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Statistical tools for discovering pseudo-periodicitiesinbiological 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 ascysteine signatures and leucine heptads. These known periodicitiesprobably represent a small percentage of the total number of sequencesperiodic structures, and it is useful to have general tools todetect such sequences and their period in large databases ofsequences. We compare three statistics adapted from those used in timeseries analysis: a generalisation of the simple autocovariance basedon a similarity score and two statistics intending to increase thepower of the method. Theoretical behaviour of these statistics arederived, and the corresponding tests are then described. In this paperwe also present an application of these tests to a protein known tohave sequence periodicity.

Keywords

Biological sequencesproteinsperiodicityautocovariance funtion.

Information

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

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