DynaStI: A Dynamic Retention Time Database for Steroidomics.

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State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_025EBCF708FD
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
DynaStI: A Dynamic Retention Time Database for Steroidomics.
Journal
Metabolites
Author(s)
Codesido S., Randazzo G.M., Lehmann F., González-Ruiz V., García A., Xenarios I., Liechti R., Bridge A., Boccard J., Rudaz S.
ISSN
2218-1989 (Print)
ISSN-L
2218-1989
Publication state
Published
Issued date
30/04/2019
Peer-reviewed
Oui
Volume
9
Number
5
Pages
85
Language
english
Abstract
: Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC-MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC-MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI (https://dynasti.vital-it.ch) is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases.
Keywords
database, metabolomics, prediction, steroidomics
Pubmed
Web of science
Open Access
Yes
Create date
16/07/2019 7:49
Last modification date
16/09/2019 5:26
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