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Review Article

用于 microRNA 目标预测的生物信息学工具的最新发展

卷 29, 期 5, 2022

发表于: 04 January, 2022

页: [865 - 880] 页: 16

弟呕挨: 10.2174/0929867328666210804090224

价格: $65

摘要

MicroRNAs (miRNAs) 是调节基因表达转录后过程的核心参与者。 miRNA与靶mRNA的结合可以通过诱导降解或通过抑制靶mRNA的翻译来抑制它们的翻译。 miRNA 靶标识别的高通量实验方法成本高且耗时，取决于各种因素。 开发用于准确预测 miRNA 靶标的生物信息学方法至关重要。 随着后基因组时代RNA序列的增加，正在开发用于miRNA研究尤其是miRNA靶标预测的生物信息学方法。 这篇综述总结了当前用于 miRNA 靶点预测的最先进的生物信息学工具的发展，指出了现有 miRNA 数据库的进展和局限性，以及它们的工作原理。 最后，我们讨论了用于预测 miRNA 靶标的下一代算法的注意事项和前景。

关键词: microRNA、基因表达、NGS、目标预测、机器学习、生物信息学工具。

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[1]
Tömböl, Z.; Szabó, P.M.; Molnár, V.; Wiener, Z.; Tölgyesi, G.; Horányi, J.; Riesz, P.; Reismann, P.; Patócs, A.; Likó, I.; Gaillard, R.C.; Falus, A.; Rácz, K.; Igaz, P. Integrative molecular bioinformatics study of human adrenocortical tumors: microRNA, tissue-specific target prediction, and pathway analysis. Endocr. Relat. Cancer, 2009, 16(3), 895-906.
[http://dx.doi.org/10.1677/ERC-09-0096] [PMID: 19546168]
[2]
Hausser, J.; Berninger, P.; Rodak, C.; Jantscher, Y.; Wirth, S.; Zavolan, M.; Mir, Z. An integrated microRNA expression atlas and target prediction resource. Nucleic acids research,, 2009, 37, 266-272.
[http://dx.doi.org/10.1093/nar/gkp412]
[3]
Bandyopadhyay, S.; Mitra, R. TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics, 2009, 25(20), 2625-2631.
[http://dx.doi.org/10.1093/bioinformatics/btp503] [PMID: 19692556]
[4]
Watanabe, Y.; Tomita, M.; Kanai, A. Computational methods for microRNA target prediction. Methods Enzymol., 2007, 427, 65-86.
[http://dx.doi.org/10.1016/S0076-6879(07)27004-1] [PMID: 17720479]
[5]
Chaudhuri, K.; Chatterjee, R. MicroRNA detection and target prediction: Integration of computational and experimental approaches. DNA Cell Biol., 2007, 26(5), 321-337.
[http://dx.doi.org/10.1089/dna.2006.0549] [PMID: 17504028]
[6]
Witkos, T.M.; Koscianska, E.; Krzyzosiak, W.J. Practical aspects of microRNA target prediction. Curr. Mol. Med., 2011, 11(2), 93-109.
[http://dx.doi.org/10.2174/156652411794859250] [PMID: 21342132]
[7]
Marín, R.M.; Vanícek, J. Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res., 2011, 39(1), 19-29.
[http://dx.doi.org/10.1093/nar/gkq768] [PMID: 20805242]
[8]
Kast, J. A quick reality check for microRNA target prediction. Expert Rev. Proteomics, 2011, 8(2), 149-152.
[http://dx.doi.org/10.1586/epr.11.18] [PMID: 21501007]
[9]
Deng, N.; Puetter, A.; Zhang, K.; Johnson, K.; Zhao, Z.; Taylor, C.; Flemington, E.K.; Zhu, D. Isoform-level microRNA-155 target prediction using RNA-seq. Nucleic Acids Res., 2011, 39(9)e61
[http://dx.doi.org/10.1093/nar/gkr042] [PMID: 21317189]
[10]
Ghoshal, A.; Shankar, R.; Bagchi, S.; Grama, A.; Chaterji, S. MicroRNA target prediction using thermodynamic and sequence curves. BMC Genomics, 2015, 16, 999.
[http://dx.doi.org/10.1186/s12864-015-1933-2] [PMID: 26608597]
[11]
Chen, J.; Sun, D.; Chu, H.; Gong, Z.; Zhang, C.; Gong, B.; Li, Y.; Li, N.; Jiang, L. Screening of differential microRNA expression in gastric signet ring cell carcinoma and gastric adenocarcinoma and target gene prediction. Oncol. Rep., 2015, 33(6), 2963-2971.
[http://dx.doi.org/10.3892/or.2015.3935] [PMID: 25964059]
[12]
Wang, F.; Chan, L.W.; Law, H.K.; Cho, W.C.; Tang, P.; Yu, J.; Shyu, C.R.; Wong, S.C.; Yip, S.P.; Yung, B.Y. Exploring microRNA-mediated alteration of EGFR signaling pathway in non-small cell lung cancer using an mRNA:miRNA regression model supported by target prediction databases. Genomics, 2014, 104(6 Pt B), 504-511.
[http://dx.doi.org/10.1016/j.ygeno.2014.09.004] [PMID: 25257143]
[13]
Ritchie, W.; Rasko, J.E.; Flamant, S. MicroRNA target prediction and validation. Adv. Exp. Med. Biol., 2013, 774, 39-53.
[http://dx.doi.org/10.1007/978-94-007-5590-1_3] [PMID: 23377967]
[14]
Pinzón, N.; Li, B.; Martinez, L.; Sergeeva, A.; Presumey, J.; Apparailly, F.; Seitz, H. microRNA target prediction programs predict many false positives. Genome Res., 2017, 27(2), 234-245.
[http://dx.doi.org/10.1101/gr.205146.116] [PMID: 28148562]
[15]
Le, D.H.; Verbeke, L.; Son, L.H.; Chu, D.T.; Pham, V.H. Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs. BMC Bioinformatics, 2017, 18(1), 479.
[http://dx.doi.org/10.1186/s12859-017-1924-1] [PMID: 29137601]
[16]
Bong, I.P.N.; Ng, C.C.; Baharuddin, P.; Zakaria, Z. MicroRNA expression patterns and target prediction in multiple myeloma development and malignancy. Genes Genomics, 2017, 39(5), 533-540.
[http://dx.doi.org/10.1007/s13258-017-0518-7] [PMID: 28458781]
[17]
Hamzeiy, H.; Allmer, J.; Yousef, M. Computational methods for microRNA target prediction. Methods Mol. Biol., 2014, 1107, 207-221.
[http://dx.doi.org/10.1007/978-1-62703-748-8_12] [PMID: 24272439]
[18]
Oliveira, C.; Faoro, H.; Alves, L.R.; Goldenberg, S. RNA-binding proteins and their role in the regulation of gene expression in Trypanosoma cruzi and Saccharomyces cerevisiae. Genet. Mol. Biol., 2017, 40(1), 22-30.
[http://dx.doi.org/10.1590/1678-4685-gmb-2016-0258] [PMID: 28463381]
[19]
Yue, D.; Liu, H.; Huang, Y. Survey of computational algorithms for MicroRNA target prediction. Curr. Genomics, 2009, 10(7), 478-492.
[http://dx.doi.org/10.2174/138920209789208219] [PMID: 20436875]
[20]
Wang, H.; Li, W.H. Increasing MicroRNA target prediction confidence by the relative R(2) method. J. Theor. Biol., 2009, 259(4), 793-798.
[http://dx.doi.org/10.1016/j.jtbi.2009.05.007] [PMID: 19463832]
[21]
Wang, X.; Wang, X. Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Res., 2006, 34(5), 1646-1652.
[http://dx.doi.org/10.1093/nar/gkl068] [PMID: 16549876]
[22]
Hakguder, Z.; Shu, J.; Liao, C.; Pan, K.; Cui, J. Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model. BMC Genomics, 2018, 19(Suppl. 7), 658.
[http://dx.doi.org/10.1186/s12864-018-5029-7] [PMID: 30255782]
[23]
Wu, J.; Wang, B.; Zhou, J.; Ji, F. MicroRNA target gene prediction of ischemic stroke by using variational Bayesian inference for Gauss mixture model. Exp. Ther. Med., 2019, 17(4), 2734-2740.
[http://dx.doi.org/10.3892/etm.2019.7262] [PMID: 30906463]
[24]
Liu, W.; Wang, X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol., 2019, 20(1), 18.
[http://dx.doi.org/10.1186/s13059-019-1629-z] [PMID: 30670076]
[25]
Oliveira, A.C.; Bovolenta, L.A.; Nachtigall, P.G.; Herkenhoff, M.E.; Lemke, N.; Pinhal, D. Combining results from distinct microRNA target prediction tools enhances the performance of analyses. Front. Genet., 2017, 8, 59.
[http://dx.doi.org/10.3389/fgene.2017.00059] [PMID: 28559915]
[26]
Loganantharaj, R.; Randall, T.A. The limitations of existing approaches in improving microRNA target prediction accuracy. Methods Mol. Biol., 2017, 1617, 133-158.
[http://dx.doi.org/10.1007/978-1-4939-7046-9_10] [PMID: 28540682]
[27]
Ma, X.; Zhu, Y.; Huang, Y.; Tegeler, T.; Gao, S.J.; Zhang, J. Quantitative proteomic approach for microRNA target prediction based on 18O/16O labeling. Cancer Inform., 2016, 14(Suppl. 5), 163-173.
[PMID: 27980386]
[28]
Chen, R.; Shi, Y.H.; Zhang, H.; Hu, J.Y.; Luo, Y. Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data. Oncol. Lett., 2018, 15(6), 9994-10000.
[http://dx.doi.org/10.3892/ol.2018.8566] [PMID: 29928371]
[29]
Torkey, H.; Heath, L.S.; ElHefnawi, M. MicroTarget: MicroRNA target gene prediction approach with application to breast cancer. J. Bioinform. Comput. Biol., 2017, 15(4)1750013
[http://dx.doi.org/10.1142/S0219720017500135] [PMID: 28552033]
[30]
Roberts, J.T.; Borchert, G.M. Computational prediction of MicroRNA target genes, target prediction databases, and web resources. Methods Mol. Biol., 2017, 1617, 109-122.
[http://dx.doi.org/10.1007/978-1-4939-7046-9_8] [PMID: 28540680]
[31]
Yu, S.; Kim, J.; Min, H.; Yoon, S. Ensemble learning can significantly improve human microRNA target prediction. Methods, 2014, 69(3), 220-229.
[http://dx.doi.org/10.1016/j.ymeth.2014.07.008] [PMID: 25088780]
[32]
Wagner, M.; Vicinus, B.; Frick, V.O.; Auchtor, M.; Rubie, C.; Jeanmonod, P.; Richards, T.A.; Linder, R.; Weichert, F. MicroRNA target prediction: Theory and practice. Mol. Genet. Genomics, 2014, 289(6), 1085-1101.
[http://dx.doi.org/10.1007/s00438-014-0871-z] [PMID: 24938624]
[33]
Peterson, S.M.; Thompson, J.A.; Ufkin, M.L.; Sathyanarayana, P.; Liaw, L.; Congdon, C.B. Common features of microRNA target prediction tools. Front. Genet., 2014, 5, 23.
[http://dx.doi.org/10.3389/fgene.2014.00023] [PMID: 24600468]
[34]
Ekimler, S.; Sahin, K. Computational methods for microRNA target prediction. Genes (Basel), 2014, 5(3), 671-683.
[http://dx.doi.org/10.3390/genes5030671] [PMID: 25153283]
[35]
Bagnacani, A.; Wolfien, M.; Wolkenhauer, O. Tools for understanding miRNA-mRNA interactions for reproducible RNA analysis. Methods Mol. Biol., 2019, 1912, 199-214.
[http://dx.doi.org/10.1007/978-1-4939-8982-9_8] [PMID: 30635895]
[36]
Bertino, J.R.; Banerjee, D.; Mishra, P.J. Pharmacogenomics of microRNA: A miRSNP towards individualized therapy. Pharmacogenomics, 2007, 8(12), 1625-1627.
[http://dx.doi.org/10.2217/14622416.8.12.1625] [PMID: 18085993]
[37]
Brown, J.A.; Bourke, E. Practical bioinformatics analysis of MiRNA data using online tools. Methods Mol. Biol., 2017, 1509, 195-208.
[http://dx.doi.org/10.1007/978-1-4939-6524-3_18] [PMID: 27826929]
[38]
Canturk, K.M.; Ozdemir, M.; Can, C.; Öner, S.; Emre, R.; Aslan, H.; Cilingir, O.; Ciftci, E.; Celayir, F.M.; Aldemir, O.; Özen, M.; Artan, S. Investigation of key miRNAs and target genes in bladder cancer using miRNA profiling and bioinformatic tools. Mol. Biol. Rep., 2014, 41(12), 8127-8135.
[http://dx.doi.org/10.1007/s11033-014-3713-5] [PMID: 25189652]
[39]
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), 1836-1852.
[http://dx.doi.org/10.1093/bib/bby054] [PMID: 29982332]
[40]
Dai, E.; Lv, Y.; Meng, F.; Yu, X.; Zhang, Y.; Wang, S.; Liu, X.; Liu, D.; Wang, J.; Li, X.; Jiang, W. CREAM: a database for chemotherapy resistance-associated miRSNP. Cell Death Dis., 2014, 5e1272
[http://dx.doi.org/10.1038/cddis.2014.236] [PMID: 24874743]
[41]
Wang, W.; Guan, X.; Khan, M.T.; Xiong, Y.; Wei, D.Q. LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions. Comput. Biol. Chem., 2020, 89107406
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107406] [PMID: 33120126]
[42]
Gaca, S.; Reichert, S.; Rödel, C.; Rödel, F.; Kreuter, J. Survivin-miRNA-loaded nanoparticles as auxiliary tools for radiation therapy: Preparation, characterisation, drug release, cytotoxicity and therapeutic effect on colorectal cancer cells. J. Microencapsul., 2012, 29(7), 685-694.
[http://dx.doi.org/10.3109/02652048.2012.680511] [PMID: 22703230]
[43]
Gong, J.; Liu, C.; Liu, W.; Wu, Y.; Ma, Z.; Chen, H.; Guo, A.Y. An update of miRNASNP database for better SNP selection by GWAS data, miRNA expression and online tools. Database (Oxford), 2015, 2015bav029
[http://dx.doi.org/10.1093/database/bav029] [PMID: 25877638]
[44]
Wang, W.; Dai, Q.; Li, F.; Xiong, Y.; Wei, D.Q. MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs. Brief. Bioinform., 2021, 22(3), bbaa104.,
[PMID: 32520339]
[45]
Heneghan, H.M.; Miller, N.; Kerin, M.J. Circulating miRNA signatures: promising prognostic tools for cancer. J. Clin. Oncol., 2010, 28(29), e573-e574.
[http://dx.doi.org/10.1200/JCO.2010.29.8901] [PMID: 20697097]
[46]
Koshy, L.; Harikrishnan, S.; Sudhakaran, P.R. Prioritizing rs7294 as a mirSNP contributing to warfarin dosing variability. Pharmacogenomics, 2020, 21(4), 257-267.
[http://dx.doi.org/10.2217/pgs-2019-0137] [PMID: 31973625]
[47]
Koshy, L.; Vijayalekshmi, S.V.; Harikrishnan, S.; Raman, K.V.; Jissa, V.T.; Jayakumaran Nair, A.; Gangaprasad, A.; Nair, G.M.; Sudhakaran, P.R. Lack of association of mirSNP rs11174811 in AVPR1A gene with arterial blood pressure and hypertension in South Indian population. Clin. Exp. Hypertens., 2018, 40(6), 534-538.
[http://dx.doi.org/10.1080/10641963.2017.1403624] [PMID: 29182374]
[48]
Liu, C.; Zhang, F.; Li, T.; Lu, M.; Wang, L.; Yue, W.; Zhang, D. MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics, 2012, 13, 661.
[http://dx.doi.org/10.1186/1471-2164-13-661] [PMID: 23173617]
[49]
Lukasik, A.; Wójcikowski, M.; Zielenkiewicz, P. Tools4miRs - one place to gather all the tools for miRNA analysis. Bioinformatics, 2016, 32(17), 2722-2724.
[http://dx.doi.org/10.1093/bioinformatics/btw189] [PMID: 27153626]
[50]
Lukasik, A.; Zielenkiewicz, P. An overview of miRNA and miRNA target analysis tools. Methods Mol. Biol., 2019, 1932, 65-87.
[http://dx.doi.org/10.1007/978-1-4939-9042-9_5] [PMID: 30701492]
[51]
Macfarlane, C.L.; Quek, S.; Pionnier, N.; Turner, J.D.; Wanji, S.; Wagstaff, S.C.; Taylor, M.J. The insufficiency of circulating miRNA and DNA as diagnostic tools or as biomarkers of treatment efficacy for Onchocerca volvulus. Sci. Rep., 2020, 10(1), 6672.
[http://dx.doi.org/10.1038/s41598-020-63249-4] [PMID: 32317658]
[52]
Mendes, N.D.; Freitas, A.T.; Sagot, M.F. Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res., 2009, 37(8), 2419-2433.
[http://dx.doi.org/10.1093/nar/gkp145] [PMID: 19295136]
[53]
Metpally, R.P.; Nasser, S.; Malenica, I.; Courtright, A.; Carlson, E.; Ghaffari, L.; Villa, S.; Tembe, W.; Van Keuren-Jensen, K. Comparison of analysis tools for mirna high throughput sequencing using nerve crush as a model. Front. Genet., 2013, 4, 20.
[http://dx.doi.org/10.3389/fgene.2013.00020] [PMID: 23459507]
[54]
Mullany, L.E.; Wolff, R.K.; Slattery, M.L. Effectiveness and usability of bioinformatics tools to analyze pathways associated with miRNA expression. Cancer Inform., 2015, 14, 121-130.
[http://dx.doi.org/10.4137/CIN.S32716] [PMID: 26560461]
[55]
Okada, Y.; Jinnin, M.; Makino, T.; Kajihara, I.; Makino, K.; Honda, N.; Nakayama, W.; Inoue, K.; Fukushima, S.; Ihn, H. MIRSNP rs2910164 of miR-146a is associated with the muscle involvement in polymyositis/dermatomyositis. Int. J. Dermatol., 2014, 53(3), 300-304.
[http://dx.doi.org/10.1111/j.1365-4632.2012.05739.x] [PMID: 24716199]
[56]
Riffo-Campos, A.L.; Riquelme, I.; Brebi-Mieville, P. Tools for Sequence-Based miRNA Target Prediction: What to Choose? Int. J. Mol. Sci., 2016, 17(12)E1987
[http://dx.doi.org/10.3390/ijms17121987] [PMID: 27941681]
[57]
Rita Balistreri, C.; Allegra, A.; Crapanzano, F.; Pisano, C.; Ruvolo, G. Matrix Metalloproteinases (MMPs), their genetic variants and miRNA in mitral valve diseases: potential biomarker tools and targets for personalized treatments. J. Heart Valve Dis., 2016, 25(4), 463-474.
[PMID: 28009951]
[58]
Shukla, V.; Varghese, V.K.; Kabekkodu, S.P.; Mallya, S.; Satyamoorthy, K. A compilation of Web-based research tools for miRNA analysis. Brief. Funct. Genomics, 2017, 16(5), 249-273.
[http://dx.doi.org/10.1093/bfgp/elw042] [PMID: 28334134]
[59]
Srivastava, P.K.; Moturu, T.R.; Pandey, P.; Baldwin, I.T.; Pandey, S.P. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics, 2014, 15, 348.
[http://dx.doi.org/10.1186/1471-2164-15-348] [PMID: 24885295]
[60]
Tsai, E.M.; Wang, Y.S.; Lin, C.S.; Lin, W.Y.; Hsi, E.; Wu, M.T.; Juo, S.H. A microRNA-520 mirSNP at the MMP2 gene influences susceptibility to endometriosis in Chinese women. J. Hum. Genet., 2013, 58(4), 202-209.
[http://dx.doi.org/10.1038/jhg.2013.1] [PMID: 23364396]
[61]
Veneziano, D.; Marceca, G.P.; Di Bella, S.; Nigita, G.; Distefano, R.; Croce, C.M. Investigating miRNA-lncRNA interactions: computational tools and resources. Methods Mol. Biol., 2019, 1970, 251-277.
[http://dx.doi.org/10.1007/978-1-4939-9207-2_14] [PMID: 30963497]
[62]
Xi, Y.; Edwards, J.R.; Ju, J. Investigation of miRNA biology by bioinformatic tools and impact of miRNAs in colorectal cancer--regulatory relationship of c-Myc and p53 with miRNAs. Cancer Inform., 2007, 3, 245-253.
[http://dx.doi.org/10.1177/117693510700300011] [PMID: 18079974]
[63]
Yousef, G.M. miRSNP-based approach identifies a miRNA that regulates prostate-specific antigen in an allele-specific manner. Cancer Discov., 2015, 5(4), 351-352.
[http://dx.doi.org/10.1158/2159-8290.CD-15-0230] [PMID: 25847955]
[64]
Huang, H.Y.; Lin, Y.C.; Li, J.; Huang, K.Y.; Shrestha, S.; Hong, H.C.; Tang, Y.; Chen, Y.G.; Jin, C.N.; Yu, Y.; Xu, J.T.; Li, Y.M.; Cai, X.X.; Zhou, Z.Y.; Chen, X.H.; Pei, Y.Y.; Hu, L.; Su, J.J.; Cui, S.D.; Wang, F.; Xie, Y.Y.; Ding, S.Y.; Luo, M.F.; Chou, C.H.; Chang, N.W.; Chen, K.W.; Cheng, Y.H.; Wan, X.H.; Hsu, W.L.; Lee, T.Y.; Wei, F.X.; Huang, H.D. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res., 2020, 48(D1), D148-D154.
[PMID: 31647101]
[65]
Chou, C.H.; Shrestha, S.; Yang, C.D.; Chang, N.W.; Lin, Y.L.; Liao, K.W.; Huang, W.C.; Sun, T.H.; Tu, S.J.; Lee, W.H.; Chiew, M.Y.; Tai, C.S.; Wei, T.Y.; Tsai, T.R.; Huang, H.T.; Wang, C.Y.; Wu, H.Y.; Ho, S.Y.; Chen, P.R.; Chuang, C.H.; Hsieh, P.J.; Wu, Y.S.; Chen, W.L.; Li, M.J.; Wu, Y.C.; Huang, X.Y.; Ng, F.L.; Buddhakosai, W.; Huang, P.C.; Lan, K.C.; Huang, C.Y.; Weng, S.L.; Cheng, Y.N.; Liang, C.; Hsu, W.L.; Huang, H.D. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res., 2018, 46(D1), D296-D302.
[http://dx.doi.org/10.1093/nar/gkx1067] [PMID: 29126174]
[66]
Chou, C.H.; Chang, N.W.; Shrestha, S.; Hsu, S.D.; Lin, Y.L.; Lee, W.H.; Yang, C.D.; Hong, H.C.; Wei, T.Y.; Tu, S.J.; Tsai, T.R.; Ho, S.Y.; Jian, T.Y.; Wu, H.Y.; Chen, P.R.; Lin, N.C.; Huang, H.T.; Yang, T.L.; Pai, C.Y.; Tai, C.S.; Chen, W.L.; Huang, C.Y.; Liu, C.C.; Weng, S.L.; Liao, K.W.; Hsu, W.L.; Huang, H.D. miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res., 2016, 44(D1), D239-D247.
[http://dx.doi.org/10.1093/nar/gkv1258] [PMID: 26590260]
[67]
Hsu, S.D.; Tseng, Y.T.; Shrestha, S.; Lin, Y.L.; Khaleel, A.; Chou, C.H.; Chu, C.F.; Huang, H.Y.; Lin, C.M.; Ho, S.Y.; Jian, T.Y.; Lin, F.M.; Chang, T.H.; Weng, S.L.; Liao, K.W.; Liao, I.E.; Liu, C.C.; Huang, H.D. miRTarBase update 2014: An information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res., 2014, 42(Database issue), D78-D85.
[http://dx.doi.org/10.1093/nar/gkt1266] [PMID: 24304892]
[68]
Hsu, S.D.; Lin, F.M.; Wu, W.Y.; Liang, C.; Huang, W.C.; Chan, W.L.; Tsai, W.T.; Chen, G.Z.; Lee, C.J.; Chiu, C.M.; Chien, C.H.; Wu, M.C.; Huang, C.Y.; Tsou, A.P.; Huang, H.D. miRTarBase: A database curates experimentally validated microRNA-target interactions. Nucleic Acids Res., 2011, 39(Database issue), D163-D169.
[http://dx.doi.org/10.1093/nar/gkq1107] [PMID: 21071411]
[69]
Kehl, T.; Kern, F.; Backes, C.; Fehlmann, T.; Stöckel, D.; Meese, E.; Lenhof, H.P.; Keller, A. miRPathDB 2.0: A novel release of the miRNA Pathway Dictionary Database. Nucleic Acids Res., 2020, 48(D1), D142-D147.
[http://dx.doi.org/10.1093/nar/gkz1022] [PMID: 31691816]
[70]
Wu, P.; Xiao, Y.; Guo, T.; Wang, Y.; Liao, S.; Chen, L.; Liu, Z. Identifying miRNA-mRNA pairs and novel miRNAs from hepatocelluar carcinoma miRNomes and TCGA database. J. Cancer, 2019, 10(11), 2552-2559.
[http://dx.doi.org/10.7150/jca.28167] [PMID: 31258761]
[71]
Liu, T.; Zhang, Q.; Zhang, J.; Li, C.; Miao, Y.R.; Lei, Q.; Li, Q.; Guo, A.Y. EVmiRNA: A database of miRNA profiling in extracellular vesicles. Nucleic Acids Res., 2019, 47(D1), D89-D93.
[http://dx.doi.org/10.1093/nar/gky985] [PMID: 30335161]
[72]
Huang, G.J.; Luo, M.S.; Chen, G.P.; Fu, M.Y. MiRNA-mRNA crosstalk in laryngeal squamous cell carcinoma based on the TCGA database. European Archives of Oto-rhino-laryngology: Official Journal of the European Federation of Oto-rhino-laryngological Societies, 2018, 275(3), 571-759.
[73]
Chitarra, W.; Pagliarani, C.; Abbà, S.; Boccacci, P.; Birello, G.; Rossi, M.; Palmano, S.; Marzachì, C.; Perrone, I.; Gambino, G. miRVIT: A novel miRNA database and its application to uncover Vitis responses to Flavescence dorée infection. Front. Plant Sci., 2018, 9, 1034.
[http://dx.doi.org/10.3389/fpls.2018.01034] [PMID: 30065744]
[74]
Karagkouni, D.; Paraskevopoulou, M.D.; Chatzopoulos, S.; Vlachos, I.S.; Tastsoglou, S.; Kanellos, I.; Papadimitriou, D.; Kavakiotis, I.; Maniou, S.; Skoufos, G.; Vergoulis, T.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res., 2018, 46(D1), D239-D245.
[http://dx.doi.org/10.1093/nar/gkx1141] [PMID: 29156006]
[75]
Vergoulis, T.; Vlachos, I.S.; Alexiou, P.; Georgakilas, G.; Maragkakis, M.; Reczko, M.; Gerangelos, S.; Koziris, N.; Dalamagas, T.; Hatzigeorgiou, A.G. TarBase 6.0: Capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res., 2012, 40(Database issue), D222-D229.
[http://dx.doi.org/10.1093/nar/gkr1161] [PMID: 22135297]
[76]
Sethupathy, P.; Corda, B.; Hatzigeorgiou, A.G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA, 2006, 12(2), 192-197.
[http://dx.doi.org/10.1261/rna.2239606] [PMID: 16373484]
[77]
Paraskevopoulou, M.D.; Vlachos, I.S.; Hatzigeorgiou, A.G. DIANA-TarBase and DIANA suite tools: Studying experimentally supported microrna targets. Current Protocols in Bioinformatics,, 2016, 55, 12 14 11-12 14 18.
[78]
Vlachos, I.S.; Paraskevopoulou, M.D.; Karagkouni, D.; Georgakilas, G.; Vergoulis, T.; Kanellos, I.; Anastasopoulos, I.L.; Maniou, S.; Karathanou, K.; Kalfakakou, D.; Fevgas, A.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res., 2015, 43(Database issue), D153-D159.
[http://dx.doi.org/10.1093/nar/gku1215] [PMID: 25416803]
[79]
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 issue), D105-D110.
[http://dx.doi.org/10.1093/nar/gkn851] [PMID: 18996891]
[80]
Chen, Y.; Wang, X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res., 2020, 48(D1), D127-D131.
[http://dx.doi.org/10.1093/nar/gkz757] [PMID: 31504780]
[81]
Wong, N.; Wang, X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res., 2015, 43(Database issue), D146-D152.
[http://dx.doi.org/10.1093/nar/gku1104] [PMID: 25378301]
[82]
Wang, X. miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA, 2008, 14(6), 1012-1017.
[http://dx.doi.org/10.1261/rna.965408] [PMID: 18426918]
[83]
Zheng, X.; Fu, X.; Wang, K.; Wang, M. Deep neural networks for human microRNA precursor detection. BMC Bioinformatics, 2020, 21(1), 17.
[http://dx.doi.org/10.1186/s12859-020-3339-7] [PMID: 31931701]
[84]
Thody, J.; Moulton, V.; Mohorianu, I. PAREameters: A tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data. Nucleic Acids Res., 2020, 48(5), 2258-2270.
[http://dx.doi.org/10.1093/nar/gkz1234] [PMID: 31943065]
[85]
Sygitowicz, G.; Maciejak-Jastrzebska, A.; Sitkiewicz, D. MicroRNAs in the development of left ventricular remodeling and postmyocardial infarction heart failure. Polish Archives of Internal Med., 2020, 130(1), 59-65.
[86]
Patil, P.G.; Singh, N.V.; Parashuram, S.; Bohra, A.; Mundewadikar, D.M.; Sangnure, V.R.; Babu, K.D.; Sharma, J. Genome wide identification, characterization and validation of novel miRNA-based SSR markers in pomegranate (Punica granatum L.). Physiology and Molecular Biology of Plants: An International Journal of Functional Plant Biology, 2020, 26(4), 683-696.
[87]
Ha, J.; Park, C.; Park, C.; Park, S. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization. J. Biomed. Inform., 2020, 102103358
[http://dx.doi.org/10.1016/j.jbi.2019.103358] [PMID: 31857202]
[88]
Chen, X.; Sun, L.G.; Zhao, Y. NCMCMDA: miRNA-disease association prediction through neighborhood constraint matrix completion. Brief. Bioinform., 2020.
[http://dx.doi.org/10.1093/bib/bbz159] [PMID: 31927572]
[89]
Lu, H.C.; Yao, J.Q.; Yang, X.; Han, J.; Wang, J.Z.; Xu, K.; Zhou, R.; Yu, H.; Lv, Q.; Gu, M. Identification of a potentially functional circRNA-miRNA-mRNA regulatory network for investigating pathogenesis and providing possible biomarkers of bladder cancer. Cancer Cell Int., 2020, 20, 31.
[http://dx.doi.org/10.1186/s12935-020-1108-3] [PMID: 32015691]
[90]
Liu, Z.; Xu, S.; Dao, J.; Gan, Z.; Zeng, X. Differential expression of lncRNA/miRNA/mRNA and their related functional networks during the osteogenic/odontogenic differentiation of dental pulp stem cells. J. Cell. Physiol., 2020, 235(4), 3350-3361.
[http://dx.doi.org/10.1002/jcp.29223] [PMID: 31549394]
[91]
Lin, Y.; Zhang, L.; Zhao, Y.; Wang, Z.; Liu, H.; Zhang, L.; Zhang, Y.; Fu, Y.; Wu, J.; Ge, Y.; Zhang, W.; Zhou, S. Comparative analysis and functional identification of temperature-sensitive miRNA in Arabidopsis anthers. Biochem. Biophys. Res. Commun., 2020, 532(1), 1-10.
[http://dx.doi.org/10.1016/j.bbrc.2020.05.033] [PMID: 32826059]
[92]
Liang, T.; Han, L.; Guo, L. Rewired functional regulatory networks among miRNA isoforms (isomiRs) from let-7 and miR-10 gene families in cancer. Comput. Struct. Biotechnol. J., 2020, 18, 1238-1248.
[http://dx.doi.org/10.1016/j.csbj.2020.05.001] [PMID: 32542110]
[93]
Lee, J.H.; Kim, S.W.; Han, J.S.; Shin, S.P.; Lee, S.I.; Park, T.S. Functional analyses of miRNA-146b-5p during myogenic proliferation and differentiation in chicken myoblasts. BMC Mol Cell Biol, 2020, 21(1), 40.
[http://dx.doi.org/10.1186/s12860-020-00284-z] [PMID: 32471354]
[94]
Kumar, P.; Traurig, M.; Baier, L.J. Identification and functional validation of genetic variants in potential miRNA target sites of established BMI genes. Int. J. Obes., 2020, 44(5), 1191-1195.
[http://dx.doi.org/10.1038/s41366-019-0488-8] [PMID: 31745258]
[95]
Just, J.; Yan, Y.; Farup, J.; Sieljacks, P.; Sloth, M.; Venø, M.; Gu, T.; de Paoli, F.V.; Nyengaard, J.R.; Bæk, R.; Jørgensen, M.M.; Kjems, J.; Vissing, K.; Drasbek, K.R. Blood flow-restricted resistance exercise alters the surface profile, miRNA cargo and functional impact of circulating extracellular vesicles. Sci. Rep., 2020, 10(1), 5835.
[http://dx.doi.org/10.1038/s41598-020-62456-3] [PMID: 32245988]
[96]
Jeyaram, A.; Lamichhane, T.N.; Wang, S.; Zou, L.; Dahal, E.; Kronstadt, S.M.; Levy, D.; Parajuli, B.; Knudsen, D.R.; Chao, W.; Jay, S.M. Enhanced loading of functional miRNA Cargo via pH gradient modification of extracellular vesicles. Mol. Ther., 2020, 28(3), 975-985.
[http://dx.doi.org/10.1016/j.ymthe.2019.12.007] [PMID: 31911034]
[97]
Hoefer, I.E. Isolating functional (Iso)miRNA targets during ischemia. Mol. Ther., 2020, 28(1), 7-8.
[http://dx.doi.org/10.1016/j.ymthe.2019.12.003] [PMID: 31870621]
[98]
Guo, J.Y.; Wang, Y.S.; Chen, T.; Jiang, X.X.; Wu, P.; Geng, T.; Pan, Z.H.; Shang, M.K.; Hou, C.X.; Gao, K.; Guo, X.J. Functional analysis of a miRNA-like small RNA derived from Bombyx mori cytoplasmic polyhedrosis virus. Insect Sci., 2020, 27(3), 449-462.
[http://dx.doi.org/10.1111/1744-7917.12671] [PMID: 30869181]
[99]
Cojocneanu, R.; Braicu, C.; Raduly, L.; Jurj, A.; Zanoaga, O.; Magdo, L.; Irimie, A.; Muresan, M.S.; Ionescu, C.; Grigorescu, M.; Berindan-Neagoe, I. Plasma and tissue specific miRNA expression pattern and functional analysis associated to colorectal cancer patients. Cancers (Basel), 2020, 12(4)E843
[http://dx.doi.org/10.3390/cancers12040843] [PMID: 32244548]
[100]
Chang, L.; Zhou, G.; Soufan, O.; Xia, J. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res., 2020, 48(W1), W244-W251.
[http://dx.doi.org/10.1093/nar/gkaa467] [PMID: 32484539]
[101]
Bai, Y.; Baker, S.; Exoo, K.; Dai, X.; Ding, L.; Khattak, N.A.; Li, H.; Liu, H.; Liu, X. MMiRNA-Viewer2, a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network. BMC Bioinformatics, 2020, 21(Suppl. 4), 247.
[http://dx.doi.org/10.1186/s12859-020-3436-7] [PMID: 32631332]
[102]
Zou, J.B.; Chai, H.B.; Zhang, X.F.; Guo, D.Y.; Tai, J.; Wang, Y.; Liang, Y.L.; Wang, F.; Cheng, J.X.; Wang, J.; Shi, Y.J. Reconstruction of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in Cerebral Infarction. Sci. Rep., 2019, 9(1), 12176.
[http://dx.doi.org/10.1038/s41598-019-48435-3] [PMID: 31434962]
[103]
Zeh, N.; Schneider, H.; Mathias, S.; Raab, N.; Kleemann, M.; Schmidt-Hertel, S.; Weis, B.; Wissing, S.; Strempel, N.; Handrick, R.; Otte, K. Human CAP cells represent a novel source for functional, miRNA-loaded exosome production. PLoS One, 2019, 14(8)e0221679
[http://dx.doi.org/10.1371/journal.pone.0221679] [PMID: 31461486]
[104]
Yue, B.; Li, H.; Liu, M.; Wu, J.; Li, M.; Lei, C.; Huang, B.; Chen, H. Characterization of lncRNA-miRNA-mRNA network to reveal potential functional ceRNAs in bovine skeletal muscle. Front. Genet., 2019, 10, 91.
[http://dx.doi.org/10.3389/fgene.2019.00091] [PMID: 30842787]
[105]
Yang, G.; Zhang, Y.; Yang, J. Identification of potentially functional CircRNA-miRNA-mRNA regulatory network in gastric carcinoma using bioinformatics analysis. Med. Sci. Monit., 2019, 25, 8777-8796.
[http://dx.doi.org/10.12659/MSM.916902] [PMID: 31747387]
[106]
Yan, Y.; Li, X.Q.; Duan, J.L.; Bao, C.J.; Cui, Y.N.; Su, Z.B.; Xu, J.R.; Luo, Q.; Chen, M.; Xie, Y.; Lu, W.L. Nanosized functional miRNA liposomes and application in the treatment of TNBC by silencing Slug gene. Int. J. Nanomedicine, 2019, 14, 3645-3667.
[http://dx.doi.org/10.2147/IJN.S207837] [PMID: 31190817]
[107]
Wang, B.H.; Zhao, Y.F.; Shen, L.R.; Zhuang, Q. Differential screening and functional prediction analysis of miRNA expression profiles in periodontitis. Shanghai Kou Qiang Yi Xue, 2019, 28(4), 408-411.
[PMID: 31792483]
[108]
Tao, L.; Yang, L.; Huang, X.; Hua, F.; Yang, X. Reconstruction and Analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in dilated cardiomyopathy. Front. Genet., 2019, 10, 1149.
[http://dx.doi.org/10.3389/fgene.2019.01149] [PMID: 31803236]
[109]
Sun, D.; Han, L.; Cao, R.; Wang, H.; Jiang, J.; Deng, Y.; Yu, X. Prediction of a miRNA-mRNA functional synergistic network for cervical squamous cell carcinoma. FEBS Open Bio, 2019, 9(12), 2080-2092.
[http://dx.doi.org/10.1002/2211-5463.12747] [PMID: 31642613]
[110]
Park, I.; Kim, H.J.; Kim, Y.; Hwang, H.S.; Kasai, H.; Kim, J.H.; Park, J.W. Nanoscale imaging reveals miRNA-mediated control of functional states of dendritic spines. Proc. Natl. Acad. Sci. USA, 2019, 116(19), 9616-9621.
[http://dx.doi.org/10.1073/pnas.1819374116] [PMID: 31019087]
[111]
Monga, I.; Kumar, M. Computational resources for prediction and analysis of functional miRNA and their targetome. Methods Mol. Biol., 2019, 1912, 215-250.
[http://dx.doi.org/10.1007/978-1-4939-8982-9_9] [PMID: 30635896]
[112]
Kyuno, D.; Zhao, K.; Bauer, N.; Ryschich, E.; Zöller, M. Therapeutic targeting cancer-initiating cell markers by exosome miRNA: efficacy and functional consequences exemplified for claudin7 and EpCAM. Transl. Oncol., 2019, 12(2), 191-199.
[http://dx.doi.org/10.1016/j.tranon.2018.08.021] [PMID: 30393102]
[113]
Kumar, N.; Dougherty, J.A.; Manring, H.R.; Elmadbouh, I.; Mergaye, M.; Czirok, A.; Greta Isai, D.; Belevych, A.E.; Yu, L.; Janssen, P.M.L.; Fadda, P.; Gyorke, S.; Ackermann, M.A.; Angelos, M.G.; Khan, M. Assessment of temporal functional changes and miRNA profiling of human iPSC-derived cardiomyocytes. Sci. Rep., 2019, 9(1), 13188.
[http://dx.doi.org/10.1038/s41598-019-49653-5] [PMID: 31515494]
[114]
Jiang, Q.; Zhao, H.; Li, R.; Zhang, Y.; Liu, Y.; Wang, J.; Wang, X.; Ju, Z.; Liu, W.; Hou, M.; Huang, J. in silico genome-wide miRNA-QTL-SNPs analyses identify a functional SNP associated with mastitis in Holsteins. BMC Genet., 2019, 20(1), 46.
[http://dx.doi.org/10.1186/s12863-019-0749-5] [PMID: 31096910]
[115]
Han, Q.; Li, C.; Cao, Y.; Bao, J.; Li, K.; Song, R.; Chen, X.; Li, J.; Wu, X. CBX2 is a functional target of miRNA let-7a and acts as a tumor promoter in osteosarcoma. Cancer Med., 2019, 8(8), 3981-3991.
[http://dx.doi.org/10.1002/cam4.2320] [PMID: 31150156]
[116]
Geng, W.; Tang, H.; Luo, S.; Lv, Y.; Liang, D.; Kang, X.; Hong, W. Exosomes from miRNA-126-modified ADSCs promotes functional recovery after stroke in rats by improving neurogenesis and suppressing microglia activation. Am. J. Transl. Res., 2019, 11(2), 780-792.
[PMID: 30899379]
[117]
Galka-Marciniak, P.; Urbanek-Trzeciak, M.O.; Nawrocka, P.M.; Dutkiewicz, A.; Giefing, M.; Lewandowska, M.A.; Kozlowski, P. Somatic mutations in miRNA genes in lung cancer-potential functional consequences of non-coding sequence variants. Cancers (Basel), 2019, 11(6)E793
[http://dx.doi.org/10.3390/cancers11060793] [PMID: 31181801]
[118]
Cai, H.; Yang, C.; Liu, S.; Qi, H.; Wu, L.; Xu, L.A.; Xu, M. MiRNA-target pairs regulate adventitious rooting in Populus: A functional role for miR167a and its target Auxin response factor 8. Tree Physiol., 2019, 39(11), 1922-1936.
[http://dx.doi.org/10.1093/treephys/tpz085] [PMID: 31504994]
[119]
Burroughs, A.M.; Ando, Y. Identifying and characterizing functional 3′ nucleotide addition in the miRNA pathway. Methods, 2019, 152, 23-30.
[http://dx.doi.org/10.1016/j.ymeth.2018.08.006] [PMID: 30138674]
[120]
Bai, M.; Sun, L.; Jia, C.; Li, J.; Han, Y.; Liu, H.; Chen, Y.; Jiang, H. Integrated analysis of miRNA and mRNA expression profiles reveals functional mirna-targets in development testes of small tail han sheep. G3 (Bethesda), 2019, 9(2), 523-533.
[http://dx.doi.org/10.1534/g3.118.200947] [PMID: 30559255]
[121]
Asadirad, A.; Hashemi, S.M.; Baghaei, K.; Ghanbarian, H.; Mortaz, E.; Zali, M.R.; Amani, D. Phenotypical and functional evaluation of dendritic cells after exosomal delivery of miRNA-155. Life Sci., 2019, 219, 152-162.
[http://dx.doi.org/10.1016/j.lfs.2019.01.005] [PMID: 30625290]
[122]
Zhu, H.; Lu, J.; Zhao, H.; Chen, Z.; Cui, Q.; Lin, Z.; Wang, X.; Wang, J.; Dong, H.; Wang, S.; Tan, J. Functional long noncoding RNAs (lncRNAs) in clear cell kidney carcinoma revealed by reconstruction and comprehensive analysis of the lncRNA-miRNA-mRNA regulatory network. Med. Sci. Monit., 2018, 24, 8250-8263.
[http://dx.doi.org/10.12659/MSM.910773] [PMID: 30444862]
[123]
Tian, L.; Hu, X.; He, Y.; Wu, Z.; Li, D.; Zhang, H. Construction of lncRNA-miRNA-mRNA networks reveals functional lncRNAs in abdominal aortic aneurysm. Exp. Ther. Med., 2018, 16(5), 3978-3986.
[http://dx.doi.org/10.3892/etm.2018.6690] [PMID: 30344676]
[124]
Qu, M.; Luo, L.; Yang, Y.; Kong, Y.; Wang, D. Nanopolystyrene-induced microRNAs response in Caenorhabditis elegans after long-term and lose-dose exposure. Sci. Total Environ., 2019, 697134131
[http://dx.doi.org/10.1016/j.scitotenv.2019.134131] [PMID: 31476495]
[125]
Kato, M.; de Lencastre, A.; Pincus, Z.; Slack, F.J. Dynamic expression of small non-coding RNAs, including novel microRNAs and piRNAs/21U-RNAs, during Caenorhabditis elegans development. Genome Biol., 2009, 10(5), R54.
[http://dx.doi.org/10.1186/gb-2009-10-5-r54] [PMID: 19460142]
[126]
Lim, L.P.; Lau, N.C.; Weinstein, E.G.; Abdelhakim, A.; Yekta, S.; Rhoades, M.W.; Burge, C.B.; Bartel, D.P. The microRNAs of Caenorhabditis elegans. Genes Dev., 2003, 17(8), 991-1008.
[http://dx.doi.org/10.1101/gad.1074403] [PMID: 12672692]
[127]
Paraskevopoulou, M.D.; Vlachos, I.S.; Karagkouni, D.; Georgakilas, G.; Kanellos, I.; Vergoulis, T.; Zagganas, K.; Tsanakas, P.; Floros, E.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res., 2016, 44(D1), D231-D238.
[http://dx.doi.org/10.1093/nar/gkv1270] [PMID: 26612864]
[128]
Enright, A.J.; John, B.; Gaul, U.; Tuschl, T.; Sander, C.; Marks, D.S. MicroRNA targets in Drosophila. Genome Biol., 2003, 5(1), R1.
[http://dx.doi.org/10.1186/gb-2003-5-1-r1] [PMID: 14709173]
[129]
Agarwal, V.; Bell, G.W.; Nam, J.W.; Bartel, D.P. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 2015, 4, 4.
[http://dx.doi.org/10.7554/eLife.05005] [PMID: 26267216]
[130]
Wen, M.; Cong, P.; Zhang, Z.; Lu, H.; Li, T. DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics, 2018, 34(22), 3781-3787.
[http://dx.doi.org/10.1093/bioinformatics/bty424] [PMID: 29868708]
[131]
Pla, A.; Zhong, X.; Rayner, S. miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts. PLOS Comput. Biol., 2018, 14(7)e1006185
[http://dx.doi.org/10.1371/journal.pcbi.1006185] [PMID: 30005074]
[132]
Manavalan, B.; Hasan, M.M.; Basith, S.; Gosu, V.; Shin, T.H.; Lee, G. Empirical comparison and analysis of web-based DNA N4-methylcytosine site prediction tools. Mol. Ther. Nucleic Acids, 2020, 22, 406-420.
[http://dx.doi.org/10.1016/j.omtn.2020.09.010] [PMID: 33230445]
[133]
Hasan, M.M.; Khatun, M.S.; Kurata, H. iLBE for computational identification of linear B-cell epitopes by integrating sequence and evolutionary features. Genomics Proteomics Bioinformatics,, 2020, S1672-0229(18), 30274-2.
[http://dx.doi.org/10.1016/j.gpb.2019.04.004] [PMID: 33099033]
[134]
Hasan, M.M.; Schaduangrat, N.; Basith, S.; Lee, G.; Shoombuatong, W.; Manavalan, B. HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics, 2020, 36(11), 3350-3356.
[http://dx.doi.org/10.1093/bioinformatics/btaa160] [PMID: 32145017]
[135]
Hasan, M.M.; Manavalan, B.; Shoombuatong, W.; Khatun, M.S.; Kurata, H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation. Plant Mol. Biol., 2020, 103(1-2), 225-234.
[http://dx.doi.org/10.1007/s11103-020-00988-y] [PMID: 32140819]
[136]
Hasan, M.M.; Manavalan, B.; Shoombuatong, W.; Khatun, M.S.; Kurata, H. i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Comput. Struct. Biotechnol. J., 2020, 18, 906-912.
[http://dx.doi.org/10.1016/j.csbj.2020.04.001] [PMID: 32322372]
[137]
Basith, S.; Manavalan, B.; Hwan Shin, T.; Lee, G. Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening. Med. Res. Rev., 2020, 40(4), 1276-1314.
[http://dx.doi.org/10.1002/med.21658] [PMID: 31922268]
[138]
Lv, H.; Dao, F.Y.; Zhang, D.; Guan, Z.X.; Yang, H.; Su, W.; Liu, M.L.; Ding, H.; Chen, W.; Lin, H. iDNA-MS: An integrated computational tool for detecting DNA modification sites in multiple genomes. iScience,, 2020, 23(4), 100991.
[139]
Hasan, M.M.; Basith, S.; Khatun, M.S.; Lee, G.; Manavalan, B.; Kurata, H. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.Brief. Bioinform.,, 2021, 22(3), bbaa202.
[http://dx.doi.org/10.1093/bib/bbaa202] [PMID: 32910169]
[140]
Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome. Int. J. Biol. Macromol., 2020, 157, 752-758.
[http://dx.doi.org/10.1016/j.ijbiomac.2019.12.009] [PMID: 31805335]
[141]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees. Comput. Struct. Biotechnol. J., 2019, 17, 972-981.
[http://dx.doi.org/10.1016/j.csbj.2019.06.024] [PMID: 31372196]
[142]
Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. Prediction of S-nitrosylation sites by integrating support vector machines and random forest. Mol Omics, 2019, 15(6), 451-458.
[http://dx.doi.org/10.1039/C9MO00098D] [PMID: 31710075]
[143]
Manavalan, B.; Subramaniyam, S.; Shin, T.H.; Kim, M.O.; Lee, G. Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy. J. Proteome Res., 2018, 17(8), 2715-2726.
[http://dx.doi.org/10.1021/acs.jproteome.8b00148] [PMID: 29893128]
[144]
Hasan, M.M.; Shoombuatong, W.; Kurata, H.; Manavalan, B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools.Brief. Funct. Genomics, 2021, elaa028.,
[http://dx.doi.org/10.1093/bfgp/elaa028] [PMID: 33491072]
[145]
Charoenkwan, P.; Chiangjong, W.; Nantasenamat, C.; Hasan, M.M.; Manavalan, B.; Shoombuatong, W. StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides. Brief. Bioinform., 2021, bbab172.,
[http://dx.doi.org/10.1093/bib/bbab172] [PMID: 33963832]
[146]
Manavalan, B.; Lee, J. SVMQA: support-vector-machine-based protein single-model quality assessment. Bioinformatics, 2017, 33(16), 2496-2503.
[http://dx.doi.org/10.1093/bioinformatics/btx222] [PMID: 28419290]
[147]
Hasan, M.M.; Alam, M.A.; Shoombuatong, W.; Deng, H.W.; Manavalan, B.; Kurata, H. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.Brief. Bioinform., 2021, bbab167.,
[http://dx.doi.org/10.1093/bib/bbab167] [PMID: 33975333]
[148]
Lv, H.; Dao, F.Y.; Guan, Z.X.; Yang, H.; Li, Y.W.; Lin, H. Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method.Brief. Bioinform., 2020, bbaa255.,
[http://dx.doi.org/10.1093/bib/bbaa255] [PMID: 33099604]
[149]
Charoenkwan, P.; Yana, J.; Schaduangrat, N.; Nantasenamat, C.; Hasan, M.M.; Shoombuatong, W. iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics, 2020, 112(4), 2813-2822.
[http://dx.doi.org/10.1016/j.ygeno.2020.03.019] [PMID: 32234434]
[150]
Liu, G.; Liu, G.J.; Tan, J.X.; Lin, H. DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and depleted regions. Genomics, 2019, 111(5), 1167-1175.
[http://dx.doi.org/10.1016/j.ygeno.2018.07.013] [PMID: 30055231]
[151]
Khatun, S.; Hasan, M.; Kurata, H. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties. FEBS Lett., 2019, 593(21), 3029-3039.
[http://dx.doi.org/10.1002/1873-3468.13536] [PMID: 31297788]
[152]
Khatun, M.S.; Hasan, M.M.; Kurata, H. PreAIP: Computational prediction of anti-inflammatory peptides by integrating multiple complementary features. Front. Genet., 2019, 10, 129.
[http://dx.doi.org/10.3389/fgene.2019.00129] [PMID: 30891059]
[153]
Hasan, M.M.; Rashid, M.M.; Khatun, M.S.; Kurata, H. Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information. Sci. Rep., 2019, 9(1), 8258.
[http://dx.doi.org/10.1038/s41598-019-44548-x] [PMID: 31164681]
[154]
Xiong, Y.; Wang, Q.; Yang, J.; Zhu, X.; Wei, D.Q. PredT4SE-Stack: prediction of bacterial type IV secreted effectors from protein sequences using a stacked ensemble method. Front. Microbiol., 2018, 9, 2571.
[http://dx.doi.org/10.3389/fmicb.2018.02571] [PMID: 30416498]
[155]
Dao, F.Y.; Lv, H.; Zulfiqar, H.; Yang, H.; Su, W.; Gao, H.; Ding, H.; Lin, H. A computational platform to identify origins of replication sites in eukaryotes. Brief. Bioinform., 2020, 22(2), 1940-1950.
[http://dx.doi.org/10.1093/bib/bbaa017] [PMID: 32065211]
[156]
Wei, L.; He, W.; Malik, A.; Su, R.; Cui, L.; Manavalan, B. Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Brief. Bioinform., 2020, bbaa275.,
[http://dx.doi.org/10.1093/bib/bbaa275] [PMID: 33152766]
[157]
Xu, Z.C.; Feng, P.M.; Yang, H.; Qiu, W.R.; Chen, W.; Lin, H. iRNAD: a computational tool for identifying D modification sites in RNA sequence. Bioinformatics, 2019, 35(23), 4922-4929.
[http://dx.doi.org/10.1093/bioinformatics/btz358] [PMID: 31077296]
[158]
Lin, H. Computational analysis in medicinal chemistry. the case of pharmacogenomics and pharmacoproteomics. Med. Chem., 2020, 16(5), 593.
[http://dx.doi.org/10.2174/157340641605200608102355] [PMID: 32787749]
[159]
Charoenkwan, P.; Yana, J.; Nantasenamat, C.; Hasan, M.M.; Shoombuatong, W. iUmami-SCM: A Novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides. J. Chem. Inf. Model., 2020, 60(12), 6666-6678.
[http://dx.doi.org/10.1021/acs.jcim.0c00707] [PMID: 33094610]
[160]
Alam, M.A.; Komori, O.; Deng, H-W.; Calhoun, D.V.; Wang, Y.P. Robust kernel canonical correlation analysis to detect gene-gene co-association: A genetics study. J. Bioinform. Comput. Biol., 2019, 17, 23.
[161]
Zhang, Z.Y.; Yang, Y.H.; Ding, H.; Wang, D.; Chen, W.; Lin, H. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Brief. Bioinform., 2020.
[http://dx.doi.org/10.1093/bib/bbz177] [PMID: 31994694]
[162]
Wang, J.; Du, P.F.; Xue, X.Y.; Li, G.P.; Zhou, Y.K.; Zhao, W.; Lin, H.; Chen, W. VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences. Bioinformatics, 2020, 36(4), 1277-1278.
[PMID: 31504195]
[163]
Kruger, J.; Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res., 2006, 34, 451-454.
[http://dx.doi.org/10.1093/nar/gkl243]
[164]
Dai, X.; Zhuang, Z.; Zhao, P.X. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res., 2018, 46(W1), W49-W54.
[http://dx.doi.org/10.1093/nar/gky316] [PMID: 29718424]
[165]
Dai, X.; Zhao, P.X. psRNATarget: A plant small RNA target analysis server. Nucleic Acids Res., 2011, 39, 155-159.
[166]
Zhang, Z.M.; Wang, J.S.; Zulfiqar, H.; Lv, H.; Dao, F.Y.; Lin, H. Early diagnosis of pancreatic ductal adenocarcinoma by combining relative expression orderings with machine-learning method. Front. Cell Dev. Biol., 2020, 8582864
[http://dx.doi.org/10.3389/fcell.2020.582864] [PMID: 33178697]
[167]
Yan, C.; Wu, F.X.; Wang, J.; Duan, G. PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences. BMC Bioinformatics, 2020, 21(1), 111.
[http://dx.doi.org/10.1186/s12859-020-3426-9] [PMID: 32183740]
[168]
Wang, X. Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies. Bioinformatics, 2016, 32(9), 1316-1322.
[http://dx.doi.org/10.1093/bioinformatics/btw002] [PMID: 26743510]
[169]
Gudyś, A.; Szcześniak, M.W.; Sikora, M.; Makałowska, I. HuntMi: an efficient and taxon-specific approach in pre-miRNA identification. BMC Bioinformatics, 2013, 14, 83.
[http://dx.doi.org/10.1186/1471-2105-14-83] [PMID: 23497112]
[170]
Bradley, T.; Moxon, S. FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals. Bioinformatics, 2020, 36(8), 2410-2416.
[http://dx.doi.org/10.1093/bioinformatics/btaa007] [PMID: 31930382]
[171]
Hackenberg, M.; Rodriguez-Ezpeleta, N.; Aransay, A.M. miRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res., 2011, 39, 132-138.
[172]
Gan, H.H.; Gunsalus, K.C. The role of tertiary structure in MicroRNA target recognition. Methods Mol. Biol., 2019, 1970, 43-64.
[http://dx.doi.org/10.1007/978-1-4939-9207-2_4] [PMID: 30963487]
[173]
Calin-Jageman, I.; Nicholson, A.W. RNA structure-depen-dent uncoupling of substrate recognition and cleavage by Escherichia coli ribonuclease III. Nucleic Acids Res., 2003, 31(9), 2381-2392.
[http://dx.doi.org/10.1093/nar/gkg329] [PMID: 12711683]
[174]
L’Yi, S.; Jung, D.; Oh, M.; Kim, B.; Freishtat, R.J.; Giri, M.; Hoffman, E.; Seo, J. miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions. Methods, 2017, 124, 78-88.
[http://dx.doi.org/10.1016/j.ymeth.2017.06.004] [PMID: 28600227]
[175]
Washietl, S.; Hofacker, I.L.; Lukasser, M.; Hüttenhofer, A.; Stadler, P.F. Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nat. Biotechnol., 2005, 23(11), 1383-1390.
[http://dx.doi.org/10.1038/nbt1144] [PMID: 16273071]
[176]
Evers, M.; Huttner, M.; Dueck, A.; Meister, G.; Engelmann, J.C. miRA: adaptable novel miRNA identification in plants using small RNA sequencing data. BMC Bioinformatics, 2015, 16, 370.
[http://dx.doi.org/10.1186/s12859-015-0798-3] [PMID: 26542525]
[177]
Gao, D.; Middleton, R.; Rasko, J.E.; Ritchie, W. miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics, 2013, 29(24), 3225-3226.
[http://dx.doi.org/10.1093/bioinformatics/btt545] [PMID: 24048357]
[178]
Buermans, H.P.; Ariyurek, Y.; van Ommen, G.; den Dunnen, J.T. ’T Hoen, P.A. New methods for next generation sequencing based microRNA expression profiling. BMC Genomics, 2010, 11, 716.
[http://dx.doi.org/10.1186/1471-2164-11-716] [PMID: 21171994]
[179]
Sablok, G.; Milev, I.; Minkov, G.; Minkov, I.; Varotto, C.; Yahubyan, G.; Baev, V. isomiRex: web-based identification of microRNAs, isomiR variations and differential expression using next-generation sequencing datasets. FEBS Lett., 2013, 587(16), 2629-2634.
[http://dx.doi.org/10.1016/j.febslet.2013.06.047] [PMID: 23831580]
[180]
An, J.; Lai, J.; Lehman, M.L.; Nelson, C.C. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res., 2013, 41(2), 727-737.
[http://dx.doi.org/10.1093/nar/gks1187] [PMID: 23221645]
[181]
Friedländer, M.R.; Chen, W.; Adamidi, C.; Maaskola, J.; Einspanier, R.; Knespel, S.; Rajewsky, N. Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol., 2008, 26(4), 407-415.
[http://dx.doi.org/10.1038/nbt1394] [PMID: 18392026]
[182]
Kuang, Z.; Wang, Y.; Li, L.; Yang, X. miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants. Bioinformatics, 2019, 35(14), 2521-2522.
[http://dx.doi.org/10.1093/bioinformatics/bty972] [PMID: 30521000]
[183]
Yang, X.; Li, L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics, 2011, 27(18), 2614-2615.
[http://dx.doi.org/10.1093/bioinformatics/btr430] [PMID: 21775303]
[184]
Wang, H.; Hu, Y.; Xie, Y.; Wang, L.; Wang, J.; Lei, L.; Huang, M.; Zhang, C. Prediction of MicroRNA and gene target in synovium-associated pain of knee osteoarthritis based on canonical correlation analysis. BioMed Res. Int., 2019, 20194506876
[http://dx.doi.org/10.1155/2019/4506876] [PMID: 31737663]
[185]
Rennie, W.; Kanoria, S.; Liu, C.; Carmack, C.S.; Lu, J.; Ding, Y. Sfold tools for MicroRNA target prediction. Methods Mol. Biol., 2019, 1970, 31-42.
[http://dx.doi.org/10.1007/978-1-4939-9207-2_3] [PMID: 30963486]
[186]
Tyagi, S.; Vaz, C.; Gupta, V.; Bhatia, R.; Maheshwari, S.; Srinivasan, A.; Bhattacharya, A. CID-miRNA: a web server for prediction of novel miRNA precursors in human genome. Biochem. Biophys. Res. Commun., 2008, 372(4), 831-834.
[http://dx.doi.org/10.1016/j.bbrc.2008.05.134] [PMID: 18522801]
[187]
Wei, G.; Sun, L.; Qin, S.; Li, R.; Chen, L.; Jin, P.; Ma, F. Dme-Hsa Disease Database (DHDD): Conserved Human Disease-Related miRNA and Their Targeting Genes in Drosophila melanogaster. Int. J. Mol. Sci., 2018, 19(9)E2642
[http://dx.doi.org/10.3390/ijms19092642] [PMID: 30200613]
[188]
Chen, F.; Zhou, H.; Wu, C.; Yan, H. Identification of miRNA profiling in prediction of tumor recurrence and progress and bioinformatics analysis for patients with primary esophageal cancer: Study based on TCGA database. Pathol. Res. Pract., 2018, 214(12), 2081-2086.
[http://dx.doi.org/10.1016/j.prp.2018.10.009] [PMID: 30477645]
[189]
Romero-Cordoba, S.L.; Salido-Guadarrama, I.; Rodriguez-Dorantes, M.; Hidalgo-Miranda, A. miRNA biogenesis: biological impact in the development of cancer. Cancer Biol. Ther., 2014, 15(11), 1444-1455.
[http://dx.doi.org/10.4161/15384047.2014.955442] [PMID: 25482951]
[190]
Chen, X.; Xie, D.; Zhao, Q.; You, Z.H. MicroRNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2019, 20(2), 515-539.
[http://dx.doi.org/10.1093/bib/bbx130] [PMID: 29045685]
[191]
Zeng, X.; Zhang, X.; Zou, Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief. Bioinform., 2016, 17(2), 193-203.
[http://dx.doi.org/10.1093/bib/bbv033] [PMID: 26059461]

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