How to cite this WIREs title:
WIREs RNA

Computational approaches to discovering noncoding RNA

Advanced Review

Paul M. Krzyzanowski, Enrique M. Muro, Miguel A. Andrade‐Navarro

Published Online: May 03 2012

DOI: 10.1002/wrna.1121

How to cite this article
Download citation
Contact the editor about this article
Authors: submit updates and notes
Comment on this article
Share

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA‐related material is leading the field toward increased use of such high‐throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs. WIREs RNA 2012, 3:567–579. doi: 10.1002/wrna.1121

This WIREs title offers downloadable PowerPoint presentations of figures for non-profit, educational use, provided the content is not modified and full credit is given to the author and publication.

Download a PowerPoint presentation of all images

Figure 1.

Evolution of the number of publications on noncoding RNA (ncRNA) sequencing studies versus ncRNA prediction. The number of Medline articles dealing with ncRNA/miRNA sequencing has increased faster than that for ncRNA/miRNA prediction, likely due to increased adoption of high‐throughput sequencing. Articles found searching for deep sequencing or next generation sequencing are shown as a proxy for high‐throughput sequencing. Also shown are release dates of analysis software for de novo ncRNA prediction (Beige; RNAz 2.014, EvoFold15, RNAz13, AlifoldZ50, MiRscan12) and analysis of high‐throughput sequencing data sets (Green: SeqCluster49, SeqGene51, MIReNA21, SeqBuster9, miRanalyzer22, miRDeep23). Figure made with MLTrends52 (http://www.ogic.ca/mltrends/) using the 2012 Medline Baseline Distribution, released December 14, 2011.

[ Normal View 78K | Magnified View 136K ]

References

1 Salzberg, SL, Delcher, AL, Kasif, S, White, O. Microbial gene identification using interpolated Markov models. Nucleic Acids Res 1998, 26:544–548.
2 Burge, C, Karlin, S. Prediction of complete gene structures in human genomic DNA. J Mol Biol 1997, 268:78–94.
3 Rivas, E, Eddy, SR. Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinform 2001, 2:8.
4 Nawrocki, EP, Kolbe, DL, Eddy, SR. Infernal 1.0: inference of RNA alignments. Bioinformatics 2009, 25:1335–1337.
5 Lowe, TM, Eddy, SR. tRNAscan‐SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997, 25:955–964.
6 Lagesen, K, Hallin, P, Rodland, EA, Staerfeldt, HH, Rognes, T, Ussery, DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007, 35:3100–3108.
7 Laslett, D, Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 2004, 32:11–16.
8 Lambert, A, Fontaine, JF, Legendre, M, Leclerc, F, Permal, E, Major, F, Putzer, H, Delfour, O, Michot, B, Gautheret, D. The ERPIN server: an interface to profile‐based RNA motif identification. Nucleic Acids Res 2004, 32:W160–W165.
9 Pantano, L, Estivill, X, Marti, E. SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res 2010, 38:e34.
10 Bussotti, G, Raineri, E, Erb, I, Zytnicki, M, Wilm, A, Beaudoing, E, Bucher, P, Notredame, C. BlastR—fast and accurate database searches for non‐coding RNAs. Nucleic Acids Res 2011, 39:6886–6895.
11 Lagana, A, Forte, S, Russo, F, Giugno, R, Pulvirenti, A, Ferro, A. Prediction of human targets for viral‐encoded microRNAs by thermodynamics and empirical constraints. J RNAi Gene Silencing 2010, 6:379–385.
12 Lim, LP, Lau, NC, Weinstein, EG, Abdelhakim, A, Yekta, S, Rhoades, MW, Burge, CB, Bartel, DP. The microRNAs of Caenorhabditis elegans. Genes Dev 2003, 17:991–1008.
13 Washietl, S, Hofacker, IL, Stadler, PF. Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A 2005, 102:2454–2459.
14 Gruber, AR, Findeiss, S, Washietl, S, Hofacker, IL, Stadler, PF. Rnaz 2.0: improved noncoding RNA detection. Pac Symp Biocomput 2010, 15:69–79.
15 Pedersen, JS, Bejerano, G, Siepel, A, Rosenbloom, K, Lindblad‐Toh, K, Lander, ES, Kent, J, Miller, W, Haussler, D. Identification and classification of conserved RNA secondary structures in the human genome. PLoS Comput Biol 2006, 2:e33.
16 Krek, A, Grun, D, Poy, MN, Wolf, R, Rosenberg, L, Epstein, EJ, MacMenamin, P, da Piedade, I, Gunsalus, KC, Stoffel, M, et al. Combinatorial microRNA target predictions. Nat Genet 2005, 37:495–500.
17 Lewis, BP, Burge, CB, Bartel, DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005, 120:15–20.
18 Miranda, KC, Huynh, T, Tay, Y, Ang, YS, Tam, WL, Thomson, AM, Lim, B, Rigoutsos, I. A pattern‐based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 2006, 126:1203–1217.
19 Kertesz, M, Iovino, N, Unnerstall, U, Gaul, U, Segal, E. The role of site accessibility in microRNA target recognition. Nat Genet 2007, 39:1278–1284.
20 Dai, X, Zhao, PX. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 2011, 39:W155–W159.
21 Mathelier, A, Carbone, A. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 2010, 26:2226–2234.
22 Hackenberg, M, Sturm, M, Langenberger, D, Falcon‐Perez, JM, Aransay, AM. miRanalyzer: a microRNA detection and analysis tool for next‐generation sequencing experiments. Nucleic Acids Res 2009, 37:W68–W76.
23 Friedlander, MR, Chen, W, Adamidi, C, Maaskola, J, Einspanier, R, Knespel, S, Rajewsky, N. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008, 26:407–415.
24 Hofacker, IL, Fontana, W, Stadler, PF, Bonhoeffer, S, Tacker, M, Schuster, P. Fast folding and comparison of RNA secondary structures. Monatsh Chem 1994, 125:167–188.
25 Hofacker, IL, Priwitzer, B, Stadler, PF. Prediction of locally stable RNA secondary structures for genome‐wide surveys. Bioinformatics 2004, 20:186–190.
26 Zuker, M. On finding all suboptimal foldings of an RNA molecule. Science 1989, 244:48–52.
27 Sewer, A, Paul, N, Landgraf, P, Aravin, A, Pfeffer, S, Brownstein, MJ, Tuschl, T, van Nimwegen, E, Zavolan, M. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinform 2005, 6:267.
28 Bentwich, I, Avniel, A, Karov, Y, Aharonov, R, Gilad, S, Barad, O, Barzilai, A, Einat, P, Einav, U, Meiri, E, et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet 2005, 37:766–770.
29 Schwartz, S, Kent, WJ, Smit, A, Zhang, Z, Baertsch, R, Hardison, RC, Haussler, D, Miller, W. Human‐mouse alignments with BLASTZ. Genome Res 2003, 13:103–107.
30 Karolchik, D, Baertsch, R, Diekhans, M, Furey, TS, Hinrichs, A, Lu, YT, Roskin, KM, Schwartz, M, Sugnet, CW, Thomas, DJ, et al. The UCSC Genome Browser Database. Nucleic Acids Res 2003, 31:51–54.
31 Kent, WJ, Sugnet, CW, Furey, TS, Roskin, KM, Pringle, TH, Zahler, AM, Haussler, D. The human genome browser at UCSC. Genome Res 2002, 12:996–1006.
32 Kuhn, RM, Karolchik, D, Zweig, AS, Wang, T, Smith, KE, Rosenbloom, KR, Rhead, B, Raney, BJ, Pohl, A, Pheasant, M, et al. The UCSC Genome Browser Database: update 2009. Nucleic Acids Res 2009, 37:D755–D761.
33 Krzyzanowski, PM, Price, FD, Muro, EM, Rudnicki, MA, Andrade‐Navarro, MA. Integration of expressed sequence tag data flanking predicted RNA secondary structures facilitates novel non‐coding RNA discovery. PLoS One 2011, 6:e20561.
34 Ott, CE, Grunhagen, J, Jager, M, Horbelt, D, Schwill, S, Kallenbach, K, Guo, G, Manke, T, Knaus, P, Mundlos, S, et al. MicroRNAs differentially expressed in postnatal aortic development downregulate elastin via 3′ UTR and coding‐sequence binding sites. PLoS One 2011, 6:e16250.
35 Zhang, Y. miRU: an automated plant miRNA target prediction server. Nucleic Acids Res 2005, 33:W701–W704.
36 Spiegelman, WG, Reichardt, LF, Yaniv, M, Heinemann, SF, Kaiser, AD, Eisen, H. Bidirectional transcription and the regulation of Phage lambda repressor synthesis. Proc Natl Acad Sci U S A 1972, 69:3156–3160.
37 Lavorgna, G, Dahary, D, Lehner, B, Sorek, R, Sanderson, CM, Casari, G. In search of antisense. Trends Biochem Sci 2004, 29:88–94.
38 Lehner, B, Williams, G, Campbell, RD, Sanderson, CM. Antisense transcripts in the human genome. Trends Genet 2002, 18:63–65.
39 Yelin, R, Dahary, D, Sorek, R, Levanon, EY, Goldstein, O, Shoshan, A, Diber, A, Biton, S, Tamir, Y, Khosravi, R, et al. Widespread occurrence of antisense transcription in the human genome. Nat Biotechnol 2003, 21:379–386.
40 Sorber, K, Dimon, MT, DeRisi, JL. RNA‐Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts. Nucleic Acids Res 2011, 39:3820–3835.
41 Watanabe, T, Totoki, Y, Toyoda, A, Kaneda, M, Kuramochi‐Miyagawa, S, Obata, Y, Chiba, H, Kohara, Y, Kono, T, Nakano, T, et al. Endogenous siRNAs from naturally formed dsRNAs regulate transcripts in mouse oocytes. Nature 2008, 453:539–543.
42 Tam, OH, Aravin, AA, Stein, P, Girard, A, Murchison, EP, Cheloufi, S, Hodges, E, Anger, M, Sachidanandam, R, Schultz, RM, et al. Pseudogene‐derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 2008, 453:534–538.
43 Muro, EM, Andrade‐Navarro, MA. Pseudogenes as an alternative source of natural antisense transcripts. BMC Evol Biol 2010, 10:338.
44 Dimon, MT, Sorber, K, DeRisi, JL. HMMSplicer: a tool for efficient and sensitive discovery of known and novel splice junctions in RNA‐Seq data. PLoS One 2010, 5:e13875.
45 Trapnell, C, Williams, BA, Pertea, G, Mortazavi, A, Kwan, G, van Baren, MJ, Salzberg, SL, Wold, BJ, Pachter, L. Transcript assembly and quantification by RNA‐Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010, 28:511–515.
46 Skvortsov, D, Abdueva, D, Curtis, C, Schaub, B, Tavare, S. Explaining differences in saturation levels for Affymetrix GeneChip arrays. Nucleic Acids Res 2007, 35:4154–4163.
47 Dodd, LE, Korn, EL, McShane, LM, Chandramouli, GV, Chuang, EY. Correcting log ratios for signal saturation in cDNA microarrays. Bioinformatics 2004, 20:2685–2693.
48 Hackl, M, Jakobi, T, Blom, J, Doppmeier, D, Brinkrolf, K, Szczepanowski, R, Bernhart, SH, Siederdissen, CH, Bort, JA, Wieser, M, et al. Next‐generation sequencing of the Chinese hamster ovary microRNA transcriptome: identification, annotation and profiling of microRNAs as targets for cellular engineering. J Biotechnol 2011, 153:62–75.
49 Pantano, L, Estivill, X, Marti, E. A non‐biased framework for the annotation and classification of the non‐miRNA small RNA transcriptome. Bioinformatics 2011, 27:3202–3203.
50 Washietl, S, Hofacker, IL. Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics. J Mol Biol 2004, 342:19–30.
51 Deng, X. SeqGene: a comprehensive software solution for mining exome‐ and transcriptome‐sequencing data. BMC Bioinform 2011, 12:267.
52 Palidwor, GA, Andrade‐Navarro, MA. MLTrends: graphing MEDLINE term usage over time. J Biomed Discov Collab 2010, 5:1–6.
53 Kan, T, Sato, F, Ito, T, Matsumura, N, David, S, Cheng, Y, Agarwal, R, Paun, BC, Jin, Z, Olaru, AV, et al. The miR‐106b‐25 polycistron, activated by genomic amplification, functions as an oncogene by suppressing p21 and Bim. Gastroenterology 2009, 136:1689–1700.
54 Richardson, K, Lai, CQ, Parnell, LD, Lee, YC, Ordovas, JM. A genome‐wide survey for SNPs altering microRNA seed sites identifies functional candidates in GWAS. BMC Genomics 2011, 12:504.
55 Taft, RJ, Pang, KC, Mercer, TR, Dinger, M, Mattick, JS. Non‐coding RNAs: regulators of disease. J Pathol 2010, 220:126–139.
56 Hawkins, SM, Creighton, CJ, Han, DY, Zariff, A, Anderson, ML, Gunaratne, PH, Matzuk, MM. Functional microRNA involved in endometriosis. Mol Endocrinol 2011, 25:821–832.
57 Pellicano, M, Bramante, S, Guida, M, Bifulco, G, Di Spiezio Sardo, A, Cirillo, D, Nappi, C. Ovarian endometrioma: postoperative adhesions following bipolar coagulation and suture. Fertil Steril 2008, 89:796–799.
58 Boutz, DR, Collins, PJ, Suresh, U, Lu, M, Ramirez, CM, Fernandez‐Hernando, C, Huang, Y, Abreu Rde, S, Le, SY, Shapiro, BA, et al. Two‐tiered approach identifies a network of cancer and liver disease‐related genes regulated by miR‐122. J Biol Chem 2011, 286:18066–18078.
59 Yang, L, Duff, MO, Graveley, BR, Carmichael, GG, Chen, LL. Genomewide characterization of non‐polyadenylated RNAs. Genome Biol 2011, 12:R16.
60 Bajak, EZ, Hagedorn, CH. Efficient 5′ cap‐dependent RNA purification : use in identifying and studying subsets of RNA. Methods Mol Biol 2008, 419:147–160.
61 Chen, Z, Duan, X. Ribosomal RNA depletion for massively parallel bacterial RNA‐sequencing applications. Methods Mol Biol 2011, 733:93–103.
62 Levin, JZ, Yassour, M, Adiconis, X, Nusbaum, C, Thompson, DA, Friedman, N, Gnirke, A, Regev, A. Comprehensive comparative analysis of strand‐specific RNA sequencing methods. Nat Methods 2010, 7:709–715.
63 Rabani, M, Levin, JZ, Fan, L, Adiconis, X, Raychowdhury, R, Garber, M, Gnirke, A, Nusbaum, C, Hacohen, N, Friedman, N, et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat Biotechnol 2011, 29:436–442.
64 Babitzke, P, Romeo, T. CsrB sRNA family: sequestration of RNA‐binding regulatory proteins. Curr Opin Microbiol 2007, 10:156–163.
65 Benito, Y, Kolb, FA, Romby, P, Lina, G, Etienne, J, Vandenesch, F. Probing the structure of RNAIII, the Staphylococcus aureus agr regulatory RNA, and identification of the RNA domain involved in repression of protein A expression. RNA 2000, 6:668–679.
66 Breaker, RR. Prospects for riboswitch discovery and analysis. Mol Cell 2011, 43:867–879.
67 Altuvia, S. Identification of bacterial small non‐coding RNAs: experimental approaches. Curr Opin Microbiol 2007, 10:257–261.
68 Sharma, CM, Vogel, J. Experimental approaches for the discovery and characterization of regulatory small RNA. Curr Opin Microbiol 2009, 12:536–546.
69 Huttenhofer, A, Vogel, J. Experimental approaches to identify non‐coding RNAs. Nucleic Acids Res 2006, 34:635–646.
70 Argaman, L, Hershberg, R, Vogel, J, Bejerano, G, Wagner, EG, Margalit, H, Altuvia, S. Novel small RNA‐encoding genes in the intergenic regions of Escherichia coli. Curr Biol 2001, 11:941–950.
71 Backofen, R, Hess, WR. Computational prediction of sRNAs and their targets in bacteria. RNA Biol 2010, 7:33–42.
72 Lu, X, Goodrich‐Blair, H, Tjaden, B. Assessing computational tools for the discovery of small RNA genes in bacteria. RNA 2011, 17:1635–1647.
73 Herbig, A, Nieselt, K. nocoRNAc: characterization of non‐coding RNAs in prokaryotes. BMC Bioinform 2011, 12:40.
74 Bateman, A, Agrawal, S, Birney, E, Bruford, EA, Bujnicki, JM, Cochrane, G, Cole, JR, Dinger, ME, Enright, AJ, Gardner, PP, et al. RNAcentral: a vision for an international database of RNA sequences. RNA 2011, 17:1941–1946.
75 Sayers, EW, Barrett, T, Benson, DA, Bolton, E, Bryant, SH, Canese, K, Chetvernin, V, Church, DM, DiCuccio, M, Federhen, S, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2011, 39:D38–D51.
76 Dreszer, TR, Karolchik, D, Zweig, AS, Hinrichs, AS, Raney, BJ, Kuhn, RM, Meyer, LR, Wong, M, Sloan, CA, Rosenbloom, KR, et al. The UCSC Genome Browser database: extensions and updates 2011. Nucleic Acids Res 2011, 40:D918–D923.
77 Yamasaki, C, Murakami, K, Takeda, J, Sato, Y, Noda, A, Sakate, R, Habara, T, Nakaoka, H, Todokoro, F, Matsuya, A, et al. H‐InvDB in 2009: extended database and data mining resources for human genes and transcripts. Nucleic Acids Res 2010, 38:D626–D632.
78 Gardner, PP, Daub, J, Tate, J, Moore, BL, Osuch, IH, Griffiths‐Jones, S, Finn, RD, Nawrocki, EP, Kolbe, DL, Eddy, SR, et al. Rfam: Wikipedia, clans and the “decimal” release. Nucleic Acids Res 2011, 39:D141–D145.
79 Mituyama, T, Yamada, K, Hattori, E, Okida, H, Ono, Y, Terai, G, Yoshizawa, A, Komori, T, Asai, K. The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res 2009, 37:D89–D92.
80 Kozomara, A, Griffiths‐Jones, S. miRBase: integrating microRNA annotation and deep‐sequencing data. Nucleic Acids Res 2011, 39:D152–D157.
81 Cros, MJ, de Monte, A, Mariette, J, Bardou, P, Grenier‐Boley, B, Gautheret, D, Touzet, H, Gaspin, C. RNAspace.org: an integrated environment for the prediction, annotation, and analysis of ncRNA. RNA 2011, 17:1947–1956.
82 Betel, D, Wilson, M, Gabow, A, Marks, DS, Sander, C. The microRNA.org resource: targets and expression. Nucleic Acids Res 2008, 36:D149–D153.
83 Pang, KC, Stephen, S, Dinger, ME, Engstrom, PG, Lenhard, B, Mattick, JS. RNAdb 2.0—an expanded database of mammalian non‐coding RNAs. Nucleic Acids Res 2007, 35:D178–D182.
84 Lestrade, L, Weber, MJ. snoRNA‐LBME‐db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res 2006, 34:D158–D162.
85 He, S, Liu, C, Skogerbo, G, Zhao, H, Wang, J, Liu, T, Bai, B, Zhao, Y, Chen, R. NONCODE v2.0: decoding the non‐coding. Nucleic Acids Res 2008, 36:D170–D172.
86 Sai Lakshmi, S, Agrawal, S. piRNABank: a web resource on classified and clustered Piwi‐interacting RNAs. Nucleic Acids Res 2008, 36:D173–D177.
87 Lagana, A, Forte, S, Giudice, A, Arena, MR, Puglisi, PL, Giugno, R, Pulvirenti, A, Shasha, D, Ferro, A. miRo: a miRNA knowledge base. Database (Oxford) 2009, 2009:bap008.
88 Papadopoulos, GL, Reczko, M, Simossis, VA, Sethupathy, P, Hatzigeorgiou, AG. The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res 2009, 37:D155–D158.
89 Yang, JH, Li, JH, Shao, P, Zhou, H, Chen, YQ, Qu, LH. starBase: a database for exploring microRNA‐mRNA interaction maps from Argonaute CLIP‐Seq and Degradome‐Seq data. Nucleic Acids Res 2011, 39:D202–D209.
90 Hsu, SD, Lin, FM, Wu, WY, Liang, C, Huang, WC, Chan, WL, Tsai, WT, Chen, GZ, Lee, CJ, Chiu, CM, et al. miRTarBase: a database curates experimentally validated microRNA‐target interactions. Nucleic Acids Res 2011, 39:D163–D169.
91 Griffiths‐Jones, S, Saini, HK, van Dongen, S, Enright, AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res 2008, 36:D154–D158.
92 Flicek, P, Amode, MR, Barrell, D, Beal, K, Brent, S, Denise, CS, Clapham, P, Coates, G, Fairley, S, Fitzgerald, S, et al. Ensembl 2012. Nucleic Acids Res 2011, 40:D84–D90.
93 Gautheret, D, Lambert, A. Direct RNA motif definition and identification from multiple sequence alignments using secondary structure profiles. J Mol Biol 2001, 313:1003–1011.
94 Mattick, JS. The double life of RNA. Biochimie 2011, 93:viii–ix.
95 Clark, MB, Amaral, PP, Schlesinger, FJ, Dinger, ME, Taft, RJ, Rinn, JL, Ponting, CP, Stadler, PF, Morris, KV, Morillon, A, et al. The reality of pervasive transcription. PLoS Biol 2011, 9:e1000625; discussion e1001102.
96 Dinger, ME, Amaral, PP, Mercer, TR, Pang, KC, Bruce, SJ, Gardiner, BB, Askarian‐Amiri, ME, Ru, K, Solda, G, Simons, C, et al. Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. Genome Res 2008, 18:1433–1445.
97 He, H, Wang, J, Liu, T, Liu, XS, Li, T, Wang, Y, Qian, Z, Zheng, H, Zhu, X, Wu, T, et al. Mapping the C. elegans noncoding transcriptome with a whole‐genome tiling microarray. Genome Res 2007, 17:1471–1477.
98 Mercer, TR, Wilhelm, D, Dinger, ME, Solda, G, Korbie, DJ, Glazov, EA, Truong, V, Schwenke, M, Simons, C, Matthaei, KI, et al. Expression of distinct RNAs from 3′ untranslated regions. Nucleic Acids Res 2011, 39:2393–2403.
99 Guttman, M, Amit, I, Garber, M, French, C, Lin, MF, Feldser, D, Huarte, M, Zuk, O, Carey, BW, Cassady, JP, et al. Chromatin signature reveals over a thousand highly conserved large non‐coding RNAs in mammals. Nature 2009, 458:223–227.
100 Ingolia, NT, Lareau, LF, Weissman, JS. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 2011, 147:789–802.
101 German, MA, Pillay, M, Jeong, DH, Hetawal, A, Luo, S, Janardhanan, P, Kannan, V, Rymarquis, LA, Nobuta, K, German, R, et al. Global identification of microRNA‐target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 2008, 26:941–946.
102 Chi, SW, Zang, JB, Mele, A, Darnell, RB. Argonaute HITS‐CLIP decodes microRNA‐mRNA interaction maps. Nature 2009, 460:479–486.
103 Zhao, J, Ohsumi, TK, Kung, JT, Ogawa, Y, Grau, DJ, Sarma, K, Song, JJ, Kingston, RE, Borowsky, M, Lee, JT. Genome‐wide identification of polycomb‐associated RNAs by RIP‐seq. Mol Cell 2010, 40:939–953.
104 Ameur, A, Zaghlool, A, Halvardson, J, Wetterbom, A, Gyllensten, U, Cavelier, L, Feuk, L. Total RNA sequencing reveals nascent transcription and widespread co‐transcriptional splicing in the human brain. Nat Struct Mol Biol 2011, 18:1435–1440.

blog comments powered by Disqus

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

In the Spotlight

Yingqun Huang

and her research team focus on the dissection of the molecular mechanisms and pathways involved in Lin28-mediated regulation. First, they will analyze Lin28 expression in mouse and human ES cells to determine whether its expression is regulated during the cell cy-cle. Then, they will characterize the interactions between Lin28 and its associated mRNAs to gain molecular insights into their assembly, function and regulation in the cellular milieu. Finally, they will strive to identify Lin28-interacting protein partners and new target mRNAs to establish a comprehensive and global understanding of Lin28 function.

Learn More

Article Title	Computational approaches to discovering noncoding RNA
* Name
* E-mail
* Note

Computational approaches to discovering noncoding RNA

Related Articles

Browse by Topic

Access to this WIREs title is by subscription only.

The latest WIREs articles in your inbox

In the Spotlight

Twitter: WIREsrna Follow us on Twitter