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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The author would like to thank the NIH through R01 HG001696 grant.
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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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DOI: https://doi.org/10.1007/978-3-642-16345-6_4
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