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DynaStI: A Dynamic Retention Time Database for Steroidomics
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 an...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6572260/ https://www.ncbi.nlm.nih.gov/pubmed/31052310 http://dx.doi.org/10.3390/metabo9050085 |
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author | Codesido, Santiago Randazzo, Giuseppe Marco Lehmann, Fabio González-Ruiz, Víctor García, Arnaud Xenarios, Ioannis Liechti, Robin Bridge, Alan Boccard, Julien Rudaz, Serge |
author_facet | Codesido, Santiago Randazzo, Giuseppe Marco Lehmann, Fabio González-Ruiz, Víctor García, Arnaud Xenarios, Ioannis Liechti, Robin Bridge, Alan Boccard, Julien Rudaz, Serge |
author_sort | Codesido, Santiago |
collection | PubMed |
description | 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 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. |
format | Online Article Text |
id | pubmed-6572260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65722602019-06-18 DynaStI: A Dynamic Retention Time Database for Steroidomics Codesido, Santiago Randazzo, Giuseppe Marco Lehmann, Fabio González-Ruiz, Víctor García, Arnaud Xenarios, Ioannis Liechti, Robin Bridge, Alan Boccard, Julien Rudaz, Serge Metabolites Article 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 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. MDPI 2019-04-30 /pmc/articles/PMC6572260/ /pubmed/31052310 http://dx.doi.org/10.3390/metabo9050085 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Codesido, Santiago Randazzo, Giuseppe Marco Lehmann, Fabio González-Ruiz, Víctor García, Arnaud Xenarios, Ioannis Liechti, Robin Bridge, Alan Boccard, Julien Rudaz, Serge DynaStI: A Dynamic Retention Time Database for Steroidomics |
title | DynaStI: A Dynamic Retention Time Database for Steroidomics |
title_full | DynaStI: A Dynamic Retention Time Database for Steroidomics |
title_fullStr | DynaStI: A Dynamic Retention Time Database for Steroidomics |
title_full_unstemmed | DynaStI: A Dynamic Retention Time Database for Steroidomics |
title_short | DynaStI: A Dynamic Retention Time Database for Steroidomics |
title_sort | dynasti: a dynamic retention time database for steroidomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6572260/ https://www.ncbi.nlm.nih.gov/pubmed/31052310 http://dx.doi.org/10.3390/metabo9050085 |
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