Designing and interpreting 'multi-omic' experiments that may change our understanding of biology
Reviewartikel, 2017

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.


Robert Haas

The Francis Crick Institute

Aleksej Zelezniak

The Francis Crick Institute

Chalmers, Biologi och bioteknik, Systembiologi

Jacopo Iacovacci

The Francis Crick Institute

Imperial College London

Stephan Kamrad

University College London (UCL)

The Francis Crick Institute

St John Townsend

The Francis Crick Institute

University College London (UCL)

M. Ralser

The Francis Crick Institute

University of Cambridge

Current Opinion in Systems Biology

24523100 (eISSN)

Vol. 6 37-45



Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi



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