Abstract
With the completion of genomes of many species and the advances of microarray technologies, we begin to possess a tremendous amount of valuable biological data — but these raw products are still far from usable. One of the most challenging problems of this century is to decipher this huge amount of biological information, turning the data into knowledge. The past decade has witnessed a number of successful applications of statistical models in computational biology. This article focuses on one of these success stories: Using Bayesian models and Monte Carlo methods to find short repetitive patterns in a set of DNA or protein sequences, a task often referred to as themotif discovery. We review a few probabilistic models that have recently been shown useful for motif discovery and provide a novel framework based on a Bayesian segmentation model to unify these approaches. We show how to combine the dictionary model with the Gibbs sampler and how a segmentation-based data augmentation scheme can be implemented. A few interesting open problems are also discussed.
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Liu, J.S., Gupta, M., Liu, X., Mayerhofere, L., Lawrence, C.E. (2002). Statistical Models for Biological Sequence Motif Discovery. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 167. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2078-7_1
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DOI: https://doi.org/10.1007/978-1-4612-2078-7_1
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