Supervised Detection of Regulatory Motifs in DNA Sequences
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Published 2003 in Statistical Applications in Genetics and Molecular Biology, Vol 2, No 1, Article 5
Abstract:
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary biology. We propose a new likelihood based method, COMODE, for identifying structural motifs in DNA sequences. Commonly used methods (e.g. MEME, Gibbs sampler) model binding sites as families of sequences described by a position weight matrix (PWM) and identify PWMs that maximize the likelihood of observed sequence data under a simple multinomial mixture model. This model assumes that the positions of the PWM correspond to independent multinomial distributions with four cell probabilities. We address supervising the search for DNA binding sites using the information derived from structural characteristics of protein-DNA interactions. We extend the simple multinomial mixture model by incorporating constraints on the information content profiles or on specific parameters of the motif PWMs. The parameters of this extended model are estimated by maximum likelihood using a nonlinear constraint optimization method. Likelihood-based cross-validation is used to select model parameters such as motif width and constraint type. The performance of COMODE is compared with existing motif detection methods on simulated data that incorporate real motif examples from Saccharomyces cerevisiae. The proposed method is especially effective when the motif of interest appears as a weak signal in the data. Some of the transcription factor binding data of Lee et al. (2002) were also analyzed using COMODE and biologically verified sites were identified.
Subject Area:
Categorical Data Analysis, Human Genetics, Statistical Models, Statistical Theory and Methods
Suggested Citation:
Sunduz Keles, Mark J. van der Laan, Sandrine Dudoit, Biao Xing, and Michael B. Eisen, "Supervised Detection of Regulatory Motifs in DNA Sequences" (May 2003). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 131.
http://www.bepress.com/ucbbiostat/paper131