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Computational methods for discovering patterns of local correlations insequences are important in computational biology. Here we show how to determinethe optimal partitioning of aligned sequences into non-overlapping segmentssuch that positions in the same segment are strongly correlated while positionsin different segments are not. Our approach involves discovering the hiddenvariables of a Bayesian network that interact with observed sequences so as toform a set of independent mixture models. We introduce a dynamic program toefficiently discover the optimal segmentation, or equivalently the optimal setof hidden variables. We evaluate our approach on two computational biologytasks. One task is related to the design of vaccines against polymorphicpathogens and the other task involves analysis of single nucleotidepolymorphisms (SNPs) in human DNA. We show how common tasks in these problemsnaturally correspond to inference procedures in the learned models. Error ratesof our learned models for the prediction of missing SNPs are up to 1/3 lessthan the error rates of a state-of-the-art SNP prediction method. Source codeis available at www.uwm.edu/~joebock/segmentation.

Discovering Patterns in Biological Sequences by Optimal Segmentation