KEPAMOD

Knowledge exchange in processing and analysis of multi-omic data

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 Obiettivo del progetto (Objective)

'High-throughput experiments produce large datasets providing information about cellular processes, by simultaneously observing different types of variables (e.g. genes, proteins, metabolites). Collectively, data from such experiments are referred to as omic data, to indicate their genome-wide coverage. Advances in biotechnology have resulted in the possibility to obtain a growing variety of omic datasets. However, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptomic, etc.) separately and combining the results in order to explore the biology of a system. An important output of such analyses is the identification of networks of interactions, between molecules. However, at the molecular level, the networks of interactions that can be observed from such datasets are interconnected and thus separate analysis of omic data can result in important information being lost. Our aim is to combine the skills from multiple labs in order to advance in the non-trivial task of multi-omic data integration. In particular, we aim to apply techniques developed in this project to gain a better understanding of rheumatoid arthritis. To achieve these aims we propose a collaboration between three institutes (from Denmark, Italy, UK and China), with complementary experience in collection and analysis of omic data. The collaboration will produce results that are attractive to all institutes and researchers dealing with high-throughput technology and will therefore promote the EU as an attractive research base. This exchange will also promote China as an attractive host for Marie Curie Outgoing Fellowships, potentially leading to a higher number of EU researchers undertaking longer research periods in China in the future. Therefore, this specific network is not only relevant in terms of the scientific impact and quality of the exchange but also in terms of its geographical width and breadth.'

Introduzione (Teaser)

To understand how cells work in health and disease, we must delineate the complex molecular events that take place. For this purpose, scientists are combining information at the genomic, transcriptomic and regulatory level.

Descrizione progetto (Article)

Recent technological advances enable the monitoring of various biological molecules in a high-throughput manner. The data from such experiments are collectively referred to as omics and provide an estimate of gene expression, protein levels or abundance of metabolites within cells.

Mining this information overload is currently performed using computational and algorithmic tools. However, simultaneous analysis would provide a better picture regarding the molecular network within a cell without losing essential information.

Seeking to advance multi-omic data integration, the EU-funded 'Knowledge exchange in processing and analysis of multi-omic data' (KEPAMOD) collaborative project will combine the expertise of three different institutes. The teams have used the aquaporin 2 (AQP2) channel as a model protein for proteomic analysis.

AQP2 is found in kidney collecting ducts and contributes to water homeostasis in mammals. It can also bind to integrin receptors and promote the migration of renal epithelial cells. Following expression and purification, AQP2 was analysed using mass spectrometry.

With respect to multi-omics analysis techniques, project members have concluded that the issue with existing methods is that they transform data in multiple manual steps. They decided to reduce the resulting errors by introducing one automated step for use in simulation models. In this context, they have developed a freely available application that enables the transfer of multi-omic data from commonly used network representation software. Testing of the application in simple and large networks has proved its validity.

Further work will include the integration of gene expression data with regulatory data such as those reflecting miRNA abundance. The long-term plan of the KEPAMOD project is to apply the multi-omic data integration techniques to gain a better understanding of rheumatoid arthritis.