ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 07 2023
01 07 2023
Historique:
received:
13
12
2022
revised:
14
06
2023
accepted:
24
07
2023
medline:
31
7
2023
pubmed:
25
7
2023
entrez:
25
7
2023
Statut:
ppublish
Résumé
The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy. The source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2.
Identifiants
pubmed: 37490466
pii: 7230779
doi: 10.1093/bioinformatics/btad455
pmc: PMC10382385
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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