Autor segons l'article: Baquer G; Sementé L; Ràfols P; Martín-Saiz L; Bookmeyer C; Fernández JA; Correig X; García-Altares M
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/s de la URV: Correig Blanchar, Francesc Xavier / Ràfols Soler, Pere
Paraules clau: Mass spectrometry imaging Maldi Lipids In-source fragmentation In-source decay Computation Cheminformatics Bioinformatics Annotation
Resum: Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.© 2023. Springer Nature Switzerland AG.
Àrees temàtiques: Química Physical and theoretical chemistry Library and information sciences Computer science, interdisciplinary applications Computer science, information systems Computer science applications Computer graphics and computer-aided design Ciencias sociales Chemistry, multidisciplinary Biotecnología
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: pere.rafols@urv.cat xavier.correig@urv.cat
Identificador de l'autor: 0000-0002-9240-4058 0000-0002-6902-3054
Data d'alta del registre: 2024-08-03
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00756-2
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Journal Of Cheminformatics. 15 (1): 80-80
Referència de l'ítem segons les normes APA: Baquer G; Sementé L; Ràfols P; Martín-Saiz L; Bookmeyer C; Fernández JA; Correig X; García-Altares M (2023). rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation. Journal Of Cheminformatics, 15(1), 80-80. DOI: 10.1186/s13321-023-00756-2
DOI de l'article: 10.1186/s13321-023-00756-2
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2023
Tipus de publicació: Journal Publications