Segmentation with an isochore distribution

Authors:
Miklós Csűrös;Ming-Te Cheng;Andreas Grimm;Amine Halawani;Perrine Landreau
Affiliations:
Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada;Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada;Lehr- und Forschungseinheit für Bioinformatik, Ludwig-Maximilians-Universität München, München, Germany;Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada;Institut Scientifique Polytechnique Galilée, Université Paris XIII, Villetaneuse, France
Venue:
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Year:
2006

Citing 2
Cited 0

Maximum-Scoring Segment Sets

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An optimal DNA segmentation based on the MDL principle

International Journal of Bioinformatics Research and Applications

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Abstract

We introduce a novel generative probabilistic model for segmentation problems in molecular sequence analysis. All segmentations that satisfy given minimum segment length requirements are equally likely in the model. We show how segmentation-related problems can be solved with similar efficacy as in hidden Markov models. In particular, we show how the best segmentation, as well as posterior segment class probabilities in individual sequence positions can be computed in O(nC) time in case of C segment classes and a sequence of length n.