Conservative extraction of over-represented extensible motifs.
BACKGROUND
The discovery of motifs in biosequences is frequently torn between the rigidity of the model on the one hand and the abundance of candidates on the other. In particular, the variety of motifs described by strings that include 'don't care' (dot) patterns escalates exponentially with the length of the motif, and this gets only worse if a dot is allowed to stretch up to some prescribed maximum length. This circumstance tends to generate daunting computational burdens, and often gives rise to tables that are impossible to visualize and digest. This is unfortunate, as it seems to preclude precisely those massive analyses that have become conceivable with the increasing availability of massive genomic and protein data. Although a part of the problem is endemic, another part of it seems rooted in the various characterizations offered for the notion of a motif, that are typically based either on syntax or on statistics alone. It seems worthwhile to consider alternatives that result from a prudent combination of these two aspects in the model.


RESULTS
We introduce and study a notion of extensible motif in a sequence which tightly combines the structure of the motif pattern, as described by its syntactic specification, with the statistical measure of its occurrence count. We show that a combination of appropriate saturation conditions (expressed in terms of minimum number of dots compatible with a given list of occurrences) and the monotonicity of probabilistic scores over regions of constant frequency afford us significant parsimony in the generation and testing of candidate over-represented motifs. The merits of the method are documented by the results obtained in implementation, which specifically targeted protein sequence families. In all cases tested, the motif reported in PROSITE as the most important in terms of functional/structural relevance emerges among the top 30 extensible motifs returned by our algorithm, often right at the top. Of equal importance seems the fact that the sets of all surprising motifs returned in each experiment are extracted faster and come in much more manageable sizes than would be obtained in the absence of saturation constrains.


BACKGROUND
This software will be available for use with the suite of tools at www.research.ibm.com/bioinformatics.
Apostolico
Alberto
A
Department of Computer Sciences, Purdue University, Computer Sciences Building West Lafayette, IN 47907, USA. axa@dei.unipd.it
Comin
Matteo
M
Parida
Laxmi
L
D000465:Algorithms; D020816:Amino Acid Motifs; D001426:Bacterial Proteins / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D015233:Models, Statistical; D011336:Probability; D011381:Programming Languages; D012984:Software
D001426:Bacterial Proteins