Autor según el artículo: Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimerà R, Sales-Pardo M

Departamento: Enginyeria Electrònica, Elèctrica i Automàtica Enginyeria Química

Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta / Yanes Torrado, Óscar

Palabras clave: Spectra extraction R package Prediction Identification

Resumen: The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, for example, how many metabolites are there in a given sample.Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass; and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.Supplementary data, figures and text are available at Bioinformatics online.© The Author(s) 2019. Published by Oxford University Press.

Áreas temáticas: Statistics and probability Statistics & probability Odontología Nutrição Molecular biology Medicina veterinaria Medicina ii Medicina i Mathematics, interdisciplinary applications Mathematical & computational biology Matemática / probabilidade e estatística Interdisciplinar General medicine Engenharias iv Economia Computer science, interdisciplinary applications Computer science applications Computational theory and mathematics Computational mathematics Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência da computação Biotecnología Biotechnology & applied microbiology Biology, miscellaneous Biodiversidade Biochemistry Biochemical research methods Astronomia / física

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 13674803

Direcció de correo del autor: roger.guimera@urv.cat oscar.yanes@urv.cat marta.sales@urv.cat

Identificador del autor: 0000-0002-3597-4310 0000-0003-3695-7157 0000-0002-8140-6525

Fecha de alta del registro: 2023-02-18

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://academic.oup.com/bioinformatics/article/35/20/4089/5418951

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Bioinformatics. 35 (20): 4089-4097

Referencia de l'ítem segons les normes APA: Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimerà R, Sales-Pardo M (2019). CliqueMS: A computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics, 35(20), 4089-4097. DOI: 10.1093/bioinformatics/btz207

DOI del artículo: 10.1093/bioinformatics/btz207

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2019

Tipo de publicación: Journal Publications