Cargando…

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

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 o...

Descripción completa

Detalles Bibliográficos
Autores principales: He, Xin, Ling, Xu, Sinha, Saurabh
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657044/
https://www.ncbi.nlm.nih.gov/pubmed/19293946
http://dx.doi.org/10.1371/journal.pcbi.1000299
_version_ 1782165553337597952
author He, Xin
Ling, Xu
Sinha, Saurabh
author_facet He, Xin
Ling, Xu
Sinha, Saurabh
author_sort He, Xin
collection PubMed
description 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.
format Text
id pubmed-2657044
institution National Center for Biotechnology Information
language English
publishDate 2009
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-26570442009-03-18 Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution He, Xin Ling, Xu Sinha, Saurabh PLoS Comput Biol Research Article 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. Public Library of Science 2009-03-13 /pmc/articles/PMC2657044/ /pubmed/19293946 http://dx.doi.org/10.1371/journal.pcbi.1000299 Text en He et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
He, Xin
Ling, Xu
Sinha, Saurabh
Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title_full Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title_fullStr Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title_full_unstemmed Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title_short Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution
title_sort alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657044/
https://www.ncbi.nlm.nih.gov/pubmed/19293946
http://dx.doi.org/10.1371/journal.pcbi.1000299
work_keys_str_mv AT hexin alignmentandpredictionofcisregulatorymodulesbasedonaprobabilisticmodelofevolution
AT lingxu alignmentandpredictionofcisregulatorymodulesbasedonaprobabilisticmodelofevolution
AT sinhasaurabh alignmentandpredictionofcisregulatorymodulesbasedonaprobabilisticmodelofevolution