Latest Posters

In the multivariate analysis of NMR-based metabolomics data, small variations in the resonance position of the individual peaks caused by experimental and instrument induced variations can adversely impact the PCA results. Techniques to address the NMR peak misalignment issue include the commonly used binning or bucketing, and the more advanced algorithms that aim at shifting the individual peaks to reach a better alignment across the spectra.

In order to improve the efficiency of NMR-based metabolomics studies, we have developed KnowItAll® Informatics System, Metabolomics Edition as a consistent and integrated software environment that covers the entire process from processing raw NMR data —to multivariate analysis—to biomarker identification. Using two datasets of 1H NMR spectra of mouse urine and human serum, we demonstrate the high efficiency of this integrated workflow for metabolomics data analysis.

Characteristic 2D fractions are rapidly identified among thousands using interactive visualization, filtering and data mining with VisualCockpit. Biomarker candidates are found therein after identifying proteins from sequence tags. VisualCockpit correlates their concentrations, reflected by normalized MS peptide peak height sum, enzyme activity and immunoreactivity, to donor conditions.

Regardless of research disciplines, scientists need to easily reach the information pertinent to their research. Ideally this data access is easy. Researchers also need the ability to ‘move the data around’ to gain a better view or different perspective. This data manipulation needs to be straightforward. Incorporating the varying views and information required by different scientific disciplines is a considerable challenge.

20 tuberculosis (12 slow-responders and 8 fast responders) patients were treated with directly observed short course anti-tuberculosis chemotherapy. Chest X-ray was performed. sICAM-1 and suPAR were measured in serum by ELISA, TNFRs using the luminex technology. General discrimination analysis on selected analytes gave, 91.66% and 87,50% correctly classify fast responders and slow responder respectively. The support vector machine analysis gave 100% correct classification.

An investigation on the plasma and urine samples of healthy male volunteers designed to evaluate the variability in metabonomic data, when severe life-style and dietary restrictions are imposed.

Here, we describe two investigations on healthy subjects designed to evaluate the variability in metabonomic data, in view of dietary influences and cultural trends.

A microarray test for in vitro diagnostic use that contains 27 SNPs from 8 different genes with a proven role in the metabolism of major drugs and that may be processed using regular laboratory instrumentation and a range of different scanners.

This poster provides an overview of the data standards work initiated between several parties, our current work, and the plans towards showcasing an example of a set of proteomics data standards at work in a large scale clinical proteomics setting, in a biomarker discovery context.

Results suggest that random priming is the optimal method for expression analysis of long genes. This method of RT may be appropriate for alternatively spliced genes and for genes without unique probes in the 3’-region.