Skip to main content

CLIP: viewing the RNA world from an RNA-protein interactome perspective

Abstract

The pervasive transcription of the genome creates many types of non-coding RNAs (ncRNAs). However, we know very little regarding the functions and the regulatory mechanisms of these ncRNAs. Exploring the interactions of RNA and RNA binding proteins (RBPs) is vital because it can allow us to truly understand how these ncRNAs behave in vivo. High-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP or CLIP-seq) and its variants have been successfully used as systemic techniques to study RBP binding sites. In this review, we will explain the major differences between the CLIP techniques, summarize successful applications of these techniques, discuss limitations of CLIP, present some suggested solutions and project their promising future roles in studying the RNA world.

References

  1. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Roder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Bar NS, Batut P, Bell K, Bell I, Chakrabortty S, Chen X, Chrast J, Curado J, Derrien T, Drenkow J, Dumais E, Dumais J, Duttagupta R, Falconnet E, Fastuca M, Fejes-Toth K, Ferreira P, Foissac S, Fullwood MJ, Gao H, Gonzalez D, Gordon A, Gunawardena H, Howald C, Jha S, Johnson R, Kapranov P, King B, Kingswood C, Luo OJ, Park E, Persaud K, Preall JB, Ribeca P, Risk B, Robyr D, Sammeth M, Schaffer L, See LH, Shahab A, Skancke J, Suzuki AM, Takahashi H, Tilgner H, Trout D, Walters N, Wang H, Wrobel J, Yu Y, Ruan X, Hayashizaki Y, Harrow J, Gerstein M, Hubbard T, Reymond A, Antonarakis SE, Hannon G, Giddings MC, Ruan Y, Wold B, Carninci P, Guigo R, Gingeras TR. Landscape of transcription in human cells. Nature, 2012, 489: 101–108

    PubMed  PubMed Central  Google Scholar 

  2. Consortium EP, Birney E, Stamatoyannopoulos JA, Dutta A, Guigó R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE, Kuehn MS, Taylor CM, Neph S, Koch CM, Asthana S, Malhotra A, Adzhubei I, Greenbaum JA, Andrews RM, Flicek P, Boyle PJ, Cao H, Carter NP, Clelland GK, Davis S, Day N, Dhami P, Dillon SC, Dorschner MO, Fiegler H, Giresi PG, Goldy J, Hawrylycz M, Haydock A, Humbert R, James KD, Johnson BE, Johnson EM, Frum TT, Rosenzweig ER, Karnani N, Lee K, Lefebvre GC, Navas PA, Neri F, Parker SCJ, Sabo PJ, Sandstrom R, Shafer A, Vetrie D, Weaver M, Wilcox S, Yu M, Collins FS, Dekker J, Lieb JD, Tullius TD, Crawford GE, Sunyaev S, Noble WS, Dunham I, Denoeud F, Reymond A, Kapranov P, Rozowsky J, Zheng D, Castelo R, Frankish A, Harrow J, Ghosh S, Sandelin A, Hofacker IL, Baertsch R, Keefe D, Dike S, Cheng J, Hirsch HA, Sekinger EA, Lagarde J, Abril JF, Shahab A, Flamm C, Fried C, Hackermüller J, Hertel J, Lindemeyer M, Missal K, Tanzer A, Washietl S, Korbel J, Emanuelsson O, Pedersen JS, Holroyd N, Taylor R, Swarbreck D, Matthews N, Dickson MC, Thomas DJ, Weirauch MT, Gilbert J, Drenkow J, Bell I, Zhao X, Srinivasan KG, Sung W-K, Ooi HS, Chiu KP, Foissac S, Alioto T, Brent M, Pachter L, Tress ML, Valencia A, Choo SW, Choo CY, Ucla C, Manzano C, Wyss C, Cheung E, Clark TG, Brown JB, Ganesh M, Patel S, Tammana H, Chrast J, Henrichsen CN, Kai C, Kawai J, Nagalakshmi U, Wu J, Lian Z, Lian J, Newburger P, Zhang X, Bickel P, Mattick JS, Carninci P, Hayashizaki Y, Weissman S, Hubbard T, Myers RM, Rogers J, Stadler PF, Lowe TM, Wei C-L, Ruan Y, Struhl K, Gerstein M, Antonarakis SE, Fu Y, Green ED, Karaöz U, Siepel A, Taylor J, Liefer LA, Wetterstrand KA, Good PJ, Feingold EA, Guyer MS, Cooper GM, Asimenos G, Dewey CN, Hou M, Nikolaev S, Montoya-Burgos JI, Löytynoja A, Whelan S, Pardi F, Massingham T, Huang H, Zhang NR, Holmes I, Mullikin JC, Ureta-Vidal A, Paten B, Seringhaus M, Church D, Rosenbloom K, Kent WJ, Stone EA, Program NCS, Center BCoMHGS, Center WUGS, Institute B, Institute CsHOR, Batzoglou S, Goldman N, Hardison RC, Haussler D, Miller W, Sidow A, Trinklein ND, Zhang ZD, Barrera L, Stuart R, King DC, Ameur A, Enroth S, Bieda MC, Kim J, Bhinge AA, Jiang N, Liu J, Yao F, Vega VB, Lee CWH, Ng P, Shahab A, Yang A, Moqtaderi Z, Zhu Z, Xu X, Squazzo S, Oberley MJ, Inman D, Singer MA, Richmond TA, Munn KJ, Rada-Iglesias A, Wallerman O, Komorowski J, Fowler JC, Couttet P, Bruce AW, Dovey OM, Ellis PD, Langford CF, Nix DA, Euskirchen G, Hartman S, Urban AE, Kraus P, Van Calcar S, Heintzman N, Kim TH, Wang K, Qu C, Hon G, Luna R, Glass CK, Rosenfeld MG, Aldred SF, Cooper SJ, Halees A, Lin JM, Shulha HP, Zhang X, Xu M, Haidar JNS, Yu Y, Ruan Y, Iyer VR, Green RD, Wadelius C, Farnham PJ, Ren B, Harte RA, Hinrichs AS, Trumbower H, Clawson H, Hillman-Jackson J, Zweig AS, Smith K, Thakkapallayil A, Barber G, Kuhn RM, Karolchik D, Armengol L, Bird CP, de Bakker PIW, Kern AD, Lopez-Bigas N, Martin JD, Stranger BE, Woodroffe A, Davydov E, Dimas A, Eyras E, Hallgrímsdóttir IB, Huppert J, Zody MC, Abecasis GR, Estivill X, Bouffard GG, Guan X, Hansen NF, Idol JR, Maduro VVB, Maskeri B, McDowell JC, Park M, Thomas PJ, Young AC, Blakesley RW, Muzny DM, Sodergren E, Wheeler DA, Worley KC, Jiang H, Weinstock GM, Gibbs RA, Graves T, Fulton R, Mardis ER, Wilson RK, Clamp M, Cuff J, Gnerre S, Jaffe DB, Chang JL, Lindblad-Toh K, Lander ES, Koriabine M, Nefedov M, Osoegawa K, Yoshinaga Y, Zhu B, de Jong PJ. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 2007, 447: 799–816

    Google Scholar 

  3. Ho JJD, Marsden PA. Competition and collaboration between RNA-binding proteins and microRNAs. Wiley Interdiscip Rev RNA, 2014, 5: 69–86

    PubMed  Google Scholar 

  4. Moore MJ, Zhang C, Gantman EC, Mele A, Darnell JC, Darnell RB. Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat Protoc, 2014, 9: 263–293

    PubMed  PubMed Central  Google Scholar 

  5. Darnell RB. HITS-CLIP: panoramic views of protein-RNA regulation in living cells. Wiley Interdiscip Rev RNA, 2010, 1: 266–286

    PubMed  PubMed Central  Google Scholar 

  6. Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, Darnell RB. CLIP identifies Nova-regulated RNA networks in the brain. Science, 2003, 302: 1212–1215

    PubMed  Google Scholar 

  7. Ule J, Jensen K, Mele A, Darnell RB. CLIP: a method for identifying protein-RNA interaction sites in living cells. Methods, 2005, 37: 376–386

    PubMed  Google Scholar 

  8. Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, Clark TA, Schweitzer AC, Blume JE, Wang X, Darnell JC, Darnell RB. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature, 2008, 456: 464–469

    PubMed  PubMed Central  Google Scholar 

  9. Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 2009, 460: 479–486

    PubMed  PubMed Central  Google Scholar 

  10. Licatalosi DD, Darnell RB. RNA processing and its regulation: global insights into biological networks. Nat Rev Genet, 2010, 11: 75–87

    PubMed  PubMed Central  Google Scholar 

  11. Favre A, Saintomé C, Fourrey JL, Clivio P, Laugâa P. Thionucleobases as intrinsic photoaffinity probes of nucleic acid structure and nucleic acid-protein interactions. J Photochem Photobiol B, 1998, 42: 109–124

    PubMed  Google Scholar 

  12. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jr., Jungkamp AC, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell, 2010, 141: 129–141

    PubMed  PubMed Central  Google Scholar 

  13. Ascano M, Hafner M, Cekan P, Gerstberger S, Tuschl T. Identification of RNA-protein interaction networks using PAR-CLIP. Wiley Interdiscip Rev RNA, 2012, 3: 159–177

    PubMed  PubMed Central  Google Scholar 

  14. Corcoran DL, Georgiev S, Mukherjee N, Gottwein E, Skalsky RL, Keene JD, Ohler U. PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol, 2011, 12: R79

    PubMed  PubMed Central  Google Scholar 

  15. Kemény-Beke A, Berényi E, Facskó A, Damjanovich J, Horváth A, Bodnár A, Berta A, Aradi J. Antiproliferative effect of 4-thiouridylate on OCM-1 uveal melanoma cells. Eur J Ophthalmol, 2006, 16: 680–685

    PubMed  Google Scholar 

  16. Burger K, Mühl B, Kellner M, Rohrmoser M, Gruber-Eber A, Windhager L, Friedel CC, Dölken L, Eick D. 4-thiouridine inhibits rRNA synthesis and causes a nucleolar stress response. RNA Biol, 2013, 10: 1623–1630

    PubMed  PubMed Central  Google Scholar 

  17. Zhang C, Darnell RB. Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat Biotechnol, 2011, 29: 607–614

    PubMed  PubMed Central  Google Scholar 

  18. König J, Zarnack K, Rot G, Curk T, Kayikci M, Zupan B, Turner DJ, Luscombe NM, Ule J. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol, 2010, 17: 909–915

    PubMed  PubMed Central  Google Scholar 

  19. Urlaub H, Hartmuth K, Lührmann R. A two-tracked approach to analyze RNA-protein crosslinking sites in native, nonlabeled small nuclear ribonucleoprotein particles. Methods, 2002, 26: 170–181

    PubMed  Google Scholar 

  20. Granneman S, Kudla G, Petfalski E, Tollervey D. Identification of protein binding sites on U3 snoRNA and pre-rRNA by UV cross-linking and high-throughput analysis of cDNAs. Proc Natl Acad Sci USA, 2009, 106: 9613–9618

    PubMed  PubMed Central  Google Scholar 

  21. Kudla G, Granneman S, Hahn D, Beggs JD, Tollervey D. Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc Natl Acad Sci USA, 2011, 108: 10010–10015

    PubMed  PubMed Central  Google Scholar 

  22. Helwak A, Kudla G, Dudnakova T, Tollervey D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell, 2013, 153: 654–665

    PubMed  PubMed Central  Google Scholar 

  23. Hüttenhofer A, Schattner P. The principles of guiding by RNA: chimeric RNA-protein enzymes. Nat Rev Genet, 2006, 7: 475–482

    PubMed  Google Scholar 

  24. Akbari Moqadam F, Pieters R, den Boer ML. The hunting of targets: challenge in miRNA research. Leukemia, 2013, 27: 16–23

    PubMed  Google Scholar 

  25. Broughton JP, Pasquinelli AE. Identifying Argonaute binding sites in Caenorhabditis elegans using iCLIP. Methods, 2013, 63: 119–125

    PubMed  PubMed Central  Google Scholar 

  26. Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp A-C, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T. PAR-CliP—a method to identify transcriptome-wide the binding sites of RNA binding proteins. J Vis Exp, 2010, doi: 10.3791/2034

    Google Scholar 

  27. Wang Z, Tollervey J, Briese M, Turner D, Ule J. CLIP: construction of cDNA libraries for high-throughput sequencing from RNAs cross-linked to proteins in vivo. Methods, 2009, 48: 287–293

    PubMed  Google Scholar 

  28. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics, 2011, 27: 863–864

    PubMed  PubMed Central  Google Scholar 

  29. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinformatics, 2009, 10: 421

    PubMed  PubMed Central  Google Scholar 

  30. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009, 25: 1754–1760

    PubMed  PubMed Central  Google Scholar 

  31. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods, 2012, 9: 357–359

    PubMed  PubMed Central  Google Scholar 

  32. Uren PJ, Bahrami-Samani E, Burns SC, Qiao M, Karginov FV, Hodges E, Hannon GJ, Sanford JR, Penalva LOF, Smith AD. Site identification in high-throughput RNA-protein interaction data. Bioinformatics, 2012, 28: 3013–3020

    PubMed  PubMed Central  Google Scholar 

  33. Althammer S, González-Vallinas J, Ballaré C, Beato M, Eyras E. Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data. Bioinformatics, 2011, 27: 3333–3340

    PubMed  PubMed Central  Google Scholar 

  34. Li Y, Zhao DY, Greenblatt JF, Zhang Z. RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments. Nucleic Acids Res, 2013, 41: e94

    PubMed  PubMed Central  Google Scholar 

  35. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol, 1994, 2: 28–36

    PubMed  Google Scholar 

  36. Thompson WA, Newberg LA, Conlan S, McCue LA, Lawrence CE. The Gibbs Centroid Sampler. Nucleic Acids Res, 2007, 35: W232–W237

    PubMed  PubMed Central  Google Scholar 

  37. Roth FP, Hughes JD, Estep PW, Church GM. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol, 1998, 16: 939–945

    PubMed  Google Scholar 

  38. van Helden J. Regulatory sequence analysis tools. Nucleic Acids Res, 2003, 31: 3593–3596

    PubMed  PubMed Central  Google Scholar 

  39. Fukunaga T, Ozaki H, Terai G, Asai K, Iwasaki W, Kiryu H. CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol, 2014, 15: R16

    PubMed  PubMed Central  Google Scholar 

  40. Maticzka D, Lange SJ, Costa F, Backofen R. GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol, 2014, 15: R17

    PubMed  PubMed Central  Google Scholar 

  41. Khorshid M, Rodak C, Zavolan M. CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins. Nucleic Acids Res, 2011, 39: D245–252

    PubMed  PubMed Central  Google Scholar 

  42. Anders G, Mackowiak SD, Jens M, Maaskola J, Kuntzagk A, Rajewsky N, Landthaler M, Dieterich C. doRiNA: a database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res, 2012, 40: D180–186

    PubMed  PubMed Central  Google Scholar 

  43. Yang JH, Li JH, Shao P, Zhou H, Chen YQ, Qu LH. starBase: a da tabase for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res, 2011, 39: D202–209

    PubMed  PubMed Central  Google Scholar 

  44. Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res, 2014, 42: D92–97

    PubMed  PubMed Central  Google Scholar 

  45. Sievers C, Schlumpf T, Sawarkar R, Comoglio F, Paro R. Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data. Nucleic Acids Res, 2012, 40: e160

    PubMed  PubMed Central  Google Scholar 

  46. Yun J, Wang T, Xiao G. Bayesian hidden Markov models to identify RNA-protein interaction sites in PAR-CLIP. Biometrics, 2014, doi: 10.1111/biom.12147

    Google Scholar 

  47. Wang T, Xie Y, Xiao G. dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol, 2014, 15: R11

    PubMed  PubMed Central  Google Scholar 

  48. Kucukural A, Özadam H, Singh G, Moore MJ, Cenik C. ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq. Bioinformatics, 2013, 29: 2485–2486

    PubMed  PubMed Central  Google Scholar 

  49. Chen B, Yun J, Kim MS, Mendell JT, Xie Y. PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis. Genome Biol, 2014, 15: R18

    PubMed  PubMed Central  Google Scholar 

  50. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell, 2009, 136: 215–233

    PubMed  PubMed Central  Google Scholar 

  51. Ambros V. The functions of animal microRNAs. Nature, 2004, 431: 350–355

    PubMed  Google Scholar 

  52. 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

    PubMed  Google Scholar 

  53. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N. Combinatorial microRNA target predictions. Nat Genet, 2005, 37: 495–500

    PubMed  Google Scholar 

  54. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA, 2004, 10: 1507–1517

    PubMed  PubMed Central  Google Scholar 

  55. Rajewsky N. microRNA target predictions in animals. Nat Genet, 2006, 38(Suppl): S8–13

    PubMed  Google Scholar 

  56. Bentwich I. Prediction and validation of microRNAs and their targets. FEBS Lett, 2005, 579: 5904–5910

    PubMed  Google Scholar 

  57. Wang Y, Juranek S, Li H, Sheng G, Tuschl T, Patel DJ. Structure of an argonaute silencing complex with a seed-containing guide DNA and target RNA duplex. Nature, 2008, 456: 921–926

    PubMed  PubMed Central  Google Scholar 

  58. Friedman RC, Farh KK-H, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res, 2009, 19: 92–105

    PubMed  PubMed Central  Google Scholar 

  59. Chi SW, Hannon GJ, Darnell RB. An alternative mode of microRNA target recognition. Nat Struct Mol Biol, 2012, 19: 321–327

    PubMed  PubMed Central  Google Scholar 

  60. Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS, Rudensky AY. Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol Cell, 2012, 48: 760–770

    PubMed  PubMed Central  Google Scholar 

  61. Kim KK, Ham J, Chi SW. miRTCat: a comprehensive map of human and mouse microRNA target sites including non-canonical nucleation bulges. Bioinformatics, 2013, 29: 1898–1899

    PubMed  Google Scholar 

  62. Betel D, Koppal A, Agius P, Sander C, Leslie C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol, 2010, 11: R90

    PubMed  PubMed Central  Google Scholar 

  63. Khorshid M, Hausser J, Zavolan M, van Nimwegen E. A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets. Nat Methods, 2013, 10: 253–255

    PubMed  Google Scholar 

  64. Skalsky RL, Cullen BR. Viruses, microRNAs, and host interactions. Annu Rev Microbiol, 2010, 64: 123–141

    PubMed  PubMed Central  Google Scholar 

  65. Cullen BR. Viruses and microRNAs: RISCy interactions with serious consequences. Genes Dev, 2011, 25: 1881–1894

    PubMed  PubMed Central  Google Scholar 

  66. Gottwein E, Mukherjee N, Sachse C, Frenzel C, Majoros WH, Chi JTA, Braich R, Manoharan M, Soutschek J, Ohler U, Cullen BR. A viral microRNA functions as an orthologue of cellular miR-155. Nature, 2007, 450: 1096–1099

    PubMed  PubMed Central  Google Scholar 

  67. Gottwein E, Corcoran DL, Mukherjee N, Skalsky RL, Hafner M, Nusbaum JD, Shamulailatpam P, Love CL, Dave SS, Tuschl T, Ohler U, Cullen BR. Viral microRNA targetome of KSHV-infected primary effusion lymphoma cell lines. Cell Host Microbe, 2011, 10: 515–526

    PubMed  PubMed Central  Google Scholar 

  68. Haecker I, Gay LA, Yang Y, Hu J, Morse AM, McIntyre LM, Renne R. Ago HITS-CLIP expands understanding of Kaposi’s sarcomaassociated herpesvirus miRNA function in primary effusion lymphomas. PLoS Pathog, 2012, 8: e1002884

    PubMed  PubMed Central  Google Scholar 

  69. Skalsky RL, Corcoran DL, Gottwein E, Frank CL, Kang D, Hafner M, Nusbaum JD, Feederle R, Delecluse H-J, Luftig MA, Tuschl T, Ohler U, Cullen BR. The viral and cellular microRNA targetome in lymphoblastoid cell lines. PLoS Pathog, 2012, 8: e1002484

    PubMed  PubMed Central  Google Scholar 

  70. Riley KJ, Rabinowitz GS, Yario TA, Luna JM, Darnell RB, Steitz JA. EBV and human microRNAs co-target oncogenic and apoptotic viral and human genes during latency. EMBO J, 2012, 31: 2207–2221

    PubMed  PubMed Central  Google Scholar 

  71. Wang Y, Medvid R, Melton C, Jaenisch R, Blelloch R. DGCR8 is essential for microRNA biogenesis and silencing of embryonic stem cell self-renewal. Nat Genet, 2007, 39: 380–385

    PubMed  PubMed Central  Google Scholar 

  72. Anokye-Danso F, Trivedi CM, Juhr D, Gupta M, Cui Z, Tian Y, Zhang Y, Yang W, Gruber PJ, Epstein JA, Morrisey EE. Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell, 2011, 8: 376–388

    PubMed  PubMed Central  Google Scholar 

  73. Judson RL, Babiarz JE, Venere M, Blelloch R. Embryonic stem cell-specific microRNAs promote induced pluripotency. Nat Biotechnol, 2009, 27: 459–461

    PubMed  PubMed Central  Google Scholar 

  74. Miyoshi N, Ishii H, Nagano H, Haraguchi N, Dewi DL, Kano Y, Nishikawa S, Tanemura M, Mimori K, Tanaka F, Saito T, Nishimura J, Takemasa I, Mizushima T, Ikeda M, Yamamoto H, Sekimoto M, Doki Y, Mori M. Reprogramming of mouse and human cells to pluripotency using mature microRNAs. Cell stem cell, 2011, 8: 633–638

    PubMed  Google Scholar 

  75. Xie S, Zhang Y, Qu L, Xu H. A Helm model for microRNA regulation in cell fate decision and conversion. Sci China Life Sci, 2013, 56: 897–906

    PubMed  Google Scholar 

  76. Leonardo TR, Schultheisz HL, Loring JF, Laurent LC. The functions of microRNAs in pluripotency and reprogramming. Nat Cell Biol, 2012, 14: 1114–1121

    PubMed  Google Scholar 

  77. Leung AK, Young AG, Bhutkar A, Zheng GX, Bosson AD, Nielsen CB, Sharp PA. Genome-wide identification of Ago2 binding sites from mouse embryonic stem cells with and without mature microRNAs. Nat Struct Mol Biol, 2011, 18: 237–244

    PubMed  PubMed Central  Google Scholar 

  78. Lipchina I, Elkabetz Y, Hafner M, Sheridan R, Mihailovic A, Tuschl T, Sander C, Studer L, Betel D. Genome-wide identification of microRNA targets in human ES cells reveals a role for miR-302 in modulating BMP response. Genes Dev, 2011, 25: 2173–2186

    PubMed  PubMed Central  Google Scholar 

  79. Mendell JT. miRiad roles for the miR-17-92 cluster in development and disease. Cell, 2008, 133: 217–222

    PubMed  PubMed Central  Google Scholar 

  80. Jin HY, Oda H, Lai M, Skalsky RL, Bethel K, Shepherd J, Kang SG, Liu WH, Sabouri-Ghomi M, Cullen BR, Rajewsky K, Xiao C. MicroRNA-17∼92 plays a causative role in lymphomagenesis by coordinating multiple oncogenic pathways. EMBO J, 2013, 32: 2377–2391

    PubMed  PubMed Central  Google Scholar 

  81. Boudreau RL, Jiang P, Gilmore BL, Spengler RM, Tirabassi R, Nelson JA, Ross CA, Xing Y, Davidson BL. Transcriptome-wide Discovery of microRNA Binding Sites in Human Brain. Neuron, 2013, doi: 10.1016/j.neuron.2013.10.062

    Google Scholar 

  82. Kameswaran V, Bramswig NC, McKenna LB, Penn M, Schug J, Hand NJ, Chen Y, Choi I, Vourekas A, Won K-J, Liu C, Vivek K, Naji A, Friedman JR, Kaestner KH. Epigenetic regulation of the DLK1-MEG3 microRNA cluster in human type 2 diabetic islets. Cell Metab, 2014, 19: 135–145

    PubMed  PubMed Central  Google Scholar 

  83. Black DL. Mechanisms of alternative pre-messenger RNA splicing. Annu Rev Biochem, 2003, 72: 291–336

    PubMed  Google Scholar 

  84. Kornblihtt AR, Schor IE, Alló M, Dujardin G, Petrillo E, Muñoz MJ. Alternative splicing: a pivotal step between eukaryotic transcription and translation. Nat Rev Mol Cell Biol, 2013, 14: 153–165

    PubMed  Google Scholar 

  85. Xue Y, Zhou Y, Wu T, Zhu T, Ji X, Kwon YS, Zhang C, Yeo G, Black DL, Sun H, Fu XD, Zhang Y. Genome-wide analysis of PTB-RNA interactions reveals a strategy used by the general splicing repressor to modulate exon inclusion or skipping. Mol Cell, 2009, 36: 996–1006

    PubMed  PubMed Central  Google Scholar 

  86. Licatalosi DD, Yano M, Fak JJ, Mele A, Grabinski SE, Zhang C, Darnell RB. Ptbp2 represses adult-specific splicing to regulate the generation of neuronal precursors in the embryonic brain. Genes Dev, 2012, 26: 1626–1642

    PubMed  PubMed Central  Google Scholar 

  87. Yeo GW, Coufal NG, Liang TY, Peng GE, Fu X-D, Gage FH. An RNA code for the FOX2 splicing regulator revealed by mapping RNA-protein interactions in stem cells. Nat Struct Mol Biol, 2009, 16: 130–137

    PubMed  PubMed Central  Google Scholar 

  88. Sanford JR, Wang X, Mort M, Vanduyn N, Cooper DN, Mooney SD, Edenberg HJ, Liu Y. Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts. Genome Res, 2009, 19: 381–394

    PubMed  PubMed Central  Google Scholar 

  89. Wang Z, Kayikci M, Briese M, Zarnack K, Luscombe NM, Rot G, Zupan B, Curk T, Ule J. iCLIP predicts the dual splicing effects of TIA-RNA interactions. PLoS Biol, 2010, 8: e1000530

    PubMed  PubMed Central  Google Scholar 

  90. Wang ET, Cody NAL, Jog S, Biancolella M, Wang TT, Treacy DJ, Luo S, Schroth GP, Housman DE, Reddy S, Lécuyer E, Burge CB. Transcriptome-wide regulation of pre-mRNA splicing and mRNA localization by muscleblind proteins. Cell, 2012, 150: 710–724

    PubMed  PubMed Central  Google Scholar 

  91. Daughters RS, Tuttle DL, Gao W, Ikeda Y, Moseley ML, Ebner TJ, Swanson MS, Ranum LPW. RNA gain-of-function in spinocerebellar ataxia type 8. PLoS Genet, 2009, 5: e1000600

    PubMed  PubMed Central  Google Scholar 

  92. Pandit S, Zhou Y, Shiue L, Coutinho-Mansfield G, Li H, Qiu J, Huang J, Yeo GW, Ares M Jr., Fu XD. Genome-wide analysis reveals SR protein cooperation and competition in regulated splicing. Mol Cell, 2013, 50: 223–235

    PubMed  PubMed Central  Google Scholar 

  93. Zarnack K, König J, Tajnik M, Martincorena I, Eustermann S, Stévant I, Reyes A, Anders S, Luscombe NM, Ule J. Direct competition between hnRNP C and U2AF65 protects the transcriptome from the exonization of Alu elements. Cell, 2013, 152: 453–466

    PubMed  PubMed Central  Google Scholar 

  94. Huelga SC, Vu AQ, Arnold JD, Liang TY, Liu PP, Yan BY, Donohue JP, Shiue L, Hoon S, Brenner S, Ares M, Yeo GW. Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins. Cell Rep, 2012, 1: 167–178

    PubMed  PubMed Central  Google Scholar 

  95. Mayeda A, Krainer AR. Regulation of alternative pre-mRNA splicing by hnRNP A1 and splicing factor SF2. Cell, 1992, 68: 365–375

    PubMed  Google Scholar 

  96. Mayeda A, Munroe SH, Cáceres JF, Krainer AR. Function of conserved domains of hnRNP A1 and other hnRNP A/B proteins. EMBO J, 1994, 13: 5483–5495

    PubMed  PubMed Central  Google Scholar 

  97. Cáceres JF, Stamm S, Helfman DM, Krainer AR. Regulation of alternative splicing in vivo by overexpression of antagonistic splicing factors. Science, 1994, 265: 1706–1709

    PubMed  Google Scholar 

  98. Guil S, Cáceres JF. The multifunctional RNA-binding protein hnRNP A1 is required for processing of miR-18a. Nat Struct Mol Biol, 2007, 14: 591–596

    PubMed  Google Scholar 

  99. Xue Y, Ouyang K, Huang J, Zhou Y, Ouyang H, Li H, Wang G, Wu Q, Wei C, Bi Y, Jiang L, Cai Z, Sun H, Zhang K, Zhang Y, Chen J, Fu XD. Direct conversion of fibroblasts to neurons by reprogramming PTB-regulated microRNA circuits. Cell, 2013, 152: 82–96

    PubMed  PubMed Central  Google Scholar 

  100. Makeyev EV, Zhang J, Carrasco MA, Maniatis T. The microRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing. Mol Cell, 2007, 27: 435–448

    PubMed  PubMed Central  Google Scholar 

  101. Sparmann A, van Lohuizen M. Polycomb silencers control cell fate, development and cancer. Nat Rev Cancer, 2006, 6: 846–856

    PubMed  Google Scholar 

  102. Zhao J, Sun BK, Erwin JA, Song JJ, Lee JT. Polycomb proteins targeted by a short repeat RNA to the mouse X chromosome. Science, 2008, 322: 750–756

    PubMed  PubMed Central  Google Scholar 

  103. Rinn JL, Kertesz M, Wang JK, Squazzo SL, Xu X, Brugmann SA, Goodnough LH, Helms JA, Farnham PJ, Segal E, Chang HY. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell, 2007, 129: 1311–1323

    PubMed  PubMed Central  Google Scholar 

  104. Guil S, Soler M, Portela A, Carrère J, Fonalleras E, Gómez A, Villanueva A, Esteller M. Intronic RNAs mediate EZH2 regulation of epigenetic targets. Nat Struct Mol Biol, 2012, 19: 664–670

    PubMed  Google Scholar 

  105. Kaneko S, Son J, Shen SS, Reinberg D, Bonasio R. PRC2 binds active promoters and contacts nascent RNAs in embryonic stem cells. Nat Struct Mol Biol, 2013, 20: 1258–1264

    PubMed  Google Scholar 

  106. Castel SE, Martienssen RA. RNA interference in the nucleus: roles for small RNAs in transcription, epigenetics and beyond. Nat Rev Genet, 2013, 14: 100–112

    PubMed  PubMed Central  Google Scholar 

  107. Lee JT. Epigenetic regulation by long noncoding RNAs. Science, 2012, 338: 1435–1439

    PubMed  Google Scholar 

  108. Jeffery L, Nakielny S. Components of the DNA methylation system of chromatin control are RNA-binding proteins. J Biol Chem, 2004, 279: 49479–49487

    PubMed  Google Scholar 

  109. Davis BN, Hilyard AC, Nguyen PH, Lagna G, Hata A. Smad proteins bind a conserved RNA sequence to promote microRNA maturation by Drosha. Mol Cell, 2010, 39: 373–384

    PubMed  PubMed Central  Google Scholar 

  110. Riley KJ-L, James Maher L. Analysis of p53-RNA interactions in cultured human cells. Biochem Biophys Res Commun, 2007, 363: 381–387

    PubMed  PubMed Central  Google Scholar 

  111. Riley KJL, Maher LJ. p53 RNA interactions: new clues in an old mystery. RNA, 2007, 13: 1825–1833

    PubMed  PubMed Central  Google Scholar 

  112. Cassiday LA, Maher LJ. Having it both ways: transcription factors that bind DNA and RNA. Nucleic Acids Res, 2002, 30: 4118–4126

    PubMed  PubMed Central  Google Scholar 

  113. Suswam EA, Li YY, Mahtani H, King PH. Novel DNA-binding properties of the RNA-binding protein TIAR. Nucleic Acids Res, 2005, 33: 4507–4518

    PubMed  PubMed Central  Google Scholar 

  114. Law WJ, Cann KL, Hicks GG. TLS, EWS and TAF15: a model for transcriptional integration of gene expression. Brief Funct Genomic Proteomic, 2006, 5: 8–14

    PubMed  Google Scholar 

  115. Huang V, Zheng J, Qi Z, Wang J, Place RF, Yu J, Li H, Li LC. Ago1 Interacts with RNA polymerase II and binds to the promoters of actively transcribed genes in human cancer cells. PLoS Genet, 2013, 9: e1003821

    PubMed  PubMed Central  Google Scholar 

  116. Phillips JE, Corces VG. CTCF: master weaver of the genome. Cell, 2009, 137: 1194–1211

    PubMed  PubMed Central  Google Scholar 

  117. Saldana-Meyer R, Gonzalez-Buendia E, Guerrero G, Narendra V, Bonasio R, Recillas-Targa F, Reinberg D. CTCF regulates the human p53 gene through direct interaction with its natural antisense transcript, Wrap53. Genes Dev, 2014, 28: 723–734

    PubMed  PubMed Central  Google Scholar 

  118. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell, 2011, 146: 353–358

    PubMed  PubMed Central  Google Scholar 

  119. Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature, 2014, 505: 344–352

    PubMed  PubMed Central  Google Scholar 

  120. Poliseno L, Salmena L, Zhang J, Carver B, Haveman WJ, Pandolfi PP. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 2010, 465: 1033–1038

    PubMed  PubMed Central  Google Scholar 

  121. Cesana M, Cacchiarelli D, Legnini I, Santini T, Sthandier O, Chinappi M, Tramontano A, Bozzoni I. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell, 2011, 147: 358–369

    PubMed  PubMed Central  Google Scholar 

  122. Kallen AN, Zhou XB, Xu J, Qiao C, Ma J, Yan L, Lu L, Liu C, Yi JS, Zhang H, Min W, Bennett AM, Gregory RI, Ding Y, Huang Y. The imprinted H19 lncRNA antagonizes let-7 microRNAs. Mol Cell, 2013, 52: 101–112

    PubMed  Google Scholar 

  123. Franco-Zorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, Rubio-Somoza I, Leyva A, Weigel D, García JA, Paz-Ares J. Target mimicry provides a new mechanism for regulation of microRNA activity. Nat Genet, 2007, 39: 1033–1037

    PubMed  Google Scholar 

  124. Karreth FA, Tay Y, Perna D, Ala U, Tan SM, Rust AG, DeNicola G, Webster KA, Weiss D, Perez-Mancera PA, Krauthammer M, Halaban R, Provero P, Adams DJ, Tuveson DA, Pandolfi PP. In vivo identification of tumor-suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma. Cell, 2011, 147: 382–395

    PubMed  PubMed Central  Google Scholar 

  125. Sumazin P, Yang X, Chiu HS, Chung WJ, Iyer A, Llobet-Navas D, Rajbhandari P, Bansal M, Guarnieri P, Silva J, Califano A. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell, 2011, 147: 370–381

    PubMed  PubMed Central  Google Scholar 

  126. Kumar MS, Armenteros-Monterroso E, East P, Chakravorty P, Matthews N, Winslow MM, Downward J. HMGA2 functions as a competing endogenous RNA to promote lung cancer progression. Nature, 2014, 505: 212–217

    PubMed  Google Scholar 

  127. Thomas M, Lieberman J, Lal A. Desperately seeking microRNA tar gets. Nat Struct Mol Biol, 2010, 17: 1169–1174

    PubMed  Google Scholar 

  128. Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak SD, Gregersen LH, Munschauer M, Loewer A, Ziebold U, Landthaler M, Kocks C, le Noble F, Rajewsky N. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 2013, 495: 333–338

    PubMed  Google Scholar 

  129. Ledford H. Circular RNAs throw genetics for a loop. Nature, 2013, 494: 415

    PubMed  Google Scholar 

  130. Libri V, Helwak A, Miesen P, Santhakumar D, Borger JG, Kudla G, Grey F, Tollervey D, Buck AH. Murine cytomegalovirus encodes a miR-27 inhibitor disguised as a target. Proc Natl Acad Sci USA, 2012, 109: 279–284

    PubMed  PubMed Central  Google Scholar 

  131. Friedersdorf MB, Keene JD. Advancing the functional utility of PAR-CLIP by quantifying background binding to mRNAs and lncRNAs. Genome Biol, 2014, 15: R2

    PubMed  PubMed Central  Google Scholar 

  132. Hafner M, Lianoglou S, Tuschl T, Betel D. Genome-wide identification of miRNA targets by PAR-CLIP. Methods, 2012, 58: 94–105

    PubMed  PubMed Central  Google Scholar 

  133. Liu ZR, Wilkie AM, Clemens MJ, Smith CW. Detection of doublestranded RNA-protein interactions by methylene blue-mediated photo-crosslinking. RNA, 1996, 2: 611–621

    PubMed  PubMed Central  Google Scholar 

  134. Wang Z, Rana TM. Probing RNA-protein interactions by psoralen photocrosslinking. Methods Mol Biol, 1999, 118: 49–62

    PubMed  Google Scholar 

  135. Hafner M, Renwick N, Farazi TA, Mihailovic A, Pena JT, Tuschl T. Barcoded cDNA library preparation for small RNA profiling by next-generation sequencing. Methods, 2012, 58: 164–170

    PubMed  PubMed Central  Google Scholar 

  136. Zhang Z, Lee JE, Riemondy K, Anderson EM, Yi R. High-efficiency RNA cloning enables accurate quantification of miRNA expression by deep sequencing. Genome Biol, 2013, 14: R109

    PubMed  PubMed Central  Google Scholar 

  137. Grosswendt S, Filipchyk A, Manzano M, Klironomos F, Schilling M, Herzog M, Gottwein E, Rajewsky N. Unambiguous identification of miRNA: target site interactions by different types of ligation reactions. Mol Cell, 2014, 54: 1042–1054

    PubMed  PubMed Central  Google Scholar 

  138. Jayaprakash AD, Jabado O, Brown BD, Sachidanandam R. Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res, 2011, 39: e141

    PubMed  PubMed Central  Google Scholar 

  139. Alon S, Vigneault F, Eminaga S, Christodoulou DC, Seidman JG, Church GM, Eisenberg E. Barcoding bias in high-throughput multiplex sequencing of miRNA. Genome Res, 2011, 21: 1506–1511

    PubMed  PubMed Central  Google Scholar 

  140. Kishore S, Jaskiewicz L, Burger L, Hausser J, Khorshid M, Zavolan M. A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins. Nat Methods, 2011, 8: 559–564

    PubMed  Google Scholar 

  141. Schadt EE, Turner S, Kasarskis A. A window into third-generation sequencing. Hum Mol Genet, 2010, 19: R227–240

    PubMed  Google Scholar 

  142. Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM, Krijgsveld J, Hentze MW. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell, 2012, 149: 1393–1406

    PubMed  Google Scholar 

  143. Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, Gueroussov S, Albu M, Zheng H, Yang A, Na H, Irimia M, Matzat LH, Dale RK, Smith SA, Yarosh CA, Kelly SM, Nabet B, Mecenas D, Li W, Laishram RS, Qiao M, Lipshitz HD, Piano F, Corbett AH, Carstens RP, Frey BJ, Anderson RA, Lynch KW, Penalva LOF, Lei EP, Fraser AG, Blencowe BJ, Morris QD, Hughes TR. A compendium of RNA-binding motifs for decoding gene regulation. Nature, 2013, 499: 172–177

    PubMed  PubMed Central  Google Scholar 

  144. Kwon SC, Yi H, Eichelbaum K, Fohr S, Fischer B, You KT, Castello A, Krijgsveld J, Hentze MW, Kim VN. The RNA-binding protein repertoire of embryonic stem cells. Nat Struct Mol Biol, 2013, 20: 1122–1130

    PubMed  Google Scholar 

  145. Preitner N, Quan J, Nowakowski DW, Hancock ML, Shi J, Tcherkezian J, Young-Pearse TL, Flanagan JG. APC is an RNA-binding protein, and its interactome provides a link to neural development and microtubule assembly. Cell, 2014, 158: 368–382

    PubMed  PubMed Central  Google Scholar 

  146. Gagnon KT, Li L, Chu Y, Janowski BA, Corey DR. RNAi factors are present and active in human cell nuclei. Cell Rep, 2014, 6: 211–221

    PubMed  PubMed Central  Google Scholar 

  147. Truesdell SS, Mortensen RD, Seo M, Schroeder JC, Lee JH, LeTonqueze O, Vasudevan S. MicroRNA-mediated mRNA translation activation in quiescent cells and oocytes involves recruitment of a nuclear microRNP. Sci Rep, 2012, 2: 842

    PubMed  PubMed Central  Google Scholar 

  148. Gagnon KT, Corey DR. Argonaute and the nuclear RNAs: new pathways for RNA-mediated control of gene expression. Nucleic Acid Ther, 2012, 22: 3–16

    PubMed  PubMed Central  Google Scholar 

  149. Liao JY, Ma LM, Guo YH, Zhang YC, Zhou H, Shao P, Chen YQ, Qu LH. Deep sequencing of human nuclear and cytoplasmic small RNAs reveals an unexpectedly complex subcellular distribution of miRNAs and tRNA 3′ trailers. tPLoS One, 2010, 5: e10563

    Google Scholar 

  150. van Kouwenhove M, Kedde M, Agami R. MicroRNA regulation by RNA-binding proteins and its implications for cancer. Nat Rev Cancer, 2011, 11: 644–656

    PubMed  Google Scholar 

  151. Kim HH, Kuwano Y, Srikantan S, Lee EK, Martindale JL, Gorospe M. HuR recruits let-7/RISC to repress c-Myc expression. Genes Dev, 2009, 23: 1743–1748

    PubMed  PubMed Central  Google Scholar 

  152. Kundu P, Fabian MR, Sonenberg N, Bhattacharyya SN, Filipowicz W. HuR protein attenuates miRNA-mediated repression by promoting miRISC dissociation from the target RNA. Nucleic Acids Res, 2012, 40: 5088–5100

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to LiangHu Qu.

Additional information

This article is published with open access at link.springer.com

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xie, S., Xu, H. et al. CLIP: viewing the RNA world from an RNA-protein interactome perspective. Sci. China Life Sci. 58, 75–88 (2015). https://doi.org/10.1007/s11427-014-4764-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11427-014-4764-5

Keywords

  • CLIP
  • CLASH
  • RPBs
  • ncRNAs
  • functional RNomics