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Prediction of miRNA Targets

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RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1269))

Abstract

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

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References

  1. 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(5):843–854

    Article  CAS  PubMed  Google Scholar 

  2. Huttenhofer A, Vogel J (2006) Experimental approaches to identify non-coding RNAs. Nucleic Acids Res 34(2):635–646

    Article  PubMed Central  PubMed  Google Scholar 

  3. Miranda KC, Huynh T, Tay Y et al (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126(6):1203–1217

    Article  CAS  PubMed  Google Scholar 

  4. Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284

    Article  CAS  PubMed  Google Scholar 

  5. Griffiths-Jones S, Grocock RJ, van Dongen S et al (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(Database issue):D140–D144

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Betel D, Wilson M, Gabow A et al (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res 36(Database issue):D149–D153

    CAS  PubMed Central  PubMed  Google Scholar 

  7. 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(1):15–20

    Article  CAS  PubMed  Google Scholar 

  8. Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495–500

    Article  CAS  PubMed  Google Scholar 

  9. Maragkakis M, Reczko M, Simossis VA et al (2009) DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 37(Web Server issue):W273–W276

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Maragkakis M, Alexiou P, Papadopoulos GL et al (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinform 10:295

    Article  Google Scholar 

  11. Khvorova A, Reynolds A, Jayasena SD (2003) Functional siRNAs and miRNAs exhibit strand bias. Cell 115(2):209–216

    Article  CAS  PubMed  Google Scholar 

  12. Lee Y, Jeon K, Lee JT et al (2002) MicroRNA maturation: stepwise processing and subcellular localization. EMBO J 21(17):4663–4670

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Lim LP, Glasner ME, Yekta S et al (2003) Vertebrate microRNA genes. Science 299(5612):1540

    Article  CAS  PubMed  Google Scholar 

  14. Brennecke J, Stark A, Russell RB et al (2005) Principles of microRNA-target recognition. PLoS Biol 3(3):e85

    Article  PubMed Central  PubMed  Google Scholar 

  15. Helvik SA, Snove O Jr, Saetrom P (2006) Reliable prediction of Drosha processing sites improves microRNA gene prediction. Bioinformatics 23(2):142–149

    Article  PubMed  Google Scholar 

  16. Hertel J, Stadler PF (2006) Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics 22(14):e197–e202

    Article  CAS  PubMed  Google Scholar 

  17. Lim LP, Lau NC, Weinstein EG et al (2003) The microRNAs of Caenorhabditis elegans. Genes Dev 16(8):991–1008

    Article  Google Scholar 

  18. Sewer A, Paul N, Landgraf P et al (2005) Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinform 6:267–281

    Article  Google Scholar 

  19. Yousef M, Nebozhyn M, Shatkay H et al (2006) Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics 22(11):1325–1334

    Article  CAS  PubMed  Google Scholar 

  20. Kiriakidou M, Nelson PT, Kouranov A et al (2004) A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18(10):1165–1178

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Friedman RC, Farh KK, Burge CB et al (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19(1):92–105

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Witkos TM, Koscianska E, Krzyzosiak WJ (2011) Practical aspects of microRNA target prediction. Curr Mol Med 11(2):93–109

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Lewis BP, Shih IH, Jones-Rhoades MW et al (2003) Prediction of mammalian microRNA targets. Cell 115(7):787–798

    Article  CAS  PubMed  Google Scholar 

  24. Enright AJ, John B, Gaul U et al (2003) MicroRNA targets in Drosophila. Genome Biol 5(1):R1

    Article  PubMed Central  PubMed  Google Scholar 

  25. Long D, Lee R, Williams P et al (2007) Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14(4):287–294

    Article  CAS  PubMed  Google Scholar 

  26. Marin RM, Vanicek J (2011) Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res 39(1):19–29

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Grimson A, Farh KK, Johnston WK et al (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27(1):91–105

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Schmidt T, Mewes HW, Stumpflen V (2009) A novel putative miRNA target enhancer signal. PLoS One 4(7):e6473

    Article  PubMed Central  PubMed  Google Scholar 

  29. Baek D, Villen J, Shin C et al (2008) The impact of microRNAs on protein output. Nature 455(7209):64–71

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Selbach M, Schwanhausser B, Thierfelder N et al (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455(7209):58–63

    Article  CAS  PubMed  Google Scholar 

  31. Friedlander MR, Chen W, Adamidi C et al (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26(4):407–415

    Article  PubMed  Google Scholar 

  32. Cimmino A, Calin GA, Fabbri M et al (2005) miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102(39):13944–13949

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Mayr C, Hemann MT, Bartel DP (2007) Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation. Science 315(5818):1576–1579

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Sylvestre Y, De Guire V, Querido E et al (2007) An E2F/miR-20a autoregulatory feedback loop. J Biol Chem 282(4):2135–2143

    Article  CAS  PubMed  Google Scholar 

  35. Papadopoulos GL, Reczko M, Simossis VA et al (2009) The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res 37(Database issue):D155–D158

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Lee Y, Yang X, Huang Y et al (2010) Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis. PLoS Comput Biol 6(4):e1000730

    Article  PubMed Central  PubMed  Google Scholar 

  37. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Shin C, Nam JW, Farh KK et al (2010) Expanding the microRNA targeting code: functional sites with centered pairing. Mol Cell 38(6):789–802

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Chi SW, Zang JB, Mele A et al (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460(7254):479–486

    CAS  PubMed Central  PubMed  Google Scholar 

  40. Chi SW, Hannon GJ, Darnell RB (2012) An alternative mode of microRNA target recognition. Nat Struct Mol Biol 19(3):321–327

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Tay Y, Zhang J, Thomson AM et al (2008) MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature 455(7216):1124–1128

    Article  CAS  PubMed  Google Scholar 

  42. Lal A, Navarro F, Maher CA et al (2009) miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to “seedless” 3′UTR microRNA recognition elements. Mol Cell 35(5):610–625

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic Acids Res 31(13):3429–3431

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Rehmsmeier M, Steffen P, Hochsmann M et al (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10(10):1507–1517

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Kruger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34(Web Server issue):W451–W454

    Article  PubMed Central  PubMed  Google Scholar 

  46. Rusinov V, Baev V, Minkov IN et al (2005) MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res 33(Web Server issue):W696–W700

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Kozomara A, Griffiths-Jones S (2010) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39(Database issue):D152–D157

    PubMed Central  PubMed  Google Scholar 

  48. Betel D, Koppal A, Agius P et al (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11(8):R90

    Article  PubMed Central  PubMed  Google Scholar 

  49. Reczko M, Maragkakis M, Alexiou P et al (2012) Functional microRNA targets in protein coding sequences. Bioinformatics 28(6):771–776

    Article  CAS  PubMed  Google Scholar 

  50. Garcia DM, Baek D, Shin C et al (2011) Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol 18(10):1139–1146

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Xiao F, Zuo Z, Cai G et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Vergoulis T, Vlachos IS, Alexiou P et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40(Database issue):D222–D229

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Maglott D, Ostell J, Pruitt KD et al (2005) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 39(Database issue):D52–D57

    Google Scholar 

  54. Griffiths-Jones S, Moxon S, Marshall M et al (2005) Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res 33(Database issue):D121–D124

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Seal RL, Gordon SM, Lush MJ et al (2011) genenames.org: the HGNC resources in 2011. Nucleic Acids Res 39(Database issue):D514–D519

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Landgraf P, Rusu M, Sheridan R et al (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129(7):1401–1414

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Thomson DW, Bracken CP, Goodall GJ (2011) Experimental strategies for microRNA target identification. Nucleic Acids Res 39(16):6845–6853

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  58. Hsu SD, Lin FM, Wu WY et al (2010) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39(Database issue):D163–D169

    PubMed Central  PubMed  Google Scholar 

  59. Naeem H, Kuffner R, Csaba G et al (2010) miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature. BMC Bioinform 11:135

    Article  Google Scholar 

  60. Yang JH, Li JH, Shao P et al (2010) starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res 39(Database issue):D202–D209

    PubMed Central  PubMed  Google Scholar 

  61. John B, Enright AJ, Aravin A et al (2004) Human MicroRNA targets. PLoS Biol 2(11):e363

    Article  PubMed Central  PubMed  Google Scholar 

  62. Oulas A, Karathanasis N, Louloupi A et al (2012) A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2. RNA Biol 9(9):1196–1207

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Vlachos IS, Kostoulas N, Vergoulis T et al (2012) DIANA miRPath v. 2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 40(Web Server issue):W498–W504

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Kowarsch A, Preusse M, Marr C et al (2011) miTALOS: analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs. RNA 17(5):809–819

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Backes C, Keller A, Kuentzer J et al (2007) GeneTrail–advanced gene set enrichment analysis. Nucleic Acids Res 35(Web Server issue):W186–W192

    Article  PubMed Central  PubMed  Google Scholar 

  66. Cho S, Jun Y, Lee S et al (2010) miRGator v2.0: an integrated system for functional investigation of microRNAs. Nucleic Acids Res 39(Database issue):D158–D162

    PubMed Central  PubMed  Google Scholar 

  67. Nam S, Kim B, Shin S et al (2008) miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res 36(Database issue):D159–D164

    CAS  PubMed Central  PubMed  Google Scholar 

  68. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Jiang Q, Wang Y, Hao Y et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–D104

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  70. Lu M, Zhang Q, Deng M et al (2008) An analysis of human microRNA and disease associations. PLoS One 3(10):e3420

    Article  PubMed Central  PubMed  Google Scholar 

  71. Ruepp A, Kowarsch A, Schmidl D et al (2010) PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol 11(1):R6

    Article  PubMed Central  PubMed  Google Scholar 

  72. Hiard S, Charlier C, Coppieters W et al (2010) Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res 38(Database issue):D640–D651

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  73. Yang Q, Qiu C, Yang J et al (2011) miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes. Bioinformatics 27(23):3329–3330

    Article  CAS  PubMed  Google Scholar 

  74. Friedlander MR, Mackowiak SD, Li N et al (2012) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40(1):37–52

    Article  PubMed Central  PubMed  Google Scholar 

  75. Berezikov E, Robine N, Samsonova A et al (2011) Deep annotation of Drosophila melanogaster microRNAs yields insights into their processing, modification, and emergence. Genome Res 21(2):203–215

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  76. Li N, You X, Chen T et al (2013) Global profiling of miRNAs and the hairpin precursors: insights into miRNA processing and novel miRNA discovery. Nucleic Acids Res 41(6):3619–3634

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  77. Chou CH, Lin FM, Chou MT et al (2013) A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing. BMC Genomics 14(Suppl 1):S2

    PubMed Central  PubMed  Google Scholar 

  78. Mathelier A, Carbone A (2010) MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 26(18):2226–2234

    Article  CAS  PubMed  Google Scholar 

  79. Konig J, Zarnack K, Luscombe NM et al (2011) Protein-RNA interactions: new genomic technologies and perspectives. Nat Rev Genet 13(2):77–83

    Article  Google Scholar 

  80. Hafner M, Landthaler M, Burger L et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141(1):129–141

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  81. Khorshid M, Rodak C, Zavolan M (2011) CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins. Nucleic Acids Res 39(Database issue):D245–D252

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  82. Wu J, Liu Q, Wang X, Zheng J, Wang T, You M, Sheng Sun Z, Shi Q (2013), mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol 10(7): 1087–1092

    Google Scholar 

  83. Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM (2011) miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 39(Web Server issue):W132–W138

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  84. Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25

    Article  PubMed Central  PubMed  Google Scholar 

  85. Zhao W, Liu W, Tian D et al (2011) wapRNA: a web-based application for the processing of RNA sequences. Bioinformatics 27(21):3076–3077

    Article  CAS  PubMed  Google Scholar 

  86. Vlachos IS, Hatzigeorgiou AG (2013) Online resources for miRNA analysis. Clin Biochem 46(10–11):879–900. doi:10.1016/j.clinbiochem.2013.03.006, Epub 2013 Mar 18. Review. PMID: 23518312 [PubMed—indexed for MEDLINE]

    Article  CAS  PubMed  Google Scholar 

  87. Oulas A, Boutla A, Gkirtzou K et al (2009) Prediction of novel microRNA genes in cancer-associated genomic regions – a combined computational and experimental approach. Nucleic Acids Res 37(10):3276–3287

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  88. Simoneau M, Aboulkassim TO, LaRue H et al (1999) Four tumor suppressor loci on chromosome 9q in bladder cancer: evidence for two novel candidate regions at 9q22.3 and 9q31. Oncogene 18(1):157–163

    Article  CAS  PubMed  Google Scholar 

  89. Han Y, Chen J, Zhao X et al (2011) MicroRNA expression signatures of bladder cancer revealed by deep sequencing. PLoS One 6(3):e18286

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  90. Kapranov P, Cheng J, Dike S et al (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316(5830):1484–1488

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Institute of Marine Biology, Biotechnology and Aquaculture-HCMR, Heraklion, Crete, Greece

    Anastasis Oulas

  2. Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, Crete, Greece

    Nestoras Karathanasis, Panayiota Poirazi & Kriton Kalantidis

  3. Department of Biology, University of Crete, Heraklion, Crete, Greece

    Nestoras Karathanasis & Kriton Kalantidis

  4. Biomedical Science-Medical Biology, University of Amsterdam, Amsterdam, Netherlands

    Annita Louloupi

  5. Division of Basic Sciences, University of Crete Medical School, Heraklion, Crete, Greece

    Georgios A. Pavlopoulos & Ioannis Iliopoulos

  6. Faculty of Medicine, Division of Basic Sciences, University of Crete, Wing 2c/Pteryga 2Γ, 2208, 71003, Heraklion, Crete, Greece

    Ioannis Iliopoulos

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  1. Anastasis Oulas
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  2. Nestoras Karathanasis
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  4. Georgios A. Pavlopoulos
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  5. Panayiota Poirazi
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  6. Kriton Kalantidis
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  7. Ioannis Iliopoulos
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Correspondence to Ioannis Iliopoulos .

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Editors and Affiliations

  1. Department of Biosciences, Biotechnology and Biopharmaceutics, and National Research Council, University of Bari; Institute of Biomembranes and Bioenergetics, Bari, Italy, and National Institute of Biostructures and Biosystems (INBB), Rome, Italy

    Ernesto Picardi

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Oulas, A. et al. (2015). Prediction of miRNA Targets. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 1269. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2291-8_13

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  • DOI: https://doi.org/10.1007/978-1-4939-2291-8_13

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