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Computational Promoter Prediction in a Vertebrate Genome

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Computational prediction of vertebrate gene promoters from genomic DNA sequences is one of the most difficult problems in computational genomics, but it is essential for understanding genome organization, improving gene annotation and for further comprehensive studies of gene expression and regulation networks. The advent of new genomic technologies has ushered forth the era of deeper understanding of molecular biology at systems level, more accurate and diverse large-scale molecular data have been fueling the development of new predictive methods and computational tools in this rapidly moving field. In this chapter, I will give an introduction on structure and function of promoters in typical vertebrate genes, as well as experimental methods for determining them. I then describe generic statistical methods for promoter prediction and a few computational approaches as examples. I will further review and update on more recent advances in promoter prediction methodologies and give a future prospect in the conclusion.

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References

  1. Abeel, T., Van de Peer, Y., & Saeys, Y. (2009). Toward a gold standard for promoter prediction evaluation. Bioinformatics, 25(12), i313–i320.

    Article  Google Scholar 

  2. Bajic, V. B., Tan, S. L., Suzuki, Y., & Sugano, S. (2004). Promoter prediction analysis on the whole human genome. Nature Biotechnology, 22(11), 1467–1473.

    Article  Google Scholar 

  3. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group.

    MATH  Google Scholar 

  4. Buck, M. J., & Lieb, J. D. (2006). A chromatin-mediated mechanism for specification of conditional transcription factor targets. Nature Genetics, 38(12), 1446–1451.

    Article  Google Scholar 

  5. Cairns, B. R. (2009). The logic of chromatin architecture and remodeling at promoters. Nature, 461(7261), 193–198.

    Article  Google Scholar 

  6. Dettling, M., & Buhlmann, P. (2003). Boosting for tumor classification with gene expression data. Bioinformatics, 19(9), 1061–1069.

    Article  Google Scholar 

  7. Down, T. A., & Hubbard, T. J. P. (2002). Computational detection and location of transcription start sites in mammalian genomic DNA. Genome Research, 12(3), 458–461.

    Article  Google Scholar 

  8. Faulkner, G. J., & Carninci, P. (2009). Altruistic functions for selfish DNA. Cell Cycle, 8(18), 2895–2900.

    Article  Google Scholar 

  9. Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. Machine learning: Proceedings of the thirteenth international conference (pp. 148–156). Italy.

    Google Scholar 

  10. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 28(2), 337–407.

    Article  MathSciNet  MATH  Google Scholar 

  11. Frith, M. C., Valen, E., Krogh, A., Hayashizaki, Y., Carninci, P., & Sandelin, A. (2008). A code for transcription initiation in mammalian genomes. Genome Research, 18, 1–12.

    Article  Google Scholar 

  12. Fuda, N. J., Ardehali, M. B., & Lis, J. T. (2009). Defining mechanisms that regulate RNA polymerase II transcription in vivo. Nature, 461(7261), 186–192.

    Article  Google Scholar 

  13. Kearns, M., & Valiant, L. (1994). Cryptographic limitations on learning boolean formulae and finite automata. Journal of the ACM (JACM), 41(1), 67–95.

    Article  MathSciNet  MATH  Google Scholar 

  14. McCullagh, P., & Nelder, J. A. (1983). Generalized linear models. London: Chapman and Hall.

    MATH  Google Scholar 

  15. Park, P. J. (2009). ChIP–seq: Advantages and challenges of a maturing technology. Nature Reviews Genetics, 10, 669–680.

    Article  Google Scholar 

  16. Sandelin, A., Carninci, P., Lenhard, B., Ponjavic, J., Hayashizaki, Y., & Hume, D. (2007). Mammalian RNA polymerase II core promoters: Insights from genome-wide studies. Nature Reviews Genetics, 8(6), 424–436.

    Article  Google Scholar 

  17. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.

    Google Scholar 

  18. Sonnenburg, S., Zien, A., & Ratsch, G. (2006). ARTS: Accurate recognition of transcription starts in human. Bioinformatics, 22(14), e472–e480.

    Article  Google Scholar 

  19. Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning, 1, 211–244.

    MathSciNet  MATH  Google Scholar 

  20. Wang, X., Xuan, Z., Zhao, X., Li, Y., & Zhang, M. (2009). High-resolution human core-promoter prediction with CoreBoost_HM. Genome Research, 19(2), 266–275.

    Article  Google Scholar 

  21. Zeng, J., Zhu, S., & Yan, H. (2009). Towards accurate human promoter recognition: A review of currently used sequence features and classification methods. Briefings in Bioinformatics, 10(5), 498–508.

    Article  Google Scholar 

  22. Zhang, M. Q. (2007). Computational analyses of eukaryotic promoters. BMC Bioinformatics, 8(Suppl. 6), S3.

    Article  Google Scholar 

  23. Zhao, X., Xuan, Z., & Zhang, M. (2007). Boosting with stumps for predicting transcription start sites. Genome Biology, 8(2), R17.

    Article  Google Scholar 

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Acknowledgements

The author would like to thank the NIH through R01 HG001696 grant.

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Correspondence to Michael Q. Zhang .

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Zhang, M.Q. (2011). Computational Promoter Prediction in a Vertebrate Genome. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_4

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