Evidence map›Paper›PMID 30577788›Full record

ArticleBMC bioinformatics2018

FELLA: an R package to enrich metabolomics data.

Sergio Picart-Armada, Francesc Fernández-Albert, Maria Vinaixa, Oscar Yanes, Alexandre Perera-Lluna

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Squamocin as the Principal Toxic Constituent ofInternational journal of molecular sciences · 2026
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17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sergio Picart-ArmadaB2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain. sergi.picart@upc.edu.ORCID http://orcid.org/0000-0002-6426-8204
Francesc Fernández-AlbertB2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.
Maria VinaixaMetabolomics Platform, IISPV, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona, 43003, Spain.
Oscar YanesMetabolomics Platform, IISPV, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona, 43003, Spain.
Alexandre Perera-LlunaB2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.

Funding

Ministerio de Economía y Competitividad BFU2014-57466-PMinisterio de Economía y Competitividad DPI2017-89827-RMinisterio de Economía y Competitividad TEC2014-60337-R
6 · The paper itself

Abstract

backgroundPathway 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.

resultsWe 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.

conclusionsThe 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.

Indexed as

Metabolic Networks and PathwaysSoftwareAnimalsComputational BiologyComputer GraphicsDatasets as TopicFemaleHumansMalariaMetabolomicsMiceModels, BiologicalNon-alcoholic Fatty Liver DiseaseOvarian NeoplasmsZebrafishData miningKnowledge representationMetabolomicsNetwork analysisPathways

Identifiers

PMID30577788
PMCPMC6303911

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.