Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution

PLoS Computational Biology
Xin HeSaurabh Sinha


Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs) and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs) and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to im...Continue Reading


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Related Concepts

DNA, Double-Stranded
Plasma Protein Binding Capacity
Transcription Factor
Two-Parameter Models
Determination, Sequence Homology
Sequence Determinations, DNA
Evolution, Molecular
Regulatory Elements, Transcriptional
Conserved Sequence

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