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.
KEPAMOD Knowledge exchange in processing and analysis of multi-omic data (FP7-PEOPLE-2011-IRSES-294935)
2012
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
