|Title||Insights in working with complex datasets: Benefits and limitations using the bioinformatics tool MetaCore|
|Author(s)||Schothorst, E.M. van; Ost, Mario|
|Event||Nugoweek, Copenhagen, 2016-09-05/2016-09-08|
Human and Animal Physiology
|Publication type||Abstract in scientific journal or proceedings|
|Keyword(s)||MetaCore - complex datasets|
|Abstract||Advances in data acquisition, such as whole genome microarray analysis, have changed how we address and investigate basic and applied scientific questions. Today, we are able to generate complex datasets to tackle the challenges of the 21st century, including a growing and ageing population, metabolic diseases such as type 2 diabetes and obesity as well as human nutrition underlying health and disease. The ability to analyse datasets effectively is of critical importance
for a deep understanding of modern biology and physiology. This ultimately requires skills in he field of quantitative research in order to uncover and understand the underlying molecular mechanism of complex biological processes. This tutorial will focus on introducing and discussing the bioinformatics tool MetaCore and its ability to underpin modern bioscience and nutrigenomics in the context of physiological and pathophysiological interpretations. We aim to demonstrate key analytical tools for microarray analysis using MetaCore in order to extract valuable knowledge from complex transcriptomic datasets generated in your research projects. The session will be designed for participants to acquire essential skills for the pathway analysis and interpretation of differential gene expression data of human and animal studies. This will be combined with the presentation and discussion of successful published studies regarding a metabolic remodelling and endocrine regulatory function of skeletal muscle. Finally, participants will be made familiar with benefits and limitations of using MetaCore to accelerate their scientific research as well as future perspectives to gain extensive understanding of biological systems.