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A Statistical Analysis of MicroRNA: Classification, Identification and Conservation Based on Structure and Function

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Growth Curve and Structural Equation Modeling

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 132))

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Abstract

The microRNAs (miRNAs) are small non-coding RNAs which play an important role in gene regulation and are involved in several biological functions. Studies have shown that there are several hundreds of them across (human) genome. And one miRNA may be involved in several genes and several miRNA may target a gene. In this regard it is interesting to know whether these several known miRNAs show structural and functional similarities. Do they fall into recognisable groups with respect to their structure and function and does the length of miRNA follow evolutionary principles and are highly conserved?. This study with the help of statistical tools explores characterising, identification of (human) miRNA based on their structure and function, network analysis of their relationship and target genes and conservation of their length and sequence structure across species.

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References

  • Abelson JF, Kwan KY, O’Roak BJ, Baek DY, Stillman AA, Morgan TM, Mathews CA, Pauls DL, Rasin MR, Gunel M, Davis NR, Ercan-Sencicek AG, Guez DH, Spertus JA, Leckman JF, Dure LS, Kurlan R, Singer HS, Gilbert DL, Farhi A, Louvi A, Lifton RP, Sestan N, State MW (2005) Sequence variants in SLITRK1 are associated with Tourett’s syndrome. Science 310(5746):317–320

    Article  Google Scholar 

  • Agostini F, Dapas B, Farra R, Grassi M, Racchi G, Klingel K, Kandolf R, Heidenreich O, Mercatanti A, Rainaldi G et al (2006) Potential applications of small interfering RNAs in the cardiovascular field. Drugs Future 31(6):513–525

    Google Scholar 

  • Ambros V (2004) The functions of animal microRNAs. Nature 431:350–355

    Article  Google Scholar 

  • Ambros V, Bartel B, Bartel DP, Burge CB et al (2003) A uniform system for microRNA annotation. RNA 9:277–279

    Article  Google Scholar 

  • Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism and function. Cell 116(2): 281–297

    Article  Google Scholar 

  • Basu S, Burma DP, Chaudhuri P (2003) Words in DNA sequences: some case studies based on their frequency statistics. J Math Biol 46:479–503

    MATH  MathSciNet  Google Scholar 

  • Bouamar H, Jiang D, Wang L, Lin AP, Ortega M, Aguiar RC (2015) MicroRNA 155 control of p53 activity is context dependent and mediated by Aicda and Socs1. Mol Cell Biol 35(8): 1329–1340

    Google Scholar 

  • Brennecke J, Hipfner DR, Stark A, Russel RB, Cohen SM (2003) Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113:25–36

    Article  Google Scholar 

  • Brennecke J, Stark A, Russel RB, Cohen SM (2005) Principles of micro-RNA-target recognition. PLoS Biol 3:e85

    Google Scholar 

  • Brody E, Abelson J (1985) The “spliceosome” yeast pre-messenger RNA associates with a 40S complex in a splicing-dependent reaction. Science 228(4702):963–967

    Article  Google Scholar 

  • Chaudhuri P, Das S (2001) Statistical analysis of large DNA sequences using distribution of DNA words. Curr Sci 80:1161–1166

    Google Scholar 

  • Chaudhuri P, Das S (2002) SWORDS: a statistical tool for analyzing large DNA sequences. J Biosci 27:1–6

    Google Scholar 

  • Crick FHC (1958) On protein synthesis. Symp Soc Exp Biol 12:138–163

    Google Scholar 

  • Crick FHC (1968) The origin of the genetic code. J Mol Biol 38:367–379

    Google Scholar 

  • Crick FHC (1970) Central dogma of molecular biology. Nature 227:561–563

    Article  Google Scholar 

  • Crick FHC (1988) What made pursuit. Basic Books, New York

    Google Scholar 

  • Denli AM, Tops BB, Plasterk RH, Ketting RF, Hannon GJ (2004) Processing of primary microRNAs by the microprocessor complex. Nature 432(7014):231–240

    Article  Google Scholar 

  • Doolittle WF (1978) Genes in pieces: were they ever together? Nature 272:581–582

    Article  Google Scholar 

  • Doolittle WF, Fraser P, Gerstein MB, Graveley BR (2013) Sixty years of genome biology. Genome Biol 14(4):113–119

    Google Scholar 

  • Dusl M, Senderek J, Müller JS, Vogel JG, Pertl A, Stucka R, Lochmüller H, David R, Abicht A (2015) A 3′-UTR mutation creates a microRNA target site in the GFPT1 gene of patients with congenital myasthenic syndrome. Hum Mol Genet 24(8). doi:10.1093/hmg/ddv090

  • Fantini B (2006) History of central dogma of molecular biology and its epistemological status today, Geneva, February 22–23, 2007. Hist Philos Life Sci 28:487–609

    Google Scholar 

  • Lian F, Cui Y, Zhou C, Gao K, Wu L (2015) Identification of a plasma four-microRNA panel as potential noninvasive biomarker for osteosarcoma. PLoS One 10(3):e0121499. doi:10.1371/journal.pone.0121499

    Article  Google Scholar 

  • Franklin RE, Gosling RG (1953) Molecular configuration in sodium thymonucleate. Nature 171(4356):740–741

    Article  Google Scholar 

  • Gilbert W (1978) Why genes in pieces? Nature 271(5645):501

    Article  Google Scholar 

  • Gregory RI, Yan KP, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R (2004) The microprocessor complex mediates the genesis of microRNAs. Nature 432(7014):235–240

    Article  Google Scholar 

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

    Google Scholar 

  • Griffiths-Jones S et al (2005) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(Suppl 1):D140–D144

    Google Scholar 

  • Gu W, Wang X, Zhail C, Xie X, Zhou T (2012) Selection on synonymous sites for increased accessibility around miRNA binding sites in plants. Mol Biol Evol 29:3037–3044

    Google Scholar 

  • Han J, Han J, Lee Y, Yeom K, Nam J, Heo I, Rhee J, Sohn SY, Cho Y, Zhang BT, Kim VN (2004) The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev 18(24):3016–3027

    Google Scholar 

  • Huang Y, Gu X (2007) A bootstrap based analysis pipeline for efficient classification of phylogenetically related animal miRNAs. BMC Genomics 8:66. doi:10.1186/1471-2164-8-66

    Article  Google Scholar 

  • Jeffreys AJ, Flavel RA (1977) The rabbit beta-globin gene contains a large large insert in the coding sequence. Cell 12(4):1097–1108

    Article  Google Scholar 

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

    Google Scholar 

  • John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets. PLoS Biol 2:e363. doi:10.1371/journal.pbio.0020363, pmid:15502875

    Google Scholar 

  • Kamanu TKK, Radovanovic A, Archer JAC, Bajic VB (2013) Exploration of miRNA families for hypotheses generation. Nat Sci Rep 3:2940. doi:10.1038/srep02940

    Google Scholar 

  • Kefas B, Comeau L, Floyd DH, Seleverstov O, Godlewski J, Schmittgen T et al (2009) The neuronal microRNA miR-326 acts in a feedback loop with notch and has therapeutic potential against brain tumors. J Neurosci 29:15161–15168. doi:10.1523/JNEUROSCI.4966-09.2009, pmid:19955368

    Google Scholar 

  • Kefas B, Comeau L, Erdle N, Montgomery E, Amos S, Purow B (2010) Pyruvate kinase M2 is a target of the tumor-suppressive microRNA-326 and regulates the survival of glioma cells. Neuro Oncol 12:1102–1112. doi:10.1093/neuonc/noq080, pmid:20667897

    Google Scholar 

  • Kruger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454. doi:10.1093/nar/gkl243, pmid:16845047

    Google Scholar 

  • Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599

    Google Scholar 

  • Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294(5543):858–862

    Article  Google Scholar 

  • Lee RC, Ambros V (2001) An extensive class of small RNAs in Caenorhabditis elegans. Science 294:862–864

    Article  Google Scholar 

  • Lee RC, Feinbaum RL, Ambros V (1993) The C elegans heterochronic gene lin-4 encodes small RNAs with artisense complementarity to lin-14. Cell 75:843–854

    Article  Google Scholar 

  • Lee Y et al (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J 23: 4051–4060

    Google Scholar 

  • Lorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65(16):7065–7070

    Google Scholar 

  • Ma L, Teruya-Feldstein J, Weinberg RA (2007) Tumour invasion and metastasis initiated bu microRNA-10b in breast cancer. Nature 449(7163):682–688

    Article  Google Scholar 

  • Mathews DH, Sabina J, Zuker M, Turner DH (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 288:911–940

    Google Scholar 

  • miRBase (2010) Sanger miRBase sequence database. http://microrna.sanger.ac.uk/sequences/

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

    Article  Google Scholar 

  • miRBase (2010) Sanger miRBase sequence database. microrna.sanger.ac.uk/sequences/

    Google Scholar 

  • Morange M (2006) The protein side of the central dogma:permanence and change. Hist Philos Life Sci 28:513–524

    Google Scholar 

  • Morange M (2008) What history tells us XIII. Fifty years of central dogma. J Biosci 33(2):171–175

    Google Scholar 

  • Mulder C, Arrand JR, Delius H, Keller W, Pettersson U, Roberts RJ, Sharp PA (1975) Cleavage maps of DNA from adenovirus types 2 and 5 by restriction endonucleases EcoRI and HpaI. Cold Spring Harb Symp Quant Biol 39(Pt 1):397–400

    Google Scholar 

  • Olsen PH, Ambros V (1999) The lin-4 regulatory RNA controls developmental timing in C. elegans by blocking LIN-14 protein synthesis after the initiation of translation. Dev Biol 2:671–680

    Google Scholar 

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

    Article  Google Scholar 

  • Prabhakar S, Noonan JP, Pääbo S, Rubin EM (2007) Accelerated evolution of conserved noncoding sequences in humans. Science 314(5800):786

    Article  Google Scholar 

  • Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G (2000) The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403:901–906

    Google Scholar 

  • Rhee J-K, Shin S-Y, Zhang B-T (2013) Construction of microRNA functional families by a mixture model of position weight matrices. Peer J 1:e199. doi:10.7717/peerj.199

    Google Scholar 

  • Ruvkun G (2001) Molecular biology: glimpses of a tiny RNA world. Science 294:797–799

    Article  Google Scholar 

  • Sarazin A, Voinnet O (2014) Exploring new models of easiRNA biogenesis. Nat Genet 46(6):530. doi:10.1038/ng.2993

    Google Scholar 

  • Sharp PA, Sugen B, Sambrook J (1973) Detection of two restriction endonuclease activities

    Google Scholar 

  • Sinha S, Vasulu TS, Rajat KD (2009) Performance and evaluation of microRNA gene identification tools. J Proteomics Bioinform 2(8):336–343

    Google Scholar 

  • Smielewska MM (2008) The role of miRNAs and PiRNAs in planarian regeneration. UMI, Ann Arbor

    Google Scholar 

  • Soifer H et al (2007) MicroRNAs in disease and potential therapeutic applications. Mol Ther 15:2070–2079

    Google Scholar 

  • Chung SH, Gillies M, Sugiyama Y, Zhu L, Lee S-R, Shen W (2015) Profiling of microRNAs involved in retinal degeneration caused by selective Müller cell ablation. PLoS One 10(3):e0118949

    Article  Google Scholar 

  • van Rooij E et al (2007) MicroRNAs: powerful new regulators of heart disease and provocative therapeutic targets. J Clin Invest 117:2369–2376

    Google Scholar 

  • Vergoulis T et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229

    Google Scholar 

  • Vergoulis T, Kanellos I, Kostoulas N, Georgakilas G, Sellis T, Hatzigeorgiou A, Dalamagas T (2015) mirPub: a databse for searching microRNA publications. Bioinformatics 31(2):1–3

    Google Scholar 

  • Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Lorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM (2006) A microRNA expression signature of human solid tumor cancer gene targets. Proc Natl Acad Sci U S A 103(7):2257–2261

    Google Scholar 

  • Watson JD (1965) Molecular biology of the gene. W A Benjamin, New York

    Google Scholar 

  • Watson JD, Crick FH (1953) Molecular structure of nucleic acids: a structure for deoxy rebose nucleic acid. Nature 153:737–738

    Article  Google Scholar 

  • Wightman B, Ha I, Ruvkun G (1993) Posttransacriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75(5):855–862

    Article  Google Scholar 

  • Wilkins MHF, Stokes AR, Wilson HR (1953) Molecular structure of deoxypentose nucleic acids. Nature 171(4356):738–740

    Article  Google Scholar 

  • Woese CR (1967) The genetic code: the molecular basis for genetic expression. Harper and Row, New York

    Google Scholar 

  • Woese CR (2001) Translation: in retrospect and prospect. RNA 7:1055–1067

    Article  Google Scholar 

  • 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(Database issue):D105–D110. doi:10.1093/nar/gkn851, Epub 2008 Nov 7

    Google Scholar 

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

    Article  Google Scholar 

  • Xu P, Vernooy SY, Guo M, Hay BA (2003) The Drosophila microRNA Mir-14 suppresses cell death and is required for normal fat metabolism. Curr Biol 13(9):790–795

    Google Scholar 

  • Yu X, Lin J, Zack DJ et al (2008) Analysis of regulatory network topology reveals functionally distinct classes of microRNAs. Nucleic Acids Res 36(20):6494–6503

    Google Scholar 

  • Yu X, Lin J, Zack DJ et al (2008) Analysis of regulatory network topology reveals functionally distinct classes of microRNAs. Nucleic Acids Res 36(20):6494–503

    Google Scholar 

  • Zamore PD (2002) Ancient pathways programmed by small RNAs. Science 296:1265–1269

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Zhang H-M, Kuang S, Xiong X, Gao T, Liu C, Guo A-Y (2015) Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Brief Bioinform 16:45–58

    Google Scholar 

  • Zhang Y, Li M, Wang H, Fisher WE, Lin PH, Yao Q et al (2009) Profiling of 95 microRNAs in pancreatic cancer cell lines and surgical specimens by real-time PCR analysis. World J Surg 33:698–709. doi:10.1007/s00268-008-9833-0, pmid:19030927

    Google Scholar 

  • Zhang Z-L, Bai Z-H, Wang X-B, Bai L, Miao F, Pei H-H (2015) miR-186 and 326 predict the prognosis of pancreatic ductal adenocarcinoma and affect the proliferation and migration of cancer cells. PLoS One 10(3). doi:10.1371/journal.pone.0118814

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Authors and Affiliations

  1. Assam University, Silchar, India

    Mohua Chakraborty

  2. Tata Consultancy Services, Kolkata, India

    Ananya Chatterjee

  3. College of London, London, UK

    S Krithika

  4. Indian Statistical Institute, Kolkata, India

    T. S. Vasulu

Authors
  1. Mohua Chakraborty
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  2. Ananya Chatterjee
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  3. S Krithika
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  4. T. S. Vasulu
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Correspondence to T. S. Vasulu .

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Editors and Affiliations

  1. Indian Statistical Institute, Theoretical Statistics and Mathematics unit, Kolkata, India

    Ratan Dasgupta

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Chakraborty, M., Chatterjee, A., Krithika, S., Vasulu, T.S. (2015). A Statistical Analysis of MicroRNA: Classification, Identification and Conservation Based on Structure and Function. In: Dasgupta, R. (eds) Growth Curve and Structural Equation Modeling. Springer Proceedings in Mathematics & Statistics, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-17329-0_13

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