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
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
2062-2068Références
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