Autor según el artículo: Picart-Armada S; Fernández-Albert F; Vinaixa M; Yanes O; Perera-Lluna A

Departamento: Enginyeria Electrònica, Elèctrica i Automàtica

Autor/es de la URV: Vinaixa Crevillent, Maria / Yanes Torrado, Óscar

Palabras clave: Reveals alterations Pathways Network analysis Metabolomics Liver Knowledge representation Fumarate-hydratase Data mining Cells Cancer network analysis metabolomics knowledge representation data mining

Resumen: © 2018 The Author(s). Background: Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects. Results: We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature. Conclusions: The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

Áreas temáticas: Structural biology Saúde coletiva Química Molecular biology Medicina veterinaria Medicina ii Medicina i Mathematical & computational biology Matemática / probabilidade e estatística Interdisciplinar Farmacia Engenharias iv Engenharias iii Computer science applications Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência da computação Biotecnología Biotechnology & applied microbiology Biodiversidade Biochemistry Biochemical research methods Applied mathematics

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

ISSN: 14712105

Direcció de correo del autor: maria.vinaixa@urv.cat oscar.yanes@urv.cat maria.vinaixa@urv.cat

Identificador del autor: 0000-0001-9804-0171 0000-0003-3695-7157 0000-0001-9804-0171

Fecha de alta del registro: 2023-02-18

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

Enlace a la fuente original: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2487-5

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

Referencia al articulo segun fuente origial: Bmc Bioinformatics. 19 (1): 538-

Referencia de l'ítem segons les normes APA: Picart-Armada S; Fernández-Albert F; Vinaixa M; Yanes O; Perera-Lluna A (2018). FELLA: An R package to enrich metabolomics data. Bmc Bioinformatics, 19(1), 538-. DOI: 10.1186/s12859-018-2487-5

DOI del artículo: 10.1186/s12859-018-2487-5

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2018

Tipo de publicación: Journal Publications