Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration.

Bayesian statistics label-free quantification mass spectrometry proteomics quantification

Journal

Journal of proteome research
ISSN: 1535-3907
Titre abrégé: J Proteome Res
Pays: United States
ID NLM: 101128775

Informations de publication

Date de publication:
02 04 2021
Historique:
pubmed: 5 3 2021
medline: 22 6 2021
entrez: 4 3 2021
Statut: ppublish

Résumé

Error estimation for differential protein quantification by label-free shotgun proteomics is challenging due to the multitude of error sources, each contributing uncertainty to the final results. We have previously designed a Bayesian model, Triqler, to combine such error terms into one combined quantification error. Here we present an interface for Triqler that takes MaxQuant results as input, allowing quick reanalysis of already processed data. We demonstrate that Triqler outperforms the original processing for a large set of both engineered and clinical/biological relevant data sets. Triqler and its interface to MaxQuant are available as a Python module under an Apache 2.0 license from https://pypi.org/project/triqler/.

Identifiants

pubmed: 33661646
doi: 10.1021/acs.jproteome.0c00902
pmc: PMC8041382
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2062-2068

Références

Biomed Res Int. 2018 Mar 1;2018:5238760
pubmed: 29687002
Mol Cell Proteomics. 2019 Mar;18(3):561-570
pubmed: 30482846
J Proteome Res. 2019 Nov 1;18(11):4020-4026
pubmed: 31547658
PeerJ. 2020 Apr 23;8:e8779
pubmed: 32351780
Nat Commun. 2020 Jun 26;11(1):3234
pubmed: 32591519
J Proteome Res. 2018 Jan 5;17(1):590-599
pubmed: 29195270
Nucleic Acids Res. 2019 Jan 8;47(D1):D442-D450
pubmed: 30395289
J Proteome Res. 2015 May 1;14(5):1993-2001
pubmed: 25855118
Mol Cell Proteomics. 2014 Sep;13(9):2513-26
pubmed: 24942700
Mol Cell Proteomics. 2019 Oct;18(10):2108-2120
pubmed: 31311848
Mol Cell Proteomics. 2016 Apr;15(4):1467-78
pubmed: 26729709
J Proteome Res. 2019 Sep 6;18(9):3305-3316
pubmed: 31310545
PLoS Comput Biol. 2017 Nov 3;13(11):e1005752
pubmed: 29099853
Nat Methods. 2016 Nov 29;13(12):964-966
pubmed: 27898063
Nat Methods. 2016 Sep;13(9):731-40
pubmed: 27348712
Clin Proteomics. 2019 May 8;16:19
pubmed: 31080378
Nat Methods. 2020 Oct;17(10):981-984
pubmed: 32929271
J Proteome Res. 2016 Apr 1;15(4):1116-25
pubmed: 26906401

Auteurs

Matthew The (M)

Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany.

Lukas Käll (L)

Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology - KTH, Box 1031, 17121 Solna, Sweden.

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Classifications MeSH