Abstract
BackgroundIn metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity.FindingsIn this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise.ConclusionsWe propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.
Highlights
In metabolomics the bioinformatics-driven search for highly-discriminatory biomarker candidates has become a key task in the biomarker discovery process with the objective of introducing novel biomarkers aiding in diagnosis or therapeutic management [1,2,3,4].A wide spectrum of feature selection methods including filter, wrapper or embedded algorithms is available for the identification of significant features in biomedical datasets [5,6,7,8,9]
Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/
The quantitative analysis of networks has increasingly become an important technique for the biological interpretation of changes in disease-associated metabolic pathways, allowing the study of interconnectivity, interaction or correlation among analytes. For this type of analysis, different types of topological graph descriptors can be used to analyze such complex biological networks [10,11]. In this short report we propose a new computational strategy that identifies metabolic biomarker candidates according to their discriminatory ability from dependent samples, and we review and interpret them using a network-based approach
Summary
We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/
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