Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1785022488305991680 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10088044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT truongpatrick triqlerforproteinsummarizationofdatafromdataindependentacquisitionmassspectrometry AT thematthew triqlerforproteinsummarizationofdatafromdataindependentacquisitionmassspectrometry AT kalllukas triqlerforproteinsummarizationofdatafromdataindependentacquisitionmassspectrometry |