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Review of databases for experimentally validated human microRNA–mRNA interactions

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cat...

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Autores principales: Kariuki, Dorian, Asam, Kesava, Aouizerat, Bradley E, Lewis, Kimberly A, Florez, Jose C, Flowers, Elena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129384/
https://www.ncbi.nlm.nih.gov/pubmed/37098414
http://dx.doi.org/10.1093/database/baad014
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author Kariuki, Dorian
Asam, Kesava
Aouizerat, Bradley E
Lewis, Kimberly A
Florez, Jose C
Flowers, Elena
author_facet Kariuki, Dorian
Asam, Kesava
Aouizerat, Bradley E
Lewis, Kimberly A
Florez, Jose C
Flowers, Elena
author_sort Kariuki, Dorian
collection PubMed
description MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR–messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR–mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/
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spelling pubmed-101293842023-04-26 Review of databases for experimentally validated human microRNA–mRNA interactions Kariuki, Dorian Asam, Kesava Aouizerat, Bradley E Lewis, Kimberly A Florez, Jose C Flowers, Elena Database (Oxford) Review MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR–messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR–mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/ Oxford University Press 2023-04-25 /pmc/articles/PMC10129384/ /pubmed/37098414 http://dx.doi.org/10.1093/database/baad014 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Kariuki, Dorian
Asam, Kesava
Aouizerat, Bradley E
Lewis, Kimberly A
Florez, Jose C
Flowers, Elena
Review of databases for experimentally validated human microRNA–mRNA interactions
title Review of databases for experimentally validated human microRNA–mRNA interactions
title_full Review of databases for experimentally validated human microRNA–mRNA interactions
title_fullStr Review of databases for experimentally validated human microRNA–mRNA interactions
title_full_unstemmed Review of databases for experimentally validated human microRNA–mRNA interactions
title_short Review of databases for experimentally validated human microRNA–mRNA interactions
title_sort review of databases for experimentally validated human microrna–mrna interactions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129384/
https://www.ncbi.nlm.nih.gov/pubmed/37098414
http://dx.doi.org/10.1093/database/baad014
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