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Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving

[Image: see text] Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity...

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Autores principales: Mardal, Marie, Dalsgaard, Petur W., Rasmussen, Brian S., Linnet, Kristian, Mollerup, Christian B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018448/
https://www.ncbi.nlm.nih.gov/pubmed/36802528
http://dx.doi.org/10.1021/acs.analchem.2c03769
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author Mardal, Marie
Dalsgaard, Petur W.
Rasmussen, Brian S.
Linnet, Kristian
Mollerup, Christian B.
author_facet Mardal, Marie
Dalsgaard, Petur W.
Rasmussen, Brian S.
Linnet, Kristian
Mollerup, Christian B.
author_sort Mardal, Marie
collection PubMed
description [Image: see text] Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.
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spelling pubmed-100184482023-03-17 Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving Mardal, Marie Dalsgaard, Petur W. Rasmussen, Brian S. Linnet, Kristian Mollerup, Christian B. Anal Chem [Image: see text] Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data. American Chemical Society 2023-02-20 /pmc/articles/PMC10018448/ /pubmed/36802528 http://dx.doi.org/10.1021/acs.analchem.2c03769 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Mardal, Marie
Dalsgaard, Petur W.
Rasmussen, Brian S.
Linnet, Kristian
Mollerup, Christian B.
Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title_full Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title_fullStr Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title_full_unstemmed Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title_short Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving
title_sort scalable analysis of untargeted lc-hrms data by means of sql database archiving
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018448/
https://www.ncbi.nlm.nih.gov/pubmed/36802528
http://dx.doi.org/10.1021/acs.analchem.2c03769
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