Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

Author(s): Muniba Faiza , Khushnuma Tanveer , Saman Fatihi , Yonghua Wang , Khalid Raza* .

Journal Name: Current Bioinformatics

Volume 14 , Issue 5 , 2019

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Abstract:

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets.

Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study.

Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools.

Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives.

Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

Keywords: microRNA target prediction, target prediction algorithm, transcript prediction, feature extraction, miRNA Homo sapiens, miRNA Drosophila melanogaster.

[1]
Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75(5): 843-54.
[2]
Liu B, Li J, Cairns MJ. Identifying miRNAs, targets and functions. Brief Bioinform 2014; 15(1): 1-19.
[3]
He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 2004; 5(7): 522-31.
[4]
Yanaihara N, Caplen N, Bowman E, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9(3): 189-98.
[5]
Porkka KP, Pfeiffer MJ, Waltering KK, Vessella RL, Tammela TL, Visakorpi T. MicroRNA expression profiling in prostate cancer. Cancer Res 2007; 67(13): 6130-5.
[6]
Yang H, Kong W, He L, et al. MicroRNA expression profiling in human ovarian cancer: miR-214 induces cell survival and cisplatin resistance by targeting PTEN. Cancer Res 2008; 68(2): 425-33.
[7]
Hébert SS, Horré K, Nicolaï L, et al. MicroRNA regulation of Alzheimer’s Amyloid precursor protein expression. Neurobiol Dis 2009; 33(3): 422-8.
[8]
Beveridge NJ, Gardiner E, Carroll AP, Tooney PA, Cairns MJ. Schizophrenia is associated with an increase in cortical microRNA biogenesis. Mol Psychiatry 2010; 15(12): 1176-89.
[9]
Cox MB, Cairns MJ, Gandhi KS, et al. MicroRNAs miR-17 and miR-20a inhibit T cell activation genes and are under-expressed in MS whole blood. PLoS One 2010; 5(8): e12132.
[10]
Mendes ND, Freitas AT, Sagot MF. Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 2009; 37(8): 2419-33.
[11]
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009; 136(2): 215-33.
[12]
Krek A, Grün D, Poy MN, et al. Combinatorial microRNA target predictions. Nat Genet 2005; 37(5): 495-500.
[13]
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.
[14]
Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM. Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution. Cell 2005; 123(6): 1133-46.
[15]
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.
[16]
Alexiou P, Maragkakis M, Papadopoulos GL, Reczko M, Hatzigeorgiou AG. Lost in translation: an assessment and perspective for computational microRNA target identification. Bioinformatics 2009; 25(23): 3049-55.
[17]
Fan X, Kurgan L. Comprehensive overview and assessment of computational prediction of microRNA targets in animals. Brief Bioinform 2015; 16(5): 780-94.
[18]
Srivastava PK, Moturu TR, Pandey P, Baldwin IT, Pandey SP. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics 2014; 15(1): 348.
[19]
Akhtar MM, Micolucci L, Islam MS, Olivieri F, Procopio AD. Bioinformatic tools for microRNA dissection. Nucleic Acids Res 2016; 44(1): 24-44.
[20]
Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB. Common features of microRNA target prediction tools. Front Genet 2014; 5: 23.
[21]
Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell 2003; 115(7): 787-98.
[22]
Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biol 2005; 3(3): e85.
[23]
Yue D, Liu H, Huang Y. Survey of computational algorithms for MicroRNA target prediction. Curr Genomics 2009; 10(7): 478-92.
[24]
Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 2009; 19(1): 92-105.
[25]
Doench JG, Sharp PA. Specificity of microRNA target selection in translational repression. Genes Dev 2004; 18(5): 504-11.
[26]
Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 2007; 27(1): 91-105.
[27]
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.
[28]
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.
[29]
Griffiths-Jones S, Grocock RJ, Van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006; 1; 34(Suppl-1): D140-4.
[30]
Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res 2008; 36(Database issue): D154-8.
[31]
Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006; 12(2): 192-7.
[32]
Vlachos IS, Paraskevopoulou MD, Karagkouni D, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res 2015; 43(Database issue): D153-9.
[33]
Chou CH, Chang NW, Shrestha S, et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 2016; 44(D1): D239-47.
[34]
Wang X. miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 2008; 14(6): 1012-7.
[35]
Wang X, El Naqa IM. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 2008; 24(3): 325-32.
[36]
Hsu PW, Huang HD, Hsu SD, Lin LZ, Tsou AP, Tseng CP, et al. miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res 2006; 34(Suppl. 1): D135-9.
[37]
Washietl S, Hofacker IL, Stadler PF. Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci USA 2005; 102(7): 2454-9.
[38]
Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol 2003; 5(1): R1.
[39]
Andrés-León E, González Peña D, Gómez-López G, Pisano DG. miRGate: a curated database of human, mouse and rat miRNAmRNA targets. Database (Oxford) 2015; 2015bav035.
[40]
Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015; 4
[http://dx.doi.org/10.7554/eLife.05005]
[41]
Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 2006; 34(Suppl. 1): W451-4.
[42]
Thadani R, Tammi MT. MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinformatics 2006; 7(Suppl. 5): S20.
[43]
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.
[44]
Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42(Database issue): D68-73.
[45]
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): D105-10.
[46]
Vergoulis T, Vlachos IS, Alexiou P, et al. TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 2012; 40(Database issue): D222-9.
[47]
Wang D, Gu J, Wang T, Ding Z. OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs. Bioinformatics 2014; 30(15): 2237-8.
[48]
Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res 2008; 36(Suppl. 1): D149-53.
[49]
Coronnello C, Benos PV. ComiR: Combinatorial microRNA target prediction tool. Nucleic Acids Res 2013; 41(Web Server issue): W159-64.
[50]
Paraskevopoulou MD, Georgakilas G, Kostoulas N, et al. DIANAmicroT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 2013; 41(Web Server issue) W169-73.
[51]
Friedman Y, Naamati G, Linial M. MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 2010; 26(15): 1920-1.
[52]
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.
[53]
Carrington JC, Ambros V. Role of microRNAs in plant and animal development. Science 2003; 301(5631): 336-8.
[54]
Martin G, Schouest K, Kovvuru P, Spillane C. Prediction and validation of microRNA targets in animal genomes. J Biosci 2007; 32(6): 1049-52.
[55]
Guo L, Zhang H, Zhao Y, Yang S, Chen F. In-depth exploration of miRNA: a new approach to study miRNA at the miRNA/isomiR levels. Curr Bioinform 2014; 9(5): 522-30.
[56]
Coronnello C, Hartmaier R, Arora A, et al. Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density. PLOS Comput Biol 2012; 8(12): e1002830.
[57]
Zhao Y, Granas D, Stormo GD. Inferring binding energies from selected binding sites. PLOS Comput Biol 2009; 5(12): e1000590.
[58]
Dweep H, Gretz N, Sticht C. miRWalk database for miRNA–target interactions. RNA mapping: methods and protocols 2014; 289-305.
[59]
John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. PLoS Biol 2004; 2(11): e363.
[60]
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.
[61]
Hausser J, Berninger P, Rodak C, Jantscher Y, Wirth S, Zavolan M. MirZ: an integrated microRNA expression atlas and target prediction resource. Nucleic Acids Res 2009; 37(Suppl. 2): W266-72.
[62]
Tsang JS, Ebert MS, van Oudenaarden A. Genome-wide dissection of microRNA functions and cotargeting networks using gene set signatures. Mol Cell 2010; 38(1): 140-53.
[63]
Vejnar CE, Zdobnov EM. MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res 2012; 40(22): 11673-83.
[64]
Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 2010; 20(1): 110-21.
[65]
German MA, Pillay M, Jeong DH, et al. Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 2008; 26(8): 941-6.
[66]
Beauclair L, Yu A, Bouché N. microRNA-directed cleavage and translational repression of the copper chaperone for superoxide dismutase mRNA in Arabidopsis. Plant J 2010; 62(3): 454-62.


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Article Details

VOLUME: 14
ISSUE: 5
Year: 2019
Page: [432 - 445]
Pages: 14
DOI: 10.2174/1574893614666190103101033
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