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On-Line Viterbi Algorithm for Analysis of Long Biological Sequences

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Algorithms in Bioinformatics (WABI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4645))

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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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Author information

Authors and Affiliations

  1. Department of Computer Science, Comenius University, 842 48 Bratislava, Slovakia

    Rastislav Šrámek

  2. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA

    Broňa Brejová & Tomáš Vinař

Authors
  1. Rastislav Šrámek
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  2. Broňa Brejová
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  3. Tomáš Vinař
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Raffaele Giancarlo Sridhar Hannenhalli

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© 2007 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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