Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

miRNA, siRNA, and lncRNA: Recent Development of Bioinformatics Tools and Databases in Support of Combating Different Diseases

Author(s): Chiranjib Chakraborty*, Manojit Bhattacharya and Ashish Ranjan Sharma

Volume 19, Issue 1, 2024

Published on: 30 May, 2023

Page: [39 - 60] Pages: 22

DOI: 10.2174/1574893618666230411104945

Price: $65

Abstract

Today, the bioinformatics tool and database development are one of the most significant research areas in computational biology. Computational biologists are developing diverse bioinformatics tools and databases in the various fields of biological science. Nowadays, several non-coding RNAs (ncRNA) have been studied extensively, which act as a mediator of the regulation of gene expression. ncRNA is a functional RNA molecule that is transcribed from the mammalian genome. It also controls the disease regulation pathway. Based on the size, ncRNA can be classified into three categories such as small ncRNA (~18–30 nt), medium ncRNA (~30–200 nt), and long ncRNA (from 200 nt to several hundred kb). The miRNA and siRNAs are two types of ncRNA. Various bioinformatics tools and databases have recently been developed to understand the different ncRNAs (miRNAs, siRNAs, and lncRNAs) disease association. We have illustrated different bioinformatics resources, such as in silico tools and databases, currently available for researching miRNAs, siRNAs, and lncRNAs. Some bioinformatics- based miRNA tools are miRbase, miRecords, miRCancer, miRSystem, miRGator, miRNEST, mirtronPred and miRIAD, etc. Bioinformatics-based siRNA tools are siPRED, siDRM, sIR, siDirect 2.0. Bioinformatics-based lncRNAs tools are lncRNAdb v2, lncRNAtor, LncDisease, iLoc-lncRNA, etc. These tools and databases benefit molecular biologists, biomedical researchers, and computational biologists.

« Previous Next »
Graphical Abstract

[1]
Nagarajan N, Yapp EKY, Le NQK, Kamaraj B, Al-Subaie AM, Yeh HY. Application of computational biology and artificial intelligence technologies in cancer precision drug discovery. BioMed Res Int 2019; 2019: 1-15.
[http://dx.doi.org/10.1155/2019/8427042] [PMID: 31886259]
[2]
Koumakis L. Deep learning models in genomics; Are we there yet? Comput Struct Biotechnol J 2020; 18: 1466-73.
[http://dx.doi.org/10.1016/j.csbj.2020.06.017] [PMID: 32637044]
[3]
Tramontano A. Bioinformatics. In: Encyclopedia of Life Sciences (ELS). Chichester: John Wiley & Sons, Ltd 2009.
[http://dx.doi.org/10.1002/9780470015902.a0001900.pub2]
[4]
Rigden DJ, Fernández XM. The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2018; 46(D1): D1-7.
[http://dx.doi.org/10.1093/nar/gkx1235] [PMID: 29316735]
[5]
Galperin MY, Fernández-Suárez XM. The 2012 nucleic acids research database issue and the online molecular biology database collection. Nucleic Acids Res 2012; 40(D1): D1-8.
[http://dx.doi.org/10.1093/nar/gkr1196] [PMID: 22144685]
[6]
Oliveira AL. Biotechnology, big data and artificial intelligence. Biotechnol J 2019; 14(8): 1800613.
[http://dx.doi.org/10.1002/biot.201800613] [PMID: 30927505]
[7]
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.
[8]
Mello CC, Conte D Jr. Revealing the world of RNA interference. Nature 2004; 431(7006): 338-42.
[http://dx.doi.org/10.1038/nature02872] [PMID: 15372040]
[9]
Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998; 391(6669): 806-11.
[http://dx.doi.org/10.1038/35888] [PMID: 9486653]
[10]
Agrawal N, Dasaradhi PVN, Mohmmed A, Malhotra P, Bhatnagar RK, Mukherjee SK. RNA interference: Biology, mechanism, and applications. Microbiol Mol Biol Rev 2003; 67(4): 657-85.
[http://dx.doi.org/10.1128/MMBR.67.4.657-685.2003] [PMID: 14665679]
[11]
Jana S, Chakraborty C, Nandi S, Deb JK. RNA interference: Potential therapeutic targets. Appl Microbiol Biotechnol 2004; 65(6): 649-57.
[http://dx.doi.org/10.1007/s00253-004-1732-1] [PMID: 15372214]
[12]
Valencia-Sanchez MA, Liu J, Hannon GJ, Parker R. Control of translation and mRNA degradation by miRNAs and siRNAs: Table 1. Genes Dev 2006; 20(5): 515-24.
[http://dx.doi.org/10.1101/gad.1399806] [PMID: 16510870]
[13]
Nagano T, Fraser P. No-nonsense functions for long noncoding RNAs. Cell 2011; 145(2): 178-81.
[http://dx.doi.org/10.1016/j.cell.2011.03.014] [PMID: 21496640]
[14]
Ghildiyal M, Zamore PD. Small silencing RNAs: An expanding universe. Nat Rev Genet 2009; 10(2): 94-108.
[http://dx.doi.org/10.1038/nrg2504] [PMID: 19148191]
[15]
Bartel DP. MicroRNAs. Cell 2004; 116(2): 281-97.
[http://dx.doi.org/10.1016/S0092-8674(04)00045-5] [PMID: 14744438]
[16]
Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet 2010; 11(9): 597-610.
[http://dx.doi.org/10.1038/nrg2843] [PMID: 20661255]
[17]
Mallick B, Sharma AR, Lee SS, Chakraborty C. Understanding the molecular interaction of human argonaute‐2 and miR‐20a complex: A molecular dynamics approach. J Cell Biochem 2019; 120(12): 19915-24.
[http://dx.doi.org/10.1002/jcb.29300] [PMID: 31318096]
[18]
Wilson RC, Tambe A, Kidwell MA, Noland CL, Schneider CP, Doudna JA. Dicer-TRBP complex formation ensures accurate mammalian microRNA biogenesis. Mol Cell 2015; 57(3): 397-407.
[http://dx.doi.org/10.1016/j.molcel.2014.11.030] [PMID: 25557550]
[19]
Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol 2014; 15(8): 509-24.
[http://dx.doi.org/10.1038/nrm3838] [PMID: 25027649]
[20]
de Rie D, Abugessaisa I, Alam T, et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat Biotechnol 2017; 35(9): 872-8.
[http://dx.doi.org/10.1038/nbt.3947] [PMID: 28829439]
[21]
Kim YK, Kim VN. Processing of intronic microRNAs. EMBO J 2007; 26(3): 775-83.
[http://dx.doi.org/10.1038/sj.emboj.7601512] [PMID: 17255951]
[22]
Okamura K, Hagen JW, Duan H, Tyler DM, Lai EC. The mirtron pathway generates microRNA-class regulatory RNAs in Drosophila. Cell 2007; 130(1): 89-100.
[http://dx.doi.org/10.1016/j.cell.2007.06.028] [PMID: 17599402]
[23]
Broughton JP, Lovci MT, Huang JL, Yeo GW, Pasquinelli AE. Pairing beyond the seed supports microRNA targeting specificity. Mol Cell 2016; 64(2): 320-33.
[http://dx.doi.org/10.1016/j.molcel.2016.09.004] [PMID: 27720646]
[24]
Makarova JA, Shkurnikov MU, Wicklein D, et al. Intracellular and extracellular microRNA: An update on localization and biological role. Prog Histochem Cytochem 2016; 51(3-4): 33-49.
[http://dx.doi.org/10.1016/j.proghi.2016.06.001] [PMID: 27396686]
[25]
Chakraborty C, Doss CGP, Sarin R, Hsu MJ, Agoramoorthy G. Can the chemotherapeutic agents perform anticancer activity though miRNA expression regulation? Proposing a new hypothesis. Protoplasma 2015; 252(6): 1603-10.
[http://dx.doi.org/10.1007/s00709-015-0776-7] [PMID: 25698235]
[26]
Bhattacharya M, Sharma AR, Sharma G, et al. The crucial role and regulations of miRNAs in zebrafish development. Protoplasma 2017; 254(1): 17-31.
[http://dx.doi.org/10.1007/s00709-015-0931-1] [PMID: 26820151]
[27]
Tüfekci KU, Meuwissen RLJ, Genç Ş. The role of microRNAs in biological processes, in miRNomics: microRNA biology and computational analysis. In:Methods in Molecular Biology Springer(1107). Totowa, NJ: Humana Press 2014.
[28]
Paul P, Chakraborty A, Sarkar D, et al. Interplay between miRNAs and human diseases. J Cell Physiol 2018; 233(3): 2007-18.
[http://dx.doi.org/10.1002/jcp.25854] [PMID: 28181241]
[29]
Sharma AR, Sharma G, Lee SS, Chakraborty C. miRNA-regulated key components of cytokine signaling pathways and inflammation in rheumatoid arthritis. Med Res Rev 2016; 36(3): 425-39.
[http://dx.doi.org/10.1002/med.21384] [PMID: 26786912]
[30]
Small EM, Olson EN. Pervasive roles of microRNAs in cardiovascular biology. Nature 2011; 469(7330): 336-42.
[http://dx.doi.org/10.1038/nature09783] [PMID: 21248840]
[31]
Chakraborty C, Chin KY, Das S. miRNA-regulated cancer stem cells: understanding the property and the role of miRNA in carcinogenesis. Tumour Biol 2016; 37(10): 13039-48.
[http://dx.doi.org/10.1007/s13277-016-5156-1] [PMID: 27468722]
[32]
Chakraborty C, Sharma AR, Patra BC, Bhattacharya M, Sharma G, Lee SS. MicroRNAs mediated regulation of MAPK signaling pathways in chronic myeloid leukemia. Oncotarget 2016; 7(27): 42683-97.
[http://dx.doi.org/10.18632/oncotarget.7977] [PMID: 26967056]
[33]
Chakraborty C, Sharma AR, Sharma G, Lee SS. The interplay among miRNAs, major cytokines, and cancer-related inflammation. Mol Ther Nucleic Acids 2020; 20: 606-20.
[http://dx.doi.org/10.1016/j.omtn.2020.04.002] [PMID: 32348938]
[34]
Chakraborty C, Doss CGP, Bandyopadhyay S, Agoramoorthy G. Influence of miRNA in insulin signaling pathway and insulin resistance: Micro‐molecules with a major role in type‐2 diabetes. Wiley Interdiscip Rev RNA 2014; 5(5): 697-712.
[http://dx.doi.org/10.1002/wrna.1240] [PMID: 24944010]
[35]
Chakraborty C, George Priya Doss C, Bandyopadhyay S. miRNAs in insulin resistance and diabetes-associated pancreatic cancer: The ‘minute and miracle’ molecule moving as a monitor in the ‘genomic galaxy’. Curr Drug Targets 2013; 14(10): 1110-7.
[http://dx.doi.org/10.2174/13894501113149990182] [PMID: 23834149]
[36]
Bhattacharya M, Sharma AR, Sharma G, Patra BC, Lee SS, Chakraborty C. Interaction between miRNAs and signaling cascades of Wnt pathway in chronic lymphocytic leukemia. J Cell Biochem 2020; 121(11): 4654-66.
[http://dx.doi.org/10.1002/jcb.29683] [PMID: 32100920]
[37]
Gupta P, Bhattacharjee S, Sharma AR, Sharma G, Lee SS, Chakraborty C. miRNAs in Alzheimer Disease-a therapeutic perspective. Curr Alzheimer Res 2017; 14(11): 1198-206.
[PMID: 28847283]
[38]
Ling H, Fabbri M, Calin GA. MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat Rev Drug Discov 2013; 12(11): 847-65.
[http://dx.doi.org/10.1038/nrd4140] [PMID: 24172333]
[39]
Samanta S, Balasubramanian S, Rajasingh S, et al. MicroRNA: A new therapeutic strategy for cardiovascular diseases. Trends Cardiovasc Med 2016; 26(5): 407-19.
[http://dx.doi.org/10.1016/j.tcm.2016.02.004] [PMID: 27013138]
[40]
Chakraborty C, Sharma AR, Sharma G, Doss CGP, Lee SS. Therapeutic miRNA and siRNA: Moving from bench to clinic as next generation medicine. Mol Ther Nucleic Acids 2017; 8: 132-43.
[http://dx.doi.org/10.1016/j.omtn.2017.06.005] [PMID: 28918016]
[41]
Chakraborty C, Sharma AR, Sharma G, Lee SS. Therapeutic advances of miRNAs: A preclinical and clinical update. J Adv Res 2021; 28: 127-38.
[http://dx.doi.org/10.1016/j.jare.2020.08.012] [PMID: 33364050]
[42]
Chakraborty C, Das S. Profiling cell-free and circulating miRNA: A clinical diagnostic tool for different cancers. Tumour Biol 2016; 37(5): 5705-14.
[http://dx.doi.org/10.1007/s13277-016-4907-3] [PMID: 26831657]
[43]
Kim VN, Han J, Siomi MC. Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 2009; 10(2): 126-39.
[http://dx.doi.org/10.1038/nrm2632] [PMID: 19165215]
[44]
Axtell MJ, Westholm JO, Lai EC. Vive la différence: Biogenesis and evolution of microRNAs in plants and animals. Genome Biol 2011; 12(4): 221.
[http://dx.doi.org/10.1186/gb-2011-12-4-221] [PMID: 21554756]
[45]
Meister G, Tuschl T. Mechanisms of gene silencing by double-stranded RNA. Nature 2004; 431(7006): 343-9.
[http://dx.doi.org/10.1038/nature02873] [PMID: 15372041]
[46]
Hammond SM, Bernstein E, Beach D, Hannon GJ. An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature 2000; 404(6775): 293-6.
[http://dx.doi.org/10.1038/35005107] [PMID: 10749213]
[47]
Wang J, Lu Z, Wientjes MG, Au JLS. Delivery of siRNA therapeutics: barriers and carriers. AAPS J 2010; 12(4): 492-503.
[http://dx.doi.org/10.1208/s12248-010-9210-4] [PMID: 20544328]
[48]
Chakraborty C. Potentiality of small interfering RNAs (siRNA) as recent therapeutic targets for gene-silencing. Curr Drug Targets 2007; 8(3): 469-82.
[http://dx.doi.org/10.2174/138945007780058988] [PMID: 17348839]
[49]
Patzel V. In silico selection of active siRNA. Drug Discov Today 2007; 12(3-4): 139-48.
[http://dx.doi.org/10.1016/j.drudis.2006.11.015] [PMID: 17275734]
[50]
Marchese FP, Raimondi I, Huarte M. The multidimensional mechanisms of long noncoding RNA function. Genome Biol 2017; 18(1): 206.
[http://dx.doi.org/10.1186/s13059-017-1348-2] [PMID: 29084573]
[51]
Fernandes J, Acuña S, Aoki J, Floeter-Winter L, Muxel S. Long Non-Coding RNAs in the regulation of gene expression: physiology and disease. Noncoding RNA 2019; 5(1): 17.
[http://dx.doi.org/10.3390/ncrna5010017] [PMID: 30781588]
[52]
Hinske LC, França GS, Torres HAM, et al. miRIAD—integrating microRNA inter- and intragenic data. Database 2014; 2014: bau099.
[http://dx.doi.org/10.1093/database/bau099] [PMID: 25288656]
[53]
Chan WC, Lin W. MetaMirClust: Discovery and exploration of evolutionarily conserved miRNA cluster. Methods Mol Biol 2015; 1375: 75-89.
[http://dx.doi.org/10.1007/7651_2015_237] [PMID: 25861770]
[54]
Loraine K. Winning strategies when the game is confrontation. RN 1989; 52(3): 18-20.
[PMID: 2734552]
[55]
Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23(5): bbac358.
[http://dx.doi.org/10.1093/bib/bbac358] [PMID: 36056743]
[56]
Chen X, Xie D, Zhao Q, You ZH. From experimental results to computational models: From experimental results to computational models. Brief Bioinform 2019; 20(2): 515-39.
[http://dx.doi.org/10.1093/bib/bbx130] [PMID: 29045685]
[57]
Chen L, Heikkinen L, Wang C, Yang Y, Knott KE, Wong G. miRToolsGallery: A tag-based and rankable microRNA bioinformatics resources database portal. Database 2018; 2018: bay004.
[http://dx.doi.org/10.1093/database/bay004] [PMID: 29688355]
[58]
Gardner PP, Daub J, Tate JG, et al. Rfam: updates to the RNA families database. Nucleic Acids Res 2009; 37: D136-40.
[http://dx.doi.org/10.1093/nar/gkn766] [PMID: 18953034]
[59]
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]
[60]
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.
[http://dx.doi.org/10.1093/nar/gkn851] [PMID: 18996891]
[61]
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]
[62]
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]
[63]
Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015; 4: e05005.
[http://dx.doi.org/10.7554/eLife.05005] [PMID: 26267216]
[64]
Brown JR, Sanseau P. A computational view of microRNAs and their targets. Drug Discov Today 2005; 10(8): 595-601.
[http://dx.doi.org/10.1016/S1359-6446(05)03399-4] [PMID: 15837603]
[65]
Yuan C, Meng X, Li X, et al. PceRBase: A database of plant competing endogenous RNA. Nucleic Acids Res 2017; 45(D1): D1009-14.
[http://dx.doi.org/10.1093/nar/gkw916] [PMID: 28053167]
[66]
Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42(D1): D92-7.
[http://dx.doi.org/10.1093/nar/gkt1248] [PMID: 24297251]
[67]
Keerthikumar S, Chisanga D, Ariyaratne D, et al. ExoCarta: A web-based compendium of exosomal cargo. J Mol Biol 2016; 428(4): 688-92.
[http://dx.doi.org/10.1016/j.jmb.2015.09.019] [PMID: 26434508]
[68]
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]
[69]
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]
[70]
Chien CH, Sun YM, Chang WC, et al. Identifying transcriptional start sites of human microRNAs based on high-throughput sequencing data. Nucleic Acids Res 2011; 39(21): 9345-56.
[http://dx.doi.org/10.1093/nar/gkr604] [PMID: 21821656]
[71]
Georgakilas G, Vlachos IS, Paraskevopoulou MD, et al. microTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs. Nat Commun 2014; 5(1): 5700.
[http://dx.doi.org/10.1038/ncomms6700] [PMID: 25492647]
[72]
Wang J, Lu M, Qiu C, Cui Q, Transmi R. TransmiR: A transcription factor–microRNA regulation database. Nucleic Acids Res 2010; 38 (Suppl. 1): D119-22.
[http://dx.doi.org/10.1093/nar/gkp803] [PMID: 19786497]
[73]
Xie B, Ding Q, Han H, Wu D. miRCancer: A microRNA-cancer association database constructed by text mining on literature. Bioinformatics 2013; 29(5): 638-44.
[http://dx.doi.org/10.1093/bioinformatics/btt014] [PMID: 23325619]
[74]
Lu TP, Lee CY, Tsai MH, et al. miRSystem: An integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS One 2012; 7(8): e42390.
[http://dx.doi.org/10.1371/journal.pone.0042390] [PMID: 22870325]
[75]
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 2013; 41(Database issue): D252-7.
[PMID: 23193297]
[76]
Szcześniak MW, Makałowska I. miRNEST 2.0: A database of plant and animal microRNAs. Nucleic Acids Res 2014; 42(D1): D74-7.
[http://dx.doi.org/10.1093/nar/gkt1156] [PMID: 24243848]
[77]
Zhou KR, Liu S, Sun WJ, et al. ChIPBase v2.0: Decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res 2017; 45(D1): D43-50.
[http://dx.doi.org/10.1093/nar/gkw965] [PMID: 27924033]
[78]
Piriyapongsa J, Bootchai C, Ngamphiw C, Tongsima S. microPIR: An integrated database of microRNA target sites within human promoter sequences. PLoS One 2012; 7(3): e33888.
[http://dx.doi.org/10.1371/journal.pone.0033888] [PMID: 22439011]
[79]
Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: Tools for microRNA genomics. Nucleic Acids Res 2008; 36: D154-8.
[PMID: 17991681]
[80]
Williams KP, Lau BY. RNAcentral: A comprehensive database of non-coding RNA sequences. Nucleic Acids Res 2017; 45(D1): D128-34.
[81]
Laganà A, Forte S, Giudice A, et al. miRo: a miRNA knowledge base. Database 2009; 2009: bap008.
[http://dx.doi.org/10.1093/database/bap008] [PMID: 20157481]
[82]
Oak N, Ghosh R, Huang K, Wheeler DA, Ding L, Plon SE. Framework for microRNA variant annotation and prioritization using human population and disease datasets. Hum Mutat 2019; 40(1): 73-89.
[http://dx.doi.org/10.1002/humu.23668] [PMID: 30302893]
[83]
Maselli V, Di Bernardo D, Banfi S. CoGemiR: A comparative genomics microRNA database. BMC Genomics 2008; 9(1): 457.
[http://dx.doi.org/10.1186/1471-2164-9-457] [PMID: 18837977]
[84]
Dai E, Lv Y, Meng F, et al. CREAM: A database for chemotherapy resistance-associated miRSNP. Cell Death Dis 2014; 5(5): e1272-2.
[http://dx.doi.org/10.1038/cddis.2014.236] [PMID: 24874743]
[85]
Yang Z, Wu L, Wang A, et al. dbDEMC 2.0: Updated database of differentially expressed miRNAs in human cancers. Nucleic Acids Res 2017; 45(D1): D812-8.
[http://dx.doi.org/10.1093/nar/gkw1079] [PMID: 27899556]
[86]
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]
[87]
Dai E, Yu X, Zhang Y, et al. EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications. Database 2014; 2014: bau023-3.
[http://dx.doi.org/10.1093/database/bau023] [PMID: 24682734]
[88]
Gennarino VA, Sardiello M, Mutarelli M, et al. HOCTAR database: A unique resource for microRNA target prediction. Gene 2011; 480(1-2): 51-8.
[http://dx.doi.org/10.1016/j.gene.2011.03.005] [PMID: 21435384]
[89]
Joshi PK, Gupta D, Nandal UK, Khan Y, Mukherjee SK, Sanan-Mishra N. Identification of mirtrons in rice using MirtronPred: A tool for predicting plant mirtrons. Genomics 2012; 99(6): 370-5.
[http://dx.doi.org/10.1016/j.ygeno.2012.04.002] [PMID: 22546559]
[90]
Liu Q, Wang J, Zhao Y, et al. Identification of active miRNA promoters from nuclear run-on RNA sequencing. Nucleic Acids Res 2017; 45(13): e121.
[http://dx.doi.org/10.1093/nar/gkx318] [PMID: 28460090]
[91]
Lorenz R, Bernhart SH, Höner zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol Biol 2011; 6(1): 26.
[http://dx.doi.org/10.1186/1748-7188-6-26] [PMID: 22115189]
[92]
Bellaousov S, Reuter JS, Seetin MG. RNAstructure: Web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res 2013; 41(W471)
[93]
Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012; 40(1): 37-52.
[http://dx.doi.org/10.1093/nar/gkr688] [PMID: 21911355]
[94]
Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM. miRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011; 39: W132-138.
[95]
Tav C, Tempel S, Poligny L, Tahi F. miRNAFold: A web server for fast miRNA precursor prediction in genomes. Nucleic Acids Res 2016; 44(W1): W181-4.
[http://dx.doi.org/10.1093/nar/gkw459] [PMID: 27242364]
[96]
Lall S, Grün D, Krek A, et al. A genome-wide map of conserved microRNA targets in C. elegans. Curr Biol 2006; 16(5): 460-71.
[http://dx.doi.org/10.1016/j.cub.2006.01.050] [PMID: 16458514]
[97]
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.
[http://dx.doi.org/10.1038/ng2135] [PMID: 17893677]
[98]
Kruger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 2006; 34: W451-454.
[http://dx.doi.org/10.1093/nar/gkl243]
[99]
Tyagi S, Vaz C, Gupta V, et al. CID-miRNA: A web server for prediction of novel miRNA precursors in human genome. Biochem Biophys Res Commun 2008; 372(4): 831-4.
[http://dx.doi.org/10.1016/j.bbrc.2008.05.134] [PMID: 18522801]
[100]
Mhuantong W, Wichadakul D. MicroPC (μPC): A comprehensive resource for predicting and comparing plant microRNAs. BMC Genomics 2009; 10(1): 366.
[http://dx.doi.org/10.1186/1471-2164-10-366] [PMID: 19660144]
[101]
Hansen TB, Venø MT, Kjems J, Damgaard CK. miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs. Nucleic Acids Res 2014; 42(16): e124.
[http://dx.doi.org/10.1093/nar/gku598] [PMID: 25053842]
[102]
Vitsios DM, Kentepozidou E, Quintais L, et al. Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Res 2017; 45(21): e177.
[http://dx.doi.org/10.1093/nar/gkx836] [PMID: 29036314]
[103]
Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007; 35 (Suppl. 2): W339-44.
[http://dx.doi.org/10.1093/nar/gkm368] [PMID: 17553836]
[104]
Wang WC, Lin FM, Chang WC, Lin KY, Huang HD, Lin NS. miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 2009; 10(1): 328.
[http://dx.doi.org/10.1186/1471-2105-10-328] [PMID: 19821977]
[105]
Sablok G, Milev I, Minkov G, et al. isomiRex: Web-based identification of microRNAs, isomiR variations and differential expression using next-generation sequencing datasets. FEBS Lett 2013; 587(16): 2629-34.
[http://dx.doi.org/10.1016/j.febslet.2013.06.047] [PMID: 23831580]
[106]
Zhang Y. miRU: An automated plant miRNA target prediction server. Nucleic Acids Res 2005; 33: W701-704.
[107]
Gaidatzis D, van Nimwegen E, Hausser J, Zavolan M. Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 2007; 8(1): 69.
[http://dx.doi.org/10.1186/1471-2105-8-69] [PMID: 17331257]
[108]
Reczko M, Maragkakis M, Alexiou P, Grosse I, Hatzigeorgiou AG. Functional microRNA targets in protein coding sequences. Bioinformatics 2012; 28(6): 771-6.
[http://dx.doi.org/10.1093/bioinformatics/bts043] [PMID: 22285563]
[109]
Miranda KC, Huynh T, Tay Y, et al. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 2006; 126(6): 1203-17.
[http://dx.doi.org/10.1016/j.cell.2006.07.031] [PMID: 16990141]
[110]
Jeggari A, Marks DS, Larsson E. miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 2012; 28(15): 2062-3.
[http://dx.doi.org/10.1093/bioinformatics/bts344] [PMID: 22718787]
[111]
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]
[112]
Heikkinen L, Kolehmainen M, Wong G. Prediction of microRNA targets in Caenorhabditis elegans using a self-organizing map. Bioinformatics 2011; 27(9): 1247-54.
[http://dx.doi.org/10.1093/bioinformatics/btr144] [PMID: 21422073]
[113]
Fahlgren N, Carrington JC. miRNA target prediction in plants. Methods Mol Biol 2010; 592: 51-7.
[http://dx.doi.org/10.1007/978-1-60327-005-2_4] [PMID: 19802588]
[114]
Huang JC, Babak T, Corson TW, et al. Using expression profiling data to identify human microRNA targets. Nat Methods 2007; 4(12): 1045-9.
[http://dx.doi.org/10.1038/nmeth1130] [PMID: 18026111]
[115]
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]
[116]
Wang X. Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies. Bioinformatics 2016; 32(9): 1316-22.
[http://dx.doi.org/10.1093/bioinformatics/btw002] [PMID: 26743510]
[117]
Bottini S, Hamouda-Tekaya N, Tanasa B, et al. From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data. Nucleic Acids Res 2017; 45(9): e71.
[http://dx.doi.org/10.1093/nar/gkx007] [PMID: 28108660]
[118]
Pan X, Shen HB. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics 2017; 18(1): 136.
[http://dx.doi.org/10.1186/s12859-017-1561-8] [PMID: 28245811]
[119]
Lee B, Baek J, Park S, Yoon S. deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks. Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics:. Seattle, USA. New York, USA: Association for Computing Machinery: 2016; pp. 434-42.
[http://dx.doi.org/10.1145/2975167.2975212]
[120]
Cheng S, Guo M, Wang C, Liu X, Liu Y. MiRTDL: A deep learning approach for miRNA target prediction. IEEE/ACM Trans Comput Biol Bioinform 2016; 13(6): 1161-9.
[121]
Gruber AR, Findeiß S, Washietl S, Hofacker IL, Stadler PF, Wu X. RNAz 2.0: Improved noncoding RNA detection. Pac Symp Biocomput 2010; 13(6): 69-79.
[PMID: 19908359]
[122]
Lim LP, Lau NC, Weinstein EG, et al. The microRNAs of Caenorhabditis elegans. Genes Dev 2003; 17(8): 991-1008.
[http://dx.doi.org/10.1101/gad.1074403] [PMID: 12672692]
[123]
Alon S, Eisenberg E. Identifying RNA editing sites in miRNAs by deep sequencing. Methods Mol Biol 2013; 1038: 159-70.
[http://dx.doi.org/10.1007/978-1-62703-514-9_9] [PMID: 23872974]
[124]
Xue B, Lipps D, Devineni S. Integrated strategy improves the prediction accuracy of miRNA in large dataset. PLoS One 2016; 11(12): e0168392.
[http://dx.doi.org/10.1371/journal.pone.0168392] [PMID: 28002428]
[125]
Xue C, Li F, He T, Liu GP, Li Y, Zhang X. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005; 6(1): 310.
[http://dx.doi.org/10.1186/1471-2105-6-310] [PMID: 16381612]
[126]
Wu Y, Wei B, Liu H, Li T, Rayner S. MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 2011; 12(1): 107.
[http://dx.doi.org/10.1186/1471-2105-12-107] [PMID: 21504621]
[127]
Mathelier A, Carbone A. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 2010; 26(18): 2226-34.
[http://dx.doi.org/10.1093/bioinformatics/btq329] [PMID: 20591903]
[128]
Nam JW, Kim J, Kim SK, Zhang BT. ProMiR II: A web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res 2006; 34: W455-458.
[http://dx.doi.org/10.1093/nar/gkl321]
[129]
Mapleson D, Moxon S, Dalmay T, Moulton V. MirPlex: A tool for identifying miRNAs in high-throughput sRNA datasets without a genome. J Exp Zoolog B Mol Dev Evol 2013; 320(1): 47-56.
[http://dx.doi.org/10.1002/jez.b.22483] [PMID: 23184675]
[130]
Pan WJ, Chen CW, Chu YW. siPRED: Predicting siRNA efficacy using various characteristic methods. PLoS One 2011; 6(11): e27602.
[http://dx.doi.org/10.1371/journal.pone.0027602] [PMID: 22102913]
[131]
Gong W, Ren Y, Zhou H, Wang Y, Kang S, Li T. siDRM: An effective and generally applicable online siRNA design tool. Bioinformatics 2008; 24(20): 2405-6.
[http://dx.doi.org/10.1093/bioinformatics/btn442] [PMID: 18718944]
[132]
Shah JK, Garner HR, White MA, Shames DS, Minna JD. sIR: siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database. BMC Bioinformatics 2007; 8(1): 178.
[http://dx.doi.org/10.1186/1471-2105-8-178] [PMID: 17540034]
[133]
Naito Y, Yoshimura J, Morishita S, Ui-Tei K. siDirect 2.0: Updated software for designing functional siRNA with reduced seed-dependent off-target effect. BMC Bioinformatics 2009; 10(1): 392.
[http://dx.doi.org/10.1186/1471-2105-10-392] [PMID: 19948054]
[134]
Holen T. Efficient prediction of siRNAs with siRNArules 1.0: An open-source JAVA approach to siRNA algorithms. RNA 2006; 12(9): 1620-5.
[http://dx.doi.org/10.1261/rna.81006] [PMID: 16870995]
[135]
Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, Khvorova A. Rational siRNA design for RNA interference. Nat Biotechnol 2004; 22(3): 326-30.
[http://dx.doi.org/10.1038/nbt936] [PMID: 14758366]
[136]
Ding Y, Chan CY, Lawrence CE. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res 2004; 32: W135-141.
[http://dx.doi.org/10.1093/nar/gkh449]
[137]
Chalk AM, Warfinge RE, Georgii-Hemming P, Sonnhammer EL. siRNAdb: A database of siRNA sequences. Nucleic Acids Res 2004; 33: D131-4.
[http://dx.doi.org/10.1093/nar/gki136] [PMID: 15608162]
[138]
Truss M, Swat M, Kielbasa SM, Schäfer R, Herzel H, Hagemeier C. HuSiDa--the human siRNA database: an open-access database for published functional siRNA sequences and technical details of efficient transfer into recipient cells. Nucleic Acids Res 2004; 33: D108-11.
[http://dx.doi.org/10.1093/nar/gki131] [PMID: 15608157]
[139]
Boudreau RL, Spengler RM, Hylock RH, et al. siSPOTR: a tool for designing highly specific and potent siRNAs for human and mouse. Nucleic Acids Res 2013; 41(1): e9-9.
[http://dx.doi.org/10.1093/nar/gks797] [PMID: 22941647]
[140]
Thody J, Folkes L, Moulton V. NATpare: a pipeline for high-throughput prediction and functional analysis of nat-siRNAs. Nucleic Acids Res 2020; 48(12): 6481-90.
[http://dx.doi.org/10.1093/nar/gkaa448] [PMID: 32463462]
[141]
Sciabola S, Xi H, Cruz D, et al. PFRED: A computational platform for siRNA and antisense oligonucleotides design. PLoS One 2021; 16(1): e0238753.
[http://dx.doi.org/10.1371/journal.pone.0238753] [PMID: 33481821]
[142]
Quek XC, Thomson DW, Maag JLV, et al. lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res 2015; 43(D1): D168-73.
[http://dx.doi.org/10.1093/nar/gku988] [PMID: 25332394]
[143]
Park C, Yu N, Choi I, Kim W, Lee S. lncRNAtor: A comprehensive resource for functional investigation of long non-coding RNAs. Bioinformatics 2014; 30(17): 2480-5.
[http://dx.doi.org/10.1093/bioinformatics/btu325] [PMID: 24813212]
[144]
Wang J, Ma R, Ma W, et al. LncDisease: A sequence based bioinformatics tool for predicting lncRNA-disease associations. Nucleic Acids Res 2016; 44(9): e90.
[http://dx.doi.org/10.1093/nar/gkw093] [PMID: 26887819]
[145]
Su ZD, Huang Y, Zhang ZY, et al. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 2018; 34(24): 4196-204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID: 29931187]
[146]
Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS. lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res 2011; 39 (Suppl. 1): D146-51.
[http://dx.doi.org/10.1093/nar/gkq1138] [PMID: 21112873]
[147]
He S, Liu C, Skogerbø G, et al. NONCODE v2.0: decoding the non-coding. Nucleic Acids Res 2008; 36: D170-2.
[PMID: 18000000]
[148]
Mituyama T, Yamada K, Hattori E, et al. The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res 2009; 37: D89-92.
[http://dx.doi.org/10.1093/nar/gkn805] [PMID: 18948287]
[149]
Pang KC, Stephen S, Dinger ME, Engström PG, Lenhard B, Mattick JS. RNAdb 2.0--an expanded database of mammalian non-coding RNAs. Nucleic Acids Res 2007; 35: D178-82.
[http://dx.doi.org/10.1093/nar/gkl926] [PMID: 17145715]
[150]
Zhang Y, Guan DG, Yang JH, Shao P, Zhou H, Qu LH. ncRNAimprint: A comprehensive database of mammalian imprinted noncoding RNAs. RNA 2010; 16(10): 1889-901.
[http://dx.doi.org/10.1261/rna.2226910] [PMID: 20801769]
[151]
Seifuddin F, Singh K, Suresh A, et al. lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA. Sci Data 2020; 7(1): 326.
[http://dx.doi.org/10.1038/s41597-020-00659-z] [PMID: 33020484]
[152]
Volders PJ, Helsens K, Wang X, et al. LNCipedia: A database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res 2013; 41(D1): D246-51.
[http://dx.doi.org/10.1093/nar/gks915] [PMID: 23042674]
[153]
Vancura A, Lanzós A, Bosch-Guiteras N, et al. Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs. NAR Cancer 2021; 3(2): zcab013.
[http://dx.doi.org/10.1093/narcan/zcab013] [PMID: 34316704]
[154]
Liu CJ, Fu X, Xia M, Zhang Q, Gu Z, Guo AY. miRNASNP-v3: A comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets. Nucleic Acids Res 2021; 49(D1): D1276-81.
[http://dx.doi.org/10.1093/nar/gkaa783] [PMID: 32990748]
[155]
Xie GY, Xia M, Miao YR, Luo M, Zhang Q, Guo AY. FFLtool: A web server for transcription factor and miRNA feed forward loop analysis in human. Bioinformatics 2020; 36(8): 2605-7.
[http://dx.doi.org/10.1093/bioinformatics/btz929] [PMID: 31830251]
[156]
Gong J, Liu W, Zhang J, Miao X, Guo AY. lncRNASNP: A database of SNPs in lncRNAs and their potential functions in human and mouse. Nucleic Acids Res 2015; 43(D1): D181-6.
[http://dx.doi.org/10.1093/nar/gku1000] [PMID: 25332392]
[157]
Shirley M. Casimersen: First Approval. Drugs 2021; 81(7): 875-9.
[http://dx.doi.org/10.1007/s40265-021-01512-2] [PMID: 33861387]
[158]
Clemens PR, Rao VK, Connolly AM, et al. Safety, tolerability, and efficacy of viltolarsen in boys with duchenne muscular dystrophy amenable to exon 53 skipping: a phase 2 randomized clinical trial. JAMA Neurol 2020; 77(8): 982-91.
[http://dx.doi.org/10.1001/jamaneurol.2020.1264] [PMID: 32453377]
[159]
Wagner KR, Kuntz NL, Koenig E, et al. Safety, tolerability, and pharmacokinetics of casimersen in patients with D uchenne muscular dystrophy amenable to exon 45 skipping: A randomized, double‐blind, placebo‐controlled, dose‐titration trial. Muscle Nerve 2021; 64(3): 285-92.
[http://dx.doi.org/10.1002/mus.27347] [PMID: 34105177]
[160]
Group VS. A randomized controlled clinical trial of intravitreous fomivirsen for treatment of newly diagnosed peripheral cytomegalovirus retinitis in patients with AIDS. Am J Ophthalmol 2002; 133(4): 467-74.
[PMID: 11931780]
[161]
Mendell JR, Rodino-Klapac LR, Sahenk Z, et al. Eteplirsen for the treatment of Duchenne muscular dystrophy. Ann Neurol 2013; 74(5): 637-47.
[http://dx.doi.org/10.1002/ana.23982] [PMID: 23907995]
[162]
Adams D, Gonzalez-Duarte A, O’Riordan WD, et al. Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis. N Engl J Med 2018; 379(1): 11-21.
[http://dx.doi.org/10.1056/NEJMoa1716153] [PMID: 29972753]
[163]
Santos RD, Raal FJ, Donovan JM, Cromwell WC. Mipomersen preferentially reduces small low-density lipoprotein particle number in patients with hypercholesterolemia. J Clin Lipidol 2015; 9(2): 201-9.
[http://dx.doi.org/10.1016/j.jacl.2014.12.008] [PMID: 25911076]
[164]
Lamb YN. Inclisiran: First approval. Drugs 2021; 81(3): 389-95.
[http://dx.doi.org/10.1007/s40265-021-01473-6] [PMID: 33620677]
[165]
Lee TB, Yang K, Ko HJ, et al. Successful defibrotide treatment of a patient with veno-occlusive disease after living-donor liver transplantation. Medicine 2021; 100(25): e26463.
[http://dx.doi.org/10.1097/MD.0000000000026463] [PMID: 34160449]
[166]
Richardson PG, Smith AR, Triplett BM, et al. Defibrotide for patients with hepatic veno-occlusive disease/sinusoidal obstruction syndrome: interim results from a treatment IND study. Biol Blood Marrow Transplant 2017; 23(6): 997-1004.
[http://dx.doi.org/10.1016/j.bbmt.2017.03.008] [PMID: 28285079]
[167]
Liebow A, Li X, Racie T, et al. An investigational RNAi therapeutic targeting glycolate oxidase reduces oxalate production in models of primary hyperoxaluria. J Am Soc Nephrol 2017; 28(2): 494-503.
[http://dx.doi.org/10.1681/ASN.2016030338] [PMID: 27432743]
[168]
Kim J, Hu C, Moufawad El Achkar C, et al. Patient-customized oligonucleotide therapy for a rare genetic disease. N Engl J Med 2019; 381(17): 1644-52.
[http://dx.doi.org/10.1056/NEJMoa1813279] [PMID: 31597037]
[169]
Gragoudas ES, Adamis AP, Cunningham ET Jr, Feinsod M, Guyer DR. Pegaptanib for neovascular age-related macular degeneration. N Engl J Med 2004; 351(27): 2805-16.
[http://dx.doi.org/10.1056/NEJMoa042760] [PMID: 15625332]
[170]
Balwani M, Sardh E, Ventura P, et al. Phase 3 trial of RNAi therapeutic givosiran for acute intermittent porphyria. N Engl J Med 2020; 382(24): 2289-301.
[http://dx.doi.org/10.1056/NEJMoa1913147] [PMID: 32521132]
[171]
Szcześniak MW, Deorowicz S, Gapski J, Kaczyński Ł, Makałowska I. miRNEST database: An integrative approach in microRNA search and annotation. Nucleic Acids Res 2012; 40(D1): D198-204.
[http://dx.doi.org/10.1093/nar/gkr1159] [PMID: 22135287]
[172]
Liu H, Jin T, Liao R, et al. miRFANs: an integrated database for Arabidopsis thalianamicroRNA function annotations. BMC Plant Biol 2012; 12(1): 68.
[http://dx.doi.org/10.1186/1471-2229-12-68] [PMID: 22583976]
[173]
Piriyapongsa J, Bootchai C, Ngamphiw C, Tongsima S. micro-PIR2: a comprehensive database for human-mouse comparative study of microRNA-promoter interactions. Database 2014; 2014(0): bau115.
[http://dx.doi.org/10.1093/database/bau115] [PMID: 25425035]
[174]
Ritchie W, Flamant S, Rasko JEJ. mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets. Bioinformatics 2010; 26(2): 223-7.
[http://dx.doi.org/10.1093/bioinformatics/btp649] [PMID: 19933167]
[175]
Andrés-León E, González Peña D, Gómez-López G, Pisano DG. miRGate: a curated database of human, mouse and rat miRNA–mRNA targets. Database 2015; 2015: bav035.
[http://dx.doi.org/10.1093/database/bav035] [PMID: 25858286]
[176]
Fromm B, Billipp T, Peck LE, et al. A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome. Annu Rev Genet 2015; 49(1): 213-42.
[http://dx.doi.org/10.1146/annurev-genet-120213-092023] [PMID: 26473382]
[177]
Wang D, Gu J, Wang T, Ding Z, Oncomi RDB. OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs. Bioinformatics 2014; 30(15): 2237-8.
[http://dx.doi.org/10.1093/bioinformatics/btu155] [PMID: 24651967]
[178]
Ruepp A, Kowarsch A, Schmidl D, et al. PhenomiR: A knowledgebase for microRNA expression in diseases and biological processes. Genome Biol 2010; 11(1): R6-6.
[http://dx.doi.org/10.1186/gb-2010-11-1-r6] [PMID: 20089154]
[179]
Liu J, Liu X, Zhang S, et al. TarDB: An online database for plant miRNA targets and miRNA-triggered phased siRNAs. BMC Genomics 2021; 22(1): 348.
[http://dx.doi.org/10.1186/s12864-021-07680-5] [PMID: 33985427]
[180]
Hackenberg M, Sturm M, Langenberger D, Falcón-Pérez JM, Aransay AM. miRanalyzer: A microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 2009; 37 (Suppl. 2): W68-76.
[http://dx.doi.org/10.1093/nar/gkp347] [PMID: 19433510]
[181]
Lim LP, Glasner ME, Yekta S, Burge CB, Bartel DP. Vertebrate microRNA genes. Science 2003; 299(5612): 1540-0.
[http://dx.doi.org/10.1126/science.1080372] [PMID: 12624257]
[182]
Yang X, Li L. miRDeep-P: A computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 2011; 27(18): 2614-5.
[http://dx.doi.org/10.1093/bioinformatics/btr430] [PMID: 21775303]
[183]
Washietl S, Hofacker IL, Stadler PF. Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci USA 2005; 102(7): 2454-9.
[http://dx.doi.org/10.1073/pnas.0409169102] [PMID: 15665081]
[184]
Kadri S, Hinman V, Benos PV. HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics 2009; 10 (Suppl. 1): S35.
[http://dx.doi.org/10.1186/1471-2105-10-S1-S35] [PMID: 19208136]
[185]
Stegmayer G, Yones C, Kamenetzky L, Milone DH. High class-imbalance in pre-miRNA prediction: A novel approach based on deepSOM. IEEE/ACM Trans Comput Biol Bioinform 2016; 14(6): 1316-26.
[http://dx.doi.org/10.1109/TCBB.2016.2576459]
[186]
Gkirtzou K, Tsamardinos I, Tsakalides P, Poirazi P. MatureBayes: A probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 2010; 5(8): e11843.
[http://dx.doi.org/10.1371/journal.pone.0011843] [PMID: 20700506]
[187]
Jha A, Shankar R. miReader: Discovering novel miRNAs in species without sequenced genome. PLoS One 2013; 8(6): e66857.
[http://dx.doi.org/10.1371/journal.pone.0066857] [PMID: 23805282]
[188]
Bandyopadhyay S, Bhattacharyya M. PuTmiR: A database for extracting neighboring transcription factors of human microRNAs. BMC Bioinformatics 2010; 11(1): 190.
[http://dx.doi.org/10.1186/1471-2105-11-190] [PMID: 20398296]
[189]
Ronen R, Gan I, Modai S, et al. miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 2010; 26(20): 2615-6.
[http://dx.doi.org/10.1093/bioinformatics/btq493] [PMID: 20801911]
[190]
Shi J, Dong M, Li L, et al. mirPRo–a novel standalone program for differential expression and variation analysis of miRNAs. Sci Rep 2015; 5(1): 14617.
[http://dx.doi.org/10.1038/srep14617] [PMID: 26434581]
[191]
Wu J, Liu Q, Wang X, et al. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol 2013; 10(7): 1087-92.
[http://dx.doi.org/10.4161/rna.25193] [PMID: 23778453]
[192]
Zhao W, Liu W, Tian D, et al. wapRNA: a web-based application for the processing of RNA sequences. Bioinformatics 2011; 27(21): 3076-7.
[http://dx.doi.org/10.1093/bioinformatics/btr504] [PMID: 21896507]
[193]
Fahlgren N, Carrington JC. miRNA target prediction in plants, in Plant MicroRNAs. Springer 2010; pp. 51-7.
[194]
Shirdel EA, Xie W, Mak TW, Jurisica I. NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One 2011; 6(2): e17429.
[http://dx.doi.org/10.1371/journal.pone.0017429] [PMID: 21364759]
[195]
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]
[196]
Hsu SD, Lin FM, Wu WY, et al. miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res 2011; 39 (Suppl. 1): D163-9.
[http://dx.doi.org/10.1093/nar/gkq1107] [PMID: 21071411]
[197]
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]
[198]
Ahadi A, Sablok G, Hutvagner G. miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA–target interaction data. Nucleic Acids Res 2017; 45(6): e42-2.
[http://dx.doi.org/10.1093/nar/gkw1185] [PMID: 27903911]
[199]
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]
[200]
Loher P, Rigoutsos I. Interactive exploration of RNA22 microRNA target predictions. Bioinformatics 2012; 28(24): 3322-3.
[http://dx.doi.org/10.1093/bioinformatics/bts615] [PMID: 23074262]
[201]
Coronnello C, Benos PV, Comi R. ComiR: Combinatorial microRNA target prediction tool. Nucleic Acids Res 2013; 41: W159-64.
[PMID: 23703208]
[202]
Ahmadi H, Ahmadi A, Azimzadeh-Jamalkandi S, et al. HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens. Genomics 2013; 101(2): 94-100.
[http://dx.doi.org/10.1016/j.ygeno.2012.11.005] [PMID: 23174671]
[203]
Thadani R, Tammi MT. MicroTar: Predicting microRNA targets from RNA duplexes. BMC Bioinformatics 2006; 7 (Suppl. 5): S20-0.
[http://dx.doi.org/10.1186/1471-2105-7-S5-S20]
[204]
Quillet A, Saad C, Ferry G, et al. Improving bioinformatics prediction of microRNA targets by ranks aggregation. Front Genet 2020; 10: 1330.
[http://dx.doi.org/10.3389/fgene.2019.01330] [PMID: 32047509]
[205]
Friedman Y, Karsenty S, Linial M. miRror-Suite: decoding coordinated regulation by microRNAs. Database 2014; 2014(0): bau043.
[http://dx.doi.org/10.1093/database/bau043] [PMID: 24907353]
[206]
Ding J, Li X, Hu H. TarPmiR: a new approach for microRNA target site prediction. Bioinformatics 2016; 32(18): 2768-75.
[http://dx.doi.org/10.1093/bioinformatics/btw318] [PMID: 27207945]
[207]
Chae H, Rhee S, Nephew KP, Kim S. BioVLAB-MMIA-NGS: microRNA–mRNA integrated analysis using high-throughput sequencing data. Bioinformatics 2015; 31(2): 265-7.
[http://dx.doi.org/10.1093/bioinformatics/btu614] [PMID: 25270639]
[208]
Ji BY, Pan LR, Zhou JR, You ZH, Peng SL. SMMDA: Predicting miRNA-Disease associations by incorporating multiple similarity profiles and a novel disease representation. Biology 2022; 11(5): 777.
[http://dx.doi.org/10.3390/biology11050777] [PMID: 35625505]
[209]
Wong NW, Chen Y, Chen S, Wang X. OncomiR: An online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics 2018; 34(4): 713-5.
[http://dx.doi.org/10.1093/bioinformatics/btx627] [PMID: 29028907]
[210]
Liu X, Wang S, Meng F, et al. SM2miR: A database of the experimentally validated small molecules’ effects on microRNA expression. Bioinformatics 2013; 29(3): 409-11.
[http://dx.doi.org/10.1093/bioinformatics/bts698] [PMID: 23220571]
[211]
Vlachos IS, Zagganas K, Paraskevopoulou MD, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res 2015; 43(W1): W460-6.
[http://dx.doi.org/10.1093/nar/gkv403] [PMID: 25977294]
[212]
Jiang Q, Wang Y, Hao Y, et al. miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009; 37: D98-D104.
[http://dx.doi.org/10.1093/nar/gkn714] [PMID: 18927107]
[213]
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]
[214]
Preusse M, Theis FJ, Mueller NS. miTALOS v2: Analyzing tissue specific microRNA function. PLoS One 2016; 11(3): e0151771.
[http://dx.doi.org/10.1371/journal.pone.0151771] [PMID: 26998997]
[215]
Vitsios DM, Enright AJ. Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics 2015; 31(20): 3365-7.
[http://dx.doi.org/10.1093/bioinformatics/btv380] [PMID: 26093149]
[216]
Fehlmann T, Ludwig N, Backes C, Meese E, Keller A. Distribution of microRNA biomarker candidates in solid tissues and body fluids. RNA Biol 2016; 13(11): 1084-8.
[http://dx.doi.org/10.1080/15476286.2016.1234658] [PMID: 27687236]
[217]
Kim J, Levy E, Ferbrache A, et al. MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure. Bioinformatics 2014; 30(19): 2826-7.
[http://dx.doi.org/10.1093/bioinformatics/btu377] [PMID: 24907367]
[218]
Müller S, Rycak L, Winter P, Kahl G, Koch I, Rotter B. omiRas: a Web server for differential expression analysis of miRNAs derived from small RNA-Seq data. Bioinformatics 2013; 29(20): 2651-2.
[http://dx.doi.org/10.1093/bioinformatics/btt457] [PMID: 23946503]
[219]
Fasold M, Langenberger D, Binder H, Stadler PF, Hoffmann S. DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 2011; 39: W112-W11.
[http://dx.doi.org/10.1093/nar/gkr357]
[220]
Monfort-Lanzas P, Gronauer R, Madersbacher L, Schatz C, Rieder D, Hackl H. MIO: microRNA target analysis system for immuno-oncology. Bioinformatics 2022; 38(14): 3665-7.
[http://dx.doi.org/10.1093/bioinformatics/btac366] [PMID: 35642895]
[221]
Ichihara M, Murakumo Y, Masuda A, et al. Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities. Nucleic Acids Res 2007; 35(18): e123.
[http://dx.doi.org/10.1093/nar/gkm699] [PMID: 17884914]
[222]
Yamasaki C, Murakami K, Fujii Y, et al. The H-Invitational Database (H-InvDB), a comprehensive annotation resource for human genes and transcripts. Nucleic Acids Res 2008; 36: D793-9.
[PMID: 18089548]
[223]
Cabili MN, Trapnell C, Goff L, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 2011; 25(18): 1915-27.
[http://dx.doi.org/10.1101/gad.17446611] [PMID: 21890647]
[224]
Ma L, Cao J, Liu L, et al. LncBook: a curated knowledgebase of human long non-coding RNAs. Nucleic Acids Res 2019; 47(D1): D128-34.
[http://dx.doi.org/10.1093/nar/gky960] [PMID: 30329098]
[225]
Dinger ME, Pang KC, Mercer TR, Crowe ML, Grimmond SM, Mattick JS. NRED: A database of long noncoding RNA expression. Nucleic Acids Res 2009; 37 (Suppl. 1): D122-6.
[http://dx.doi.org/10.1093/nar/gkn617] [PMID: 18829717]

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