Paper Info
Title
Alignment And Prediction Of Cis-Regulatory Modules Based On A Probabilistic Model Of Evolution
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.
Year
DOI
Venue
2009
10.1371/journal.pcbi.1000299
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
binding site turnover,statistical alignment,cis-regulatory modules,regulatory evolution,dna sequence,probabilistic model,transcription factor,transcription factor binding site,transcription factors,sequence alignment,cis regulatory module,binding site,protein binding,binding sites,transcription start site,dna
Sequence alignment,Alignment-free sequence analysis,Human evolutionary genetics,DNA binding site,Biology,Statistical model,Bioinformatics,Genetics,Multiple sequence alignment,Cis-regulatory module,Sequence analysis
Journal
Volume
Issue
ISSN
5
3
1553-734X
Citations 
PageRank 
References 
11
0.64
28
Authors
3
Name
Order
Citations
PageRank
Xin He1312.38
Xu Ling2895.94
Saurabh Sinha352948.96