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Computational Prediction of Functional MicroRNA–mRNA Interactions

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Computational Biology of Non-Coding RNA

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1912))

Abstract

Proteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as recognition sequences for their target mRNAs within a larger regulatory machinery. A miRNA can have many target mRNAs and an mRNA can be targeted by many miRNAs which makes it difficult to experimentally discover all miRNA–mRNA interactions. Therefore, computational methods have been developed for miRNA detection and miRNA target prediction. An abundance of available computational tools makes selection difficult. Additionally, interactions are not currently the focus of investigation although they more accurately define the regulation than pre-miRNA detection or target prediction could perform alone. We define an interaction including the miRNA source and the mRNA target. We present computational methods allowing the investigation of these interactions as well as how they can be used to extend regulatory pathways. Finally, we present a list of points that should be taken into account when investigating miRNA–mRNA interactions. In the future, this may lead to better understanding of functional interactions which may pave the way for disease marker discovery and design of miRNA-based drugs.

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References

  1. Crick F (1970) Central dogma of molecular biology. Nature 227:561–563

    Article  CAS  PubMed  Google Scholar 

  2. Noble D (2012) A theory of biological relativity: no privileged level of causation. Interface Focus 2:55–64. https://doi.org/10.1098/rsfs.2011.0067

    Article  PubMed  Google Scholar 

  3. Liu H, Lei C, He Q, Pan Z, Xiao D, Tao Y (2018) Nuclear functions of mammalian MicroRNAs in gene regulation, immunity and cancer. Mol Cancer 17:64. https://doi.org/10.1186/s12943-018-0765-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yousef M, Allmer J (2014) miRNomics: microRNA biology and computational analysis. Humana Press, Totowa, NJ

    Book  Google Scholar 

  5. Iwakawa H, Tomari Y (2015) The functions of microRNAs: mRNA decay and translational repression. Trends Cell Biol 25:651–665. https://doi.org/10.1016/j.tcb.2015.07.011

    Article  CAS  PubMed  Google Scholar 

  6. Ørom UA, Nielsen FC, Lund AH (2008) MicroRNA-10a binds the 5’UTR of ribosomal protein mRNAs and enhances their translation. Mol Cell 30:460–471. https://doi.org/10.1016/j.molcel.2008.05.001

    Article  CAS  PubMed  Google Scholar 

  7. Grundhoff A, Sullivan CS (2011) Virus-encoded microRNAs. Virology 411:325–343. https://doi.org/10.1016/j.virol.2011.01.002

    Article  CAS  PubMed  Google Scholar 

  8. Skalsky RL, Cullen BR (2010) Viruses, microRNAs, and host interactions. Annu Rev Microbiol 64:123–141. https://doi.org/10.1146/annurev.micro.112408.134243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Saçar Demirci MD, Bağcı C, Allmer J (2016) Differential expression of Toxoplasma gondii microRNAs in murine and human hosts. In: Non-coding RNAs and inter-kingdom communication. Springer International Publishing, Cham, pp 143–159

    Chapter  Google Scholar 

  10. Saçar MD, Bağcı C, Allmer J (2014) Computational prediction of MicroRNAs from toxoplasma gondii potentially regulating the hosts’ gene expression. Genomics, Proteomics Bioinformatics 12:228–238. https://doi.org/10.1016/j.gpb.2014.09.002

    Article  PubMed  PubMed Central  Google Scholar 

  11. Liu S, Weiner HL (2016) Control of the gut microbiome by fecal microRNA. Microb cell (Graz, Austria) 3:176–177. https://doi.org/10.15698/mic2016.04.492

    Article  Google Scholar 

  12. Williams MR, Stedtfeld RD, Tiedje JM, Hashsham SA (2017) MicroRNAs-based inter-domain communication between the host and members of the gut microbiome. Front Microbiol 8. https://doi.org/10.3389/fmicb.2017.01896

  13. Baker M (2010) MicroRNA profiling: separating signal from noise. Nat Methods 7:687–692. https://doi.org/10.1038/nmeth0910-687

    Article  CAS  PubMed  Google Scholar 

  14. Chugh P, Dittmer DP (2012) Potential pitfalls in microRNA profiling. Wiley Interdiscip Rev RNA 3:601–616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dong H, Lei J, Ding L, Wen Y, Ju H, Zhang X (2013) MicroRNA: function, detection, and bioanalysis. Chem Rev 113:6207–6233. https://doi.org/10.1021/cr300362f

    Article  CAS  PubMed  Google Scholar 

  16. Saçar MD, Allmer J (2014) Machine learning methods for microRNA gene prediction. In: Yousef M, Allmer J (eds) miRNomics: microRNA biology and computational analysis SE-10. Humana Press, pp 177–187

    Google Scholar 

  17. Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, Clark TA, Schweitzer AC, Blume JE, Wang X, Darnell JC, Darnell RB (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456:464–469. https://doi.org/10.1038/nature07488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hamzeiy H, Allmer J, Yousef M (2014) Computational methods for microRNA target prediction. Methods Mol Biol 1107:207–221. https://doi.org/10.1007/978-1-62703-748-8_12

    Article  CAS  PubMed  Google Scholar 

  19. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843–854

    Article  CAS  PubMed  Google Scholar 

  20. Berezikov E, Cuppen E, RH P (2006) Approaches to microRNA discovery. Nat Genet 38(Suppl):S2–S7. https://doi.org/10.1038/ng1794

    Article  CAS  PubMed  Google Scholar 

  21. Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP (2003) The microRNAs of Caenorhabditis elegans. Genes Dev 17:991–1008. https://doi.org/10.1101/gad.1074403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang X, Zhang J, Li F, Gu J, He T, Zhang X, Li Y (2005) MicroRNA identification based on sequence and structure alignment. Bioinformatics 21:3610–3614. https://doi.org/10.1093/bioinformatics/bti562

    Article  CAS  PubMed  Google Scholar 

  23. Nam J-W, Kim J, Kim S-K, Zhang B-T (2006) ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res 34:W455–W458. https://doi.org/10.1093/nar/gkl321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Xue C, Li F, He T, Liu G-P, Li Y, Zhang X (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6:310. https://doi.org/10.1186/1471-2105-6-310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sewer A, Paul N, Landgraf P, Aravin A, Pfeffer S, Brownstein MJ, Tuschl T, van Nimwegen E, Zavolan M (2005) Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 6:267. https://doi.org/10.1186/1471-2105-6-267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hertel J, Stadler PF (2006) Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics 22:e197–e202. https://doi.org/10.1093/bioinformatics/btl257

    Article  CAS  PubMed  Google Scholar 

  27. Yousef M, Nebozhyn M, Shatkay H, Kanterakis S, Showe LC, Showe MK (2006) Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics 22:1325–1334. https://doi.org/10.1093/bioinformatics/btl094

    Article  CAS  PubMed  Google Scholar 

  28. Huang T-H, Fan B, Rothschild MF, Hu Z-L, Li K, Zhao S-H (2007) MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics 8:341. https://doi.org/10.1186/1471-2105-8-341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z (2007) MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 35:W339–W344. https://doi.org/10.1093/nar/gkm368

    Article  PubMed  PubMed Central  Google Scholar 

  30. Terai G, Komori T, Asai K (2081–2090) Kin T (2007) miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity. https://doi.org/10.1261/rna.655107.been

    Book  Google Scholar 

  31. Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26:407–415. https://doi.org/10.1038/nbt1394

    Article  CAS  PubMed  Google Scholar 

  32. Hackenberg M, Sturm M, Langenberger D, Falcón-Pérez JM, Aransay AM (2009) miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37:W68–W76. https://doi.org/10.1093/nar/gkp347

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P (2009) Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach. Nucleic Acids Res 37:3276–3287. https://doi.org/10.1093/nar/gkp120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kadri S, Hinman V, Benos PV (2009) HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics 10(Suppl 1):S35. https://doi.org/10.1186/1471-2105-10-S1-S35

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mathelier A, Carbone A (2010) MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 26:2226–2234. https://doi.org/10.1093/bioinformatics/btq329

    Article  CAS  PubMed  Google Scholar 

  36. Wu Y, Wei B, Liu H, Li T, Rayner S (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12:107. https://doi.org/10.1186/1471-2105-12-107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tempel S, Tahi F (2012) A fast ab-initio method for predicting miRNA precursors in genomes. Nucleic Acids Res 40:e80. https://doi.org/10.1093/nar/gks146

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Gao D, Middleton R, Rasko JEJ, Ritchie W (2013) miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 29:3225–3226. https://doi.org/10.1093/bioinformatics/btt545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lei J, Sun Y (2014) miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data. Bioinformatics 30:2837–2839. https://doi.org/10.1093/bioinformatics/btu380

    Article  CAS  PubMed  Google Scholar 

  40. Tran VDT, Tempel S, Zerath B, Zehraoui F, Tahi F (2015) miRBoost: boosting support vector machines for microRNA precursor classification. RNA 21:775–785. https://doi.org/10.1261/rna.043612.113

    Article  CAS  PubMed Central  Google Scholar 

  41. Chen J, Wang X, Liu B (2016) iMiRNA-SSF: improving the identification of microRNA precursors by combining negative sets with different distributions. Sci Rep 6:19062. https://doi.org/10.1038/srep19062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Saçar Demirci MD, Baumbach J, Allmer J (2017) On the performance of pre-microRNA detection algorithms. Nat Commun 8:330. https://doi.org/10.1038/s41467-017-00403-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lu Yi, Aras AS, Halushka MK (2018) miRge 2.0: an updated tool to comprehensively analyze microRNA sequencing data, bioRxiv, https://doi.org/10.1101/250779

  44. Gomes CPC, Cho J-H, Hood L, Franco OL, Pereira RW, Wang K (2013) A review of computational tools in microRNA discovery. Front Genet 4:81. https://doi.org/10.3389/fgene.2013.00081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. van der Burgt A, Fiers MWJE, Nap J-P, van Ham RCHJ (2009) In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity. BMC Genomics 10:204. https://doi.org/10.1186/1471-2164-10-204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic Acids Res 31:3429–3431. https://doi.org/10.1093/nar/gkg599

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zeng C, Wang W, Zheng Y, Chen X, Bo W, Song S, Zhang W, Peng M (2010) Conservation and divergence of microRNAs and their functions in Euphorbiaceous plants. Nucleic Acids Res 38:981–995. https://doi.org/10.1093/nar/gkp1035

    Article  CAS  PubMed  Google Scholar 

  48. Liang H, Li W-H (2009) Lowly expressed human microRNA genes evolve rapidly. Mol Biol Evol 26:1195–1198. https://doi.org/10.1093/molbev/msp053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Saçar Demirci MD, Allmer J (2017) Delineating the impact of machine learning elements in pre-microRNA detection. PeerJ 5:e3131. https://doi.org/10.7717/peerj.3131

    Article  PubMed  PubMed Central  Google Scholar 

  50. Marcinkowska M, Szymanski M, Krzyzosiak WJ, Kozlowski P (2011) Copy number variation of microRNA genes in the human genome. BMC Genomics 12:183. https://doi.org/10.1186/1471-2164-12-183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Erson-Bensan AE (2014) Introduction to microRNAs in biological systems. Methods Mol Biol 1107:1–14. https://doi.org/10.1007/978-1-62703-748-8_1

    Article  CAS  PubMed  Google Scholar 

  52. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp A-C, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141. https://doi.org/10.1016/j.cell.2010.03.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486. https://doi.org/10.1038/nature08170.Ago

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654–665. https://doi.org/10.1016/j.cell.2013.03.043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42:D78–D85. https://doi.org/10.1093/nar/gkt1266

    Article  CAS  PubMed  Google Scholar 

  56. Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229. https://doi.org/10.1093/nar/gkr1161

    Article  CAS  PubMed  Google Scholar 

  57. Krüger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454. https://doi.org/10.1093/nar/gkl243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500. https://doi.org/10.1038/ng1536

    Article  CAS  PubMed  Google Scholar 

  59. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20. https://doi.org/10.1016/j.cell.2004.12.035

    Article  CAS  PubMed  Google Scholar 

  60. Miranda KC, Huynh T, Tay Y, Ang Y-S, Tam W-L, Thomson AM, Lim B, Rigoutsos I (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217. https://doi.org/10.1016/j.cell.2006.07.031

    Article  CAS  PubMed  Google Scholar 

  61. Sethupathy P, Corda B, Hatzigeorgiou AG (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12:192–197. https://doi.org/10.1261/rna.2239606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yousef M, Jung S, Kossenkov AV, Showe LC, Showe MK (2007) Naïve Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics 23:2987–2992. https://doi.org/10.1093/bioinformatics/btm484

    Article  CAS  PubMed  Google Scholar 

  63. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284. https://doi.org/10.1038/ng2135

    Article  CAS  PubMed  Google Scholar 

  64. Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Simossis VA, Sethupathy P, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 10:295. https://doi.org/10.1186/1471-2105-10-295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37:D105–D110. https://doi.org/10.1093/nar/gkn851

    Article  CAS  PubMed  Google Scholar 

  66. 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 (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39:D163–D169. https://doi.org/10.1093/nar/gkq1107

    Article  CAS  PubMed  Google Scholar 

  67. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44:839–847. https://doi.org/10.1016/j.jbi.2011.05.002

    Article  CAS  PubMed  Google Scholar 

  68. Elefant N, Berger A, Shein H, Hofree M, Margalit H, Altuvia Y (2011) RepTar: a database of predicted cellular targets of host and viral miRNAs. Nucleic Acids Res 39:D188–D194. https://doi.org/10.1093/nar/gkq1233

    Article  CAS  PubMed  Google Scholar 

  69. Li J-H, Liu S, Zhou H, Qu L-H, Yang J-H (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42:D92–D97. https://doi.org/10.1093/nar/gkt1248

    Article  CAS  PubMed  Google Scholar 

  70. Chiu H-S, Llobet-Navas D, Yang X, Chung W-J, Ambesi-Impiombato A, Iyer A, Kim HR, Seviour EG, Luo Z, Sehgal V, Moss T, Lu Y, Ram P, Silva J, Mills GB, Califano A, Sumazin P (2015) Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks. Genome Res 25:257–267. https://doi.org/10.1101/gr.178194.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z (2015) MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep 5:8004. https://doi.org/10.1038/srep08004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Liu S, Li J-H, Wu J, Zhou K-R, Zhou H, Yang J-H, Qu L-H (2015) StarScan: a web server for scanning small RNA targets from degradome sequencing data. Nucleic Acids Res 43:W480–W486. https://doi.org/10.1093/nar/gkv524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Riffo-Campos Á, Riquelme I, Brebi-Mieville P (2016) Tools for sequence-based miRNA target prediction: what to choose? Int J Mol Sci 17:1987. https://doi.org/10.3390/ijms17121987

    Article  CAS  PubMed Central  Google Scholar 

  74. Lewis BP, Shih I, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115:787–798

    Article  CAS  PubMed  Google Scholar 

  75. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS (2003) MicroRNA targets in Drosophila. Genome Biol 5:R1. https://doi.org/10.1186/gb-2003-5-1-r1

    Article  PubMed  PubMed Central  Google Scholar 

  76. Kiriakidou M, Nelson PT, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos Z, Hatzigeorgiou A (2004) A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18:1165–1178. https://doi.org/10.1101/gad.1184704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Peterson SM, JA T, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB (2014) Common features of microRNA target prediction tools. Front Genet 5:23. https://doi.org/10.3389/fgene.2014.00023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Yousef M, Allmer J, Khalifa W (2016) Feature selection for microRNA target prediction comparison of one-class feature selection methodologies. In: BIOINFORMATICS 2016—7th international conference on bioinformatics models, methods and algorithms, Proceedings; Part of 9th international joint conference on biomedical engineering systems and technologies, BIOSTEC 2016

    Google Scholar 

  79. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets. PLoS Biol 2:e363. https://doi.org/10.1371/journal.pbio.0020363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  PubMed  Google Scholar 

  81. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517. https://doi.org/10.1261/rna.5248604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lai EC (2004) Predicting and validating microRNA targets. Genome Biol 5:115. https://doi.org/10.1186/gb-2004-5-9-115

    Article  PubMed  PubMed Central  Google Scholar 

  83. Yousef M, Nigatu D, Levy D, Allmer J, Henkel W (2017) Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers. EURASIP J Adv Signal Process 2017:70. https://doi.org/10.1186/s13634-017-0506-8

  84. Heyn J, Hinske LC, Ledderose C, Limbeck E, Kreth S (2013) Experimental miRNA target validation. Methods Mol Biol 936:83–90. https://doi.org/10.1007/978-1-62703-083-0_7

    Article  CAS  PubMed  Google Scholar 

  85. Thomson DW, Bracken CP, Goodall GJ (2011) Experimental strategies for microRNA target identification. Nucleic Acids Res 39:6845–6853. https://doi.org/10.1093/nar/gkr330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 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 FL, 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 (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46:D296–D302. https://doi.org/10.1093/nar/gkx1067

    Article  CAS  PubMed  Google Scholar 

  87. Saçar MD, Allmer J (2013) Current limitations for computational analysis of miRNAs in cancer. Pakistan J Clin Biomed Res 1:3–5

    Google Scholar 

  88. Koo J, Zhang J, Chaterji S (2018) Tiresias: context-sensitive approach to decipher the presence and strength of microRNA regulatory interactions. Theranostics 8:277–291. https://doi.org/10.7150/thno.22065

    Article  PubMed  PubMed Central  Google Scholar 

  89. Kim VN, Han J, Siomi MC (2009) Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 10:126–139. https://doi.org/10.1038/nrm2632

    Article  CAS  PubMed  Google Scholar 

  90. Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H (2005) Clustering and conservation patterns of human microRNAs. Nucleic Acids Res 33:2697–2706. https://doi.org/10.1093/nar/gki567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Mechtler P, Johnson S, Slabodkin H, Cohanim AB, Brodsky L, Kandel ES (2017) The evidence for a microRNA product of human DROSHA gene. RNA Biol 14:1508–1513. https://doi.org/10.1080/15476286.2017.1342934

    Article  PubMed  PubMed Central  Google Scholar 

  92. Acar İE, Saçar Demirci MD, Groß U, Allmer J (2018) The expressed MicroRNA—mRNA interactions of Toxoplasma gondii. Front Microbiol 8. https://doi.org/10.3389/fmicb.2017.02630

  93. Leinonen R, Sugawara H, Shumway M (2011) The sequence read archive. Nucleic Acids Res 39:D19–D21. https://doi.org/10.1093/nar/gkq1019

    Article  CAS  PubMed  Google Scholar 

  94. Fei Y, Wang R, Li H, Liu S, Zhang H, Huang J (2017) DPMIND: degradome-based Plant MiRNA-target interaction and network database. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx824

    Article  Google Scholar 

  95. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73. https://doi.org/10.1093/nar/gkt1181

    Article  CAS  PubMed  Google Scholar 

  96. Brinkrolf C, Janowski SJ, Kormeier B, Lewinski M, Hippe K, Borck D, Hofestädt R (2014) VANESA—a software application for the visualization and analysis of networks in system biology applications. J Integr Bioinform 11:239. https://doi.org/10.2390/biecoll-jib-2014-239

    Article  PubMed  Google Scholar 

  97. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D’Eustachio P (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477. https://doi.org/10.1093/nar/gkt1102

    Article  CAS  PubMed  Google Scholar 

  98. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Hamzeiy H, Suluyayla R, Brinkrolf C, Janowski SJ, Hofestaedt R, Allmer J (2017) Visualization and analysis of microRNAs within KEGG pathways using VANESA. J Integr Bioinform 14. https://doi.org/10.1515/jib-2016-0004

  100. Le DH, Verbeke L, Son LH, Chu DT, Pham VH (2017) Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs. BMC Bioinformatics 18:1–13. https://doi.org/10.1186/s12859-017-1924-1

    Article  CAS  Google Scholar 

  101. Zeng X, Zhang X, Zou Q (2016) Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform 17:193–203. https://doi.org/10.1093/bib/bbv033

    Article  CAS  PubMed  Google Scholar 

  102. Jiang Q, Hao Y, Wang G, Juan L, Zhang T, Teng M, Liu Y, Wang Y (2010) Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst Biol 4(Suppl 1):S2. https://doi.org/10.1186/1752-0509-4-S1-S2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Jiang Q, Hao Y, Wang G, Zhang T, Wang Y (2010) Weighted network-based inference of human microRNA-disease associations. In: 2010 Fifth international conference on frontier of computer science and technology. IEEE, pp 431–435

    Google Scholar 

  104. Wang D, Wang J, Lu M, Song F, Cui Q (2010) Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26:1644–1650. https://doi.org/10.1093/bioinformatics/btq241

    Article  CAS  PubMed  Google Scholar 

  105. Xu J, Li C-X, Li Y-S, Lv J-Y, Ma Y, Shao T-T, Xu L-D, Wang Y-Y, Du L, Zhang Y-P, Jiang W, Li C-Q, Xiao Y, Li X (2011) MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res 39:825–836. https://doi.org/10.1093/nar/gkq832

    Article  CAS  PubMed  Google Scholar 

  106. Chen X, Yan G-Y (2015) Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep 4:5501. https://doi.org/10.1038/srep05501

    Article  CAS  Google Scholar 

  107. Kandhro AH, Shoombuatong W, Nantasenamat C, Prachayasittikul V, Nuchnoi P (2017) The microRNA interaction network of lipid diseases. Front Genet 8:1–14. https://doi.org/10.3389/fgene.2017.00116

    Article  CAS  Google Scholar 

  108. Honardoost MA, Naghavian R, Ahmadinejad F, Hosseini A, Ghaedi K (2015) Integrative computational mRNA-miRNA interaction analyses of the autoimmune-deregulated miRNAs and well-known Th17 differentiation regulators: an attempt to discover new potential miRNAs involved in Th17 differentiation. Gene 572:153–162. https://doi.org/10.1016/j.gene.2015.08.043

    Article  CAS  PubMed  Google Scholar 

  109. Robinson JM, Henderson WA (2018) Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming. BMC Res Notes 11:1–7. https://doi.org/10.1186/s13104-018-3126-y

    Article  Google Scholar 

  110. van den Bout I, Divecha N (2009) PIP5K-driven PtdIns(4,5)P2 synthesis: regulation and cellular functions. J Cell Sci 122:3837–3850. https://doi.org/10.1242/jcs.056127

    Article  CAS  PubMed  Google Scholar 

  111. Han J, Pedersen JS, Kwon SC, Belair CD, Kim Y, Yeom K, Yang W, Haussler D, Blelloch R, Kim VN (2009) Posttranscriptional crossregulation between Drosha and DGCR8. Cell 136:75–84. https://doi.org/10.1016/j.cell.2008.10.053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Crews ST, Pearson JC (2009) Transcriptional autoregulation in development. Curr Biol 19:R241–R246. https://doi.org/10.1016/j.cub.2009.01.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Cargnin F, Flora A, Di Lascio S, Battaglioli E, Longhi R, Clementi F, Fornasari D (2005) PHOX2B regulates its own expression by a transcriptional auto-regulatory mechanism. J Biol Chem 280:37439–37448. https://doi.org/10.1074/jbc.M508368200

    Article  CAS  PubMed  Google Scholar 

  114. Bracken CP, Gregory PA, Kolesnikoff N, Bert AG, Wang J, Shannon MF, Goodall GJ (2008) A double-negative feedback loop between ZEB1-SIP1 and the microRNA-200 family regulates epithelial-mesenchymal transition. Cancer Res 68:7846–7854. https://doi.org/10.1158/0008-5472.CAN-08-1942

    Article  CAS  PubMed  Google Scholar 

  115. Osella M, Bosia C, Corá D, Caselle M (2011) The role of incoherent microRNA-mediated feedforward loops in noise buffering. PLoS Comput Biol 7. https://doi.org/10.1371/journal.pcbi.1001101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Tsang J, Zhu J, van Oudenaarden A (2007) MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 26:753–767. https://doi.org/10.1016/j.molcel.2007.05.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Zhang HM, Kuang S, Xiong X, Gao T, Liu C, Guo AY (2013) Transcription factor and microRNA co-regulatory loops: Important regulatory motifs in biological processes and diseases. Brief Bioinform 16:45–58. https://doi.org/10.1093/bib/bbt085

    Article  CAS  PubMed  Google Scholar 

  118. Yousef M, Trinh HV, Allmer J (2014) Intersection of microRNA and gene regulatory networks and their implication in cancer. Curr Pharm Biotechnol 15:445–454. https://doi.org/10.2174/1389201015666140519120855

    Article  CAS  PubMed  Google Scholar 

  119. Wightman B, Ha I, Ruvkun G (1993) Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75:855–862

    Article  CAS  PubMed  Google Scholar 

  120. Hamzeiy H, Suluyayla R, Brinkrolf C, Janowski SJ, Hofestädt R, Allmer J (2018) Visualization and analysis of miRNAs implicated in amyotrophic lateral sclerosis within gene regulatory pathways. Stud Heal Technol Inform 253:183–187

    Google Scholar 

  121. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E, Castagnoli L, Cesareni G (2012) MINT, the molecular interaction database: 2012 Update. Nucleic Acids Res 40

    Google Scholar 

  122. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40

    Article  PubMed  PubMed Central  Google Scholar 

  123. Liu B, Hu B (2010) HPRD: a high performance RDF database. Int J Parallel Emergent Distrib Syst 25:123–133

    Article  CAS  Google Scholar 

  124. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Faculty of Life and Natural Sciences, Department of Bioinformatics, Abdullah Gul University, Kayseri, Turkey

    Müşerref Duygu Saçar Demirci

  2. Department of Community Information Systems, Zefat Academic College, Zefat, Israel

    Malik Yousef

  3. Applied Bioinformatics, Bioscience, Wageningen University & Research, Wageningen, The Netherlands

    Jens Allmer

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  1. Müşerref Duygu Saçar Demirci
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  2. Malik Yousef
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  3. Jens Allmer
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Correspondence to Jens Allmer .

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Editors and Affiliations

  1. Department of Dermatology, Erlangen University Hospital, Erlangen, Germany

    Xin Lai

  2. Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Shailendra K. Gupta

  3. Department of Dermatology, Erlangen University Hospital, Erlangen, Germany

    Julio Vera

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Saçar Demirci, M.D., Yousef, M., Allmer, J. (2019). Computational Prediction of Functional MicroRNA–mRNA Interactions. In: Lai, X., Gupta, S., Vera, J. (eds) Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8982-9_7

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  • DOI: https://doi.org/10.1007/978-1-4939-8982-9_7

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