EyeMirDB: a Web-Based Platform of Experimentally Supported Eye Disease-miRNA Information
Journal of Ophthalmic and Optometric Sciences,
Vol. 4 No. 3 (2020),
23 June 2020
,
Page 32-41
https://doi.org/10.22037/joos.v4i3.37853
Abstract
Background: Studies of microRNA biology have increased in numerous scientific research domains, including eye science. MicroRNAs (miRNAs) are small non-coding RNAs that operate as post-transcriptional regulators of gene expression by destroying or blocking the translation of target messenger RNA. Despite significant efforts to investigate the miRNA of eye disease, a complete platform of frequent ocular disease with genes, pathways, and miRNA is still unavailable.
Material and Methods: Three well-known databases were used as the main data source: DisGeNET, OMIM, and KEGG. The curated genes involved in each disease were manually collected. Then, the annotation information like gene’s sequence, description, chromosome’s number, start and end loci were extracted from the Ensembl data source. Gene’s pathway information was earned from KEGG and Reactome. Finally, experimentally validated gene’s related miRNA has been collected from miRecords, miRTarBase, and TarBase. In order to consider miRNAs expression in ocular tissues.
Results: we present EyeMirDB (http://eyemirdb.databanks.behrc.ir/), a web-based platform of consisting of all predicted and validated miRNAs. Information on the annotation of miRNA-related genes was also collected in order to better understand the effects of miRNA. Pathways by which these genes are active were also identified. Right now, EyeMirDB contains 429 curated genes, 1258 pathways, and 2596 validated miRNAs of 25 prevalent ocular diseases.
Conclusion: We introduce EyeMirDB, a web-based platform of Eye diseases-related interactions including disease-gene, gene-miRNA, gene-pathway curated information, and annotations, with the optionality of studying all these entities from different viewpoints. This data portal is a good entry point for ocular disease researchers.
- Database
- Eye disease
- Gene
- miRNA
- Pathway
- Web-Based Platform
How to Cite
References
ander ES. Initial sequencing and analysis of the human germane. Nature. 2001;409:860-921.
Pasquinelli AE, Hunter S, Bracht J. MicroRNAs: a developing story. Curr Opin Genet Dev. 2005;15(2):200–5.
Ahmadi H, Ahmadi A, Azimzadeh-Jamalkandi S, Shoorehdeli MA, Salehzadeh-Yazdi A, Bidkhori G, et al. HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens. Genomics. 2013;101(2):94-100.
Ghasemi M, Seidkhani H, Tamimi F, Rahgozar M, Masoudi-Nejad A. Centrality measures in biological networks. Current Bioinformatics. 2014;9(4):426-41.
Kouhsar M, Azimzadeh Jamalkandi S, Moeini A, Masoudi-Nejad A. Detection of novel biomarkers for early detection of Non-Muscle-Invasive Bladder Cancer using Competing Endogenous RNA network analysis. Scientific reports. 2019;9(1):1-15.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific reports. 2019;9(1):1-14.
Huang KM, Dentchev T, Stambolian D. MiRNA expression in the eye. Mamm genome. 2008;19(7):510–6.
Liu C-H, Huang S, Britton WR, Chen J. MicroRNAs in vascular eye diseases. Int J Mol Sci. 2020;21(2):649.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. DrugR+: a comprehensive relational database for drug repurposing, combination therapy, and replacement therapy. Computers in biology and medicine. 2019;109:254-62.
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48(D1):D845–55.
McKusick V. Online Mendelian Inheritance in Man, OMIMTM. McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University (Baltimore, MD) and National Center for Biotechnology Information, National Library of Medicine (Bethesda, MD), 2000. World Wide Web URL https//omim org. 2009;
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Masoudi-Nejad A, Goto S, Endo TR, Kanehisa M. KEGG bioinformatics resource for plant genomics research. Plant Bioinformatics: Springer; 2007. p. 437-58.
Masoudi-Nejad A, Goto S, Jauregui R, Ito M, Kawashima S, Moriya Y, et al. EGENES: transcriptome-based plant database of genes with metabolic pathway information and expressed sequence tag indices in KEGG. Plant Physiology. 2007;144(2):857-66.
Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, et al. Ensembl 2018. Nucleic Acids Res. 2018;46:D754–61.
Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2012;40(D1):D13–25.
Guberman JM, Ai J, Arnaiz O, Baran J, Blake A, Baldock R, et al. BioMart Central Portal: an open database network for the biological community. Database. 2011;2011.
Carlson M, Falcon S, Pages H, Li N. org. Hs. eg. db: Genome wide annotation for Human. R package version. 2019;3(2):3.
Griss J, Viteri G, Sidiropoulos K, Nguyen V, Fabregat A, Hermjakob H. ReactomeGSA-efficient multi-omics comparative pathway analysis. Mol Cell Proteomics. 2020;19(12):2115–25.
Ligtenberg W. reactome. db: A set of annotation maps for reactome. R Packag version 168 0. 2019.
Carlson M, Falcon S, Pages H, Li N. KEGG. db: A set of annotation maps for KEGG. R Packag version. 2016;3(3):10–18129.
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA–target interactions. Nucleic Acids Res. 2009;37(suppl_1):D105–10.
Huang H-Y, Lin Y-C-D, Li J, Huang K-Y, Shrestha S, Hong H-C, et al. miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Res. 2020;48(D1):D148–54.
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res. 2018;46(D1):D239–45.
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, et al. DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41(W1):W169–73.
Gaidatzis D, van Nimwegen E, Hausser J, Zavolan M. Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics. 2007;8(1):1–22.
Rose D. MicroRNAs in Cancer Translational Research: The Microcosm of Cancer Diagnosis, Prognosis, and Therapy. Front Genet. 2012;3:42.
Sonawane AR, Platig J, Fagny M, Chen CY, Paulson JN, Lopes-Ramos CM, et al. Understanding Tissue-Specific Gene Regulation. Cell Rep. 2017 Oct 24;21(4):1077–88.
Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P. Tools for sequence-based miRNA target prediction: what to choose? Int J Mol Sci. 2016;17(12):1987.
Krek A. Grü n D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N. 2005. Combinatorial microRNA target predictions. Nat Genet. 37:495–500.
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39(10):1278–84.
McGeary SE, Lin KS, Shi CY, Pham TM, Bisaria N, Kelley GM, et al. The biochemical basis of microRNA targeting efficacy. Science (80- ). 2019;366(6472):eaav1741.
Griffiths-Jones S, Grocock RJ, Van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006;34(suppl_1):D140–4.
Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, et al. The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations. Nucleic Acids Res. 2014;42(17):e133–e133.
Reid JF. mirbase. db: miRBase: the microRNA database. R Packag version. 2013;1(1).
Kavakiotis, Ioannis, Athanasios Alexiou, Spyros Tastsoglou, Ioannis S. Vlachos, and Artemis G. Hatzigeorgiou. "DIANA-miTED: a microRNA tissue expression database." Nucleic Acids Research 50, no. D1 (2022): D1055-D1061.
Najafi A, Bidkhori G, H Bozorgmehr J, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Current genomics. 2014;15(2):130-59.
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