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Deciphering the splicing code

Abstract

Alternative splicing has a crucial role in the generation of biological complexity, and its misregulation is often involved in human disease. Here we describe the assembly of a ‘splicing code’, which uses combinations of hundreds of RNA features to predict tissue-dependent changes in alternative splicing for thousands of exons. The code determines new classes of splicing patterns, identifies distinct regulatory programs in different tissues, and identifies mutation-verified regulatory sequences. Widespread regulatory strategies are revealed, including the use of unexpectedly large combinations of features, the establishment of low exon inclusion levels that are overcome by features in specific tissues, the appearance of features deeper into introns than previously appreciated, and the modulation of splice variant levels by transcript structure characteristics. The code detected a class of exons whose inclusion silences expression in adult tissues by activating nonsense-mediated messenger RNA decay, but whose exclusion promotes expression during embryogenesis. The code facilitates the discovery and detailed characterization of regulated alternative splicing events on a genome-wide scale.

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Figure 1: Assembling the splicing code.
Figure 2: Predicting tissue-regulated alternative splicing.
Figure 3: Graphical depiction of the splicing code.
Figure 4: Validation of a regulatory feature map.
Figure 5: The code predicts a mechanism for developmental regulation.

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Acknowledgements

We thank D. L. Black, D. Botstein, S. E. Brenner, C. B. Burge, B. Chabot, R. Durbin, X.-D. Fu, B. R. Graveley, T. R. Hughes, N. Jojic, C. W. J. Smith, S. Tavazoie and members of our various laboratories for discussions or comments on the manuscript; D. D. Licatalosi and R. B. Darnell for communicating unpublished results; and S. Chaudhry for RT–PCR work. B.J.F. also thanks C. M. Bishop and D. J. C. MacKay for hosting him during his sabbatical in Cambridge. This research was funded by a grant from Genome Canada through the OGI to B.J.B., B.J.F. and others; an NSERC/CFI/OIT CRC grant to B.J.F.; CIHR grants to B.J.F. and B.J.B.; an NCIC grant to B.J.B.; and NSERC EWR Steacie and Canadian Institute for Advanced Research Fellowships to B.J.F.

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Authors and Affiliations

Authors

Contributions

Y.B. and B.J.F. developed the predictive framework and code assembly algorithms, analysed validation rates, and with B.J.B. and J.A.C. extracted predictions for regulatory mechanisms. Y.B., B.J.B. and B.J.F. produced the feature compendium. J.A.C. performed wet laboratory experiments. Q.P. generated exon and intron datasets. W.G. and Y.B. developed the web tool with input from the other authors. X.W. analysed exons from neurological disorder-associated genes. O.S. estimated the percentage inclusion values. B.J.F., B.J.B. and Y.B. designed the study and wrote the manuscript with input from the other authors.

Corresponding authors

Correspondence to Benjamin J. Blencowe or Brendan J. Frey.

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Barash, Y., Calarco, J., Gao, W. et al. Deciphering the splicing code. Nature 465, 53–59 (2010). https://doi.org/10.1038/nature09000

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