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Statistical tools for discovering pseudo-periodicities inbiological sequences

Published online by Cambridge University Press:  15 August 2002

Bernard Prum ,
Élisabeth de Turckheim  and
Martin Vingron
Bernard Prum
Affiliation:
Laboratoire Statistique et Génome, URA 8071 du CNRS, La Génopole, Université d'Evry, France; prum@genopole.cnrs.fr.
Élisabeth de Turckheim
Affiliation:
Institut National de la Recherche Agronomique, BIA, 78352 Jouy-en-Josas, France; et@jouy.inra.fr.
Martin Vingron
Affiliation:
Max-Planck-Institut für Molekulare Genetik, Ihnestr. 73, 14195 Berlin, Germany; vingron@molgem.mpg.de.
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Abstract


Many protein sequences present non trivial periodicities, such as cysteine signatures and leucine heptads. These known periodicities probably represent a small percentage of the total number of sequences periodic structures, and it is useful to have general tools to detect such sequences and their period in large databases of sequences. We compare three statistics adapted from those used in time series analysis: a generalisation of the simple autocovariance based on a similarity score and two statistics intending to increase the power of the method. Theoretical behaviour of these statistics are derived, and the corresponding tests are then described. In this paper we also present an application of these tests to a protein known to have sequence periodicity.

Keywords

Biological sequencesproteinsperiodicityautocovariance funtion.
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 5 , 2001 , pp. 171 - 181
Copyright
© EDP Sciences, SMAI, 2001

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