Large deviation properties for patterns - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Discrete Algorithms Year : 2013

Large deviation properties for patterns


Deciding whether a given pattern is over- or under-represented according to a given background model is a key question in computational biology. Such a decision is usually made by computing some p-values reflecting the ''exceptionality'' of a pattern in a given sequence or set of sequences. In the simplest cases (short and simple patterns, simple background model, small number of sequences), an exact p-value can be computed with a tractable complexity. The realistic cases are in general too complicated to get such an exact $p$-value. Approximations are thus proposed (Gaussian, Poisson, Large deviation approximations). These approximations are applicable under some conditions: Gaussian approximations are valid in the central domain while Poisson and Large deviation approximations are valid for rare events. In the present paper, we prove a large deviation approximation to the double strands counting problem that refers to a counting of a given pattern in a set of sequences that arise from both strands of the genome. In that case, dependencies between a sequence and its reverse complement cannot be neglected. They are captured here for a Bernoulli model from general combinatorial properties of the pattern. A large deviation result is also provided for a set of small sequences.
Ce papier établit un résultat de grande déviations pour des ensembles de séquences courtes ou pour des ensembles de mots surrepresentés ou sous-représentés dans de longues séquences geenomiques. Ce résultat s'applique à la détection de mots exceptionnels dans des séquences génomiques.

Dates and versions

hal-00868462 , version 1 (01-10-2013)



Mireille Regnier, Jérémie Bourdon. Large deviation properties for patterns. Journal of Discrete Algorithms, 2013, ⟨10.1016/j.jda.2013.09.004⟩. ⟨hal-00868462⟩
193 View
0 Download



Gmail Facebook X LinkedIn More