Posters
« Back
Just how many unknowns are there in a metabolomics data set?  Really?
EP26429
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
Poster Views: 377
View poster »
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.
Report abuse »
Questions
Ask the author a question about this poster.
Ask a Question »

Creative Commons

Related Posters


Comprehensive, Non-Target Characterization of Environmental Exposome Samples Using GCxGC and High Resolution Time-of-Flight Mass Spectrometry
Todd Richards, Joe Binkley, and Lorne Fell

Aroma Profile of Coffee with GC, GCxGC, and TOFMS
Elizabeth Humston-Fulmer and Joseph E. Binkley

Robust Characterization of Inhalation Information Using a Deep Neural Network and a Smartphone
Kian Min Lim, Szer Ming Lee, David Harris, Phil Seeney

BIONET 2nd Generation Premium Fragment Library
Andrew Lowerson1, Patrick McCarren2, Steven Laplante3, and Michael Serrano-Wu2

New BIONET Compounds for CNS Diseases
Steve Brough1, Andrew Lowerson1 and Nikolay T. Tzvetkov2