biological variability and are not a technical artifact of the data.

multi-omicsFigure 1 Cartoon depicting the perspective about findings when using single omics approach and multi-omics approach [3,4].


Student’s section: Summer of 2017 at University of Cambridge understanding Multi-Omics

Rohit Thakur (ESR12) and Joey Mark Santiago Diaz (ESR13), at University Leeds, are exploring transcriptomic and copy number data generated from primary melanoma tumors. They are currently applying numerous methods to analyse the tumor data with an ultimate aim to derive prognostic biomarkers of melanoma which could be clinically applicable. One of the research aims is to identify factors influencing survival using copy number variation and transcriptomic data.Motivated by their research aims they attended a Multi-omics workshop ( organized by MIMOmics and and University of Cambridge on 20-27 August, 2017 at University of Cambridge Computer Laboratory situated in William Gates Building. The workshop comprised of seminars from esteemed scientists from omics background and hands on experience with multi-omics data analysis. Overall they both found this workshop engaging and useful.


“The field of “Multi-omics” is truly exciting because it harness information from different omic levels which could be utilised to develop strong weapons for fighting complex diseases such as cancer. However, there lies a pool of challenges which should be considered while using Multi-omics” – Rohit Thakur (PhD student at University of Leeds)

“Multi-omics provides a more holistic approach of addressing a biological problem by looking at it in different dimensions using integrated information from different platforms.” – Joey Diaz (PhD student at University of Leeds)




  1. Hasin, Y., M. Seldin, and A. Lusis, Multi-omics approaches to disease. Genome Biology, 2017. 18(1): p. 83.
  2. Palsson, B. and K. Zengler, The challenges of integrating multi-omic data sets. Nature chemical biology, 2010. 6(11): p. 787-789.