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RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods pp 437–456Cite as

Computational Prediction of MicroRNA Genes

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1097))

Abstract

The computational identification of novel microRNA (miRNA) genes is a challenging task in bioinformatics. Massive amounts of data describing unknown functional RNA transcripts have to be analyzed for putative miRNA candidates with automated computational pipelines. Beyond those miRNAs that meet the classical definition, high-throughput sequencing techniques have revealed additional miRNA-like molecules that are derived by alternative biogenesis pathways. Exhaustive bioinformatics analyses on such data involve statistical issues as well as precise sequence and structure inspection not only of the functional mature part but also of the whole precursor sequence of the putative miRNA. Apart from a considerable amount of species-specific miRNAs, the majority of all those genes are conserved at least among closely related organisms. Some miRNAs, however, can be traced back to very early points in the evolution of eukaryotic species. Thus, the investigation of the conservation of newly found miRNA candidates comprises an important step in the computational annotation of miRNAs.

Topics covered in this chapter include a review on the obvious problem of miRNA annotation and family definition, recommended pipelines of computational miRNA annotation or detection, and an overview of current computer tools for the prediction of miRNAs and their limitations. The chapter closes discussing how those bioinformatic approaches address the problem of faithful miRNA prediction and correct annotation.

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References

  1. Bartel DP (2004) Micro RNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297

    CAS  PubMed  Google Scholar 

  2. Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T (2002) Identification of tissue-specific microRNAs from mouse. Curr Biol 12:735–739

    CAS  PubMed  Google Scholar 

  3. Trang P, Weidhaas JB, Slack FJ (2008) MicroRNAs as potential cancer therapeutics. Oncogene 27(2):S52–S57

    CAS  PubMed  Google Scholar 

  4. Fasanaro P, Greco S, Ivan M, Capogrossi MC, Martelli F (2010) MicroRNA: emerging therapeutic targets in acute ischemic diseases. Pharmacol Ther 125:92–104

    CAS  PubMed  Google Scholar 

  5. Cifuentes D, Xue H, Taylor DW, Patnode H, Mishima Y, Cheloufi S, Ma E, Mane S, Hannon GJ, Lawson ND, Wolfe SA, Giraldez AJ (2010) A novel miRNA processing pathway independent of Dicer requires Argonaute2 catalytic activity. Science 328:1694– 1698

    CAS  PubMed Central  PubMed  Google Scholar 

  6. Grimson A, Srivastava M, Fahey B, Woodcroft BJ, Chiang HR, King N, Degnan BM, Rokhsar DS, Bartel DP (2008) Early origins and evolution of microRNAs and Piwi-interacting RNAs in animals. Nature 455:1193–1197

    CAS  PubMed  Google Scholar 

  7. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR (2003) Rfam: an rna family database. Nucleic Acids Res 31(1):439–441

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Cai X, Hagedorn CH, Cullen BR (2004) Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs. RNA 10:1957–1966

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Pawlicki JM, Steitz JA (2008) Primary microRNA transcript retention at sites of transcription leads to enhanced microRNA production. J Cell Biol 182:61–76

    CAS  PubMed Central  PubMed  Google Scholar 

  10. Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in caenorhabditis elegans. Science 294:858–862

    CAS  PubMed  Google Scholar 

  11. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T (2001) Identification of novel genes coding for small expressed RNAs. Science 294:853–858

    CAS  PubMed  Google Scholar 

  12. Morlando M, Ballarino M, Gromak N, Pagano F, Bozzoni I, Proudfoot NJ (2008) Primary microRNA transcripts are processed co-transcriptionally. Nat Struct Mol Biol 15:902–909

    CAS  PubMed  Google Scholar 

  13. Jones-Rhoades MW, Bartel DP, Bartel B (2006) MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol 57: 19–53

    CAS  PubMed  Google Scholar 

  14. Hertel J, Lindemeyer M, Missal K, Fried C, Tanzer A, Flamm C, Hofacker IL, Stadler PF (2006) The expansion of the metazoan microRNA repertoire. BMC Genomics 7:25

    PubMed Central  PubMed  Google Scholar 

  15. Li A, Mao L (2007) Evolution of plant microRNA gene families. Cell Res 17:212–218

    CAS  PubMed  Google Scholar 

  16. Zhao T, Li G, Mi S, Li S, Hannon GJ, Wang XJ, Qi Y (2007) A complex system of small RNAs in the unicellular green alga Chlamydomonas reinhardtii. Genes Dev 21:1190–1203

    CAS  PubMed Central  PubMed  Google Scholar 

  17. Molnár A, Schwach F, Studholme DJ, Thuenemann EC, Baulcombe DC (2007) miRNAs control gene expression in the single-cell alga Chlamydomonas reinhardtii. Nature 447:1126–1129

    PubMed  Google Scholar 

  18. Hinas A, Reimegård J, Wagner EGH, Nellen W, Ambros VR, Söderbom F (2007) The small RNA repertoire of the unicellular amoeba Dictyostelium discoideum: microRNA candidates, small antisense RNAs that may be derived from longer transcripts and multiple classes of repeat-associated RNAs. Nucleic Acids Res 35:6714–6726

    CAS  PubMed Central  PubMed  Google Scholar 

  19. WanZhong J, Zhi L, ZhaoRong L (2008) Discoveries and functions of virus-encoded microRNAs. Chin Sci Bull 53(2):169–177

    Google Scholar 

  20. Wheeler BM, Heimberg AM, Moy VN, Sperling EA, Holstein TW, Heber S, Peterson KJ (2009) The deep evolution of metazoan microRNAs. Evol Dev 11:50–68

    CAS  PubMed  Google Scholar 

  21. Peterson KJ, Dietrich MR, McPeek MA (2009) MicroRNAs and metazoan macroevolution: insights into canalization, complexity, and the cambrian explosion. Bioessays 31:736–747

    CAS  PubMed  Google Scholar 

  22. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T (2003) A uniform system for microRNA annotation. RNA 9:277–279

    CAS  PubMed Central  PubMed  Google Scholar 

  23. Grun D, Wang Y-L, Langenberger D, Gunsalus KC, Rajewsky N (2005) MicroRNA target predictions across seven drosophila species and comparison to mammalian targets. PLoS Comput Biol 1:e13

    PubMed Central  PubMed  Google Scholar 

  24. Huang Y, Gu X (2007) A bootstrap based analysis pipeline for efficient classification of phylogenetically related animal miRNAs. BMC Genomics 8:66

    PubMed Central  PubMed  Google Scholar 

  25. Heimberg AM, Sempere LF, Moy VN, Donoghue PJC, Peterson KJ (2008) MicroRNAs and the advent of vertebrate morphological complexity. Proc Natl Acad Sci USA 105:2946–2950

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Griffiths-Jones S, Hui JLH, Marco A, Ronshaugen M (2011) MicroRNA evolution by arm switching. EMBO Rep 12:172–177

    CAS  PubMed Central  PubMed  Google Scholar 

  27. Grad Y, Aach J, Hayes GD, Reinhart BJ, Church GM, Ruvkun G, Kim J (2003) Computational and experimental identification of C. elegans microRNAs. Mol Cell 11:1253–1263

    CAS  PubMed  Google Scholar 

  28. 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

    CAS  PubMed Central  PubMed  Google Scholar 

  29. Hayes GD, Frand AR, Ruvkun G (2006) The mir-84 and let-7 paralogous microRNA genes of Caenorhabditis elegans direct the cessation of molting via the conserved nuclear hormone receptors NHR-23 and NHR-25. Development 133:4631–4641

    CAS  PubMed  Google Scholar 

  30. Roush S, Slack F (2008) The let-7 family of micrornas. Trends Cell Biol 18(10):505–516

    CAS  PubMed  Google Scholar 

  31. Buermans HJP, Ariyurek Y, van Gertjan O, den Dunnen JT, ’t Hoen PCA (2010) New methods for next generation sequencing based microRNA expression profiling. BMC Genomics 11:716

    Google Scholar 

  32. Miyoshi K, Miyoshi T, Siomi H (2010) Many ways to generate microrna-like small RNAs: non-canonical pathways for microRNA production. Mol Genet Genomics 284:95–103

    CAS  PubMed  Google Scholar 

  33. Schopman NCT, Heynen S, Haasnoot J, Berkhout B (2010) A miRNA–tRNA mix-up: tRNA origin of proposed miRNA. RNA Biol 7:573–576

    CAS  PubMed  Google Scholar 

  34. Langenberger D, Bartschat S, Hertel J, Hoffmann S, Tafer H, Stadler PF (2011) MicroRNA or not microRNA? In: BSB 2011. Lecture notes in computer science. Springer, Berlin, pp 1–9 (accepted)

    Google Scholar 

  35. Hansen TB, Kjems J, Bramsen JB (2011) Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis. RNA Biol 8(3):378–383

    CAS  PubMed Central  PubMed  Google Scholar 

  36. Lee YS, Shibata Y, Malhotra A, Dutta A (2009) A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes Dev 23:2639–2649

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Cole C, Sobala A, Lu C, Thatcher SR, Bowman A, Brown JW, Green PJ, Barton GJ, Hutvagner G (2009) Filtering of deep sequencing data reveals the existence of abundant Dicer-dependent small RNAs derived from tRNAs. RNA 15:2147–2160

    CAS  PubMed Central  PubMed  Google Scholar 

  38. Haussecker D, Huang Y, Lau A, Parameswaran P, Fire AZ, Kay MA (2010) Human tRNA-derived small RNAs in the global regulation of RNA silencing. RNA 16:673–695

    CAS  PubMed Central  PubMed  Google Scholar 

  39. Findeiß S, Langenberger D, Stadler PF, Hoffmann S (2011) Traces of post-transcriptional RNA modifications in deep sequencing data. Biol Chem 392:305–313

    PubMed  Google Scholar 

  40. Kawaji H, Nakamura M, Takahashi1 Y, Sandelin A, Katayama S, Fukuda S, Daub C, Kai C, Jun Kawai J, Yasuda J, Carninci P, Hayashizaki Y (2008) Hidden layers of human small RNAs. BMC Genomics 9:157

    Google Scholar 

  41. Taft RJ, Glazov EA, Lassmann T, Hayashizaki Y, Carninci P, Mattick JS (2009) Small RNAs derived from snoRNAs. RNA 15:1233–1240

    CAS  PubMed Central  PubMed  Google Scholar 

  42. Langenberger D, Bermudez-Santana C, Stadler PF, Hoffmann S (2010) Identification and classification of small RNAs in transcriptome sequence data. Pac Symp Biocomput 15:80–87

    Google Scholar 

  43. Brameier M, Herwig A, Reinhardt R, Walter L, Gruber J (2011) Human box C/D snoRNAs with miRNA like functions: expanding the range of regulatory rnas. Nucleic Acids Res 39:675–686

    CAS  PubMed Central  PubMed  Google Scholar 

  44. Canella D, Praz V, Reina JH, Cousin P, Hernandez N (2010) Defining the RNA polymerase III transcriptome: genome-wide localization of the RNA polymerase III transcription machinery in human cells. Genome Res 20:710–721

    CAS  PubMed Central  PubMed  Google Scholar 

  45. Griffiths-Jones S (2004) The microrna registry. Nucleic Acids Res 32:D109–D111

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Berezikov E, Chung WJ, Willis J, Cuppen E, Lai EC (2007) Mammalian mirtron genes. Mol Cell 28:328–336

    CAS  PubMed Central  PubMed  Google Scholar 

  47. Ruby GJ, Jan CH, Bartell DP (2007) Intronic microRNA precursors that bypass Drosha processing. Nature 48:83–86

    Google Scholar 

  48. Okamura K, Hagen JW, Duan H, Tyler DM, Lai EC (2007) The mirtron pathway generates microRNA-class regulatory RNAs in Drosophila. Cell 130:89–100

    CAS  PubMed Central  PubMed  Google Scholar 

  49. Chung W-J, Agius P, Westholm JO, Chen M, Okamura K, Robine N, Leslie CS, Lai EC (2011) Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans. Genome Res 21(2):286–300. doi: 10.1101/gr.113050.110

    CAS  PubMed Central  PubMed  Google Scholar 

  50. Lee Y, Kim M, Han J, Yeom K-H, Lee S, Baek SH, Kim VN (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J 23:4051–4060

    CAS  PubMed Central  PubMed  Google Scholar 

  51. Denli AM, Tops BJB, Plasterk RAH, Ketting RF, Hannon GJ (2004) Processing of primary microRNAs by the microprocessor complex. Nature 432:231–235

    CAS  PubMed  Google Scholar 

  52. Gregory RI, Yan K-P, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R (2004) The microprocessor complex mediates the genesis of microRNAs. Nature 432:235–240

    CAS  PubMed  Google Scholar 

  53. Kurihara Y, Takashi Y, Watanabe Y (2006) The interaction between DCL1 and HYL1 is important for efficient and precise processing of pri-miRNA in plant microRNA biogenesis. RNA 12:206–212

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Yi R, Qin Y, Macara IG, Cullen BR (2003) Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 17:3011–3016

    CAS  PubMed Central  PubMed  Google Scholar 

  55. Lund E, Guttinger S, Calado A, Dahlberg JE, Kutay U (2004) Nuclear export of microRNA precursors. Science 303:95–98

    CAS  PubMed  Google Scholar 

  56. Hutvagner G, Zamore PD (2002) A microRNA in a multiple-turnover RNAi enzyme complex. Science 297:2056–2060

    CAS  PubMed  Google Scholar 

  57. Zeng Y, Cullen BR (2003) Sequence requirements for micro RNA processing and function in human cells. RNA 9:112–123

    CAS  PubMed Central  PubMed  Google Scholar 

  58. Zeng Y, Wagner EJ, Cullen BR (2002) Both natural and designed microRNAs can inhibit the expression of cognate mRNAs when expressed in human cells. Mol Cell 9:1327–1333

    CAS  PubMed  Google Scholar 

  59. Cullen BR (2004) Derivation and function of small interfering RNAs and microRNAs. Virus Res 102:3–9

    CAS  PubMed  Google Scholar 

  60. Cai X, Hagedorn CH, Cullen BR (2004) Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs. RNA 10:1957–1966

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Piriyapongsa J, Marino-Ramirez L, Jordan IK (2007) Origin and evolution of human microRNAs from transposable elements. Genetics 176:1323–1337

    CAS  PubMed Central  PubMed  Google Scholar 

  62. Klase Z, Kale P, Winograd R, Gupta MV, Heydarian M, Berro R, McCaffrey T, Kashanchi F (2007) HIV-1 TAR element is processed by dicer to yield a viral micro-RNA involved in chromatin remodeling of the viral LTR. BMC Mol Biol 8:63

    PubMed Central  PubMed  Google Scholar 

  63. Saraiya AA, Wang CC (2008) SnoRNA, a novel precursor of microRNA in giardia lamblia. PLoS Pathog 4:e1000224

    PubMed Central  PubMed  Google Scholar 

  64. Ender C, Krek A, Friedlander MR, Beitzinger M, Weinmann L, Chen W, Pfeffer S, Rajewsky N, Meister G (2008) A human snoRNA with microRNA-like functions. Mol Cell 32:519–528

    CAS  PubMed  Google Scholar 

  65. Scott MS, Avolio F, Ono M, Lamond AI, Barton GJ (2009) Human miRNA precursors with box H/ACA snoRNA features. PLoS Comput Biol 5:e1000507

    PubMed Central  PubMed  Google Scholar 

  66. Ono M, Scott MS, Yamada K, Avolio F, Barton GJ, Lamond AI (2011) Identification of human miRNA precursors that resemble box C/d snoRNAs. Nucleic Acids Res 39(9):3879–3891

    CAS  PubMed Central  PubMed  Google Scholar 

  67. Zhang B, Pan X, Cobb GP, Anderson TA (2006) Plant microRNA: a small regulatory molecule with big impact. Dev Biol 289:3–16

    CAS  PubMed  Google Scholar 

  68. Brodersen P, Voinnet O (2009) Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol 10:141–148

    CAS  PubMed  Google Scholar 

  69. Sun YH, Lu S, Shi R, Chiang VL (2011) Computational prediction of plant miRNA targets. Methods Mol Biol 744:175–186

    CAS  PubMed  Google Scholar 

  70. Allen E, Xie Z, Gustafson AM, Sung GH, Spatafora JW, Carrington JC (2004) Evolution of microRNA genes by inverted duplication of target gene sequences in Arabidopsis thaliana. Nat Genet 36:1282–1290

    CAS  PubMed  Google Scholar 

  71. Zhang W, Gao S, Zhou X, Xia J, Chellappan P, Zhou X, Zhang X, Jin H (2010) Multiple distinct small RNAs originate from the same microRNA precursors. Genome Biol 11:8

    Google Scholar 

  72. Shi W, Hendrix D, Levine M, Haley B (2009) A distinct class of small RNAs arises from pre-miRNA-proximal regions in a simple chordate. Nat Struct Mol Biol 16:183–189

    CAS  PubMed Central  PubMed  Google Scholar 

  73. Tweedie S, Ashburner M, Falls K, Leyland P, McQuilton P, Marygold S, Millburn G, Osumi-Sutherland D, Schroeder A, Seal R, Zhang H, Consortium TF (2009) Flybase: enhancing drosophila gene ontology annotations. Nucleic Acids Res 37:D555–D559

    Google Scholar 

  74. Stein L, Sternberg P, Durbin R, Thierry-Mieg J, Spieth J (2001) Wormbase: network access to the genome and biology of caenorhabditis elegans. Nucleic Acids Res 29(1):82–86

    CAS  PubMed Central  PubMed  Google Scholar 

  75. Mhuantong W, Wichadakul D (2009) MicroPC (μPC): a comprehensive resource for predicting and comparing plant microRNAs. BMC Genomics 10:366

    PubMed Central  PubMed  Google Scholar 

  76. Axtell MJ, Bowman JL (2008) Evolution of plant microRNAs and their targets. Trends Plant Sci 13:343–349

    CAS  PubMed  Google Scholar 

  77. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    CAS  PubMed  Google Scholar 

  78. Hertel J, de Jong D, Marz M, Rose D, Tafer H, Tanzer A, Schierwater B, Stadler PF (2009) Non-coding RNA annotation of the genome of Trichoplax adhaerens. Nucleic Acids Res 37:1602–1615

    Google Scholar 

  79. Thakur V, Wanchana S, Xu M, Bruskiewich R, Quick WP, Mosig A, Zhu XG (2011) Characterization of statistical features for plant microRNA prediction. BMC Genomics 12:108

    CAS  PubMed Central  PubMed  Google Scholar 

  80. Xuan P, Guo M, Liu X, Huang Y, Li W, Huang Y (2011) PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs. Bioinformatics 27(10):1368–1376

    CAS  PubMed  Google Scholar 

  81. Jones-Rhoades MW (2010) Prediction of plant miRNA genes. Methods Mol Biol 592:19–30

    CAS  PubMed  Google Scholar 

  82. Xue C, Li F, He T, Liu GP, 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

    PubMed Central  PubMed  Google Scholar 

  83. 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

    CAS  PubMed Central  PubMed  Google Scholar 

  84. Kadri S, Hinman V, Benos PV (2009) HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden markov models. BMC Bioinformatics 10(1):S35

    PubMed Central  PubMed  Google Scholar 

  85. Teune JH, Steger G (2010) NOVOMIR: de novo prediction of MicroRNA-coding regions in a single plant-genome. J Nucleic Acids 2010:495904

    PubMed Central  PubMed  Google Scholar 

  86. Hofacker IL, Priwitzer B, Stadler PF (2004) Prediction of locally stable RNA secondary structures for genome-wide surveys. Bioinformatics 20:191–198

    Google Scholar 

  87. Giegerich R, Voss B, Rehmsmeier M (2004) Abstract shapes of RNA. Nucleic Acids Res 32:4843–4851

    CAS  PubMed Central  PubMed  Google Scholar 

  88. Wang X-J, Reyes JL, Chua N-H, Gaasterland T (2004) Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol 5:R65

    PubMed Central  PubMed  Google Scholar 

  89. Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14:787–799

    CAS  PubMed  Google Scholar 

  90. Dezulian T, Remmert M, Palatnik JF, Weigel D, Huson DH (2006) Identification of plant microRNA homologs. Bioinformatics 22:359–360

    CAS  PubMed  Google Scholar 

  91. Wen J, Frickey T, Weiller GF (2008) Computational prediction of candidate miRNAs and their targets from Medicago truncatula non-protein-coding transcripts. In Silico Biol 8:291–306

    CAS  PubMed  Google Scholar 

  92. Adai A, Johnson C, Mlotshwa S, Archer-Evans S, Manocha V, Vance V, Sundaresan V (2005) Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res 15:78–91

    CAS  PubMed Central  PubMed  Google Scholar 

  93. Lindow M, Jacobsen A, Nygaard S, Mang Y, Krogh A (2007) Intragenomic matching reveals a huge potential for miRNA-mediated regulation in plants. PLoS Comput Biol 3:e238

    PubMed Central  PubMed  Google Scholar 

  94. Sunkar R, Girke T, Jain PK, Zhu JK (2005) Cloning and characterization of micrornas from rice. Plant Cell 17:1397–1411

    CAS  PubMed Central  PubMed  Google Scholar 

  95. Lu C, Jeong DH, Kulkarni K, Pillay M, Nobuta K, German R, Thatcher SR, Maher C, Zhang L, Ware D, Liu B, Cao X, Meyers BC, Green PJ (2008) Genome-wide analysis for discovery of rice microRNAs reveals natural antisense micrornas (nat-miRNAs). Proc Natl Acad Sci USA 105:4951–4956

    CAS  PubMed Central  PubMed  Google Scholar 

  96. Thieme CJ, Gramzow L, Lobbes D, Theißen G (2011) SplamiR—prediction of spliced miRNAs in plants. Bioinformatics 27:1215–1223

    CAS  PubMed  Google Scholar 

  97. Pertea M, Lin X, Salzberg SL (2001) GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 29:1185–1190

    CAS  PubMed Central  PubMed  Google Scholar 

  98. 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

    PubMed Central  PubMed  Google Scholar 

  99. Bentley D, Balasubramanian S, Swerdlow H, Smith G, Milton J, Brown C, Hall K, Evers D, Barnes C, Bignell H et al (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456(7218):53–59

    Google Scholar 

  100. Margulies M, Egholm M, Altman W, Attiya S, Bader J, Bemben L, Berka J, Braverman M, Chen Y, Chen Z et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057):376–380

    Google Scholar 

  101. Hoffmann S, Otto C, Kurtz S, Sharma C, Khaitovich P, Vogel J, Stadler P, Hackermüller J (2009) Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput Biol 5(9):e1000502

    PubMed Central  PubMed  Google Scholar 

  102. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14):1754

    CAS  PubMed Central  PubMed  Google Scholar 

  103. Li R, Yu C, Li Y, Lam T, Yiu S, Kristiansen K, Wang J (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25(15):1966

    CAS  PubMed  Google Scholar 

  104. Knoop V (2010) When you cannot trust the DNA: RNA editing changes transcript sequences. Cell Mol Life Sci 68(4):567–586

    PubMed  Google Scholar 

  105. Friedlaender M, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26(4):407–415

    CAS  Google Scholar 

  106. Hackenberg M, Sturm M, Langenberger D, Falcon-Perez J, Aransay A (2009) miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 37(2):W68

    Google Scholar 

  107. Hackenberg M, Rodríguez-Ezpeleta N, Aransay A (2011) miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 39(2):W132

    Google Scholar 

  108. Hendrix D, Levine M, Shi W (2010) miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data. Genome Biol 11(4):R39

    Google Scholar 

  109. Shi W, Hendrix D, Levine M, Haley B (2009) A distinct class of small RNAs arises from pre-miRNA—proximal regions in a simple chordate. Nat Struct Mol Biol 16(2):183–189

    CAS  PubMed Central  PubMed  Google Scholar 

  110. Langenberger D, Bermudez-Santana C, Hertel J, Hoffmann S, Khaitovich P, Stadler P (2009) Evidence for human microRNA-offset RNAs in small RNA sequencing data. Bioinformatics 25(18):2298

    CAS  PubMed  Google Scholar 

  111. Fasold M, Langenberger D, Binder H, Stadler P, Hoffmann S (2011) Dario: a ncRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 39(2):W112–W117

    CAS  PubMed Central  PubMed  Google Scholar 

  112. Erhard F, Zimmer R (2010) Classification of ncRNAs using position and size information in deep sequencing data. Bioinformatics 26(18):i426

    CAS  PubMed Central  PubMed  Google Scholar 

  113. Lu Z, Yip K, Wang G, Shou C, Hillier L, Khurana E, Agarwal A, Auerbach R, Rozowsky J, Cheng C et al (2011) Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data. Genome Res 21(2):276

    Google Scholar 

  114. Ding J, Zhou S, Guan J (2010) MirenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features. BMC Bioinformatics 11(11):S11

    PubMed Central  PubMed  Google Scholar 

  115. Batuwita R, Palade V (2009) Micropred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25:989–995

    CAS  PubMed  Google Scholar 

  116. Zhao D, Wang Y, Luo D, Shi X, Wang L, Xu D, Yu J, Liang Y (2010) PMirp: a pre-microRNA prediction method based on structure-sequence hybrid features. Artif Intell Med 49:127–132

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  118. Washietl S, Hofacker IL, Stadler PF (2005) Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci USA 102:2454–2459

    CAS  PubMed Central  PubMed  Google Scholar 

  119. 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

    CAS  PubMed Central  PubMed  Google Scholar 

  120. Nawrocki EP, Kolbe DL, Eddy SR (2009) Infernal 1.0: inference of rna alignments. Bioinformatics 25(13):1713

    Google Scholar 

  121. 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

    CAS  PubMed  Google Scholar 

  122. Gkirtzou K, Tsamardinos I, Tsakalides P, Poirazi P (2010) Maturebayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 5(8):e11843

    PubMed Central  PubMed  Google Scholar 

  123. Grundhoff A, Sullivan CS, Ganem D (2006) A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses. RNA 12:733–750

    CAS  PubMed Central  PubMed  Google Scholar 

  124. Alexiou P, Maragkakis M, Papadopoulos GL, Simmosis VA, Zhang L, Hatzigeorgiou AG (2010) The DIANA-mirextra web server: from gene expression data to microRNA function. PLoS One 5(2):e9171

    PubMed Central  PubMed  Google Scholar 

  125. Nam J-W, Shin K-R, Han J, Lee Y, Kim VN, Zhang B-T (2005) Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res 33(11):3570–3581

    CAS  PubMed Central  PubMed  Google Scholar 

  126. Thakur V, Wanchana S, Xu M, Bruskiewich R, Quick WP, Mosig A, Zhu X-G (2011) Characterization of statistical features for plant microRNA prediction. BMC Genomics 12:108

    CAS  PubMed Central  PubMed  Google Scholar 

  127. Zhu E, Zhao F, Xu G, Hou H, Zhou L, Li X, Sun Z, Wu J (2010) mirTools: microRNA profiling and discovery based on high-throughput sequencing. Nucleic acids Res 38(2):W392

    Google Scholar 

  128. Yang J, Shao P, Zhou H, Chen Y, Qu L (2010) DeepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Res 38(1):D123

    CAS  PubMed Central  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany

    Jana Hertel & David Langenberger

  2. Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany

    Peter F. Stadler

  3. Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany

    Peter F. Stadler

  4. Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany

    Peter F. Stadler

  5. Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria

    Peter F. Stadler

  6. Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark

    Peter F. Stadler

  7. Santa Fe Institute, Santa Fe, NM, USA

    Peter F. Stadler

Authors
  1. Jana Hertel
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  2. David Langenberger
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  3. Peter F. Stadler
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Editor information

Editors and Affiliations

  1. Center for non-coding RNA in Technology and Health, IKVH University of Copenhagen, Frederiksberg, Denmark

    Jan Gorodkin

  2. University of Washington Dept. Computer Science & Engineering, Seattle, Washington, USA

    Walter L. Ruzzo

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Hertel, J., Langenberger, D., Stadler, P.F. (2014). Computational Prediction of MicroRNA Genes. In: Gorodkin, J., Ruzzo, W. (eds) RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods. Methods in Molecular Biology, vol 1097. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-709-9_20

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  • DOI: https://doi.org/10.1007/978-1-62703-709-9_20

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-708-2

  • Online ISBN: 978-1-62703-709-9

  • eBook Packages: Springer Protocols

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