Advertisement

mCarts: Genome-Wide Prediction of Clustered Sequence Motifs as Binding Sites for RNA-Binding Proteins

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1421)

Abstract

RNA-binding proteins (RBPs) are critical components of post-transcriptional gene expression regulation. However, their binding sites have until recently been difficult to determine due to the apparent low specificity of RBPs for their target transcripts and the lack of high-throughput assays for analyzing binding sites genome wide. Here we present a bioinformatics method for predicting RBP binding motif sites on a genome-wide scale that leverages motif conservation, RNA secondary structure, and the tendency of RBP binding sites to cluster together. A probabilistic model is learned from bona fide binding sites determined by CLIP and applied genome wide to generate high specificity binding site predictions.

Key words

RNA-binding protein (RBP) CLIP tag clusters motif Binding site prediction mCarts 

Notes

Acknowledgements

The authors would like to thank Lauren E. Fairchild and Huijuan Feng for their assistance in testing the protocol and for providing feedback on the manuscript. This work was supported by grants from the National Institutes of Health (NIH) (R00GM95713) and the Simons Foundation Autism Research Initiative (297990 and 307711) to C.Z.

References

  1. 1.
    Licatalosi DD, Darnell RB (2010) RNA processing and its regulation: global insights into biological networks. Nat Rev Genet 11:75–87CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Ray D, Kazan H, Cook KB et al (2013) A compendium of RNA-binding motifs for decoding gene regulation. Nature 499:172–177CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Cook KB, Kazan H, Zuberi K et al (2011) RBPDB: a database of RNA-binding specificities. Nucleic Acids Res 39:D301–D308CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chasin LA (2007) Searching for splicing motifs. Adv Exp Med Biol 623:85–106CrossRefPubMedGoogle Scholar
  5. 5.
    Galarneau A, Richard S (2005) Target RNA motif and target mRNAs of the Quaking STAR protein. Nat Struct Mol Biol 12:691–698CrossRefPubMedGoogle Scholar
  6. 6.
    Licatalosi DD, Mele A, Fak JJ et al (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456:464–469CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    König J, Zarnack K, Rot G et al (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17:909–915CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Moore MJ, Zhang C, Gantman EC et al (2014) Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat Protoc 9:263–293CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Darnell RB (2010) HITS‐CLIP: panoramic views of protein–RNA regulation in living cells. Wiley Interdiscipl Rev RNA 1:266–286CrossRefGoogle Scholar
  10. 10.
    Blencowe BJ, Ahmad S, Lee LJ (2009) Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes. Genes Dev 23:1379–1386CrossRefPubMedGoogle Scholar
  11. 11.
    Maticzka D, Lange SJ, Costa F et al (2014) GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 15:R17CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Cereda M, Pozzoli U, Rot G et al (2014) RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing. Genome Biol 15:R20CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Han A, Stoilov P, Linares AJ et al (2014) De novo prediction of PTBP1 binding and splicing targets reveals unexpected features of its RNA recognition and function. PLoS Comput Biol 10:e1003442CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Zhang C, Lee K-Y, Swanson MS et al (2013) Prediction of clustered RNA-binding protein motif sites in the mammalian genome. Nucleic Acids Res 41:6793–6807CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Jelen N, Ule J, Živin M et al (2007) Evolution of nova-dependent splicing regulation in the brain. PLoS Genet 3:e173–e1847CrossRefPubMedCentralGoogle Scholar
  16. 16.
    Stein LD (2002) Unix survival guide. John Wiley, Hoboken, NJCrossRefGoogle Scholar
  17. 17.
    Buffalo V (2015) Bioinformatics data skills. O'Reilly Media, Sebastopol, CAGoogle Scholar
  18. 18.
    Zhang C, Frias MA, Mele A et al (2010) Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science 329:439–443CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Weyn-Vanhentenryck SM, Mele A, Yan Q et al (2014) HITS-CLIP and integrative modeling define the rbfox splicing-regulatory network linked to brain development and autism. Cell Rep 6:1139–1152CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Hinrichs AS, Karolchik D, Baertsch R et al (2006) The UCSC genome browser database: update 2006. Nucleic Acids Res 34:D590–D598CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Systems Biology, Department of Biochemistry and Molecular BiophysicsCenter for Motor Neuron Biology and Disease, Columbia UniversityNew YorkUSA

Personalised recommendations