Advertisement

Finding RNA–Protein Interaction Sites Using HMMs

  • Tao Wang
  • Jonghyun Yun
  • Yang Xie
  • Guanghua Xiao
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1552)

Abstract

RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein–RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling.

Key words

Hidden Markov models RNA-binding proteins Interaction sites 

References

  1. 1.
    Keene JD (2007) RNA regulons: coordination of post-transcriptional events. Nat Rev Genet 8:533–543CrossRefPubMedGoogle Scholar
  2. 2.
    Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M et al (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456:464–469CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Ule J, Jensen K, Mele A, Darnell RB (2005) CLIP: a method for identifying protein-RNA interaction sites in living cells. Methods 37:376–386CrossRefPubMedGoogle Scholar
  4. 4.
    Ule J, Jensen KB, Ruggiu M, Mele A, Ule A et al (2003) CLIP identifies Nova-regulated RNA networks in the brain. Science 302:1212–1215CrossRefPubMedGoogle Scholar
  5. 5.
    Zhang C, Darnell RB (2011) Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat Biotechnol 29:607–614CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486PubMedPubMedCentralGoogle Scholar
  7. 7.
    Hafner M, Lianoglou S, Tuschl T, Betel D (2012) Genome-wide identification of miRNA targets by PAR-CLIP. Methods 58:94–105CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J et al (2010) PAR-CliP--a method to identify transcriptome-wide the binding sites of RNA binding proteins. J Vis Exp 41. doi: 10.3791/2034. pii: 2034
  9. 9.
    Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Granneman S, Kudla G, Petfalski E, Tollervey D (2009) 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 U S A 106:9613–9618CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Konig J, Zarnack K, Rot G, Curk T, Kayikci M et al (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17:909–915CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ince-Dunn G, Okano HJ, Jensen KB, Park WY, Zhong R et al (2012) Neuronal Elav-like (Hu) proteins regulate RNA splicing and abundance to control glutamate levels and neuronal excitability. Neuron 75:1067–1080CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Boudreau RL, Jiang P, Gilmore BL, Spengler RM, Tirabassi R et al (2014) Transcriptome-wide discovery of microRNA binding sites in human brain. Neuron 81:294–305CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Wang T, Xie Y, Xiao G (2014) dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol 15:R11CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Neumann M, Bentmann E, Dormann D, Jawaid A, DeJesus-Hernandez M et al (2011) FET proteins TAF15 and EWS are selective markers that distinguish FTLD with FUS pathology from amyotrophic lateral sclerosis with FUS mutations. Brain 134:2595–2609CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Corcoran DL, Georgiev S, Mukherjee N, Gottwein E, Skalsky RL et al (2011) PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol 12:R79CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Welch LR (2003) Hidden Markov models and the Baum-Welch algorithm. IEEE Inform Theory Soc Newslett:1–14Google Scholar
  18. 18.
    Viterbi AJ (1967) Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Trans Inform Theory 13:260–269CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Tao Wang
    • 2
  • Jonghyun Yun
    • 1
  • Yang Xie
    • 2
  • Guanghua Xiao
    • 1
  1. 1.Department of MathematicsUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Quantitative Biomedical Research Center, Department of Clinical ScienceUniversity of Texas Southwestern Medical CenterDallasUSA

Personalised recommendations