Sequential modeling for identifying CpG island locations in human genome

We consider several sequential processing algorithms for identifying genes in human DNA, based on detecting CpG ("C proceeds G") islands. The algorithms are designed to capture the underlying statistical structure in a DNA sequence. Sequential processing using a Markov model and a hidden Markov model are shown to identify most CpG islands in annotated (marked) DNA subsequences available from publicly available DNA datasets. We also consider a wavelet-based hidden Markov tree (HMT). In the context of the HMT, we address design of adaptive wavelets matched to CpG islands, this accomplished via lifting and genetic-algorithm optimization.

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