ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0 – an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

Francesco Del Carratore, William Eagles, Juraj Borka, Rainer Breitling

Research output: Contribution to journalArticlepeer-review

Abstract

The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LCMS-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.
Original languageEnglish
JournalBioinformatics (Oxford, England)
Publication statusAccepted/In press - 20 Jul 2023

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