MargheRita: an R package for LC-MS/MS SWATH metabolomics data analysis and confident metabolite identification based on a spectral library of reference standards
Mosca, E.; Ulaszewska, M.; Alavikakhki, Z.; Bellini, E. N.; Mannella, V.; Frigerio, G.; Drago, D.; Andolfo, A.
Show abstract
Short Structured AbstractUntargeted metabolomics by mass spectrometry technologies generates huge numbers of metabolite signals, requiring computational analyses for post-acquisition processing and databases for metabolite identification. Web-based data processing solutions frequently include only a part of the entire workflow thus requiring the use of different platforms. The R package "margheRita" enhances fragment matching accuracy and addresses the complete workflow for metabolomic profiling in untargeted studies based on liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS), especially in the case of data-independent acquisition, where all MS/MS spectra are acquired with high quantitative accuracy. Availability and Implementationsource code and documentation are available at https://github.com/emosca-cnr/margheRita. Contactettore.mosca@itb.cnr.it, andolfo.annapaola@hsr.it
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Rapid Development of Improved Data-dependent Acquisition Strategies. 96%
- Met-ID: An Open-Source Software for Comprehensive Annotation of Multiple On-Tissue Chemical Modifications in MALDI-MSI 96%
- MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics 96%
Similar papers in this journal
- Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics 95%
- Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides 94%
- Moiety Modeling Framework for Deriving Moiety Abundances from Mass Spectrometry Measured Isotopologues 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.