Pattern statistics on Markov chains and sensitivity to parameter estimation.
BACKGROUND
In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...).


RESULTS
In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of sigma, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered.


CONCLUSIONS
We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.
Nuel
Grégory
G
Laboratoire Statistique et Génome, University of Evry, CNRS (8071), INRA(1152), 523, place des terrasses de I'Agora, 91034 Evry CEDEX, France. nuel@genopole.cnrs.fr