Abstract
Protein–RNA interactions are integral components of posttranscriptional gene regulatory processes including mRNA processing and assembly of cellular architectures. Dysregulation of RNA-binding protein (RBP) expression or disruptions in RBP–RNA interactions underlie a variety of human pathologies and genetic diseases including cancer and neurodegenerative diseases (reviewed in (Cooper et al., Cell 136(4):777–793, 2009; Darnell, Cancer Res Treat 42(3):125–129, 2010; Lukong et al., Trends Genet 24 (8):416–425, 2008)). Recent studies have uncovered only a small proportion of the extensive RBP–RNA interactome in any organism (Baltz et al., Mol Cell 46(5):674–690, 2012; Castello et al., Cell 149(6):1393–1406, 2012; Freeberg et al., Genome Biol 14(2):R13, 2013; Hogan et al., PLoS Biol 6(10):e255, 2008; Mitchell et al., Nat Struct Mol Biol 20(1):127–133, 2013; Tsvetanova et al. PLoS One 5(9): pii: e12671, 2010; Schueler et al., Genome Biol 15(1):R15, 2014; Silverman et al., Genome Biol 15(1):R3, 2014). To expand our understanding of how RBP–RNA interactions govern RNA-related processes, we developed gPAR-CLIP-seq (global photoactivatable-ribonucleoside-enhanced cross-linking and precipitation followed by deep sequencing) for capturing and sequencing all regions of the Saccharomyces cerevisiae transcriptome bound by RBPs (Freeberg et al., Genome Biol 14(2):R13, 2013). This chapter describes a pipeline for bioinformatic analysis of gPAR-CLIP-seq data. The first half of this pipeline can be implemented by running locally installed programs or by running the programs using the Galaxy platform (Blankenberg et al., Curr Protoc Mol Biol. Chapter 19:Unit 19 10 11–21, 2010; Giardine et al., Genome Res 15 (10):1451–1455, 2005; Goecks et al., Genome Biol 11(8):R86, 2010). The second half of this pipeline can be implemented by user-generated code in any language using the pseudocode provided as a template.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Darnell RB (2010) HITS-CLIP: panoramic views of protein-RNA regulation in living cells. Wiley Interdiscip Rev RNA 1(2):266–286. doi:10.1002/wrna.31
Ule J, Jensen KB, Ruggiu M, Mele A, Ule A, Darnell RB (2003) CLIP identifies Nova-regulated RNA networks in the brain. Science 302(5648):1212–1215. doi:10.1126/science.1090095
Licatalosi DD, Darnell RB (2006) Splicing regulation in neurologic disease. Neuron 52(1):93–101. doi:10.1016/j.neuron.2006.09.017
Jensen KB, Dredge BK, Stefani G, Zhong R, Buckanovich RJ, Okano HJ, Yang YY, Darnell RB (2000) Nova-1 regulates neuron-specific alternative splicing and is essential for neuronal viability. Neuron 25(2):359–371
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 (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141(1):129–141. doi:10.1016/j.cell.2010.03.009
Corcoran DL, Georgiev S, Mukherjee N, Gottwein E, Skalsky RL, Keene JD, Ohler U (2011) PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol 12(8):R79. doi:10.1186/gb-2011-12-8-r79
Erhard F, Dolken L, Jaskiewicz L, Zimmer R (2013) PARma: identification of microRNA target sites in AGO-PAR-CLIP data. Genome Biol 14(7):R79. doi:10.1186/gb-2013-14-7-r79
Chou CH, Lin FM, Chou MT, Hsu SD, Chang TH, Weng SL, Shrestha S, Hsiao CC, Hung JH, Huang HD (2013) A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing. BMC Genomics 14(Suppl 1):S2. doi:10.1186/1471-2164-14-S1-S2
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628. doi:10.1038/nmeth.1226
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. doi:10.1038/nrg2484
Team RDC (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. doi:10.1186/gb-2009-10-3-r25
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. doi:10.1093/bioinformatics/btp352
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43(5):491–498. doi:10.1038/ng.806
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303. doi:10.1101/gr.107524.110
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515. doi:10.1038/nbt.1621
Garber M, Grabherr MG, Guttman M, Trapnell C (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8(6):469–477. doi:10.1038/nmeth.1613
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25(1):25–29. doi:10.1038/75556
da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. doi:10.1038/nprot.2008.211
da Huang W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. doi:10.1093/nar/gkn923
Reimand J, Arak T, Vilo J (2011) g:Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res 39(Web Server issue):W307–W315. doi:10.1093/nar/gkr378
Reimand J, Kull M, Peterson H, Hansen J, Vilo J (2007) g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 35(Web Server issue):W193–W200. doi:10.1093/nar/gkm226
Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37(Web Server issue):W202–W208. doi:10.1093/nar/gkp335
Lorenz R, Bernhart SH, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL (2011) ViennaRNA Package 2.0. Algorithms Mol Biol 6:26. doi:10.1186/1748-7188-6-26
Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A (2010) Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20(1):110–121. doi:10.1101/gr.097857.109
Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, Weinstock GM, Wilson RK, Gibbs RA, Kent WJ, Miller W, Haussler D (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15(8):1034–1050. doi:10.1101/gr.3715005
Favre A (1990) 4-Thiouridine as an intrinsic photoaffinity probe of nucleic acid structure and interactions. In: Morrison H (ed) Bioorganic photochemistry, vol 1. Wiley, New York, pp 379–425
Acknowledgements
This work was supported by the National Science Foundation Open Data IGERT grant 0903629 (M.A.F.), the National Institutes of Health grant GM088565 (J.K.K.), and the Pew Charitable Trusts (J.K.K.). The authors would like to thank Danny Yang, Ting Han, and James Taylor for helpful comments on the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Freeberg, M.A., Kim, J.K. (2016). Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis. In: Devaux, F. (eds) Yeast Functional Genomics. Methods in Molecular Biology, vol 1361. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3079-1_6
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3079-1_6
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3078-4
Online ISBN: 978-1-4939-3079-1
eBook Packages: Springer Protocols