A new era in omics data analysis
Advances in high-throughput technologies have made possible monitoring at the genomic, transcriptomic and regulatory levels of various biological molecules. These analyses lead to an overload of data that require computational methods for their mining and analysis. It is desirable to combine data from different omics technologies to obtain a more complete picture of gene expression, protein abundance or various intracellular processes. The primary objective of the EU-funded KEPAMOD (Knowledge exchange in processing and analysis of multi-omic data) initiative was to integrate multi-omic data. For this purpose, partners performed staff exchanges of both experienced and young researchers across research institutes to obtain knowledge in the analysis of omics technology data. The goal was to understand the proteomics and functional genomics techniques and methodologies. Researchers used the aquaporin 2 (AQP2) channel as a model protein for proteomic analysis. Apart from its role in water homeostasis in mammals, the AQP2 protein serves to bind integrins and promote renal epithelial cell migration and morphogenesis. AQP2 was isolated, purified and prepared for mass spectrometry analysis. In addition, researchers obtained bioinformatics skills and training in analysing RNA sequencing data. Through collaboration with China, they determined that the major issue with current multi-omic data integration techniques is that they rely on multiple manual steps to transform data. For this purpose, they developed an application that allowed the transfer of multi-omic data to simulation software of molecular interaction networks. The introduction of an automated step immediately reduced error, as observed following extensive testing on simple and large networks. The KEPAMOD multi-omic data integration techniques were applied to understand mechanotransduction phenomena, and relate them to mechanical manipulations such as acupuncture, a therapeutic approach used in rheumatoid arthritis. Various data sets from heterogeneous biochemical origin were analysed to delineate the molecular network on rheumatoid arthritis and identify novel targets for therapy.
Keywords
Omics, data analysis, genomic, transcriptomic, multi-omic data, aquaporin 2, rheumatoid arthritis
