With the main aims of improving statistical power and being able to carry out metabolomics-based epidemiological studies, it is seen today as a key objective in analyzing data sets composed of thousands of samples. Nevertheless, this will require reproducible analyses over long time scales, as well as sophisticated and efficient strategies for processing the acquired data to be able to retrieve relevant metabolite information. So far, promising Inhibitors,research,lifescience,medical results have been presented using NMR in metabolome-wide associations (MWAS)[7,8] and mass spectrometry in large-scale, non-targeted studies using quality control samples as a means for

generating reference tables of putative metabolites, as well as correcting for analytical drifts in the data [9,10]. Another interesting approach that has attracted great http://www.selleckchem.com/products/Oligomycin-A.html interest this website recently in mass spectrometry-based metabolomics is the use Inhibitors,research,lifescience,medical of array-based detection and quantification of pre-defined sets of metabolites. This has been shown to work well in large-scale

association studies and is definitely providing a useful complement to non-targeted approaches [11,12,13]. However, despite being of high importance for the progress of the metabolomics field, the Inhibitors,research,lifescience,medical main objective of these studies has not been the data processing part with the aim of generating a pipeline for retrieving high quality data. Mining of sample banks is becoming increasingly important in trying to understand the complex biological interactions behind, or finding diagnostic or Inhibitors,research,lifescience,medical prognostic biomarkers for, various disease states. Usually, these sample banks contain a large number of human samples collected continuously Inhibitors,research,lifescience,medical over a long period of time, often extremely well-characterized in terms of property data (metadata). Samples of this type are very attractive for research purposes. However, a problem is that regulations regarding availability, for obvious and valid reasons, are

very strict, and also, sample volumes might be limited for specific applications. For this reason, it will be of high relevance to be able to select a representative subset of samples for analysis and method development. A way to address this would be to select samples based on the metadata characterization to make sure to create a sample set Dacomitinib consisting of samples relevant for the question. The selected sample set could then be characterized using an appropriate analytical technique, and the acquired data processed to obtain a reliable quantification and identification of detected metabolites, i.e., a reference table of putative metabolites in the analyzed samples. In that way, multiple sample comparisons and biomarker or biomarker pattern extraction can be efficiently carried out by means of multivariate data analysis.