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Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum
INTRODUCTION: Technological advancements enabled the analyses of limited sample volumes on (1)H NMR. Manual spectral profiling of the data is, however, complex, and timely. OBJECTIVE: To evaluate the performance of BAYESIL for automated identification and quantification of (1)H NMR spectra of limite...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063514/ https://www.ncbi.nlm.nih.gov/pubmed/36995481 http://dx.doi.org/10.1007/s11306-023-02001-1 |
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author | van Zyl, Christiaan De Wet van Reenen, Mari Osthoff, Gernot du Preez, Ilse |
author_facet | van Zyl, Christiaan De Wet van Reenen, Mari Osthoff, Gernot du Preez, Ilse |
author_sort | van Zyl, Christiaan De Wet |
collection | PubMed |
description | INTRODUCTION: Technological advancements enabled the analyses of limited sample volumes on (1)H NMR. Manual spectral profiling of the data is, however, complex, and timely. OBJECTIVE: To evaluate the performance of BAYESIL for automated identification and quantification of (1)H NMR spectra of limited volume samples. METHOD: Aliquots of a pooled African elephant serum sample were analyzed using standard and reduced volumes. Performance was evaluated on confidence scores, non-detects and laboratory CV. RESULTS: Of the 47 compounds detected, 28 had favorable performances. The approach could differentiate samples based on biological variation. CONCLUSIONS: BAYESIL is valuable for limited sample (1)H NMR data analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-023-02001-1. |
format | Online Article Text |
id | pubmed-10063514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100635142023-04-01 Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum van Zyl, Christiaan De Wet van Reenen, Mari Osthoff, Gernot du Preez, Ilse Metabolomics Original Article INTRODUCTION: Technological advancements enabled the analyses of limited sample volumes on (1)H NMR. Manual spectral profiling of the data is, however, complex, and timely. OBJECTIVE: To evaluate the performance of BAYESIL for automated identification and quantification of (1)H NMR spectra of limited volume samples. METHOD: Aliquots of a pooled African elephant serum sample were analyzed using standard and reduced volumes. Performance was evaluated on confidence scores, non-detects and laboratory CV. RESULTS: Of the 47 compounds detected, 28 had favorable performances. The approach could differentiate samples based on biological variation. CONCLUSIONS: BAYESIL is valuable for limited sample (1)H NMR data analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-023-02001-1. Springer US 2023-03-30 2023 /pmc/articles/PMC10063514/ /pubmed/36995481 http://dx.doi.org/10.1007/s11306-023-02001-1 Text en © The Author(s) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Original Article van Zyl, Christiaan De Wet van Reenen, Mari Osthoff, Gernot du Preez, Ilse Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title | Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title_full | Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title_fullStr | Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title_full_unstemmed | Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title_short | Evaluation of BAYESIL for automated annotation of (1)H NMR data using limited sample volumes: application to African elephant serum |
title_sort | evaluation of bayesil for automated annotation of (1)h nmr data using limited sample volumes: application to african elephant serum |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063514/ https://www.ncbi.nlm.nih.gov/pubmed/36995481 http://dx.doi.org/10.1007/s11306-023-02001-1 |
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