Login Register Cart 0
Mini-Review Article

Comparative Analysis of Published Database Predicting MicroRNA Binding in 3’UTR of mRNA in Diverse Species

Author(s): Sonu Singh Ahirwar, Rehma Rizwan, Samdish Sethi, Zainab Shahid, Shivani Malviya, Rekha Khandia, Amit Agarwal and Ashwin Kotnis*

Volume 13, Issue 1, 2024

Published on: 27 October, 2023

Page: [2 - 13] Pages: 12

DOI: 10.2174/0122115366261005231018070640

Price: $65

Abstract

Background: Micro-RNAs are endogenous non-coding RNA moieties of 22-27 nucleotides that play a crucial role in the regulation of various biological processes and make them useful prognostic and diagnostic biomarkers. Discovery and experimental validation of miRNA is a laborious and time-consuming process. For early prediction, multiple bioinformatics databases are available for miRNA target prediction; however, their utility can confuse amateur researchers in selecting the most appropriate tools for their study.

Objective: This descriptive review aimed to analyse the usability of the existing database based on the following criteria: accessibility, efficiency, interpretability, updatability, and flexibility for miRNA target prediction of 3’UTR of mRNA in diverse species so that the researchers can utilize the database most appropriate to their research.

Methods: A systematic literature search was performed in PubMed, Google Scholar and Scopus databases up to November 2022. ≥10,000 articles found online, including ⁓130 miRNA tools, which contain various information on miRNA. Out of them, 31 databases that provide information on validated 3’UTR miRNAs target databases were included and analysed in this review.

Results: These miRNA database tools are being used in varied areas of biological research to select the most suitable miRNA for their experimental validation. These databases, updated until the year 2021, consist of miRNA-related data from humans, animals, mice, plants, viruses etc. They contain 525-29806351 data entries, and information from most databases is freely available on the online platform.

Conclusion: Reviewed databases provide significant information, but not all information is accurate or up-to-date. Therefore, Diana-TarBase and miRWalk are the most comprehensive and up-to-date databases.

« Previous Next »
[1]
Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75(5): 843-54.
[http://dx.doi.org/10.1016/0092-8674(93)90529-Y] [PMID: 8252621]
[2]
Dweep H, Sticht C, Gretz N. In-Silico Algorithms for the Screening of Possible microRNA Binding Sites and Their Interactions. Curr Genomics 2013; 14(2): 127-36.
[http://dx.doi.org/10.2174/1389202911314020005] [PMID: 24082822]
[3]
Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform 2019; 20(5): 1836-52.
[http://dx.doi.org/10.1093/bib/bby054] [PMID: 29982332]
[4]
Sayed D, Abdellatif M. MicroRNAs in development and disease. Physiol Rev 2011; 91(3): 827-87.
[http://dx.doi.org/10.1152/physrev.00006.2010] [PMID: 21742789]
[5]
Carleton M, Cleary MA, Linsley PS. MicroRNAs and cell cycle regulation. Cell Cycle 2007; 6(17): 2127-32.
[http://dx.doi.org/10.4161/cc.6.17.4641] [PMID: 17786041]
[6]
Lynam-Lennon N, Maher SG, Reynolds JV. The roles of microRNA in cancer and apoptosis. Biol Rev Camb Philos Soc 2009; 84(1): 55-71.
[http://dx.doi.org/10.1111/j.1469-185X.2008.00061.x] [PMID: 19046400]
[7]
Soifer HS, Rossi JJ, Sætrom P. MicroRNAs in disease and potential therapeutic applications. Mol Ther 2007; 15(12): 2070-9.
[http://dx.doi.org/10.1038/sj.mt.6300311] [PMID: 17878899]
[8]
Harfe BD. MicroRNAs in vertebrate development. Curr Opin Genet Dev 2005; 15(4): 410-5.
[http://dx.doi.org/10.1016/j.gde.2005.06.012] [PMID: 15979303]
[9]
Lu J, Getz G, Miska EA, et al. MicroRNA expression profiles classify human cancers. Nature 2005; 435(7043): 834-8.
[http://dx.doi.org/10.1038/nature03702] [PMID: 15944708]
[10]
Jiang Q, Wang Y, Hao Y, et al. miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009; 37(Database): D98-D104.
[http://dx.doi.org/10.1093/nar/gkn714] [PMID: 18927107]
[11]
Jin HY, Xiao C. MicroRNA Mechanisms of Action: What have We Learned from Mice? Front Genet 2015; 6: 328.
[http://dx.doi.org/10.3389/fgene.2015.00328] [PMID: 26635864]
[12]
Doench JG, Sharp PA. Specificity of microRNA target selection in translational repression. Genes Dev 2004; 18(5): 504-11.
[http://dx.doi.org/10.1101/gad.1184404] [PMID: 15014042]
[13]
Lee YJ, Kim V, Muth DC, Witwer KW. Validated MicroRNA Target Databases: An Evaluation. Drug Dev Res 2015; 76(7): 389-96.
[http://dx.doi.org/10.1002/ddr.21278] [PMID: 26286669]
[14]
Lindow M, Gorodkin J. Principles and limitations of computational microRNA gene and target finding. DNA Cell Biol 2007; 26(5): 339-51.
[http://dx.doi.org/10.1089/dna.2006.0551] [PMID: 17504029]
[15]
Dweep H, Sticht C, Pandey P, Gretz N. miRWalk – Database: Prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 2011; 44(5): 839-47.
[http://dx.doi.org/10.1016/j.jbi.2011.05.002] [PMID: 21605702]
[16]
Wong N, Wang X. miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 2015; 43(D1): D146-52.
[http://dx.doi.org/10.1093/nar/gku1104] [PMID: 25378301]
[17]
Dweep H, Gretz N. miRWalk2.0: A comprehensive atlas of microRNA-target interactions. Nat Methods 2015; 12(8): 697.
[http://dx.doi.org/10.1038/nmeth.3485] [PMID: 26226356]
[18]
Linsley PS, Schelter J, Burchard J, et al. Transcripts targeted by the microRNA-16 family cooperatively regulate cell cycle progression. Mol Cell Biol 2007; 27(6): 2240-52.
[http://dx.doi.org/10.1128/MCB.02005-06] [PMID: 17242205]
[19]
Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120(1): 15-20.
[http://dx.doi.org/10.1016/j.cell.2004.12.035] [PMID: 15652477]
[20]
Wang X. miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA 2008; 14(6): 1012-7.
[http://dx.doi.org/10.1261/rna.965408] [PMID: 18426918]
[21]
Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006; 12(2): 192-7.
[http://dx.doi.org/10.1261/rna.2239606] [PMID: 16373484]
[22]
Hsu SD, Tseng YT, Shrestha S, et al. miRTarBase update 2014: An information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 2014; 42(D1): D78-85.
[http://dx.doi.org/10.1093/nar/gkt1266] [PMID: 24304892]
[23]
Garcia-Moreno A, Carmona-Saez P. Computational Methods and Software Tools for Functional Analysis of miRNA Data. Biomolecules 2020; 10(9): 1252.
[http://dx.doi.org/10.3390/biom10091252] [PMID: 32872205]
[24]
Vlachos IS, Paraskevopoulou MD, Karagkouni D, et al. DIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res 2015; 43(D1): D153-9.
[http://dx.doi.org/10.1093/nar/gku1215] [PMID: 25416803]
[25]
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, et al. DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 2018; 46(D1): D239-45.
[http://dx.doi.org/10.1093/nar/gkx1141] [PMID: 29156006]
[26]
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(Database): D105-10.
[http://dx.doi.org/10.1093/nar/gkn851] [PMID: 18996891]
[27]
Chou CH, Chang NW, Shrestha S, et al. miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 2016; 44(D1): D239-47.
[http://dx.doi.org/10.1093/nar/gkv1258] [PMID: 26590260]
[28]
Hsu SD, Lin FM, Wu WY, et al. miRTarBase: A database curates experimentally validated microRNA–target interactions. Nucleic Acids Res 2011; 39(Database issue) (Suppl. 1): D163-9.
[http://dx.doi.org/10.1093/nar/gkq1107] [PMID: 21071411]
[29]
Alexiou P, Vergoulis T, Gleditzsch M, et al. miRGen 2.0: A database of microRNA genomic information and regulation. Nucleic Acids Res 2010; 38(Database issue) (Suppl. 1): D137-41.
[http://dx.doi.org/10.1093/nar/gkp888] [PMID: 19850714]
[30]
Georgakilas G, Vlachos IS, Zagganas K, et al. DIANA-miRGen v3.0: Accurate characterization of microRNA promoters and their regulators. Nucleic Acids Res 2016; 44(D1): D190-5.
[http://dx.doi.org/10.1093/nar/gkv1254] [PMID: 26586797]
[31]
Gerlach D, Kriventseva EV, Rahman N, Vejnar CE, Zdobnov EM. miROrtho: Computational survey of microRNA genes Nucleic Acids Res 2009; 37(Database): D111-7.
[http://dx.doi.org/10.1093/nar/gkn707] [PMID: 18927110]
[32]
Kriventseva EV, Rahman N, Espinosa O, Zdobnov EM. OrthoDB: The hierarchical catalog of eukaryotic orthologs. Nucleic Acids Res 2007; 36(Database): D271-5.
[http://dx.doi.org/10.1093/nar/gkm845] [PMID: 17947323]
[33]
Betel D, Koppal A, Agius P, Sander C, Leslie C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 2010; 11(8): R90.
[http://dx.doi.org/10.1186/gb-2010-11-8-r90] [PMID: 20799968]
[34]
Laganà A, Forte S, Giudice A, et al. miRo: A miRNA knowledge base. Database (Oxford) 2009; 2009(0): bap008.
[http://dx.doi.org/10.1093/database/bap008] [PMID: 20157481]
[35]
Russo F, Di Bella S, Nigita G, et al. miRandola: Extracellular circulating microRNAs database. PLoS One 2012; 7(10): e47786.
[http://dx.doi.org/10.1371/journal.pone.0047786] [PMID: 23094086]
[36]
Hiard S, Charlier C, Coppieters W, Georges M, Baurain D. Patrocles: A database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res 2010; 38(Database issue) (Suppl. 1): D640-51.
[http://dx.doi.org/10.1093/nar/gkp926] [PMID: 19906729]
[37]
Naeem H, Küffner R, Csaba G, Zimmer R. miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature. BMC Bioinformatics 2010; 11(1): 135.
[http://dx.doi.org/10.1186/1471-2105-11-135] [PMID: 20233441]
[38]
Ulitsky I, Laurent LC, Shamir R. Towards computational prediction of microRNA function and activity. Nucleic Acids Res 2010; 38(15): e160.
[http://dx.doi.org/10.1093/nar/gkq570] [PMID: 20576699]
[39]
Ruepp A, Kowarsch A, Schmidl D, et al. PhenomiR: A knowledgebase for microRNA expression in diseases and biological processes. Genome Biol 2010; 11(1): R6.
[http://dx.doi.org/10.1186/gb-2010-11-1-r6] [PMID: 20089154]
[40]
Ruepp A, Kowarsch A, Theis F, Phenomi R. PhenomiR: microRNAs in human diseases and biological processes. Methods Mol Biol 2012; 822: 249-60.
[http://dx.doi.org/10.1007/978-1-61779-427-8_17] [PMID: 22144204]
[41]
Mestdagh P, Lefever S, Pattyn F, et al. The microRNA body map: Dissecting microRNA function through integrative genomics. Nucleic Acids Res 2011; 39(20): e136.
[http://dx.doi.org/10.1093/nar/gkr646] [PMID: 21835775]
[42]
Russo F, Di Bella S, Vannini F, et al. miRandola 2017: A curated knowledge base of non-invasive biomarkers. Nucleic Acids Res 2018; 46(D1): D354-9.
[http://dx.doi.org/10.1093/nar/gkx854] [PMID: 29036351]
[43]
Kozomara A, Griffiths-Jones S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42(D1): D68-73.
[http://dx.doi.org/10.1093/nar/gkt1181] [PMID: 24275495]
[44]
Backes C, Fehlmann T, Kern F, et al. miRCarta: A central repository for collecting miRNA candidates. Nucleic Acids Res 2018; 46(D1): D160-7.
[http://dx.doi.org/10.1093/nar/gkx851] [PMID: 29036653]
[45]
Yue M, Zhou D, Zhi H, et al. MSDD: A manually curated database of experimentally supported associations among miRNAs, SNPs and human diseases. Nucleic Acids Res 2018; 46(D1): D181-5.
[http://dx.doi.org/10.1093/nar/gkx1035] [PMID: 29106642]
[46]
Bhattacharya A, Ziebarth JD, Cui Y. PolymiRTS Database 3.0: Linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res 2014; 42(D1): D86-91.
[http://dx.doi.org/10.1093/nar/gkt1028] [PMID: 24163105]
[47]
Hua X, Tang R, Xu X, et al. mirTrans: A resource of transcriptional regulation on microRNAs for human cell lines. Nucleic Acids Res 2018; 46(D1): D168-74.
[http://dx.doi.org/10.1093/nar/gkx996] [PMID: 29077896]
[48]
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006; 34(90001): D140-4.
[http://dx.doi.org/10.1093/nar/gkj112] [PMID: 16381832]
[49]
Vergoulis T, Kanellos I, Kostoulas N, et al. mirPub: A database for searching microRNA publications. Bioinformatics 2015; 31(9): 1502-4.
[http://dx.doi.org/10.1093/bioinformatics/btu819] [PMID: 25527833]
[50]
Maxwell EK, Campbell JD, Spira A, Baxevanis AD. SubmiRine: Assessing variants in microRNA targets using clinical genomic data sets. Nucleic Acids Res 2015; 43(8): 3886-98.
[http://dx.doi.org/10.1093/nar/gkv256] [PMID: 25813044]
[51]
Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol 2011; 18(10): 1139-46.
[http://dx.doi.org/10.1038/nsmb.2115] [PMID: 21909094]
[52]
Krek A, Grün D, Poy MN, et al. Combinatorial microRNA target predictions. Nat Genet 2005; 37(5): 495-500.
[http://dx.doi.org/10.1038/ng1536] [PMID: 15806104]
[53]
Coronnello C, Hartmaier R, Arora A, et al. Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density. PLOS Comput Biol 2012; 8(12): e1002830.
[http://dx.doi.org/10.1371/journal.pcbi.1002830] [PMID: 23284279]
[54]
Bruno AE, Li L, Kalabus JL, Pan Y, Yu A, Hu Z. miRdSNP: A database of disease-associated SNPs and microRNA target sites on 3'UTRs of human genes. BMC Genomics 2012; 13(1): 44.
[http://dx.doi.org/10.1186/1471-2164-13-44] [PMID: 22276777]
[55]
Barenboim M, Zoltick BJ, Guo Y, Weinberger DR. MicroSNiPer: A web tool for prediction of SNP effects on putative microRNA targets. Hum Mutat 2010; 31(11): 1223-32.
[http://dx.doi.org/10.1002/humu.21349] [PMID: 20809528]
[56]
Zorc M, Omejec S, Tercic D, Holcman A, Dovc P, Kunej T. Catalog of genetic variants within mature microRNA seed regions in chicken. Poult Sci 2015; 94(9): 2037-40.
[http://dx.doi.org/10.3382/ps/pev170] [PMID: 26175051]
[57]
Zhang S, Yue Y, Sheng L, et al. PASmiR: A literature-curated database for miRNA molecular regulation in plant response to abiotic stress. BMC Plant Biol 2013; 13(1): 33.
[http://dx.doi.org/10.1186/1471-2229-13-33] [PMID: 23448274]
[58]
Hsu PWC, Lin LZ, Hsu SD, Hsu JBK, Huang HD. ViTa: Prediction of host microRNAs targets on viruses. Nucleic Acids Res 2007; 35(Database): D381-5.
[http://dx.doi.org/10.1093/nar/gkl1009] [PMID: 17148483]
[59]
Thomas LF, Saito T, Sætrom P. Inferring causative variants in microRNA target sites. Nucleic Acids Res 2011; 39(16): e109.
[http://dx.doi.org/10.1093/nar/gkr414] [PMID: 21693556]
[60]
Li SC, Shiau CK, Lin W. Vir-Mir db: Prediction of viral microRNA candidate hairpins. Nucleic Acids Res 2007; 36(Database): D184-9.
[http://dx.doi.org/10.1093/nar/gkm610] [PMID: 17702763]
[61]
Hsu PWC, Huang HD, Hsu SD, et al. miRNAMap: Genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res 2006; 34(90001): D135-9.
[http://dx.doi.org/10.1093/nar/gkj135] [PMID: 16381831]
[62]
Vejnar CE, Zdobnov EM. miRmap: Comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 2012; 40(22): 11673-83.
[http://dx.doi.org/10.1093/nar/gks901] [PMID: 23034802]
[63]
Cho S, Jang I, Jun Y, et al. miRGator v3.0: A microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res 2012; 41(D1): D252-7.
[http://dx.doi.org/10.1093/nar/gks1168] [PMID: 23193297]
[64]
Liu C, Zhang F, Li T, et al. MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics 2012; 13(1): 661.
[http://dx.doi.org/10.1186/1471-2164-13-661] [PMID: 23173617]
[65]
Wilkins OM, Titus AJ, Salas LA, et al. MicroRNA-related genetic variants associated with survival of head and neck squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev 2019; 28(1): 127-36.
[http://dx.doi.org/10.1158/1055-9965.EPI-18-0002] [PMID: 29880533]
[66]
Lu Y, Baras AS, Halushka MK. miRge 2.0 for comprehensive analysis of microRNA sequencing data. BMC Bioinformatics 2018; 19(1): 275.
[http://dx.doi.org/10.1186/s12859-018-2287-y] [PMID: 30153801]
[67]
Baras AS, Mitchell CJ, Myers JR, et al. miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy. PLoS One 2015; 10(11): e0143066.
[http://dx.doi.org/10.1371/journal.pone.0143066] [PMID: 26571139]
[68]
Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One 2018; 13(10): e0206239.
[http://dx.doi.org/10.1371/journal.pone.0206239] [PMID: 30335862]
[69]
Bhattacharya A, Ziebarth JD, Cui Y, Somami R. A database for somatic mutations impacting microRNA function in cancer. Nucleic Acids Res 2013; 41(D1): D977-82.
[http://dx.doi.org/10.1093/nar/gks1138] [PMID: 23180788]
[70]
Zheng H, Fu R, Wang JT, Liu Q, Chen H, Jiang SW. Advances in the techniques for the prediction of microRNA targets. Int J Mol Sci 2013; 14(4): 8179-87.
[http://dx.doi.org/10.3390/ijms14048179] [PMID: 23591837]
[71]
Akhtar MM, Micolucci L, Islam MS, Olivieri F, Procopio AD. Bioinformatic tools for microRNA dissection. Nucleic Acids Res 2016; 44(1): 24-44.
[http://dx.doi.org/10.1093/nar/gkv1221] [PMID: 26578605]
[72]
Mullany LE, Wolff RK, Slattery ML. Effectiveness and usability of bioinformatics tools to analyze pathways associated with miRNA expression. Cancer Inform 2015; 14: CIN.S32716..
[http://dx.doi.org/10.4137/CIN.S32716] [PMID: 26560461]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy