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Deeper and higher confident annotation of complex metabolomics data by complementary large scale spectral libraries
EP38439
Poster Title: Deeper and higher confident annotation of complex metabolomics data by complementary large scale spectral libraries
Submitted on 09 Mar 2022
Author(s): Florian Zubeil, Nikolas Kessler, Matthias Szesny, Aiko Barsch, Ulrike Schweiger Hufnagel
Affiliations: Bruker Daltonics GmbH & Co. KG Bremen, Germany
Poster Views: 110
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Poster Information
Abstract: • Even when the same analyte is contained in complementary libraries, their MS/MS spectra might still differ due to different experimental settings, e g collision energies
• Libraries do not provide the same type of meta information (e g CAS numbers) which makes comparison of libraries difficult
Summary: How orthogonal is the content of commonly
used large spectral libraries?
References: [1] Ralaivola L. et al. (2005) Neural Networks 18(8): 1093 1110
[2] RDKit: Open source cheminformatics; http://www.rdkit.org
[3] Shannon P. et al. (2003) Genome Res. 13(11): 2498 504
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