Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

Author(s): Abbasali Emamjomeh*, Javad Zahiri, Mehrdad Asadian, Mehrdad Behmanesh, Barat A. Fakheri, Ghasem Mahdevar.

Journal Name: Medicinal Chemistry

Volume 15 , Issue 3 , 2019

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Graphical Abstract:


Abstract:

Background: Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

Objective: The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.

Method: In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

Results: The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

Conclusion: ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Keywords: ncRNAs, drug design, experimental methods, algorithm, database, tool.

[1]
Washietl, S.; Will, S.; Hendrix, D.A.; Goff, L.A.; Rinn, J.L.; Berger, B.; Kellis, M. Computational analysis of noncoding RNAs. Wiley Interdiscip. Rev. RNA, 2012, 3(6), 759-778.
[2]
Mattick, J.S.; Makunin, I.V. Non-coding RNA. Hum. Mol. Genet., 2006, 15, 17-29.
[3]
Costa, F.F. Non-coding RNAs: lost in translation? Gene, 2007, 386(1), 1-10.
[4]
Ling, H.; Fabbri, M.; Calin, G.A. MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat. Rev. Drug Discov., 2013, 12(11), 847-865.
[5]
Yogev, O.; Lagos, D. Noncoding RNAs and cancer. Silence, 2011, 2(1), 6.
[6]
Bali, K.K.; Kuner, R. Noncoding RNAs: key molecules in understanding and treating pain. Trends Mol. Med., 2014, 20(8), 437-448.
[7]
Lutz, B.M.; Bekker, A.; Tao, Y.X. Noncoding RNAs new players in chronic pain. J. Am. Soc. Anesthesio., 2014, 121(2), 409-417.
[8]
Bolha, L.; Ravnik-Glavač, M.; Glavac, D. Long noncoding RNAs as biomarkers in cancer. Dis. Markers, 2017, 2017, 7243968.
[9]
Bartel, D.P. MicroRNAs: target recognition and regulatory functions. Cell, 2009, 136(2), 215-233.
[10]
Kouhkan, F.; Soleimani, M.; Daliri, M.; Behmanesh, M.; Mobarra, N. miR-451 up-regulation, induce erythroid differentiation of CD133+ cells independent of cytokine cocktails. Iran. J. Basic Med. Sci., 2013, 16(6), 756.
[11]
Kouhkan, F.; Hafizi, M.; Mobarra, N.; Mossahebi-Mohammadi, M.; Mohammadi, S.; Behmanesh, M.; Zomorrod, M.S.; Alizadeh, S.; Lahmy, R.; Daliri, M. miRNAs: a new method for erythroid differentiation of hematopoietic stem cells without the presence of growth factors. Appl. Biochem. Biotechnol., 2014, 172(4), 2055-2069.
[12]
Lahmy, R.; Soleimani, M.; Sanati, M.H.; Behmanesh, M.; Kouhkan, F.; Mobarra, N. miRNA-375 promotes beta pancreatic differentiation in human induced pluripotent stem (hiPS) cells. Mol. Biol. Rep., 2014, 41(4), 2055-2066.
[13]
Lahmy, R.; Soleimani, M.; Sanati, M.H.; Behmanesh, M.; Kouhkan, F.; Mobarra, N. Pancreatic islet differentiation of human embryonic stem cells by microRNA overexpression. J. Tissue Eng. Regen. Med., 2016, 10(6), 527-534.
[14]
Bae, Y.H.; Mrsny, R.J.; Park, K. Cancer targeted drug delivery; Springer, 2013.
[15]
Mendes Soares, L.M.; Valcárcel, J. The expanding transcriptome: the genome as the ‘Book of Sand’. EMBO J., 2006, 25(5), 923-931.
[16]
Gunawardane, L.S.; Saito, K.; Nishida, K.M.; Miyoshi, K.; Kawamura, Y.; Nagami, T.; Siomi, H.; Siomi, M.C. A slicer-mediated mechanism for repeat-associated siRNA 5'end formation in Drosophila. Science, 2007, 315(5818), 1587-1590.
[17]
Evans, D.; Marquez, S.M.; Pace, N.R. RNase P: interface of the RNA and protein worlds. Trends Biochem. Sci., 2006, 31(6), 333-341.
[18]
Reiner, R.; Ben-Asouli, Y.; Krilovetzky, I.; Jarrous, N. A role for the catalytic ribonucleoprotein RNase P in RNA polymerase III transcription. Genes Dev., 2006, 20(12), 1621-1635.
[19]
Smit, S.; Widmann, J.; Knight, R. Evolutionary rates vary among rRNA structural elements. Nucleic Acids Res., 2007, 35(10), 3339-3354.
[20]
Yusupov, M.M.; Yusupova, G.Z.; Baucom, A.; Lieberman, K.; Earnest, T.N.; Cate, J.; Noller, H.F. Crystal structure of the ribosome at 5.5 Å resolution. Science, 2001, 292(5518), 883-896.
[21]
Agrawal, N.; Dasaradhi, P.; Mohmmed, A.; Malhotra, P.; Bhatnagar, R.K.; Mukherjee, S.K. RNA interference: biology, mechanism and applications. Microbiol. Mol. Biol. Rev., 2003, 67(4), 657-685.
[22]
Zeng, Y.; Yi, R.; Cullen, B.R. MicroRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms. Proc. Natl. Acad. Sci., 2003, 100(17), 9779-9784.
[23]
Valencia-Sanchez, M.A.; Liu, J.; Hannon, G.J.; Parker, R. Control of translation and mRNA degradation by miRNAs and siRNAs. Genes Dev., 2006, 20(5), 515-524.
[24]
Bachellerie, J.P.; Cavaillé, J.; Hüttenhofer, A. The expanding snoRNA world. Biochimie, 2002, 84(8), 775-790.
[25]
Tycowski, K.T.; Smith, C.M.; Shu, M.D.; Steitz, J.A. A small nucleolar RNA requirement for site-specific ribose methylation of rRNA in Xenopus. Proc. Nal. Aca. Sci., 1996, 93(25), 14480-14485.
[26]
Kiss, T. Biogenesis of small nuclear RNPs. J. Cell Sci., 2004, 117(25), 5949-5951.
[27]
Vogel, J.; Wagner, E.G.H. Target identification of small noncoding RNAs in bacteria. Curr. Opin. Microbiol., 2007, 10(3), 262-270.
[28]
Hershberg, R.; Altuvia, S.; Margalit, H. A survey of small RNA- encoding genes in Escherichia coli. Nucleic Acids Res., 2003, 31(7), 1813-1820.
[29]
Grotwinkel, J.T.; Wild, K.; Segnitz, B.; Sinning, I. SRP RNA remodeling by SRP68 explains its role in protein translocation. Science, 2014, 344(6179), 101-104.
[30]
Zwieb, C.; van Nues, R.W.; Rosenblad, M.A.; Brown, J.D.; Samuelsson, T. A nomenclature for all signal recognition particle RNAs. RNA, 2005, 11(1), 7-13.
[31]
Collins, K. Forms and functions of telomerase RNA.In Non-protein coding RNAs; Springer, 2009, pp. 285-301.
[32]
Xie, P. Model for processive nucleotide and repeat additions by the telomerasearXiv preprint arXiv: 0708.2527, 2007.
[33]
Goodenbour, J.M.; Pan, T. Diversity of tRNA genes in eukaryotes. Nucleic Acids Res., 2006, 34(21), 6137-6146.
[34]
Mohn, F.; Handler, D.; Brennecke, J. piRNA-guided slicing specifies transcripts for Zucchini-dependent, phased piRNA biogenesis. Science, 2015, 348(6236), 812-817.
[35]
Mao, C.; Bhardwaj, K.; Sharkady, S.M.; Fish, R.I.; Driscoll, T.; Wower, J.; Zwieb, C.; Sobral, B.W.; Williams, K.P. Variations on the tmRNA gene. RNA Biol., 2009, 6(4), 355-361.
[36]
Hajjari, M.; Behmanesh, M.; Sadeghizadeh, M.; Zeinoddini, M. Up-regulation of HOTAIR long non-coding RNA in human gastric adenocarcinoma tissues. Med. Oncol., 2013, 30(3), 1-4.
[37]
Hajjari, M.; Salavaty, A. HOTAIR: an oncogenic long non-coding RNA in different cancers. Cancer Biol. Med., 2015, 12(1), 1-9.
[38]
Sado, T.; Brockdorff, N. Advances in understanding chromosome silencing by the long non-coding RNA Xist Philos. Tran. R. Soc. B. Biol. Sci, 2013, 368(1609), 20110325.
[39]
Laganà, A.; Ferro, A.; Croce, C.M. Editorial: bioinformatics of non-coding RNAs with applications to biomedicine: recent advances and open challenges. Front. Bioeng. Biotechnol., 2015, 3, 156.
[40]
Hüttenhofer, A.; Vogel, J. Experimental approaches to identify non-coding RNAs. Nucleic Acids Res., 2006, 34(2), 635-646.
[41]
Washietl, S.; Hofacker, I.L.; Stadler, P.F. Fast and reliable prediction of noncoding RNAs. Proc. Natl. Acad. Sci. USA, 2005, 102(7), 2454-2459.
[42]
Lund, E.; Dahlberg, J.E. Spacer transfer RNAs in ribosomal RNA transcripts of E. coli: processing of 30S ribosomal RNA in vitro. Cell, 1977, 11(2), 247-262.
[43]
Ilott, N.E.; Ponting, C.P. Predicting long non-coding RNAs using RNA sequencing. Methods, 2013, 63(1), 50-59.
[44]
Ozsolak, F.; Platt, A.R.; Jones, D.R.; Reifenberger, J.G.; Sass, L.E.; McInerney, P.; Thompson, J.F.; Bowers, J.; Jarosz, M.; Milos, P.M. Direct RNA sequencing. Nature, 2009, 461(7265), 814-818.
[45]
Kang, W.; Friedländer, M.R. Computational prediction of miRNA genes from small RNA sequencing data. Front. Bioeng. Biotechnol., 2015, 3, 7.
[46]
Ebrahimi-Askari, R.; Behmanesh, M.; Soleimani, M. Analyses of methylation status of CpG islands in promoters of miR-9 genes family in human gastric adenocarcinoma. Mol. Biol. Res. Commun., 2015, 4(2), 73-82.
[47]
Behmanesh, M.; Sakumi, K.; Tsuchimoto, D.; Torisu, K.; Ohnishi-Honda, Y.; Rancourt, D.E.; Nakabeppu, Y. Characterization of the structure and expression of mouse Itpa gene and its related sequences in the mouse genome. DNA Res., 2005, 12(1), 39-51.
[48]
Kashi, K.; Lindsey, H.; Alessandro, B. Piero, Carninci. Discovery and functional analysis of lncRNAs: Methodologies to investigate an uncharacterized transcriptome. Biochim. Biophys. Acta. (BBA)-Gene. Regul. Mech, 2016, 1859(1), 3-15.
[49]
Li, Y.; Zhang, Y.; Li, S.; Lu, J.; Chen, J.; Wang, Y.; Li, Y.; Xu, J.; Li, X. Genome-wide DNA methylome analysis reveals epigenetically dysregulated non-coding RNAs in human breast cancer. Sci. Rep., 2015, 5, 8790.
[50]
Rederstorff, M.; Hüttenhofer, A. cDNA library generation from ribonucleoprotein particles. Nat. Protoc., 2011, 6(2), 166-174.
[51]
Harbers, M. The current status of cDNA cloning. Genomics, 2008, 91(3), 232-242.
[52]
Marker, C.; Zemann, A.; Terhörst, T.; Kiefmann, M.; Kastenmayer, J.P.; Green, P.; Bachellerie, J.P.; Brosius, J.; Hüttenhofer, A. Experimental RNomics: identification of 140 candidates for small non-messenger RNAs in the plant Arabidopsis thaliana. Curr. Biol., 2002, 12, 23.
[53]
Yuan, G.; Klämbt, C.; Bachellerie, J.P.; Brosius, J.; Hüttenhofer, A. RNomics in Drosophila melanogaster: identification of 66 candidates for novel non-messenger RNAs. Nucleic Acids Res., 2003, 31(10), 2495-2507.
[54]
Vogel, J.; Bartels, V.; Tang, T.H.; Churakov, G.; Slagter-Jäger, J.G.; Hüttenhofer, A.; Wagner, E.G.H. RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria. Nucleic Acids Res., 2003, 31(22), 6435-6443.
[55]
Kawano, M.; Reynolds, A.A.; Miranda-Rios, J.; Storz, G. Detection of 5′-and 3′-UTR-derived small RNAs and cis-encoded antisense RNAs in Escherichia coli. Nucleic Acids Res., 2005, 33(3), 1040-1050.
[56]
Rederstorff, M. Generation of cDNA libraries from RNP-derived regulatory noncoding RNAs in Genomic Imprinting; Springer, 2012, pp. 211-218.
[57]
Li, J.P.; Liu, L.H.; Li, J.; Chen, Y.; Jiang, X.W.; Ouyang, Y.R.; Liu, Y.Q.; Zhong, H.; Li, H.; Xiao, T. Microarray expression profile of long noncoding RNAs in human osteosarcoma. Biochem. Biophys. Res. Commun., 2013, 433(2), 200-206.
[58]
He, H.; Cai, L.; Skogerbo, G.; Deng, W.; Liu, T.; Zhu, X.; Wang, Y.; Jia, D.; Zhang, Z.; Tao, Y. Profiling Caenorhabditis elegans non-coding RNA expression with a combined microarray. Nucleic Acids Res., 2006, 34(10), 2976-2983.
[59]
Zhu, J.; Liu, S.; Ye, F.; Shen, Y.; Tie, Y.; Zhu, J.; Jin, Y.; Zheng, X.; Wu, Y.; Fu, H. The long noncoding RNA expression profile of hepatocellular carcinoma identified by microarray analysis. PLoS One, 2014, 9(7), e101707.
[60]
Raghavan, R.; Groisman, E.A.; Ochman, H. Genome-wide detection of novel regulatory RNAs in E. coli. Genome Res., 2011, 21(9), 1487-1497.
[61]
Tjaden, B.; Saxena, R.M.; Stolyar, S.; Haynor, D.R.; Kolker, E.; Rosenow, C. Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays. Nucleic Acids Res., 2002, 30(17), 3732-3738.
[62]
Zhang, A.; Wassarman, K.M.; Rosenow, C.; Tjaden, B.C.; Storz, G.; Gottesman, S. Global analysis of small RNA and mRNA targets of Hfq. Mol. Microbiol., 2003, 50(4), 1111-1124.
[63]
Inada, M.; Guthrie, C. Identification of Lhp1p-associated RNAs by microarray analysis in Saccharomycescerevisiae reveals association with coding and noncoding RNAs. Proc. Natl. Acad. Sci. USA, 2004, 101(2), 434-439.
[64]
Nookaew, I.; Papini, M.; Pornputtpong, N.; Scalcinati, G.; Fagerberg, L.; Uhlén, M.; Nielsen, J. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res., 2012, 40(20), 10084-10097.
[65]
Wang, Q.; Nowak, C.M.; Korde, A.; Oh, D-H.; Dassanayake, M.; Donze, D. Compromised RNA polymerase III complex assembly leads to local alterations of intergenic RNA polymerase II transcription in Saccharomyces cerevisiae. BMC Biol., 2014, 12(1), 89.
[66]
Shi, Y.; Shang, J. Long Noncoding RNA Expression Profiling using Arraystar LncRNA Microarrays. Long Non-Coding RNAs: Methods and Protocols; Springer, 2016, pp. 43-61.
[67]
Altuvia, S. Identification of bacterial small non-coding RNAs: experimental approaches. Curr. Opin. Microbiol., 2007, 10(3), 257-261.
[68]
Lorenz, C.; Gesell, T.; Zimmermann, B.; Schoeberl, U.; Bilusic, I.; Rajkowitsch, L.; Waldsich, C.; Von Haeseler, A.; Schroeder, R. Genomic SELEX for Hfq-binding RNAs identifies genomic aptamers predominantly in antisense transcripts. Nucleic Acids Res., 2010, 38(11), 3794-3808.
[69]
Zimmermann, B.; Bilusic, I.; Lorenz, C.; Schroeder, R. Genomic SELEX: a discovery tool for genomic aptamers. Methods, 2010, 52(2), 125-132.
[70]
Larsson, P. Computational approaches to the identification and characterization of non-coding RNA genes; Uppsala Acta Universitatis Upsaliensis, 2009, p. 57.
[71]
Lertampaiporn, S.; Thammarongtham, C.; Nukoolkit, C.; Kaewkamnerdpong, B.; Ruengjitchatchawalya, M. Identification of non-coding RNAs with a new composite feature in the hybrid random forest ensemble algorithm. Nucleic Acids Res., 2014, 42(11), e93.
[72]
Bao, M.; Cervantes, M.C.; Zhong, L.; Wang, J.T. Searching for non-coding RNAs in genomic sequences using ncRNAscout. Genomics Proteomics Bioinformatics, 2012, 10(2), 114-121.
[73]
Niazi, F.; Valadkhan, S. Computational analysis of functional long noncoding RNAs reveals lack of peptide-coding capacity and parallels with 3′ UTRs. RNA, 2012, 18(4), 825-843.
[74]
Jones, S.J. Prediction of genomic functional elements. Annu. Rev. Genomics Hum. Genet., 2006, 7, 315-338.
[75]
Zhang, R.; Zhang, L.; Yu, W. Genome-wide expression of non-coding RNA and global chromatin modification. Acta Biochim. Biophys. Sin., 2012, 44(1), 40-47.
[76]
Yamasaki, C.; Murakami, K.; Takeda, J-I.; Sato, Y.; Noda, A.; Sakate, R.; Habara, T.; Nakaoka, H.; Todokoro, F.; Matsuya, A. H-InvDB in 2009: extended database and data mining resources for human genes and transcripts. Nucleic Acids Res., 2010, 38, 626-632.
[77]
Quek, X.C.; Thomson, D.W.; Maag, J.L.; Bartonicek, N.; Signal, B.; Clark, M.B.; Gloss, B.S.; Dinger, M.E. lncRNAdb v2. 0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res., 2014, 43(D1), 68-73.
[78]
Zhang, Z.; Yu, J.; Li, D.; Zhang, Z.; Liu, F.; Zhou, X.; Wang, T.; Ling, Y.; Su, Z. PMRD: plant microRNA database. Nucleic Acids Res., 2010, 38, 806-813.
[79]
Brown, J.W.; Echeverria, M.; Qu, L-H.; Lowe, T.M.; Bachellerie, J-P.; Hüttenhofer, A.; Kastenmayer, J.P.; Green, P.J.; Shaw, P.; Marshall, D.F. Plant snoRNA database. Nucleic Acids Res., 2003, 31(1), 432-435.
[80]
Pang, K.C.; Stephen, S.; Dinger, M.E.; Engström, P.G.; Lenhard, B.; Mattick, J.S. RNAdb 2.0- an expanded database of mammalian non-coding RNAs. Nucleic Acids Res., 2007, 35, 178-182.
[81]
Kozomara, A.; Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res., 2013, 42(D1), 68-73.
[82]
Sethupathy, P.; Corda, B.; Hatzigeorgiou, A.G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA, 2006, 12(2), 192-197.
[83]
Xiao, F.; Zuo, Z.; Cai, G.; Kang, S.; Gao, X.; Li, T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res., 2009, 37, 105-110.
[84]
Chan, P.P.; Lowe, T.M. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res., 2016, 44(1), 184-189.
[85]
Leung, Y.Y.; Kuksa, P.P.; Amlie-Wolf, A.; Valladares, O.; Ungar, L.H.; Kannan, S.; Gregory, B.D.; Wang, L-S. DASHR: database of small human noncoding RNAs. Nucleic Acids Res., 2016, 44(1), 216-222.
[86]
Volders, P-J.; Verheggen, K.; Menschaert, G.; Vandepoele, K.; Martens, L.; Vandesompele, J.; Mestdagh, P. An update on LNCipedia: A database for annotated human lncRNA sequences. Nucleic Acids Res., 2015, 43(1), 174-180.
[87]
Xie, C.; Yuan, J.; Li, H.; Li, M.; Zhao, G.; Bu, D.; Zhu, W.; Wu, W.; Chen, R.; Zhao, Y. NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res., 2014, 42(1), 98-103.
[88]
Zhao, Y.; Yuan, J.; Chen, R. NONCODEv4: Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs. Long Non-Coding RNAs: Methods and Protocols; Springer, 2016, pp. 243-254.
[89]
Bonnici, V.; Russo, F.; Bombieri, N.; Pulvirenti, A.; Giugno, R. Comprehensive reconstruction and visualization of non-coding regulatory networks in human. Front. Bioeng. Biotechnol., 2014, 2, 69.
[90]
Szymanski, M.; Erdmann, V.A.; Barciszewski, J. Noncoding RNAs database (ncRNAdb). Nucleic Acids Res., 2007, 35, 162-164.
[91]
Paraskevopoulou, M.D.; Vlachos, I.S.; Karagkouni, D.; Georgakilas, G.; Kanellos, I.; Vergoulis, T.; Zagganas, K.; Tsanakas, P.; Floros, E.; Dalamagas, T. DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res., 2016, 44, 231-238.
[92]
Yang, J-H.; Li, J-H.; Jiang, S.; Zhou, H.; Qu, L-H. ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res., 2013, 41, 177-187.
[93]
Panwar, B.; Arora, A.; Raghava, G.P. Prediction and classification of ncRNAs using structural information. BMC Genomics, 2014, 15(1), 127.
[94]
Hermann, T.; Patel, D.J. RNA bulges as architectural and recognition motifs. Structure, 2000, 8(3), 47-54.
[95]
Lee, J.C.; Gutell, R.R. Diversity of base-pair conformations and their occurrence in rRNA structure and RNA structural motifs. J. Mol. Biol., 2004, 344(5), 1225-1249.
[96]
Staple, D.W.; Butcher, S.E. Pseudoknots: RNA structures with diverse functions. PLoS Biol., 2005, 3(6), e213.
[97]
Clote, P.; Ferre, F.; Kranakis, E.; Krizanc, D. Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. RNA, 2005, 11(5), 578-591.
[98]
Yoon, B-J.; Vaidyanathan, P. In RNA secondary structure prediction using context-sensitive hidden Markov models, Biomedical Circuits and Systems, 2004 IEEE International Workshop on, IEEE: 2004; pp. S2/7/INV-S2/7/1-4.
[99]
Fang, X-Y.; Luo, Z-G.; Wang, Z-H. Predicting RNA secondary structure using profile stochastic context-free grammars and phylogenic analysis. J. Comput. Sci. Technol., 2008, 23(4), 582-589.
[100]
Griffiths-Jones, S.; Moxon, S.; Marshall, M.; Khanna, A.; Eddy, S.R.; Bateman, A. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res., 2005, 33, 121-124.
[101]
Tran, T.T.; Zhou, F.; Marshburn, S.; Stead, M.; Kushner, S.R.; Xu, Y. De novo computational prediction of non-coding RNA genes in prokaryotic genomes. Bioinform, 2009, 25(22), 2897-2905.
[102]
Tong, H.; Guo, F.-B.; Ye, Y.-N. Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics, 2011.
[103]
Lowe, T.M.; Eddy, S.R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res., 1997, 25(5), 955-964.
[104]
Hofacker, I.L. RNA secondary structure analysis using the Vienna RNA package. Curr. Protoc. Bioinformatics, 2009, 26(1), 12-2.
[105]
Ding, Y.; Chan, C.Y.; Lawrence, C.E. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res., 2004, 32, 135-141.
[106]
Will, S.; Yu, M.; Berger, B. Structure-based whole-genome realignment reveals many novel noncoding RNAs. Genome Res., 2013, 23(6), 1018-1027.
[107]
Vob, B. Structural analysis of aligned RNAs. Nucleic Acids Res., 2006, 34(19), 5471-5481.
[108]
Washietl, S. Prediction of structural non-coding RNAs by comparative sequence analysis na: ., 2005.
[109]
Pedersen, J.S.; Bejerano, G.; Siepel, A.; Rosenbloom, K.; Lindblad-Toh, K.; Lander, E.S.; Kent, J.; Miller, W.; Haussler, D. Identification and classification of conserved RNA secondary structures in the human genome. PLOS Comput. Biol., 2006, 2(4), e33.
[110]
Sadeghi, B.; Ahmadi, H. Azimzadeh- Jamalkandi, S.; Nassiri, M.; Masoudi-Nejad, A. BosFinder: a novell pre-microRNA gene prediction algorithm in Bos taurus. Anim. Genet., 2014, 45(4), 479-484.
[111]
Terai, G.; Komori, T.; Asai, K.; Kin, T. miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity. RNA, 2007, 13(12), 2081-2090.
[112]
Fan, X-N.; Zhang, S-W. lncRNA-MFDL: identification of human long non-coding RNAs by fusing multiple features and using deep learning. Mol. Biosyst., 2015, 11(3), 892-897.
[113]
He, S.; Zhang, H.; Liu, H.; Zhu, H. LongTarget: a tool to predict lncRNA DNA-binding motifs and binding sites via Hoogsteen base-pairing analysis. Bioinform, 2015, 31(2), 178-186.
[114]
Wu, J.; Liu, Q.; Wang, X.; Zheng, J.; Wang, T.; You, M.; Sun, S. Z.; Shi, Q. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol., 2013, 10(7), 1087-1092.
[115]
Pantano, L.; Estivill, X.; Martí, 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-e34.
[116]
Kruger, J.; Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res., 2006, 34, 451-454.
[117]
Liao, Q.; Xiao, H.; Bu, D.; Xie, C.; Miao, R.; Luo, H.; Zhao, G.; Yu, K.; Zhao, H.; Skogerbo, G. ncFANs: a web server for functional annotation of long non-coding RNAs. Nucleic Acids Res., 2011, 39, 118-124.
[118]
Herbig, A.; Nieselt, K. nocoRNAc: characterization of non-coding RNAs in prokaryotes. BMC Bioinformatics, 2011, 12(1), 40.
[119]
Ritchie, W.; Théodule, F-X.; Gautheret, D. Mireval: a web tool for simple microRNA prediction in genome sequences. Bioinform., 2008, 24(11), 1394-1396.
[120]
Huang, P.-J.; Liu, Y.-C.; Lee, C.-C.; Lin, W.-C.; Gan, R.R.-C.; Lyu, P.-C.; Tang, P. DSAP: deep-sequencing small RNA analysis pipeline Nucleic. Acids. Res., 2010, 38(suppl_2), W385-91.
[121]
Gupta, V.; Markmann, K.; Pedersen, C.N.; Stougaard, J.; Andersen, S.U. shortran: a pipeline for small RNA-seq data analysis. Bioinform, 2012, 28(20), 2698-2700.
[122]
An, J.; Lai, J.; Lehman, M.L.; Nelson, C.C. miRDeep: An integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res., 2013, 41(2), 727-737.
[123]
Chen, L.; Liu, Y-G. Male sterility and fertility restoration in crops. Annu. Rev. Plant Biol., 2014, 65, 579-606.
[124]
Liu, X.; Hao, L.; Li, D.; Zhu, L.; Hu, S. Long non-coding RNAs and their biological roles in plants. Genomics Proteomics Bioinformatics, 2015, 13(3), 137-147.
[125]
Chekanova, J.A. Long non-coding RNAs and their functions in plants. Curr. Opin. Plant Biol., 2015, 27, 207-216.
[126]
Peschansky, V.J.; Wahlestedt, C. Non-coding RNAs as direct and indirect modulators of epigenetic regulation. Epigenetics, 2014, 9(1), 3-12.
[127]
Jöchl, C.; Rederstorff, M.; Hertel, J.; Stadler, P.F.; Hofacker, I.L.; Schrettl, M.; Haas, H.; Hüttenhofer, A. Small ncRNA transcriptome analysis from Aspergillus fumigatus suggests a novel mechanism for regulation of protein synthesis. Nucleic Acids Res., 2008, 36(8), 2677-2689.
[128]
Yamaguchi, A.; Abe, M. Regulation of reproductive development by non-coding RNA in Arabidopsis: to flower or not to flower. J. Plant Res., 2012, 125(6), 693-704.


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Article Details

VOLUME: 15
ISSUE: 3
Year: 2019
Page: [216 - 230]
Pages: 15
DOI: 10.2174/1573406414666181015151610
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