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Bioinformatics Approaches to the Study of MicroRNAs

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Non-coding RNAs and Cancer
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Abstract

The introduction of Next-Generation Sequencing (NGS) technologies has opened new avenues, including determination of the relationship of genomic and epigenomic variation and phenotypes to disease. These technologies are particularly well suited for discovery of both small RNAs, around 30–75 base pairs long, and long RNAs, longer than 75 base pairs, because the NGS sequencers can produce millions of short reads in a relatively rapid period of time. However, the large amounts (terabases) of data generated require proper computational resources and analytical methods to translate the rich source of genomic data into meaningful information for biomedical applications. In this chapter, we describe various bioinformatics methods for performing integrative analysis for the identification of miRNAs and their target mRNAs from the NGS sequencers. We describe the most commonly used databases and prediction programs that are available on the World Wide Web and demonstrate the use of some of these programs by an example. We provide a list of these programs along with their Web URLs and suggest guidelines for successful application.

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References

  1. Sharp PA. RNAi and double-strand RNA. Genes Dev. 1999;13(2):139–41.

    PubMed  CAS  Google Scholar 

  2. Golden DE, Gerbasi VR, Sontheimer EJ. An inside job for siRNAs. Mol Cell. 2008;31(3):309–12.

    PubMed  CAS  Google Scholar 

  3. Moazed D. Small RNAs in transcriptional gene silencing and genome defence. Nature. 2009;457(7228):413–20.

    PubMed  CAS  Google Scholar 

  4. Chapman EJ, Carrington JC. Specialization and evolution of endogenous small RNA pathways. Nat Rev Genet. 2007;8(11):884–96.

    PubMed  CAS  Google Scholar 

  5. Nilsen TW. Endo-siRNAs: yet another layer of complexity in RNA silencing. Nat Struct Mol Biol. 2008;15(6):546–8.

    PubMed  CAS  Google Scholar 

  6. Ghildiyal M, Zamore PD. Small silencing RNAs: an expanding universe. Nat Rev Genet. 2009;10(2):94–108.

    PubMed  CAS  Google Scholar 

  7. Kim VN, Han J, Siomi MC. Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol. 2009;10(2):126–39.

    PubMed  CAS  Google Scholar 

  8. Ambros V, Chen X. The regulation of genes and genomes by small RNAs. Development. 2007;134(9):1635–41.

    PubMed  CAS  Google Scholar 

  9. Brodersen P, Voinnet O. Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol. 2009;10(2):141–8.

    PubMed  CAS  Google Scholar 

  10. Hussain MU. Micro-RNAs (miRNAs): genomic organisation, biogenesis and mode of action. Cell Tissue Res. 2012;349(2):405–13.

    CAS  Google Scholar 

  11. Kusenda B, et al. MicroRNA biogenesis, functionality and cancer relevance. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2006;150(2):205–15.

    PubMed  CAS  Google Scholar 

  12. Faller M, Guo F. MicroRNA biogenesis: there’s more than one way to skin a cat. Biochim Biophys Acta. 2008;1779(11):663–7.

    PubMed  CAS  Google Scholar 

  13. Gregory RI, Chendrimada TP, Shiekhattar R. MicroRNA biogenesis: isolation and characterization of the microprocessor complex. Methods Mol Biol. 2006;342:33–47.

    PubMed  CAS  Google Scholar 

  14. Winter J, et al. Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol. 2009;11(3):228–34.

    PubMed  CAS  Google Scholar 

  15. Lim LP, et al. Vertebrate microRNA genes. Science. 2003;299(5612):1540.

    PubMed  CAS  Google Scholar 

  16. Lynam-Lennon N, Maher SG, Reynolds JV. The roles of microRNA in cancer and apoptosis. Biol Rev Camb Philos Soc. 2009;84(1):55–71.

    PubMed  Google Scholar 

  17. 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.

    PubMed  CAS  Google Scholar 

  18. Lodish HF, et al. Micromanagement of the immune system by microRNAs. Nat Rev Immunol. 2008;8(2):120–30.

    PubMed  CAS  Google Scholar 

  19. Berardi E, et al. microRNAs in ES cell differentiation. Am J Physiol Heart Circ Physiol. 2012;303(8):H931–9.

    PubMed  CAS  Google Scholar 

  20. Ebert MS, Sharp PA. Roles for microRNAs in conferring robustness to biological processes. Cell. 2012;149(3):515–24.

    PubMed  CAS  Google Scholar 

  21. Osman A. MicroRNAs in health and disease—basic science and clinical applications. Clin Lab. 2012;58(5–6):393–402.

    PubMed  CAS  Google Scholar 

  22. Garzon R, Calin GA, Croce CM. MicroRNAs in cancer. Annu Rev Med. 2009;60:167–79.

    PubMed  CAS  Google Scholar 

  23. Farazi TA, et al. miRNAs in human cancer. J Pathol. 2011;223(2):102–15.

    PubMed  CAS  Google Scholar 

  24. Nygaard S, et al. Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing. BMC Med Genomics. 2009;2:35.

    PubMed  Google Scholar 

  25. Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer. 2006;6(11):857–66.

    PubMed  CAS  Google Scholar 

  26. Catto JW, et al. MicroRNA in prostate, bladder, and kidney cancer: a systematic review. Eur Urol. 2011;59(5):671–81.

    PubMed  CAS  Google Scholar 

  27. Bartels CL, Tsongalis GJ. MicroRNAs: novel biomarkers for human cancer. Clin Chem. 2009;55(4):623–31.

    PubMed  CAS  Google Scholar 

  28. Cortez MA, et al. MicroRNAs in body fluids—the mix of hormones and biomarkers. Nat Rev Clin Oncol. 2011;8(8):467–77.

    PubMed  CAS  Google Scholar 

  29. Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med. 2012;4(3):143–59.

    PubMed  CAS  Google Scholar 

  30. Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet. 2008;24(3):133–41.

    PubMed  CAS  Google Scholar 

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

    PubMed  Google Scholar 

  32. Pantano L, Estivill X, Marti E. SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res. 2010;38(5):e34.

    PubMed  Google Scholar 

  33. Hackenberg M, et al. miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res. 2009;37(Web Server issue):W68–76.

    PubMed  CAS  Google Scholar 

  34. Zhu E, et al. mirTools: microRNA profiling and discovery based on high-throughput sequencing. Nucleic Acids Res. 2010;38(Web Server issue):W392–7.

    PubMed  CAS  Google Scholar 

  35. Zhang Y, et al. CPSS: a computational platform for the analysis of small RNA deep sequencing data. Bioinformatics. 2012;28(14):1925–7.

    PubMed  CAS  Google Scholar 

  36. Ronen R, et al. miRNAkey: a software for microRNA deep sequencing analysis. Bioinformatics. 2010;26(20):2615–6.

    PubMed  CAS  Google Scholar 

  37. Maragkakis M, et al. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res. 2009;37(Web Server issue):W273–6.

    PubMed  CAS  Google Scholar 

  38. 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.

    PubMed  CAS  Google Scholar 

  39. Lewis BP, et al. Prediction of mammalian microRNA targets. Cell. 2003;115(7):787–98.

    PubMed  CAS  Google Scholar 

  40. Rehmsmeier M, et al. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10(10):1507–17.

    PubMed  CAS  Google Scholar 

  41. Kertesz M, et al. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39(10):1278–84.

    PubMed  CAS  Google Scholar 

  42. Robins H, Li Y, Padgett RW. Incorporating structure to predict microRNA targets. Proc Natl Acad Sci U S A. 2005;102(11):4006–9.

    PubMed  CAS  Google Scholar 

  43. Long D, et al. Potent effect of target structure on microRNA function. Nat Struct Mol Biol. 2007;14(4):287–94.

    PubMed  CAS  Google Scholar 

  44. Muckstein U, et al. Thermodynamics of RNA-RNA binding. Bioinformatics. 2006;22(10):1177–82.

    PubMed  Google Scholar 

  45. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33.

    PubMed  CAS  Google Scholar 

  46. Alon S, et al. Systematic identification of edited microRNAs in the human brain. Genome Res. 2012;22(8):1533–40.

    PubMed  CAS  Google Scholar 

  47. Bass BL. RNA editing by adenosine deaminases that act on RNA. Annu Rev Biochem. 2002;71:817–46.

    PubMed  CAS  Google Scholar 

  48. Ekdahl Y, et al. A-to-I editing of microRNAs in the mammalian brain increases during development. Genome Res. 2012;22(8):1477–87.

    PubMed  CAS  Google Scholar 

  49. Reinhart BJ, et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000;403(6772):901–6.

    PubMed  CAS  Google Scholar 

  50. Ambros V, Lee RC. Identification of microRNAs and other tiny noncoding RNAs by cDNA cloning. Methods Mol Biol. 2004;265:131–58.

    PubMed  CAS  Google Scholar 

  51. Xu G, et al. Cloning and identification of microRNAs in bovine alveolar macrophages. Mol Cell Biochem. 2009;332(1–2):9–16.

    PubMed  CAS  Google Scholar 

  52. Long JE, Chen HX. Identification and characteristics of cattle microRNAs by homology searching and small RNA cloning. Biochem Genet. 2009;47(5–6):329–43.

    PubMed  CAS  Google Scholar 

  53. He X, et al. Cloning and identification of novel microRNAs from rat hippocampus. Acta Biochim Biophys Sin (Shanghai). 2007;39(9):708–14.

    CAS  Google Scholar 

  54. Pfeffer S, et al. Identification of virus-encoded microRNAs. Science. 2004;304(5671):734–6.

    PubMed  CAS  Google Scholar 

  55. Lagos-Quintana M, et al. Identification of tissue-specific microRNAs from mouse. Curr Biol. 2002;12(9):735–9.

    PubMed  CAS  Google Scholar 

  56. Bentwich I, et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet. 2005;37(7):766–70.

    PubMed  CAS  Google Scholar 

  57. Morin RD, et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008;18(4):610–21.

    PubMed  CAS  Google Scholar 

  58. Glazov EA, et al. A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Res. 2008;18(6):957–64.

    PubMed  CAS  Google Scholar 

  59. Creighton CJ, Reid JG, Gunaratne PH. Expression profiling of microRNAs by deep sequencing. Brief Bioinform. 2009;10(5):490–7.

    PubMed  CAS  Google Scholar 

  60. Babiarz JE, et al. Mouse ES cells express endogenous shRNAs, siRNAs, and other microprocessor-independent, dicer-dependent small RNAs. Genes Dev. 2008;22(20):2773–85.

    PubMed  CAS  Google Scholar 

  61. Lim LP, et al. The microRNAs of Caenorhabditis elegans. Genes Dev. 2003;17(8):991–1008.

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  63. Lai EC, et al. Computational identification of Drosophila microRNA genes. Genome Biol. 2003;4(7):R42.

    PubMed  Google Scholar 

  64. Hertel J, Stadler PF. Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics. 2006;22(14):e197–202.

    PubMed  CAS  Google Scholar 

  65. Terai G, et al. miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity. RNA. 2007;13(12):2081–90.

    PubMed  CAS  Google Scholar 

  66. Siepel A, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15(8):1034–50.

    PubMed  CAS  Google Scholar 

  67. Berezikov E, et al. Phylogenetic shadowing and computational identification of human microRNA genes. Cell. 2005;120(1):21–4.

    PubMed  CAS  Google Scholar 

  68. Boffelli D, et al. Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science. 2003;299(5611):1391–4.

    PubMed  CAS  Google Scholar 

  69. Li H, Homer N. A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform. 2010;11(5):473–83.

    PubMed  CAS  Google Scholar 

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

    PubMed  Google Scholar 

  71. Dohm JC, et al. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 2008;36(16):e105.

    PubMed  Google Scholar 

  72. Linsen SE, et al. Limitations and possibilities of small RNA digital gene expression profiling. Nat Methods. 2009;6(7):474–6.

    PubMed  CAS  Google Scholar 

  73. Friedlander MR, et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008;26(4):407–15.

    PubMed  Google Scholar 

  74. Cloonan N, et al. MicroRNAs and their isomiRs function cooperatively to target common biological pathways. Genome Biol. 2011;12(12):R126.

    PubMed  CAS  Google Scholar 

  75. Ryan BM, Robles AI, Harris CC. Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer. 2010;10(6):389–402.

    PubMed  CAS  Google Scholar 

  76. Fernandez-Valverde SL, Taft RJ, Mattick JS. Dynamic isomiR regulation in Drosophila development. RNA. 2010;16(10):1881–8.

    PubMed  CAS  Google Scholar 

  77. Zhou H, et al. Deep annotation of mouse iso-miR and iso-moR variation. Nucleic Acids Res. 2012;40(13):5864–75.

    PubMed  CAS  Google Scholar 

  78. Li SC, et al. miRNA arm selection and isomiR distribution in gastric cancer. BMC Genomics. 2012;13 Suppl 1:S13.

    PubMed  CAS  Google Scholar 

  79. Li R, et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009;25(15):1966–7.

    PubMed  CAS  Google Scholar 

  80. Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011;39(Database issue):D152–7.

    PubMed  CAS  Google Scholar 

  81. Griffiths-Jones S, et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005;33(Database issue):D121–4.

    PubMed  CAS  Google Scholar 

  82. Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009;Chapter 4:Unit 4 10.

    Google Scholar 

  83. Hackenberg M, Matthiesen R. Annotation-Modules: a tool for finding significant combinations of multisource annotations for gene lists. Bioinformatics. 2008;24(11):1386–93.

    PubMed  CAS  Google Scholar 

  84. Pasaniuc B, Zaitlen N, Halperin E. Accurate estimation of expression levels of homologous genes in RNA-seq experiments. J Comput Biol. 2011;18(3):459–68.

    PubMed  CAS  Google Scholar 

  85. Moxon S, et al. A toolkit for analysing large-scale plant small RNA datasets. Bioinformatics. 2008;24(19):2252–3.

    PubMed  CAS  Google Scholar 

  86. Thomson DW, Bracken CP, Goodall GJ. Experimental strategies for microRNA target identification. Nucleic Acids Res. 2011;39(16):6845–53.

    PubMed  CAS  Google Scholar 

  87. Krek A, et al. Combinatorial microRNA target predictions. Nat Genet. 2005;37(5):495–500.

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  89. Marin RM, Vanicek J. Optimal use of conservation and accessibility filters in microRNA target prediction. PLoS One. 2012;7(2):e32208.

    PubMed  CAS  Google Scholar 

  90. John B, et al. Human MicroRNA targets. PLoS Biol. 2004;2(11):e363.

    PubMed  Google Scholar 

  91. Betel D, et al. The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008;36(Database issue):D149–53.

    PubMed  CAS  Google Scholar 

  92. Maragkakis M, et al. Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics. 2009;10:295.

    PubMed  Google Scholar 

  93. Kruger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34(Web Server issue):W451–4.

    PubMed  Google Scholar 

  94. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003;31(13):3406–15.

    PubMed  CAS  Google Scholar 

  95. Zuker M, Stiegler P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 1981;9(1):133–48.

    PubMed  CAS  Google Scholar 

  96. Miranda KC, et al. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell. 2006;126(6):1203–17.

    PubMed  CAS  Google Scholar 

  97. Wiese KC, Hendriks A. Comparison of P-RnaPredict and mfold—algorithms for RNA secondary structure prediction. Bioinformatics. 2006;22(8):934–42.

    PubMed  CAS  Google Scholar 

  98. Reczko M, et al. Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data. Front Genet. 2011;2:103.

    PubMed  Google Scholar 

  99. Blow M, et al. A survey of RNA editing in human brain. Genome Res. 2004;14(12):2379–87.

    PubMed  CAS  Google Scholar 

  100. Sommer B, et al. RNA editing in brain controls a determinant of ion flow in glutamate-gated channels. Cell. 1991;67(1):11–9.

    PubMed  CAS  Google Scholar 

  101. Paz-Yaacov N, et al. Adenosine-to-inosine RNA editing shapes transcriptome diversity in primates. Proc Natl Acad Sci U S A. 2010;107(27):12174–9.

    PubMed  CAS  Google Scholar 

  102. Farajollahi S, Maas S. Molecular diversity through RNA editing: a balancing act. Trends Genet. 2010;26(5):221–30.

    PubMed  CAS  Google Scholar 

  103. Nishikura K. Functions and regulation of RNA editing by ADAR deaminases. Annu Rev Biochem. 2010;79:321–49.

    PubMed  CAS  Google Scholar 

  104. Warf MB, et al. Effects of ADARs on small RNA processing pathways in C. elegans. Genome Res. 2012;22(8):1488–98.

    PubMed  CAS  Google Scholar 

  105. Lai EC. microRNAs: runts of the genome assert themselves. Curr Biol. 2003;13(23):R925–36.

    PubMed  CAS  Google Scholar 

  106. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281–97.

    PubMed  CAS  Google Scholar 

  107. Lee Y, et al. The nuclear RNase III Drosha initiates microRNA processing. Nature. 2003;425(6956):415–9.

    PubMed  CAS  Google Scholar 

  108. Bernstein E, et al. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature. 2001;409(6818):363–6.

    PubMed  CAS  Google Scholar 

  109. Luciano DJ, et al. RNA editing of a miRNA precursor. RNA. 2004;10(8):1174–7.

    PubMed  CAS  Google Scholar 

  110. Sharma PM, et al. RNA editing in the Wilms’ tumor susceptibility gene, WT1. Genes Dev. 1994;8(6):720–31.

    PubMed  CAS  Google Scholar 

  111. Kawahara Y, et al. Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science. 2007;315(5815):1137–40.

    PubMed  CAS  Google Scholar 

  112. Kawahara Y, et al. Frequency and fate of microRNA editing in human brain. Nucleic Acids Res. 2008;36(16):5270–80.

    PubMed  CAS  Google Scholar 

  113. Habig JW, Dale T, Bass BL. miRNA editing—we should have inosine this coming. Mol Cell. 2007;25(6):792–3.

    PubMed  CAS  Google Scholar 

  114. Chiang HR, et al. Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes Dev. 2010;24(10):992–1009.

    PubMed  CAS  Google Scholar 

  115. Vesely C, et al. Adenosine deaminases that act on RNA induce reproducible changes in abundance and sequence of embryonic miRNAs. Genome Res. 2012;22(8):1468–76.

    PubMed  CAS  Google Scholar 

  116. Burroughs AM, et al. A comprehensive survey of 3′ animal miRNA modification events and a possible role for 3′ adenylation in modulating miRNA targeting effectiveness. Genome Res. 2010;20(10):1398–410.

    PubMed  CAS  Google Scholar 

  117. de Hoon MJ, et al. Cross-mapping and the identification of editing sites in mature microRNAs in high-throughput sequencing libraries. Genome Res. 2010;20(2):257–64.

    PubMed  Google Scholar 

  118. Griffiths-Jones S. The microRNA registry. Nucleic Acids Res. 2004;32(Database issue):D109–11.

    PubMed  CAS  Google Scholar 

  119. Barrett T, et al. NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res. 2011;39(Database issue):D1005–10.

    PubMed  CAS  Google Scholar 

  120. Wang X. miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA. 2008;14(6):1012–7.

    PubMed  CAS  Google Scholar 

  121. Wang X, El Naqa IM. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008;24(3):325–32.

    PubMed  Google Scholar 

  122. Piriyapongsa J, et al. microPIR: an integrated database of microRNA target sites within human promoter sequences. PLoS One. 2012;7(3):e33888.

    PubMed  CAS  Google Scholar 

  123. Younger ST, Corey DR. Transcriptional gene silencing in mammalian cells by miRNA mimics that target gene promoters. Nucleic Acids Res. 2011;39(13):5682–91.

    PubMed  CAS  Google Scholar 

  124. Hsu SD, et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011;39(Database issue):D163–9.

    PubMed  CAS  Google Scholar 

  125. Vergoulis T, et al. TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res. 2012;40(Database issue):D222–9.

    PubMed  CAS  Google Scholar 

  126. Xiao F, et al. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105–10.

    PubMed  CAS  Google Scholar 

  127. Jiang Q, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database issue):D98–104.

    PubMed  CAS  Google Scholar 

  128. Kaya KD, et al. mESAdb: microRNA expression and sequence analysis database. Nucleic Acids Res. 2011;39(Database issue):D170–80.

    PubMed  CAS  Google Scholar 

  129. Kanehisa M, et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–14.

    PubMed  CAS  Google Scholar 

  130. Ashburner M, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9.

    PubMed  CAS  Google Scholar 

  131. Yu W, et al. A navigator for human genome epidemiology. Nat Genet. 2008;40(2):124–5.

    PubMed  CAS  Google Scholar 

  132. Nicoloso MS, et al. Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility. Cancer Res. 2010;70(7):2789–98.

    PubMed  CAS  Google Scholar 

  133. Bruno AE, et al. miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3′UTRs of human genes. BMC Genomics. 2012;13:44.

    PubMed  CAS  Google Scholar 

  134. Lekprasert P, Mayhew M, Ohler U. Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements. PLoS One. 2011;6(6):e20622.

    PubMed  CAS  Google Scholar 

  135. Dweep H, et al. miRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform. 2011;44(5):839–47.

    PubMed  CAS  Google Scholar 

  136. Yang JH, et al. starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res. 2011;39(Database issue):D202–9.

    PubMed  CAS  Google Scholar 

  137. Hiard S, et al. Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res. 2010;38(Database issue):D640–51.

    PubMed  CAS  Google Scholar 

  138. Olejniczak M, et al. RNAimmuno: a database of the nonspecific immunological effects of RNA interference and microRNA reagents. RNA. 2012;18(5):930–5.

    PubMed  CAS  Google Scholar 

  139. Gennarino VA, et al. HOCTAR database: a unique resource for microRNA target prediction. Gene. 2011;480(1–2):51–8.

    PubMed  CAS  Google Scholar 

  140. Cho S, et al. miRGator v2.0: an integrated system for functional investigation of microRNAs. Nucleic Acids Res. 2011;39(Database issue):D158–62.

    PubMed  CAS  Google Scholar 

  141. Lu TP, et al. miRSystem: an integrated system for characterizing enriched functions and pathways of MicroRNA targets. PLoS One. 2012;7(8):e42390.

    PubMed  CAS  Google Scholar 

  142. Plaisier CL, Bare JC, Baliga NS. miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling. Nucleic Acids Res. 2011;39(Web Server issue):W125–31.

    PubMed  CAS  Google Scholar 

  143. Ruepp A, Kowarsch A, Theis F. PhenomiR: microRNAs in human diseases and biological processes. Methods Mol Biol. 2012;822:249–60.

    PubMed  CAS  Google Scholar 

  144. Ziebarth JD, et al. PolymiRTS database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res. 2012;40(Database issue):D216–21.

    PubMed  CAS  Google Scholar 

  145. Szczesniak MW, et al. miRNEST database: an integrative approach in microRNA search and annotation. Nucleic Acids Res. 2012;40(Database issue):D198–204.

    PubMed  CAS  Google Scholar 

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Correspondence to Ramana V. Davuluri .

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Gupta, R., Davuluri, R.V. (2014). Bioinformatics Approaches to the Study of MicroRNAs. In: Fabbri, M. (eds) Non-coding RNAs and Cancer. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8444-8_9

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