Multiomics data analysis

Cells are the basic functional and structural units of living organisms. They are the location of many different biological processes, which can be probed by various biological techniques. Until recently such data sets have been analysed separately. The aim is to better understand the underlying biological processes and how they influence each other. Therefore techniques that integrate the data from different sources might be applicable [1].

In the image below you see the four main entities that are active throughout the cell: Genome, RNA, proteins, and metabolites. All of them are in constant interaction, for example, some proteins are transcription factors and influence the transcription of DNA into RNA. Metabolites that are present in the cell also influence the activity of proteins as ligands but at the same time are altered through enzymatic activity. This ambiguity of interactions makes it clear that probing the system at a single level gives only limited insight into the structure and function of the cellular processes.



The different levels of biological information (genome, proteome, …) work mutually and influence each other through processes as transcription regulation through transcription factors. All levels are influenced by external factors, as drug treatment or nutrient availability. Multiomics is the measurement of multiple of those populations and their integrated analysis.

In the last years, different ways to integrate such data have been developed. Broadly speaking there are three levels of data integration: conceptual integration, statistical integration, and model-based integration [2]. Conceptual integration means that the data sets are analysed separately and the conclusions are compared and integrated. This method can easily use already existing analysis pipelines but the way in which conclusions are compared and integrated is non-trivial. Statistical Integration combines data sets and analyses them jointly, reaching conclusions that match all data and potentially finding signals that are not observable with the conceptual approach. Model-based integration indicates the joint analysis of the data in a combination of training of a model, which itself might incorporate prior beliefs of a system.

[1] Gehlenborg, Nils, Seán I. O’donoghue, Nitin S. Baliga, Alexander Goesmann, Matthew A. Hibbs, Hiroaki Kitano, Oliver Kohlbacher et al. “Visualization of omics data for systems biology.” Nature methods 7 (2010): S56-S68.

[2] Cavill, Rachel, Danyel Jennen, Jos Kleinjans, and Jacob Jan Briedé. “Transcriptomic and metabolomic data integration.” Briefings in bioinformatics 17, no. 5 (2016): 891-901.