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Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry

[Image: see text] A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrom...

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Autores principales: Truong, Patrick, The, Matthew, Käll, Lukas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088044/
https://www.ncbi.nlm.nih.gov/pubmed/36988210
http://dx.doi.org/10.1021/acs.jproteome.2c00607
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author Truong, Patrick
The, Matthew
Käll, Lukas
author_facet Truong, Patrick
The, Matthew
Käll, Lukas
author_sort Truong, Patrick
collection PubMed
description [Image: see text] A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.
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spelling pubmed-100880442023-04-12 Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry Truong, Patrick The, Matthew Käll, Lukas J Proteome Res [Image: see text] A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data. American Chemical Society 2023-03-29 /pmc/articles/PMC10088044/ /pubmed/36988210 http://dx.doi.org/10.1021/acs.jproteome.2c00607 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 Truong, Patrick
The, Matthew
Käll, Lukas
Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title_full Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title_fullStr Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title_full_unstemmed Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title_short Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry
title_sort triqler for protein summarization of data from data-independent acquisition mass spectrometry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088044/
https://www.ncbi.nlm.nih.gov/pubmed/36988210
http://dx.doi.org/10.1021/acs.jproteome.2c00607
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