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Significance estimation for large scale metabolomics annotations by spectral matching

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We p...

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Autores principales: Scheubert, Kerstin, Hufsky, Franziska, Petras, Daniel, Wang, Mingxun, Nothias, Louis-Félix, Dührkop, Kai, Bandeira, Nuno, Dorrestein, Pieter C., Böcker, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5684233/
https://www.ncbi.nlm.nih.gov/pubmed/29133785
http://dx.doi.org/10.1038/s41467-017-01318-5
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author Scheubert, Kerstin
Hufsky, Franziska
Petras, Daniel
Wang, Mingxun
Nothias, Louis-Félix
Dührkop, Kai
Bandeira, Nuno
Dorrestein, Pieter C.
Böcker, Sebastian
author_facet Scheubert, Kerstin
Hufsky, Franziska
Petras, Daniel
Wang, Mingxun
Nothias, Louis-Félix
Dührkop, Kai
Bandeira, Nuno
Dorrestein, Pieter C.
Böcker, Sebastian
author_sort Scheubert, Kerstin
collection PubMed
description The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
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spelling pubmed-56842332017-11-17 Significance estimation for large scale metabolomics annotations by spectral matching Scheubert, Kerstin Hufsky, Franziska Petras, Daniel Wang, Mingxun Nothias, Louis-Félix Dührkop, Kai Bandeira, Nuno Dorrestein, Pieter C. Böcker, Sebastian Nat Commun Article The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science. Nature Publishing Group UK 2017-11-14 /pmc/articles/PMC5684233/ /pubmed/29133785 http://dx.doi.org/10.1038/s41467-017-01318-5 Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Scheubert, Kerstin
Hufsky, Franziska
Petras, Daniel
Wang, Mingxun
Nothias, Louis-Félix
Dührkop, Kai
Bandeira, Nuno
Dorrestein, Pieter C.
Böcker, Sebastian
Significance estimation for large scale metabolomics annotations by spectral matching
title Significance estimation for large scale metabolomics annotations by spectral matching
title_full Significance estimation for large scale metabolomics annotations by spectral matching
title_fullStr Significance estimation for large scale metabolomics annotations by spectral matching
title_full_unstemmed Significance estimation for large scale metabolomics annotations by spectral matching
title_short Significance estimation for large scale metabolomics annotations by spectral matching
title_sort significance estimation for large scale metabolomics annotations by spectral matching
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5684233/
https://www.ncbi.nlm.nih.gov/pubmed/29133785
http://dx.doi.org/10.1038/s41467-017-01318-5
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