Autor segons l'article: Rainer J; Vicini A; Salzer L; Stanstrup J; Badia JM; Neumann S; Stravs MA; Hernandes VV; Gatto L; Gibb S; Witting M
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/s de la URV: Badia Aparicio, José María
Paraules clau: Untargeted analysis Tandem mass-spectra Small-compound databases Reproducible research R programming Metabolomics Annotation untargeted analysis tool small-compound databases search reproducible research r programming fragmentation annotation
Resum: Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
Àrees temàtiques: Molecular biology Medicina ii Farmacia Endocrinology, diabetes and metabolism Ciências biológicas ii Ciências biológicas i Biotecnología Biochemistry & molecular biology Biochemistry
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: josepmaria.badia@urv.cat josepmaria.badia@urv.cat josepmaria.badia@urv.cat
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2218-1989/12/2/173
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Metabolites. 12 (2):
Referència de l'ítem segons les normes APA: Rainer J; Vicini A; Salzer L; Stanstrup J; Badia JM; Neumann S; Stravs MA; Hernandes VV; Gatto L; Gibb S; Witting M (2022). A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites, 12(2), -. DOI: 10.3390/metabo12020173
DOI de l'article: 10.3390/metabo12020173
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications