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Non-targeted metabolomics ID workflows providing custom annotation confidence reporting: CCS-enabled annotation workflows
EP38444
Poster Title: Non-targeted metabolomics ID workflows providing custom annotation confidence reporting: CCS-enabled annotation workflows
Submitted on 09 Mar 2022
Author(s): Aiko Barsch1; Matthias Szesny1; Ulrike Schweiger-Hufnagel1; Sofie Weinkouff1, Nikolas Kessler1
Affiliations: 1Bruker Daltonics GmbH & Co. KG Bremen, Germany
Poster Views: 132
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Poster Information
Abstract: Lack of exhaustive repositories providing retention time, MS/MS spectra and CCS values hamper the annotation and identification (ID) of target compounds in Metabolomics research. Researchers require a solution that automatically and transparently annotates features. Here, we present a solution for automatic annotation of targets with up to 5 confidence criteria. Additionally, tentative annotation of knowns and predicted known compounds is supported by automatic in-silico fragmentation and CCS prediction based on the novel CCS-Predict Pro model. Paired with customizable annotation quality scoring and visualization the
presented annotation workflows enable researchers to assess and report ID level confidence suitable for the study, as recommended by Schymanski et al. [1].
Summary: Here, we present a solution for automatic annotation of targets with up to 5 confidence criteria.References: [1] https://doi.org/10.1021/es5002105
[2] https://doi.org/10.1039/C8SC04396E
[3] https://hmdb.ca/
[4] https://doi.org/10.1186/1471-2105-11-148
[5] https://doi.org/10.1186/s13321-016-0115-9
[6] https://doi.org/10.1186/s13321-018-0324-5
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