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DynaStI: A Dynamic Retention Time Database for Steroidomics

1
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland
2
Dalle Molle Institute for Artificial Intelligence, 6928 Manno, Switzerland
3
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
4
Swiss Center for Applied Human Toxicology, 4055 Basel, Switzerland
5
Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Metabolites 2019, 9(5), 85; https://doi.org/10.3390/metabo9050085
Received: 25 February 2019 / Revised: 12 April 2019 / Accepted: 24 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Compound Identification of Small Molecules)
PDF [1090 KB, uploaded 30 April 2019]

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: metabolomics; steroidomics; database; prediction metabolomics; steroidomics; database; prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Codesido, S.; Randazzo, G.M.; Lehmann, F.; González-Ruiz, V.; García, A.; Xenarios, I.; Liechti, R.; Bridge, A.; Boccard, J.; Rudaz, S. DynaStI: A Dynamic Retention Time Database for Steroidomics. Metabolites 2019, 9, 85.

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