In Silico Prediction of Splice-Affecting Nucleotide Variants

Part of the Methods in Molecular Biology book series (MIMB, volume 760)

Abstract

It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict experimental transcript analyses to the most appropriate cases. In silico tools designed to provide this type of prediction are available. In this chapter, we present in silico splice tools integrated in the Alamut (Interactive Biosoftware) application and detail their use in routine diagnostic applications. At this time, in silico predictions are useful for variants that decrease the strength of wild-type splice sites or create a cryptic splice site. Importantly, in silico predictions are not sufficient to classify variants as neutral or deleterious: they should be used as part of the decision-making process to detect potential candidates for splicing anomalies, prompting molecular geneticists to carry out transcript analyses in a limited and pertinent number of cases which could be managed in routine settings.

Key words

Unknown variants splice in silico prediction diagnosis 

References

  1. 1.
    Hastings, M. L., and Krainer, A. R. (2001) Pre-mRNA splicing in the new millennium. Curr Opin Cell Biol 13, 302–309.PubMedCrossRefGoogle Scholar
  2. 2.
    Cooper, T. A., and Mattox, W. (1997) The regulation of splice-site selection, and its role in human disease. Am J Hum Genet 61, 259–266.PubMedCrossRefGoogle Scholar
  3. 3.
    Cartegni, L., Chew, S. L., and Krainer, A. R. (2002) Listening to silence and understanding nonsense: exonic mutations that affect splicing. Nat Rev Genet 3, 285–298.PubMedCrossRefGoogle Scholar
  4. 4.
    Zatkova, A., Messiaen, L., Vandenbroucke, I., et al. (2004) Disruption of exonic splicing enhancer elements is the principal cause of exon skipping associated with seven nonsense or missense alleles of NF1. Hum Mutat 24, 491–501.PubMedCrossRefGoogle Scholar
  5. 5.
    Dehainault, C., Michaux, D., Pages-Berhouet, S., et al. (2007) A deep intronic mutation in the RB1 gene leads to intronic sequence exonisation. Eur J Hum Genet 15, 473–477.PubMedCrossRefGoogle Scholar
  6. 6.
    Reese, M. G., Eeckman, F. H., Kulp, D., and Haussler, D. (1997) Improved splice site detection in Genie. J Comput Biol 4, 311–323.PubMedCrossRefGoogle Scholar
  7. 7.
    Yeo, G., and Burge, C. B. (2004) Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 11, 377–394.PubMedCrossRefGoogle Scholar
  8. 8.
    Cartegni, L., Wang, J., Zhu, Z., et al. (2003) ESEfinder: A web resource to identify exonic splicing enhancers. Nucleic Acids Res 31, 3568–3571.PubMedCrossRefGoogle Scholar
  9. 9.
    Fairbrother, W. G., Yeh, R. F., Sharp, P. A., and Burge, C. B. (2002) Predictive identification of exonic splicing enhancers in human genes. Science 297, 1007–1013.PubMedCrossRefGoogle Scholar
  10. 10.
    Shapiro, M. B., and Senapathy, P. (1987) RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. Nucleic Acids Res 15, 7155–7174.PubMedCrossRefGoogle Scholar
  11. 11.
    Senapathy, P., Shapiro, M. B., and Harris, N. L. (1990) Splice junctions, branch point sites, and exons: sequence statistics, identification, and applications to genome project. Methods Enzymol 183, 252–278.PubMedCrossRefGoogle Scholar
  12. 12.
    Eng, L., Coutinho, G., Nahas, S., et al. (2004) Nonclassical splicing mutations in the coding and noncoding regions of the ATM Gene: maximum entropy estimates of splice junction strengths. Hum Mutat 23, 67–76.PubMedCrossRefGoogle Scholar
  13. 13.
    Desmet, F. O., Hamroun, D., Lalande, M., et al. (2009) Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 37, e67.PubMedCrossRefGoogle Scholar
  14. 14.
    Houdayer, C., Dehainault, C., Mattler, C., et al. (2008) Evaluation of in silico splice tools for decision-making in molecular diagnosis. Hum Mutat 29, 975–982.PubMedCrossRefGoogle Scholar
  15. 15.
    Zhang, M. Q. (1998) Statistical features of human exons and their flanking regions. Hum Mol Genet 7, 919–932.PubMedCrossRefGoogle Scholar
  16. 16.
    Pertea, M., Lin, X., and Salzberg, S. L. (2001) GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 29, 1185–1190.PubMedCrossRefGoogle Scholar
  17. 17.
    Walker, L. C., Whiley, P. J., Couch, F. J., et al. Detection of splicing aberrations caused by BRCA1 and BRCA2 sequence variants encoding missense substitutions: implications for prediction of pathogenicity. Hum Mutat 31, E1484–1505.Google Scholar
  18. 18.
    Rouleau, E., Lefol, C., Moncoutier, V., et al. A missense variant within BRCA1 exon 23 causing exon skipping. Cancer Genet Cytogenet 202, 144–146.Google Scholar
  19. 19.
    Nalla, V. K., and Rogan, P. K. (2005) Automated splicing mutation analysis by information theory. Hum Mutat 25, 334–342.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Faculty of Pharmacy, Institut CurieParis Descartes UniversityParisFrance

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