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Faster Algorithms for Sampling and Counting Biological Sequences

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Book cover String Processing and Information Retrieval (SPIRE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5721))

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Abstract

A set of sequences S is pairwise bounded if the Hamming distance between any pair of sequences in S is at most 2d. The Consensus Sequence problem aims to discern between pairwise bounded sets that have a consensus, and if so, finding one such sequence s *, and those that do not. This problem is closely related to the motif-recognition problem, which abstractly models finding important subsequences in biological data. We give an efficient algorithm for sampling pairwise bounded sets, referred to as MarkovSampling, and show it generates pairwise bounded sets uniformly at random. We illustrate the applicability of MarkovSampling to efficiently solving motif-recognition instances. Computing the expected number of motif sets has been a long-standing open problem in motif-recognition [1,3]. We consider the related problem of counting the number of pairwise bounded sets, give new bounds on number of pairwise bounded sets, and present an algorithmic approach to counting the number of pairwise bounded sets.

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Author information

Authors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, Canada

    Christina Boucher

Authors
  1. Christina Boucher
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Editor information

Editors and Affiliations

  1. Swedish Institute of Computer Science, Kista, Sweden

    Jussi Karlgren

  2. Department of Computer Science and Engineering, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Espoo, Finland

    Jorma Tarhio

  3. Department of Computer Sciences, University of Tampere, Tampere, Finland

    Heikki Hyyrö

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Boucher, C. (2009). Faster Algorithms for Sampling and Counting Biological Sequences. In: Karlgren, J., Tarhio, J., Hyyrö, H. (eds) String Processing and Information Retrieval. SPIRE 2009. Lecture Notes in Computer Science, vol 5721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03784-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-03784-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03783-2

  • Online ISBN: 978-3-642-03784-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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