# Statistical tools for discovering pseudo-periodicities in biological sequences

Bernard Prum; Élisabeth de Turckheim; Martin Vingron

ESAIM: Probability and Statistics (2010)

- Volume: 5, page 171-181
- ISSN: 1292-8100

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topPrum, Bernard, de Turckheim, Élisabeth, and Vingron, Martin. "Statistical tools for discovering pseudo-periodicities in biological sequences." ESAIM: Probability and Statistics 5 (2010): 171-181. <http://eudml.org/doc/197759>.

@article{Prum2010,

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.
},

author = {Prum, Bernard, de Turckheim, Élisabeth, Vingron, Martin},

journal = {ESAIM: Probability and Statistics},

keywords = {Biological sequences; proteins; periodicity; autocovariance funtion.},

language = {eng},

month = {3},

pages = {171-181},

publisher = {EDP Sciences},

title = {Statistical tools for discovering pseudo-periodicities in biological sequences},

url = {http://eudml.org/doc/197759},

volume = {5},

year = {2010},

}

TY - JOUR

AU - Prum, Bernard

AU - de Turckheim, Élisabeth

AU - Vingron, Martin

TI - Statistical tools for discovering pseudo-periodicities in biological sequences

JO - ESAIM: Probability and Statistics

DA - 2010/3//

PB - EDP Sciences

VL - 5

SP - 171

EP - 181

AB -
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.

LA - eng

KW - Biological sequences; proteins; periodicity; autocovariance funtion.

UR - http://eudml.org/doc/197759

ER -

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