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MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. A lack of preventive establishment of means of data access at...

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Detalles Bibliográficos
Autores principales: Yones, Sara A., Csombordi, Rajmund, Komorowski, Jan, Diamanti, Klev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620779/
https://www.ncbi.nlm.nih.gov/pubmed/34822401
http://dx.doi.org/10.3390/metabo11110743
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author Yones, Sara A.
Csombordi, Rajmund
Komorowski, Jan
Diamanti, Klev
author_facet Yones, Sara A.
Csombordi, Rajmund
Komorowski, Jan
Diamanti, Klev
author_sort Yones, Sara A.
collection PubMed
description Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. A lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power, and large delays in delivery of results. We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies, and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets.
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spelling pubmed-86207792021-11-27 MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data Yones, Sara A. Csombordi, Rajmund Komorowski, Jan Diamanti, Klev Metabolites Article Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. A lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power, and large delays in delivery of results. We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies, and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets. MDPI 2021-10-28 /pmc/articles/PMC8620779/ /pubmed/34822401 http://dx.doi.org/10.3390/metabo11110743 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yones, Sara A.
Csombordi, Rajmund
Komorowski, Jan
Diamanti, Klev
MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title_full MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title_fullStr MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title_full_unstemmed MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title_short MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data
title_sort metafetcher: an r package for complete mapping of small-compound data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620779/
https://www.ncbi.nlm.nih.gov/pubmed/34822401
http://dx.doi.org/10.3390/metabo11110743
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