Nathaniel Mahieu (Presenter)
Washington University
Authorship: Nathaniel G. Mahieu, Gary J. Patti
Washington University in St. Louis
| Short Abstract Features in a metabolomic dataset are highly redundant. Annotation of these relationships and redundancies is key to data reduction, lower statistical significance thresholds, and a better understanding of metabolomic results. This poster presents an overview of: the types of relationships to be annotated in these datasets; poor assumptions of current annotation approaches and their corresponding failures; the computational challenge of this search problem; and a tool to explore these relationships. Of interest are how background ions contribute detected features, how peaks which have poor EIC correlation can still be related, and additional relationships which should be considered within these datasets. |
Long Abstract
Features in a metabolomic dataset are highly redundant. Annotation of these relationships and redundancies is key to data reduction, lower statistical significance thresholds, and a better understanding of metabolomic results.
This poster presents an overview of: the types of relationships to be annotated in these datasets; poor assumptions of current annotation approaches and their corresponding failures; the computational challenge of this search problem; and a tool to explore these relationships.
Of interest are how background ions contribute detected features, how peaks which have poor EIC correlation can still be related, and additional relationships which should be considered within these datasets.
References & Acknowledgements:
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IP Royalty: no
| Planning to mention or discuss specific products or technology of the company(ies) listed above: | no |