A Unifying Framework for Seed Sensitivity and Its Application to Subset Seeds

  • Gregory Kucherov
  • Laurent Noé
  • Mikhail Roytberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3692)


We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem – a set of target alignments, an associated probability distribution, and a seed model – that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.


Hide Markov Model Regular Language Bernoulli Model Full Path Seed Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gregory Kucherov
    • 1
  • Laurent Noé
    • 1
  • Mikhail Roytberg
    • 2
  1. 1.INRIA/LORIAVillers-lès-NancyFrance
  2. 2.Institute of Mathematical Problems in BiologyPushchino, Moscow RegionRussia

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