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Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values

BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical ut...

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Autores principales: Koelmel, Jeremy P., Cochran, Jason A., Ulmer, Candice Z., Levy, Allison J., Patterson, Rainey E., Olsen, Berkley C., Yost, Richard A., Bowden, John A., Garrett, Timothy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489209/
https://www.ncbi.nlm.nih.gov/pubmed/31035918
http://dx.doi.org/10.1186/s12859-019-2803-8
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author Koelmel, Jeremy P.
Cochran, Jason A.
Ulmer, Candice Z.
Levy, Allison J.
Patterson, Rainey E.
Olsen, Berkley C.
Yost, Richard A.
Bowden, John A.
Garrett, Timothy J.
author_facet Koelmel, Jeremy P.
Cochran, Jason A.
Ulmer, Candice Z.
Levy, Allison J.
Patterson, Rainey E.
Olsen, Berkley C.
Yost, Richard A.
Bowden, John A.
Garrett, Timothy J.
author_sort Koelmel, Jeremy P.
collection PubMed
description BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. RESULTS: LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine. CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2803-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-64892092019-06-05 Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values Koelmel, Jeremy P. Cochran, Jason A. Ulmer, Candice Z. Levy, Allison J. Patterson, Rainey E. Olsen, Berkley C. Yost, Richard A. Bowden, John A. Garrett, Timothy J. BMC Bioinformatics Software BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. RESULTS: LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine. CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2803-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-29 /pmc/articles/PMC6489209/ /pubmed/31035918 http://dx.doi.org/10.1186/s12859-019-2803-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Koelmel, Jeremy P.
Cochran, Jason A.
Ulmer, Candice Z.
Levy, Allison J.
Patterson, Rainey E.
Olsen, Berkley C.
Yost, Richard A.
Bowden, John A.
Garrett, Timothy J.
Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title_full Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title_fullStr Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title_full_unstemmed Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title_short Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
title_sort software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489209/
https://www.ncbi.nlm.nih.gov/pubmed/31035918
http://dx.doi.org/10.1186/s12859-019-2803-8
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