Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution Export

@article{citeulike:4170809, abstract = {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 improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i) the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii) binding sites in distal bound sequences (relative to transcription start sites) tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis -regulatory Module Analysis), ready to be applied in a broad biological context.}, author = {He, Xin and Ling, Xu and Sinha, Saurabh}, citeulike-article-id = {4170809}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000299}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19293946}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19293946}, day = {13}, doi = {10.1371/journal.pcbi.1000299}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {evolution, tfprograms, transcription\_factor\_binding\_sites}, month = {March}, number = {3}, pages = {e1000299+}, posted-at = {2009-11-06 04:42:28}, priority = {2}, publisher = {Public Library of Science}, title = {Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000299}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:4170809 L3 - citeulike-article-id:4170809 N2 - 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 improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i) the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii) binding sites in distal bound sequences (relative to transcription start sites) tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis -regulatory Module Analysis), ready to be applied in a broad biological context. IS - 3 JF - PLoS Comput Biol SN - 1553-7358 TI - Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution PB - Public Library of Science VL - 5 SP - e1000299 KW - evolution KW - tfprograms KW - transcription_factor_binding_sites AU - He, Xin AU - Ling, Xu AU - Sinha, Saurabh PY - 2009/03/13/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000299 ER -