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Pattern discovery in biosequences

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Grammatical Inference (ICGI 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1433))

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Abstract

We discuss the problem of algorithmic discovery of patterns common to sets of sequences and its applications to computational biology. We formulate a three step paradigm for pattern discovery, which is based on choosing the hypothesis space, designing the function rating a pattern in respect to the given sequences, and developing an algorithm finding the highest rating patterns. We give some examples of implementing this paradigm, and present experimental results of discovering new patterns in sets of biosequences. In these experiments the sets of given sequences are noisy, that is, many of the sequences given as belonging to the family, actually do not belong to the family. Nevertheless our algorithms have been able to identify biologically sound patterns. In particular we present novel results of discovering transcription factor binding sites from the complete set of over 6000 sequences, taken from the yeast genome upstream to the potential genes.

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Author information

Authors and Affiliations

  1. EMBL Outstation - Hinxton, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, UK

    Alvis Brāzma

  2. Department of Informatics, University of Bergen, HIB, N5020, Bergen, Norway

    Inge Jonassen

  3. Department of Computer Science, University of Helsinki, University of Helsinki, P.O.Box 26, FIN-00014, Finland

    Jaak Vilo & Esko Ukkonen

Authors
  1. Alvis Brāzma
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  2. Inge Jonassen
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  3. Jaak Vilo
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  4. Esko Ukkonen
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Editor information

Vasant Honavar Giora Slutzki

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© 1998 Springer-Verlag Berlin Heidelberg

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Brāzma, A., Jonassen, I., Vilo, J., Ukkonen, E. (1998). Pattern discovery in biosequences. In: Honavar, V., Slutzki, G. (eds) Grammatical Inference. ICGI 1998. Lecture Notes in Computer Science, vol 1433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054081

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  • DOI: https://doi.org/10.1007/BFb0054081

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  • Print ISBN: 978-3-540-64776-8

  • Online ISBN: 978-3-540-68707-8

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