Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

  • Raheleh Amirkhah
  • Hojjat Naderi Meshkin
  • Ali Farazmand
  • John E. J. Rasko
  • Ulf SchmitzEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1580)


In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Key words

MicroRNA Computational target prediction Machine learning Next-generation sequencing Cross-linking and immunoprecipitation 


  1. 1.
    Wen J, Parker BJ, Jacobsen A, Krogh A (2011) MicroRNA transfection and AGO-bound CLIP-seq data sets reveal distinct determinants of miRNA action. RNA 17(5):820–834CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    J-i S, Tabunoki H (2011) Comprehensive analysis of human microRNA target networks. BioData Min 4:17–17CrossRefGoogle Scholar
  3. 3.
    Garzon R, Marcucci G, Croce CM (2010) Targeting microRNAs in cancer: rationale, strategies and challenges. Nat Rev Drug Discov 9(10):775–789CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Schmitz U, Naderi-Meshkin H, Gupta SK, Wolkenhauer O, Vera J (2016) The RNA world in the 21st century—a systems approach to finding non-coding keys to clinical questions. Brief Bioinform 17(3):380–392CrossRefPubMedGoogle Scholar
  5. 5.
    Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA hypothesis: the Rosetta stone of a hidden RNA language? Cell 146(3):353–358CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Su X, Xing J, Wang Z, Chen L, Cui M, Jiang B (2013) microRNAs and ceRNAs: RNA networks in pathogenesis of cancer. Chin J Cancer Res 25(2):235–239PubMedPubMedCentralGoogle Scholar
  7. 7.
    Schmitz U, Wolkenhauer O, Vera J (2013) MicroRNA cancer regulation: advanced concepts, bioinformatics and systems biology tools, vol 774. Springer Science & Business Media, DordrechtGoogle Scholar
  8. 8.
    Amirkhah R, Schmitz U, Linnebacher M, Wolkenhauer O, Farazmand A (2015) MicroRNA-mRNA interactions in colorectal cancer and their role in tumor progression. Genes Chromosomes Cancer 54(3):129–141CrossRefPubMedGoogle Scholar
  9. 9.
    Clark PM, Loher P, Quann K, Brody J, Londin ER, Rigoutsos I (2014) Argonaute CLIP-Seq reveals miRNA targetome diversity across tissue types. Sci Rep 4:5947CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310:1817–1821CrossRefPubMedGoogle Scholar
  11. 11.
    Kowarsch A, Preusse M, Marr C, Theis FJ (2011) miTALOS: Analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs. RNA 17(5):809–819CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Preusse M, Theis FJ, Mueller NS (2016) miTALOS v2: analyzing tissue specific microRNA function. PLoS One 11(3):e0151771CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ritchie W, Rajasekhar M, Flamant S, Rasko JEJ (2009) Conserved expression patterns predict microRNA targets. PLoS Comput Biol 5(9):e1000513CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Agarwal V, Bell GW, Nam J-W, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005CrossRefPubMedCentralGoogle Scholar
  15. 15.
    J-y Z, Wang F, Li Y, X-b Z, Yang L, Wang W, Xu H, D-z L, L-y Z (2015) Five miRNAs considered as molecular targets for predicting esophageal cancer. Med Sci Monit 21:3222–3230CrossRefGoogle Scholar
  16. 16.
    Pizzini S, Bisognin A, Mandruzzato S, Biasiolo M, Facciolli A, Perilli L, Rossi E, Esposito G, Rugge M, Pilati P, Mocellin S, Nitti D, Bortoluzzi S, Zanovello P (2013) Impact of microRNAs on regulatory networks and pathways in human colorectal carcinogenesis and development of metastasis. BMC Genomics 14:589CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Li X, Gill R, Cooper NG, Yoo JK, Datta S (2011) Modeling microRNA-mRNA interactions using PLS regression in human colon cancer. BMC Med Genomics 4:44CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Ritchie W, Flamant S, Rasko JE (2010) mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets. Bioinformatics 26(2):223–227CrossRefPubMedGoogle Scholar
  19. 19.
    Wu X, Watson M (2009) CORNA: testing gene lists for regulation by microRNAs. Bioinformatics 25(6):832–833CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bhattacharya A, Cui Y (2015) miR2GO: comparative functional analysis for microRNAs. Bioinformatics 31(14):2403–2405CrossRefPubMedGoogle Scholar
  21. 21.
    Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, Dalamagas T, Hatzigeorgiou AG (2015) DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res 43(W1):W460–W466CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Ritchie W, Rasko JE (2014) Refining microRNA target predictions: sorting the wheat from the chaff. Biochem Biophys Res Commun 445(4):780–784CrossRefPubMedGoogle Scholar
  23. 23.
    Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Ritchie W, Flamant S, Rasko JE (2009) Predicting microRNA targets and functions: traps for the unwary. Nat Methods 6:397–398CrossRefPubMedGoogle Scholar
  25. 25.
    Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of MicroRNA–Target Recognition PLoS Biol 3 (3), e85.Google Scholar
  26. 26.
    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):15–20CrossRefPubMedGoogle Scholar
  27. 27.
    Reczko M, Maragkakis M, Alexiou P, Papadopoulos GL, Hatzigeorgiou AG (2011) Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data. Front Genet 2:103PubMedGoogle Scholar
  28. 28.
    Legendre M, Ritchie W, Lopez F, Gautheret D (2006) Differential repression of alternative transcripts: a screen for miRNA targets. PLoS Comput Biol 2(5):e43CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10(10):1507–1517CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284CrossRefPubMedGoogle Scholar
  31. 31.
    Yue D, Liu H, Huang Y (2009) Survey of computational algorithms for microRNA target prediction. Curr Genomics 10(7):478–492CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Ritchie W, Gao D, Rasko JE (2012) Defining and providing robust controls for microRNA prediction. Bioinformatics 28(8):1058–1061CrossRefPubMedGoogle Scholar
  33. 33.
    Yousef M, Jung S, Kossenkov AV, Showe LC, Showe MK (2007) Naive bayes for microRNA target predictions--machine learning for microRNA targets. Bioinformatics 23(22):2987–2992CrossRefPubMedGoogle Scholar
  34. 34.
    Huang J, Lu J, Ling CX (2003) Comparing naive Bayes, decision trees, and SVM with AUC and accuracy. Third IEEE International Conference on Data Mining, 553–556Google Scholar
  35. 35.
    Liu H, Yue D, Chen Y, Gao SJ, Huang Y (2010) Improving performance of mammalian microRNA target prediction. BMC Bioinformatics 11:476CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    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–D110CrossRefPubMedGoogle Scholar
  37. 37.
    Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, Tsai TR, Ho SY, Jian TY, Wu HY, Chen PR, Lin NC, Huang HT, Yang TL, Pai CY, Tai CS, Chen WL, Huang CY, Liu CC, Weng SL, Liao KW, Hsu WL, Huang HD (2016) miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 44:D239–D247CrossRefPubMedGoogle Scholar
  38. 38.
    Menor M, Ching T, Zhu X, Garmire D, Garmire LX (2014) mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biol 15:500CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Hammell M (2010) Computational methods to identify miRNA targets. Semin Cell Dev Biol 21:738–744CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Laganà A, Forte S, Giudice A, Arena MR, Puglisi PL, Giugno R, Pulvirenti A, Shasha D, Ferro A (2009) miRò: a miRNA knowledge base. Database (Oxford) 2009:bap008CrossRefGoogle Scholar
  41. 41.
    Dweep H, Gretz N (2015) miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods 12:697CrossRefPubMedGoogle Scholar
  42. 42.
    Afonso-Grunz F, Muller S (2015) Principles of miRNA-mRNA interactions: beyond sequence complementarity. Cell Mol Life Sci 72(16):3127–3141CrossRefPubMedGoogle Scholar
  43. 43.
    Amirkhah R, Farazmand A, Gupta SK, Ahmadi H, Wolkenhauer O, Schmitz U (2015) Naive Bayes classifier predicts functional microRNA target interactions in colorectal cancer. Mol Biosyst 11(8):2126–2134CrossRefPubMedGoogle Scholar
  44. 44.
    Kim SK, Nam JW, Rhee JK, Lee WJ, Zhang BT (2006) miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics 7:411CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Zheng H, Fu R, Wang JT, Liu Q, Chen H, Jiang SW (2013) Advances in the techniques for the prediction of microRNA targets. Int J Mol Sci 14(4):8179–8187CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Gumienny R, Zavolan M (2015) Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G. Nucleic Acids Res 43:9095CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Ghoshal A, Shankar R, Bagchi S, Grama A, Chaterji S (2015) MicroRNA target prediction using thermodynamic and sequence curves. BMC Genomics 16:999CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27(1):91–105CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Sandberg R, Neilson JR, Sarma A, Sharp PA, Burge CB (2008) Proliferating cells express mRNAs with shortened 3′ untranslated regions and fewer microRNA target sites. Science 320:1643–1647CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Beuvink I, Kolb FA, Budach W, Garnier A, Lange J, Natt F, Dengler U, Hall J, Filipowicz W, Weiler J (2007) A novel microarray approach reveals new tissue-specific signatures of known and predicted mammalian microRNAs. Nucleic Acids Res 35:e52CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z (2015) MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep 5:8004CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Ciafrè SA, Galardi S (2013) microRNAs and RNA-binding proteins: a complex network of interactions and reciprocal regulations in cancer. RNA Biol 10(6):934–942CrossRefPubMedCentralGoogle Scholar
  53. 53.
    Nam JW, Rissland OS, Koppstein D, Abreu-Goodger C, Jan CH, Agarwal V, Yildirim MA, Rodriguez A, Bartel DP (2014) Global analyses of the effect of different cellular contexts on microRNA targeting. Mol Cell 53(6):1031–1043CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Deng N, Puetter A, Zhang K, Johnson K, Zhao Z, Taylor C, Flemington EK, Zhu D (2011) Isoform-level microRNA-155 target prediction using RNA-seq. Nucleic Acids Res 39(9):e61CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Bandyopadhyay S, Mitra R (2009) TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25(20):2625–2631CrossRefPubMedGoogle Scholar
  56. 56.
    Motameny S, Wolters S, Nurnberg P, Schumacher B (2010) Next generation sequencing of miRNAs - strategies, resources and methods. Genes (Basel) 1(1):70–84Google Scholar
  57. 57.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Kaushik A, Saraf S, Mukherjee SK, Gupta D (2015) miRMOD: a tool for identification and analysis of 5′ and 3′ miRNA modifications in Next Generation Sequencing small RNA data. Peer J 3:e1332CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Katoh T, Sakaguchi Y, Miyauchi K, Suzuki T, Kashiwabara S, Baba T (2009) Selective stabilization of mammalian microRNAs by 3′ adenylation mediated by the cytoplasmic poly(A) polymerase GLD-2. Genes Dev 23(4):433–438CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    An J, Lai J, Lehman ML, Nelson CC (2012) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 41(2):727–737CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Hackenberg M, Sturm M, Langenberger D, Falcon-Perez JM, Aransay AM (2009) miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37:W68–W76CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Ronen R, Gan I, Modai S, Sukacheov A, Dror G, Halperin E, Shomron N (2010) miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics 26(20):2615–2616CrossRefPubMedGoogle Scholar
  64. 64.
    Gao D, Middleton R, Rasko JE, Ritchie W (2013) miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 29(24):3225–3226CrossRefPubMedGoogle Scholar
  65. 65.
    Wang WC, Lin FM, Chang WC, Lin KY, Huang HD, Lin NS (2009) miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 10:328CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Zhang Y, Xu B, Yang Y, Ban R, Zhang H, Jiang X, Cooke HJ, Xue Y, Shi Q (2012) CPSS: a computational platform for the analysis of small RNA deep sequencing data. Bioinformatics 28:1925–1927CrossRefPubMedGoogle Scholar
  67. 67.
    Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, Schwach F, Dalmay T, Moulton V (2012) The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics 28:2059–2061CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Rueda A, Barturen G, Lebron R, Gomez-Martin C, Alganza A, Oliver JL, Hackenberg M (2015) sRNAtoolbox: an integrated collection of small RNA research tools. Nucleic Acids Res 43:W467–W473CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486PubMedPubMedCentralGoogle Scholar
  70. 70.
    Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M Jr, Jungkamp AC, 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 141:129–141CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Helwak A, Kudla G, Dudnakova T, Tollervey D (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654–665CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Konig J, Zarnack K, Rot G, Curk T, Kayikci M, Zupan B, Turner DJ, Luscombe NM, Ule J (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17:909–915CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Imig J, Brunschweiger A, Brummer A, Guennewig B, Mittal N, Kishore S, Tsikrika P, Gerber AP, Zavolan M, Hall J (2015) miR-CLIP capture of a miRNA targetome uncovers a lincRNA H19-miR-106a interaction. Nat Chem Biol 11:107–114CrossRefPubMedGoogle Scholar
  74. 74.
    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:D245–D252CrossRefPubMedGoogle Scholar
  75. 75.
    Li JH, Liu S, Zhou H, Qu LH, Yang JH (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–D97CrossRefPubMedGoogle Scholar
  76. 76.
    Blin K, Dieterich C, Wurmus R, Rajewsky N, Landthaler M, Akalin A (2015) DoRiNA 2.0--upgrading the doRiNA database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res 43:D160–D167CrossRefPubMedGoogle Scholar
  77. 77.
    Chen B, Yun J, Kim MS, Mendell JT, Xie Y (2014) PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis. Genome Biol 15:R18CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Uren PJ, Bahrami-Samani E, Burns SC, Qiao M, Karginov FV, Hodges E, Hannon GJ, Sanford JR, Penalva LO, Smith AD (2012) Site identification in high-throughput RNA-protein interaction data. Bioinformatics 28:3013–3020CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Corcoran DL, Georgiev S, Mukherjee N, Gottwein E, Skalsky RL, Keene JD, Ohler U (2011) PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol 12:R79CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Chou CH, Lin FM, Chou MT, Hsu SD, Chang TH, Weng SL, Shrestha S, Hsiao CC, Hung JH, Huang HD (2013) A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing. BMC Genomics 14:S2PubMedPubMedCentralGoogle Scholar
  81. 81.
    Gaidatzis D, van Nimwegen E, Hausser J, Zavolan M (2007) Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 8:69CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Jin Y, Chen Z, Liu X, Zhou X (2013) Evaluating the microRNA targeting sites by luciferase reporter gene assay. Methods Mol Biol 936:117–127CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Gäken J, Mohamedali AM, Jiang J, Malik F, Stangl D, Smith AE, Chronis C, Kulasekararaj AG, Thomas NSB, Farzaneh F, Tavassoli M, Mufti GJ (2012) A functional assay for microRNA target identification and validation. Nucleic Acids Res 40:e75–e75CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Kuhn DE, Martin MM, Feldman DS, Terry AV Jr, Nuovo GJ, Elton TS (2008) Experimental validation of miRNA targets. Methods 44:47–54CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Watanabe Y, Tomita M, Kanai A (2007) Computational methods for microRNA target prediction. Methods Enzymol 427:65–86CrossRefPubMedGoogle Scholar
  86. 86.
    Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37:D98–104CrossRefPubMedGoogle Scholar
  87. 87.
    Wang D, Gu J, Wang T, Ding Z (2014) OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs. Bioinformatics 30:2237–2238CrossRefPubMedGoogle Scholar
  88. 88.
    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–D229CrossRefPubMedGoogle Scholar
  89. 89.
    Xie B, Ding Q, Han H, Wu D (2013) miRCancer: a microRNA–cancer association database constructed by text mining on literature. Bioinformatics 29:638–644CrossRefPubMedGoogle Scholar
  90. 90.
    Schmitz U, Lai X, Winter F, Wolkenhauer O, Vera J, Gupta SK (2014) Cooperative gene regulation by microRNA pairs and their identification using a computational workflow. Nucleic Acids Res 42:7539–7552CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Guo Z, Maki M, Ding R, Yang Y, zhang B, Xiong L (2014) Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues. Sci Rep 4, 5150.Google Scholar
  92. 92.
    Naeem H, Kuffner R, Csaba G, Zimmer R (2010) miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature. BMC Bioinformatics 11:135CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Andrés-León E, Peña DG, Gómez-López G, Pisano DG (2015) miRGate: a curated database of human, mouse and rat miRNA–mRNA targets. Database 2015:bav035CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Raheleh Amirkhah
    • 1
  • Hojjat Naderi Meshkin
    • 2
  • Ali Farazmand
    • 3
  • John E. J. Rasko
    • 4
  • Ulf Schmitz
    • 4
    Email author
  1. 1.Reza Institute of Cancer Bioinformatics and Personalized MedicineMashhadIran
  2. 2.Stem Cells and Regenerative Medicine Research GroupAcademic Center for Education, Culture Research (ACECR)MashhadIran
  3. 3.Department of Cell and Molecular Biology, School of Biology, College of ScienceUniversity of TehranTehranIran
  4. 4.Gene & Stem Cell Therapy Program, Centenary Institute, Camperdown; Sydney Medical SchoolUniversity of SydneyCamperdownAustralia

Personalised recommendations