Author, as appears in the article.: Senan, Oriol; Aguilar-Mogas, Antoni; Navarro, Miriam; Capellades, Jordi; Noon, Luke; Burks, Deborah; Yanes, Oscar; Guimera, Roger; Sales-Pardo, Marta
Department: Enginyeria Electrònica, Elèctrica i Automàtica Enginyeria Química
URV's Author/s: Guimera Manrique, Roger / Sales Pardo, Marta / Yanes Torrado, Óscar
Keywords: Tandem mass spectrometry Spectra extraction Software R package Prediction Neural networks, computer Metabolomics Ions Identification Chromatography, liquid
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 13674803
Author's mail: roger.guimera@urv.cat oscar.yanes@urv.cat marta.sales@urv.cat
Author identifier: 0000-0002-3597-4310 0000-0003-3695-7157 0000-0002-8140-6525
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://academic.oup.com/bioinformatics/article/35/20/4089/5418951
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Bioinformatics. 35 (20): 4089-4097
APA: Senan, Oriol; Aguilar-Mogas, Antoni; Navarro, Miriam; Capellades, Jordi; Noon, Luke; Burks, Deborah; Yanes, Oscar; Guimera, Roger; Sales-Pardo, Marta (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
Article's DOI: 10.1093/bioinformatics/btz207
Entity: Universitat Rovira i Virgili
Journal publication year: 2019
Publication Type: Journal Publications
Repositori URV