|Biomarker Identification Combining Multivariate Analysis of NMR Spectra with an Innovative Spectral Data Analysis Approach in an Integrated Working Environment |
Regis Grenier, Yann Bidault, Chen Peng, Gregory M. Banik, Scott Ramos, Brian Rohrback, Tao Wang and Bin Xia,
A recently developed Metabolomics informatics solution from raw data processing to metabolite database searching, compatible with multiple instruments, has been successfully applied to the analysis of 1H NMR human serum spectra of diabetic and non-diabetic subjects. This study also highlights Overlap Density Heatmap, a novel technology that complements the use of chemometrics in biomarker identification.
|Constructing Directed Metabolic Networks from Microarray Data|
J. M. Easton, T. N. Arvanitis, A. Peet and M. Viant
Although it has been several years since metabolic networks became a commonly used analysis technique in bioinformatics, the question of how best to construct them from experimental data is still not satisfactorily resolved. Here we present a method for the construction of directed metabolic networks from microarray datasets using an enhanced version of the KEGG LIGAND database.
|Application of 1H NMR Metabolomics to a Murine Model of Inflammatory Arthritis|
Reza Dowlatabadi, B. Joan Miller, Frank R. Jirik, Hans J. Vogel and Aalim M. Weljie
The novel quantitative technique of "Targeted Profiling" of 1H 1D NMR data is applied to a unique model of rheumatoid arthritis. A number of candidate biomarkers not previously identified using NMR techniques are identified related to inflammation and immune response. The pharmaceutical relevance of several of these metabolites is discussed.
|Herbal Metabolic Profiling of Raw and Steamed Panax notoginseng|
Eric C.Y. Chan, Swee-Lee Yap, Aik-Jiang Lau, Pay-Chin Leow, Ding-Fung Toh, and Hwee-Ling Koh
At present, metabolite profiling is of growing importance in herbal medicine fields such as breeding, formulation, quality control and clinical trials. Herbal metabolic profiling allows direct detection of down-stream derivatives of metabolites, arising from herbal formulation process, using metabolite profiling ultra performance liquid chromatography time-of-flight mass spectrometry.
|Metabolic Profiling of Human Blood Plasma by Combined Ultra Performance Liquid Chromatography / Mass Spectrometry|
Steve Bruce, Pär Jonsson, Henrik Antti, Olivier Cloarec, Stefan Marklund and Thomas Moritz
Metabolic profiling is concerned with the analysis of low molecular weight compounds present in complex samples such as plasma. Analytical techniques such as UPLC/MS are proving to be powerful tools for metabolic profiling on complex samples such as plasma. Here we describe a protocol of metabolic profiling analysis on plasma by UPLC/MS.
|Toward Diagnosis of Diabetes by NMR and Mulitvariate Analysis: Study of 1H NMR Spectra of Human Serum in an Integrated Working Environment|
Chen Peng, Omoshile Clement, Gregory Banik, Scott Ramos, Tao Wang, and Bin Xia
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.
|Fixed-Width Binning, Variable-Width Binning or No Binning: A Study of Different Binning Methods in NMR-based Metabolomics Analysis|
Gregory Banik, Chen Peng, Omoshile Clement, Ty Abshear, Scott Ramos, Brian Rohrback, Ian Lewis and John Markley
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.
|Effective Fishing of Characteristic Proteome Fractions and Identification of Biomarkers Therein: Application of VisualCockpit to Multidimensional Chromatogram and MS Data|
S. Kreusch, M. Nagel, R. Janetzko, G. A. Cumme, A. Winter, M. Pohl, A. Meier-Hellmann and H. Rhode
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.
|Simplifying the Flow of Drug Discovery Data|
Dr. Jonathan M.R. Davies
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.