Supervised detection of regulatory motifs in DNA sequences

Statistical Applications in Genetics and Molecular Biology
Sunduz KelesMichael B Eisen

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 motif 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 to a constrained 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 moti...Continue Reading

Citations

Aug 1, 2009·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Chengpeng Bi
Jun 29, 2013·Algorithms for Molecular Biology : AMB·Alastair M KilpatrickStuart Aitken
Jul 11, 2006·Annual Review of Genomics and Human Genetics·Steven J M Jones
Nov 6, 2004·Microbiology·Dmitry A RodionovNicole Hugouvieux-Cotte-Pattat

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