Bayesian Unsupervised Learning of DNA Regulatory Binding Regions

Corander, Jukka (author): University of Helsinki,Department of mathematics and statistics

Koski, Timo, 1952- (author): KTH,Matematisk statistik,computational biostatistics

(creator_code:org_t)

Hindawi Publishing Corporation, 2009
2009
English.
In: Advances in Artificial Intelligence. - : Hindawi Publishing Corporation. - 1687-7470 .- 1687-7489. ; , s. 219743-

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Abstract Subject headings

Identification of regulatory binding motifs, that is, short specific words, within DNA sequences is a commonly occurring problem in computational bioinformatics. A wide variety of probabilistic approaches have been proposed in the literature to either scan for previously known motif types or to attempt de novo identification of a fixed number (typically one) of putative motifs. Mostapproaches assume the existence of reliable biodatabase information to build probabilistic a priori description of the motif classes. Examples of attempts to do probabilistic unsupervised learning about the number of putative de novo motif types and theirpositions within a set of DNA sequences are very rare in the literature. Here we show how such a learning problem can be formulated using a Bayesian model that targets to simultaneously maximize the marginal likelihood of sequence data arising under multiple motif types as well as under the background DNA model, which equals a variable length Markov chain. It is demonstrated how the adopted Bayesian modelling strategy combined with recently introduced nonstandard stochastic computation tools yields a more tractable learning procedure than is possible with the standard Monte Carlo approaches. Improvements and extensions of the proposed approach are also discussed.

About the subject

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