Difference between revisions of "Can we find disease fingerprints in our metabolome?"
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+ | '''Background''': The regulation of all processes taking place in a human organism is known as metabolism. Presence and concentration of metabolites, small molecules that take part in metabolism, is influenced by genetics and the environment. Many complex diseases also have a genetic and environmental background and influence on the concentration of metabolites, which may, therefore, be used as a biomarker for disease detection. Acquiring nuclear magnetic resonance (NMR) metabolomics data is relatively easy and has a low cost compared to other techniques used to characterize the metabolome. Yet, analysis of such NMR-based metabolomics data requires a sophisticated computational pipeline, for extracting information relevant to disease, like biomarkers. | ||
+ | '''Goal''': The aim of this project is to write R code to analyse NMR data from complex biological samples. We aim to extract information from urinary NMR data regarding a disease of interest and if possible identify metabolites as biomarkers. We will use data obtained from CoLaus, a longitudinal study investigating the prevalence of cardiovascular disease risk factor in the population of Lausanne. | ||
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+ | '''Tools''': Using biclustering methods students will be asked to analyze urinary NMR metabolomics data, aiming to identify a group of individuals with a potentially common disease background. Moreover, students will use linear regression analysis to identify metabolites related to a disease of interest. | ||
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+ | '''Supervisor''': [[User:Mirjam | Mirjam Mattei]] | ||
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+ | '''Project description''': | ||
+ | [[File: MetabolomeMirjam.pdf]] |
Latest revision as of 11:16, 6 June 2018
Background: The regulation of all processes taking place in a human organism is known as metabolism. Presence and concentration of metabolites, small molecules that take part in metabolism, is influenced by genetics and the environment. Many complex diseases also have a genetic and environmental background and influence on the concentration of metabolites, which may, therefore, be used as a biomarker for disease detection. Acquiring nuclear magnetic resonance (NMR) metabolomics data is relatively easy and has a low cost compared to other techniques used to characterize the metabolome. Yet, analysis of such NMR-based metabolomics data requires a sophisticated computational pipeline, for extracting information relevant to disease, like biomarkers.
Goal: The aim of this project is to write R code to analyse NMR data from complex biological samples. We aim to extract information from urinary NMR data regarding a disease of interest and if possible identify metabolites as biomarkers. We will use data obtained from CoLaus, a longitudinal study investigating the prevalence of cardiovascular disease risk factor in the population of Lausanne.
Tools: Using biclustering methods students will be asked to analyze urinary NMR metabolomics data, aiming to identify a group of individuals with a potentially common disease background. Moreover, students will use linear regression analysis to identify metabolites related to a disease of interest.
Supervisor: Mirjam Mattei
Project description: File:MetabolomeMirjam.pdf