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Just how many unknowns are there in a metabolomics data set?  Really?
Just how many unknowns are there in a metabolomics data set? Really?
Submitted on 13 Sep 2017

Chris Beecher1, Alexander Raskind1, Casey Chamberlain2, Joy Guingab2, Rick Yost2, Felice de Jong1, Tim Garrett2
[1] IROA Technologies LLC, Bolton, MA, USA , [2] University of Florida, Gainsville, FL, USA
This poster was presented at ASMS 2017
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Poster Abstract
The question of unknowns has become probably the single most common question in metabolomics.  It is generally suggested that there are hundreds or even thousands of unknowns in most datasets and that the number of unknowns is larger than the number of known (named) compounds. We have constructed a very specific dataset using IROA materials, and performed an in-depth LC-MS analysis of the peaks in these samples. Because of the IROA1,2 patterning we can easily separate peaks of biological origin from artifacts, and using a specially written program we are annotating all of the biological peaks. The majority of unknown peaks of biological origins are fragment ions, adduct ions, or in-source polymeric ions. There appear to be few true unknowns.

de Jong F, Beecher C, “Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis (IROA®), an isotopic-labeling technique for accurate biochemical profiling”, Bioanalysis 2012, 4(18), 2303-14.

Stupp GS, Clendinen CS, Ajredini R, Szewc MA, Garrett T, Menger RF, Yost RA, Beecher C, Edison AS. “Isotopic Ratio Outlier Analysis Global Metabolomics of Caenorhabditis elegans.” Analytical Chemistry 2013 85(24), 11858-11865. doi: 10.1021/ac4025413.

Mass Spectrometry Metabolite Library of Standards.
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