Skip to main content
SpringerLink
Log in

Computational Approaches and Related Tools to Identify MicroRNAs in a Species: A Bird’s Eye View

  • Review
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

MicroRNAs (miRNAs) are a family of non-coding RNAs that play a central role in fine-tuning gene expression regulation. Over the past decade, identification and annotation of miRNAs have become a major focus in epigenomics research. However, detection and characterization of miRNA are challenging due to its small size (~22 nucleotide-long) and susceptibility to degradation. The difficulties involved in experimental prediction and characterization of miRNA coding genes have led to the development of in silico-based approaches. Although several algorithms have been developed in recent years, a comprehensive assessment of the principles, methodological insights, and estimate of the strengths and weaknesses of computational methods are limited. The present review is dealt with the detailed methodological insights of different tools used for identifying miRNA coding genes falling under four computational approaches. The parameters considered in these tools along with their specificity are also delineated. Furthermore, the strengths and weaknesses of these four computational approaches, and the bioinformatics resources pertaining to target identification, expression analysis, regulatory network analysis, and SNP identification are stated in this review. The methodological details of miRNA prediction methods and bioinformatics resources related to miRNA research in one platform would facilitate the miRNA research community to develop efficient tools for uncovering novel miRNAs and understanding their role in regulatory networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Mattick JS, Makunin IV (2006) Non-coding RNA. Hum Mol Genet 15 Spec No 1:R17–R29

    Article  PubMed  CAS  Google Scholar 

  2. Macfarlane LA, Murphy PR (2010) MicroRNA: biogenesis, function and role in cancer. Curr Genom 11(7):537–561

    Article  CAS  Google Scholar 

  3. Davis BN, Hata A (2009) Regulation of MicroRNA Biogenesis: A miRiad of mechanisms. Cell Commun Signal 7:18

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Dalmay T (2013) Mechanism of miRNA-mediated repression of mRNA translation. Essays Biochem 54:29–38

    Article  PubMed  CAS  Google Scholar 

  5. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(Database issue):D68–D73

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  7. Stefani G, Slack FJ (2008) Small non-coding RNAs in animal development. Nat Rev Mol Cell Biol 9(3):219–230

    Article  PubMed  CAS  Google Scholar 

  8. Xu P et al (2003) The Drosophila microRNA Mir-14 suppresses cell death and is required for normal fat metabolism. Curr Biol 13(9):790–795

    Article  PubMed  CAS  Google Scholar 

  9. Tsuchiya S, Okuno Y, Tsujimoto G (2006) MicroRNA: biogenetic and functional mechanisms and involvements in cell differentiation and cancer. J Pharmacol Sci 101(4):267–270

    Article  PubMed  CAS  Google Scholar 

  10. Wang Y et al (2009) MicroRNAs in embryonic stem cells. J Cell Physiol 218(2):251–255

    Article  PubMed  CAS  Google Scholar 

  11. Pegoraro M, Tauber E (2008) The role of microRNAs (miRNA) in circadian rhythmicity. J Genet 87(5):505–511

    Article  PubMed  CAS  Google Scholar 

  12. Gantier MP, Sadler AJ, Williams BR (2007) Fine-tuning of the innate immune response by microRNAs. Immunol Cell Biol 85(6):458–462

    Article  PubMed  CAS  Google Scholar 

  13. Poy MN et al (2004) A pancreatic islet-specific microRNA regulates insulin secretion. Nature 432(7014):226–230

    Article  PubMed  CAS  Google Scholar 

  14. Greco SJ, Rameshwar P (2007) MicroRNAs regulate synthesis of the neurotransmitter substance P in human mesenchymal stem cell-derived neuronal cells. Proc Natl Acad Sci USA 104(39):15484–15489

    Article  PubMed  Google Scholar 

  15. Jopling CL et al (2005) Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science 309(5740):1577–1581

    Article  PubMed  CAS  Google Scholar 

  16. Johnson SM et al (2005) RAS is regulated by the let-7 microRNA family. Cell 120(5):635–647

    Article  PubMed  CAS  Google Scholar 

  17. Johnson CD et al (2007) The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res 67(16):7713–7722

    Article  PubMed  CAS  Google Scholar 

  18. Bussing I, Slack FJ, Grosshans H (2008) let-7 microRNAs in development, stem cells and cancer. Trends Mol Med 14(9):400–409

    Article  PubMed  CAS  Google Scholar 

  19. Eis PS et al (2005) Accumulation of miR-155 and BIC RNA in human B cell lymphomas. Proc Natl Acad Sci USA 102(10):3627–3632

    Article  PubMed  CAS  Google Scholar 

  20. Mendell JT (2008) miRiad roles for the miR-17-92 cluster in development and disease. Cell 133(2):217–222

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Volinia S et al (2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 103(7):2257–2261

    Article  PubMed  CAS  Google Scholar 

  22. Voorhoeve PM et al (2006) A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell 124(6):1169–1181

    Article  PubMed  CAS  Google Scholar 

  23. Calin GA et al (2002) Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 99(24):15524–15529

    Article  PubMed  CAS  Google Scholar 

  24. Cheng CY et al (2014) miR-34 cooperates with p53 in suppression of prostate cancer by joint regulation of stem cell compartment. Cell Rep 6(6):1000–1007

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Stern-Ginossar N et al (2007) Host immune system gene targeting by a viral miRNA. Science 317(5836):376–381

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Lecellier CH et al (2005) A cellular microRNA mediates antiviral defense in human cells. Science 308(5721):557–560

    Article  PubMed  CAS  Google Scholar 

  27. Mendes ND, Freitas AT, Sagot MF (2009) Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 37(8):2419–2433

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Gomes CP et al (2013) A review of computational tools in microRNA discovery. Front Genet 4:81

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Hertel J, Langenberger D, Stadler PF (2014) Computational prediction of microRNA genes. Methods Mol Biol 1097:437–456

    Article  PubMed  CAS  Google Scholar 

  30. Lee Y et al (2002) MicroRNA maturation: stepwise processing and subcellular localization. EMBO J 21(17):4663–4670

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Lee Y et al (2003) The nuclear RNase III Drosha initiates microRNA processing. Nature 425(6956):415–419

    Article  PubMed  CAS  Google Scholar 

  32. Zeng Y, Cullen BR (2003) Sequence requirements for micro RNA processing and function in human cells. RNA 9(1):112–123

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Lund E et al (2004) Nuclear export of microRNA precursors. Science 303(5654):95–98

    Article  PubMed  CAS  Google Scholar 

  34. Zeng Y, Cullen BR (2004) Structural requirements for pre-microRNA binding and nuclear export by Exportin 5. Nucleic Acids Res 32(16):4776–4785

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ketting RF et al (2001) Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15(20):2654–2659

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  37. Schwarz DS et al (2003) Asymmetry in the assembly of the RNAi enzyme complex. Cell 115(2):199–208

    Article  PubMed  CAS  Google Scholar 

  38. Chen X (2005) MicroRNA biogenesis and function in plants. FEBS Lett 579(26):5923–5931

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Voinnet O, Origin, biogenesis (2009) and activity of plant microRNAs. Cell 136(4):669–687

    Article  PubMed  CAS  Google Scholar 

  40. Dong H et al (2013) MicroRNA: function, detection, and bioanalysis. Chem Rev 113(8):6207–6233

    Article  PubMed  CAS  Google Scholar 

  41. Szittya G et al (2008) High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics 9:593

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Burnside J et al (2008) Deep sequencing of chicken microRNAs. BMC Genom 9:185

    Article  CAS  Google Scholar 

  43. Li SC, Pan CY, Lin WC (2006) Bioinformatic discovery of microRNA precursors from human ESTs and introns. BMC Genom 7:164

    Article  CAS  Google Scholar 

  44. Lee IH et al (2014) A detailed analysis of next generation sequencing reads of microRNA expression in Barrett’s esophagus: absolute versus relative quantification. BMC Res Notes 7:212

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Krol J, Krzyzosiak WJ (2006) Structure analysis of microRNA precursors. Methods Mol Biol 342:19–32

    PubMed  CAS  Google Scholar 

  46. Ohler U et al (2004) Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification. RNA 10(9):1309–1322

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Unver T, Namuth-Covert DM, Budak H (2009) Review of current methodological approaches for characterizing microRNAs in plants. Int J Plant Genom 2009:262463

    Google Scholar 

  48. Legendre M, Lambert A, Gautheret D (2005) Profile-based detection of microRNA precursors in animal genomes. Bioinformatics 21(7):841–845

    Article  PubMed  CAS  Google Scholar 

  49. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673–4680

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Hofacker IL, Fekete M, Stadler PF (2002) Secondary structure prediction for aligned RNA sequences. J Mol Biol 319(5):1059–1066

    Article  PubMed  CAS  Google Scholar 

  51. Lambert A et al (2004) The ERPIN server: an interface to profile-based RNA motif identification. Nucleic Acids Res 32(Web Server issue):W160–W165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Wang X et al (2005) MicroRNA identification based on sequence and structure alignment. Bioinformatics 21(18):3610–3614

    Article  PubMed  CAS  Google Scholar 

  54. Altschul SF et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410

    Article  PubMed  CAS  Google Scholar 

  55. Hochsmann M et al (2003) Local similarity in RNA secondary structures. Proc IEEE Comput Soc Bioinform Conf 2:159–168

    PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Hofacker IL et al (1998) Automatic detection of conserved RNA structure elements in complete RNA virus genomes. Nucleic Acids Res 26(16):3825–3836

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Hofacker IL, Stadler PF (1999) Automatic detection of conserved base pairing patterns in RNA virus genomes. Comput Chem 23(3–4):401–414

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31(13):3406–3415

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Boffelli D (2008) Phylogenetic shadowing sequence comparisons of multiple primate species. Methods Mol Biol 453:217–231

    Article  PubMed  CAS  Google Scholar 

  62. Berezikov E et al (2005) Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120(1):21–24

    Article  PubMed  CAS  Google Scholar 

  63. Olsen GJ et al (1994) fastDNAmL: a tool for construction of phylogenetic trees of DNA sequences using maximum likelihood. Comput Appl Biosci 10(1):41–48

    PubMed  CAS  Google Scholar 

  64. Bonnet E et al (2004) Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20(17):2911–2917

    Article  PubMed  CAS  Google Scholar 

  65. Bentwich I et al (2005) Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet 37(7):766–770

    Article  PubMed  CAS  Google Scholar 

  66. Siepel A, Haussler D (2004) Combining phylogenetic and hidden Markov models in biosequence analysis. J Comput Biol 11(2–3):413–428

    Article  PubMed  CAS  Google Scholar 

  67. Schwartz S et al (2003) Human-mouse alignments with BLASTZ. Genome Res 13(1):103–107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Friedlander MR et al (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26(4):407–415

    Article  PubMed  CAS  Google Scholar 

  69. Xue C et al (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6:310

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Ritchie W, Theodule FX, Gautheret D (2008) Mireval: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 24(11):1394–1396

    Article  PubMed  CAS  Google Scholar 

  71. Gao D et al (2013) miREval 2.0: a web tool for simple microRNA prediction in genome sequences. Bioinformatics 29(24):3225–3226

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Ritchie W, Legendre M, Gautheret D (2007) RNA stem-loops: to be or not to be cleaved by RNAse III. RNA 13(4):457–462

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  74. Gruber AR et al (2010) RNAz 2.0: improved noncoding RNA detection. Pac Symp Biocomput, p. 69–79

  75. Pedersen JS et al (2006) Identification and classification of conserved RNA secondary structures in the human genome. PLoS Comput Biol 2(4):e33

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  77. Ding, J., S. Zhou, and J. Guan (2010) MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features. BMC Bioinform 11(Suppl 11):S11

    Article  Google Scholar 

  78. Gkirtzou K et al (2010) MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 5(8):e11843

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Nam JW et al (2005) Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res 33(11):3570–3581

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Wu Y et al (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12:107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Markham NR, Zuker M (2008) UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 453:3–31

    Article  PubMed  CAS  Google Scholar 

  82. Xuan P et al (2011) MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One 6(11):e27422

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Shen W et al (2012) MicroRNA prediction using a fixed-order Markov model based on the secondary structure pattern. PLoS One 7(10):e48236

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. He C et al (2012) MiRmat: mature microRNA sequence prediction. PLoS One 7(12):e51673

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Tran Vdu T et al (2015) miRBoost: boosting support vector machines for microRNA precursor classification. RNA 21(5):775–785

    Article  PubMed  CAS  Google Scholar 

  86. Tempel S, Tahi F (2012) A fast ab-initio method for predicting miRNA precursors in genomes. Nucleic Acids Res 40(11):e80

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Batuwita R, Palade V (2009) microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25(8):989–995

    Article  PubMed  CAS  Google Scholar 

  88. Xu Y, Zhou X, Zhang W (2008) MicroRNA prediction with a novel ranking algorithm based on random walks. Bioinformatics 24(13):i50–i58

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Sewer A et al (2005) Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 6:267

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Jiang P et al (2007) MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 35(Web Server issue):W339–W344

    Article  PubMed  PubMed Central  Google Scholar 

  91. Higashi S et al (2015) Mirinho: an efficient and general plant and animal pre-miRNA predictor for genomic and deep sequencing data. BMC Bioinformatics 16:179

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453

    Article  PubMed  CAS  Google Scholar 

  94. Mathews DH et al (2004) Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc Natl Acad Sci USA 101(19):7287–7292

    Article  PubMed  CAS  Google Scholar 

  95. Nussinov R et al (1978) Algorithms for loop matchings. SIAM J Appl Math 35(1):68–82

    Article  Google Scholar 

  96. 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  PubMed  CAS  Google Scholar 

  97. Agarwal S et al (2010) Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM). BMC Bioinform 11(Suppl 1):S29

    Article  CAS  Google Scholar 

  98. Tyagi S et al (2008) CID-miRNA: a web server for prediction of novel miRNA precursors in human genome. Biochem Biophys Res Commun 372(4):831–834

    Article  PubMed  CAS  Google Scholar 

  99. Grundhoff A, Sullivan CS, Ganem D (2006) A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses. RNA 12(5):733–750

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Tav C et al (2016) miRNAFold: a web server for fast miRNA precursor prediction in genomes. Nucleic Acids Res 44(W1):W181–W184

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Zhang B et al (2006) Plant microRNA: a small regulatory molecule with big impact. Dev Biol 289(1):3–16

    Article  PubMed  CAS  Google Scholar 

  102. 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  PubMed  CAS  Google Scholar 

  103. Cai Y et al (2009) A brief review on the mechanisms of miRNA regulation. Genom Proteom Bioinform 7(4):147–154

    Article  CAS  Google Scholar 

  104. Agarwal V et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Lall S et al (2006) A genome-wide map of conserved microRNA targets in C. elegans. Curr Biol 16(5):460–471

    Article  PubMed  CAS  Google Scholar 

  107. Hsu JB et al (2011) miRTar: an integrated system for identifying miRNA-target interactions in human. BMC Bioinform 12:300

    Article  CAS  Google Scholar 

  108. Dweep H et al (2011) miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44(5):839–847

    Article  PubMed  CAS  Google Scholar 

  109. Loher P, Rigoutsos I (2012) Interactive exploration of RNA22 microRNA target predictions. Bioinformatics 28(24):3322–3323

    Article  PubMed  CAS  Google Scholar 

  110. Paraskevopoulou MD et al (2013) DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41(Web Server issue):W169–W173

    Article  PubMed  PubMed Central  Google Scholar 

  111. Fahlgren N et al (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2(2):e219

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 39(Web Server issue):W155–W159

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Bonnet E et al (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26(12):1566–1568

    Article  PubMed  CAS  Google Scholar 

  114. Xie F, Zhang B (2010) Target-align: a tool for plant microRNA target identification. Bioinformatics 26(23):3002–3003

    Article  PubMed  CAS  Google Scholar 

  115. Milev I et al (2011) miRTour: plant miRNA and target prediction tool. Bioinformation 6(6):248–249

    Article  PubMed  PubMed Central  Google Scholar 

  116. Jha A, Shankar R (2011) Employing machine learning for reliable miRNA target identification in plants. BMC Genomics 12:636

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Wu HJ et al (2012) PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res 40(Web Server issue):W22–W28

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Dweep H, Sticht C, Gretz N (2013) In-silico algorithms for the screening of possible microRNA binding sites and their interactions. Curr Genom 14(2):127–136

    Article  CAS  Google Scholar 

  119. Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43(Database issue):D146–D152

    Article  PubMed  CAS  Google Scholar 

  120. Hsu SD et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39(Database issue):D163–D169

    Article  PubMed  CAS  Google Scholar 

  121. Griffiths-Jones S et al (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36(Database issue):D154–D158

    PubMed  CAS  Google Scholar 

  122. Xiao F et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110

    Article  PubMed  CAS  Google Scholar 

  123. Blin K et al (2015) DoRiNA 2.0–upgrading the doRiNA database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res 43(Database issue):D160–D167

    Article  PubMed  CAS  Google Scholar 

  124. Le Brigand K et al (2010) MiRonTop: mining microRNAs targets across large scale gene expression studies. Bioinformatics 26(24):3131–3132

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  125. Alexiou P et al (2010) The DIANA-mirExTra web server: from gene expression data to microRNA function. PLoS One 5(2):e9171

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Kaya KD et al (2011) mESAdb: microRNA expression and sequence analysis database. Nucleic Acids Res 39(Database issue):D170–D180

    Article  PubMed  CAS  Google Scholar 

  127. Cho S et al (2013) MiRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res 41(Database issue):D252–D257

    PubMed  CAS  Google Scholar 

  128. Sales G et al (2010) MAGIA, a web-based tool for miRNA and genes integrated analysis. Nucleic Acids Res 38(Web Server issue): W352–W359

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  129. Huang GT, Athanassiou C, Benos PV (2011) mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic Acids Res 39(Web Server issue):W416–W423

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  130. Gennarino VA et al (2012) Identification of microRNA-regulated gene networks by expression analysis of target genes. Genome Res 22(6):1163–1172

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Chiromatzo AO et al (2007) miRNApath: a database of miRNAs, target genes and metabolic pathways. Genet Mol Res 6(4):859–865

    PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Papadopoulos GL et al (2009) DIANA-mirPath: Integrating human and mouse microRNAs in pathways. Bioinformatics 25(15):1991–1993

    Article  PubMed  CAS  Google Scholar 

  135. Gaidatzis D et al (2007) Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinform 8:69

    Article  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  137. Lagana A et al (2009) miRo: a miRNA knowledge base. Database (Oxford) 2009:bap008

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Xie B et al (2013) miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics 29(5):638–644

    Article  PubMed  CAS  Google Scholar 

  140. Wang D et al (2014) OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs. Bioinformatics 30(15):2237–2238

    Article  PubMed  CAS  Google Scholar 

  141. Li Y et al (2014) HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res 42(Database issue):D1070–D1074

    Article  PubMed  CAS  Google Scholar 

  142. Gong J et al (2012) Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis. Hum Mutat 33(1):254–263

    Article  PubMed  CAS  Google Scholar 

  143. Sherry ST, Ward M, Sirotkin K (1999) dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 9(8):677–679

    PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  145. Bruno AE et al (2012) miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3′UTRs of human genes. BMC Genom 13:44

    Article  CAS  Google Scholar 

  146. Liu C et al (2012) MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genom 13:661

    Article  CAS  Google Scholar 

  147. Barenboim M et al (2010) MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum Mutat 31(11):1223–1232

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Thomas LF, Saito T, Saetrom P (2011) Inferring causative variants in microRNA target sites. Nucleic Acids Res 39(16):e109

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Bhattacharya A, Ziebarth JD, Cui Y (2014) PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res 42(Database issue):D86–D91

    Article  PubMed  CAS  Google Scholar 

  150. Bhattacharya A, Ziebarth JD, Cui Y (2013) SomamiR: a database for somatic mutations impacting microRNA function in cancer. Nucleic Acids Res 41(Database issue):D977–D982

    Article  PubMed  CAS  Google Scholar 

  151. Bhattacharya A, Cui Y (2015) miR2GO: comparative functional analysis for microRNAs. Bioinformatics 31(14):2403–2405

    Article  PubMed  CAS  Google Scholar 

  152. van Rooij E, Kauppinen S (2014) Development of microRNA therapeutics is coming of age. EMBO Mol Med 6(7):851–864

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

AR is grateful to Pondicherry University for the pre-doctoral fellowship. Authors are thankful to P. Manivel for critical reading and his valuable suggestions. The authors also thank the Department of Biotechnology, Department of Science and Technology, and University Grants Commission, Government of India, for supporting the research work in Centre for Bioinformatics.

Author information

Authors and Affiliations

  1. Centre for Bioinformatics, Pondicherry University, Pondicherry, 605014, India

    Anjana Rajendiran & Archana Pan

  2. Department of Pathology, Dunedin School of Medicine, University of Otago, 270 Great King Street, Dunedin, 9054, New Zealand

    Aniruddha Chatterjee

  3. Maurice Wilkins Centre for Molecular Biodiscovery, Level 2, 3A Symonds Street, Auckland, New Zealand

    Aniruddha Chatterjee

Authors
  1. Anjana Rajendiran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aniruddha Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Archana Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Archana Pan.

Ethics declarations

Conflict of interest

Authors have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajendiran, A., Chatterjee, A. & Pan, A. Computational Approaches and Related Tools to Identify MicroRNAs in a Species: A Bird’s Eye View. Interdiscip Sci Comput Life Sci 10, 616–635 (2018). https://doi.org/10.1007/s12539-017-0223-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-017-0223-x

Keywords

Search

Navigation