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Discovering Almost Any Hidden Motif from Multiple Sequences in Polynomial Time with Low Sample Complexity and High Success Probability

  • Bin Fu
  • Ming-Yang Kao
  • Lusheng Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5532)

Abstract

We study a natural probabilistic model for motif discovery that has been used to experimentally test the effectiveness of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet Σ. A motif G = g 1 g 2...g m is a string of m characters. Each background sequence is implanted a probabilistically generated approximate copy of G. For a probabilistically generated approximate copy b 1 b 2...b m of G, every character is probabilistically generated such that the probability for b i  ≠ g i is at most α. It has been conjectured that multiple background sequences can help with finding faint motifs G.

In this paper, we develop an efficient algorithm that can discover a hidden motif from a set of sequences for any alphabet Σ with |Σ| ≥ 2 and is applicable to DNA motif discovery. We prove that for \(\alpha<{1\over 4}(1-{1\over |\Sigma|})\) and any constant x ≥ 8, there exist positive constants c 0, ε, δ 1 and δ 2 such that if the length ρ of motif G is at least δ 1 logn, and there are k ≥ c 0 logn input sequences, then in O(n 2 + kn) time this algorithm finds the motif with probability at least \(1-{1\over 2^x}\) for every \(G\in \Sigma^{\rho}-\Psi_{\rho, h,\epsilon}(\Sigma)\), where ρ is the length of the motif, h is a parameter with ρ ≥ 4h ≥ δ 2logn, and Ψ ρ, h,ε (Σ) is a small subset of at most \(2^{-\Theta(\epsilon^2 h)}\) fraction of the sequences in Σ ρ . The constants c 0, ε, δ 1 and δ 2 do not depend on x when x is a parameter of order O(logn). Our algorithm can take any number k sequences as input.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bin Fu
    • 1
  • Ming-Yang Kao
    • 2
  • Lusheng Wang
    • 3
  1. 1.Dept. of Computer ScienceUniversity of Texas – Pan AmericanUSA
  2. 2.Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA
  3. 3.Department of Computer ScienceThe City University of Hong Kong, KowloonHong Kong

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