Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

FELLA: An R package to enrich metabolomics data

  • Identification data

    Identifier: imarina:5873911
    Authors:
    Picart-Armada, SergioFernandez-Albert, FrancescVinaixa, MariaYanes, OscarPerera-Lluna, Alexandre
    Abstract:
    © 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.
  • Others:

    Author, as appears in the article.: Picart-Armada, Sergio; Fernandez-Albert, Francesc; Vinaixa, Maria; Yanes, Oscar; Perera-Lluna, Alexandre
    Department: Enginyeria Electrònica, Elèctrica i Automàtica
    URV's Author/s: Vinaixa Crevillent, Maria / Yanes Torrado, Óscar
    Keywords: Reveals alterations Pathways Network analysis Metabolomics Liver Knowledge representation Fumarate-hydratase Data mining Cells Cancer network analysis metabolomics knowledge representation data mining
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 14712105
    Author's mail: maria.vinaixa@urv.cat oscar.yanes@urv.cat maria.vinaixa@urv.cat
    Author identifier: 0000-0001-9804-0171 0000-0003-3695-7157 0000-0001-9804-0171
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2487-5
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Bmc Bioinformatics. 19 (1): 538-
    APA: Picart-Armada, Sergio; Fernandez-Albert, Francesc; Vinaixa, Maria; Yanes, Oscar; Perera-Lluna, Alexandre (2018). FELLA: An R package to enrich metabolomics data. Bmc Bioinformatics, 19(1), 538-. DOI: 10.1186/s12859-018-2487-5
    Article's DOI: 10.1186/s12859-018-2487-5
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2018
    Publication Type: Journal Publications
  • Keywords:

    Applied Mathematics,Biochemical Research Methods,Biochemistry,Biotechnology & Applied Microbiology,Computer Science Applications,Mathematical & Computational Biology,Molecular Biology,Structural Biology
    Reveals alterations
    Pathways
    Network analysis
    Metabolomics
    Liver
    Knowledge representation
    Fumarate-hydratase
    Data mining
    Cells
    Cancer
    network analysis
    metabolomics
    knowledge representation
    data mining
    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
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