Chemical Information Bulletin, Volume 62, Number 1, Spring 2010 Page: 41
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Chemical Information Bulletin Vol. 62(1) Spring 2010
CINF 26 Importance of benchmarking Green
Chemistry
G. Gurau, and R. D. Rogers, rdrogers@as.ua.edu.
Department of Chemistry, Center for Green
Manufacturing, The University of Alabama,
Tuscaloosa, AL, United States
Readily searchable and accessible data with which to
benchmark 'improvements' to chemical or chemical
technology are critical to the field of Green Chemistry.
Green Chemistry strives to develop chemicals and
chemical processes which are non toxic, not harmful to
the environment, and sustainable. It is however, often
difficult to determine whether improvements in one
aspect of a process (e.g., elimination of volatile
solvents), might actually be more harmful in another
(e.g., greater energy use). Green Chemistry is really
about careful decision making in order to balance
many, often competing goals. Here we will discuss how
extracting benchmarking information from the
literature can help in these decision making processes.
CINF 27 Software advancements for performing
compound identification QC on large metabolomic
datasets
C. DeHaven, lgosselin@metabolon.com. Department
of Metabolomics, Metabolon, Research Triangle Park,
NC, United States
Traditional collection, sorting and analysis of
metabolomic data has generally involved single data
files each corresponding to a single biological sample.
Data is then individually compared to spectral libraries
of known metabolites in order to identify compounds
contained in each biological sample. This labor-
intensive approach does not lend itself to
industrialization of the process; it also presents a
problem correlating the analysis of multiple biological
samples, including meta-data, with one another to
determine trends and population differences. The
ability to analyze and perform QC on large-scale,
multi-sample metabolomics data on an industrial scale
is an important step in the evolution of metabolomics
technology as a whole. This presentation will provide
an overview of software advancements which provide
options for quickly performing rapid quality-control of
metabolomic data.
CINF 28 Fold-change analysis and visualization of
multispectral datasets in NMR-based metabolomics
S. L. Robinette, slrobin@ufl.edu, and A. S. Edison.
Biochemistry and Molecular Biology, University of
Florida, Gainesville, FL, United StatesLarge dataset visualization is a critical component in
the analysis and interpretation of metabolomics data.
Nuclear magnetic resonance offers particular
challenges in terms of visualization as datasets are
generally composed of multiple highly complex spectra
with hundreds of overlapping signals arising from
many small molecules. Here, we explore methods of
fold-change based visualization using one- and two-
dimensional NMR spectra in order to identify and
interpret patterns of differential expression of
metabolites. We show that cluster analysis of peak fold-
change matrices identifies both structurally related
peaks and metabolic coregulation using statistical
relationships between signals in ID 1H NMR spectra
and that fold-change analysis of individually aligned
two-dimensional spectra provides complementary
information. We demonstrate the utility of this
methodology using tissue extract and media samples
relevant to metabolomics.
1. Robinette, S.L.; Veselkov, K.A.; Bohus, E.; Coen,
M.; Keun, H.C.; Ebbels, T.M.D.; Beckonert, O.;
Holmes, E.C.; Lindon, J.C.; Nicholson, J.K. Anal.
Chem. 2009, 81, 6581-6589.
2. Schroeder, F.C.; Gibson, D.M.; Churchill, A.C.;
Sojikul, P.; Wursthorn, E.J.; Krasnoff, S.B.; Clardy, J.
Angew. Chem. Int. Ed. 2007, 46, 901-904.
CINF 29 Data mining tool for automated metabolite
identification and quantification using J-resolved
NMR spectroscopy
S. He', C. Ludwig2, J. M. Easton3, H. Chen', S.
Tiziani2, A. Lodi2, S. Manzoor4, A. D. Southam4, T. N.
Arvanitis3, U. L. Guenther2, and M. R. Viant4,
m.viant@bham.ac.uk. 'School of Computer Science,
University of Birmingham, Birmingham, West
Midlands, United Kingdom, 2CR UK Institute for
Cancer Studies, Henry Wellcome Building for
Biomolecular NMR Spectroscopy (HWB-NMR),
University of Birmingham, Birmingham, West
Midlands, United Kingdom, 3School of Engineering,
University of Birmingham, Birmingham, West
Midlands, United Kingdom, 4School of Biosciences,
University of Birmingham, Birmingham, West
Midlands, United Kingdom
Although one-dimensional (1D) nuclear magnetic
resonance (NMR) spectroscopy remains one of the
leading analytical technologies in metabolomics, it
suffers from severe spectral overlap which limits its
ability to identify and quantify metabolites. 2D J-
resolved (JRES) NMR spectroscopy is rapidly gaining
in popularity, and benefits from a dispersion of peaks
into a second dimension, improving metabolite
specificity and potentially the accuracy of41
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American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 62, Number 1, Spring 2010, periodical, Spring 2010; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc31514/m1/41/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .