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(m log n) 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Šrámek, R., Brejová, B., & Vinař, T. (2007). On-line viterbi algorithm for analysis of long biological sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4645 LNBI, pp. 240–251). Springer Verlag. https://doi.org/10.1007/978-3-540-74126-8_23
Mendeley helps you to discover research relevant for your work.