Abstract
Hidden Markov models (HMMs) are routinely used for analysis of long genomic sequences to identify various features such as genes, CpG islands, and conserved elements. A commonly used Viterbi algorithm requires O(mn) memory to annotate a sequence of length n with an m-state HMM, which is impractical for analyzing whole chromosomes. In this paper, we introduce the on-line Viterbi algorithm for decoding HMMs in much smaller space. Our analysis shows that our algorithm has the expected maximum memory Θ(mlogn) on two-state HMMs. We also experimentally demonstrate that our algorithm significantly reduces memory of decoding a simple HMM for gene finding on both simulated and real DNA sequences, without a significant slow-down compared to the classical Viterbi algorithm.
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Šrámek, R., Brejová, B., Vinař, T. (2007). On-Line Viterbi Algorithm for Analysis of Long Biological Sequences. In: Giancarlo, R., Hannenhalli, S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science(), vol 4645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74126-8_23
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DOI: https://doi.org/10.1007/978-3-540-74126-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74125-1
Online ISBN: 978-3-540-74126-8
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