Paper Info
Title
Morph: Probabilistic Alignment Combined With Hidden Markov Models Of Cis-Regulatory Modules
Abstract
The discovery and analysis of cis-regulatory modules ( CRMs) in metazoan genomes is crucial for understanding the transcriptional control of development and many other biological processes. Cross-species sequence comparison holds much promise for improving computational prediction of CRMs, for elucidating their binding site composition, and for understanding how they evolve. Current methods for analyzing orthologous CRMs from multiple species rely upon sequence alignments produced by off-the-shelf alignment algorithms, which do not exploit the presence of binding sites in the sequences. We present here a unified probabilistic framework, called MORPH, that integrates the alignment task with binding site predictions, allowing more robust CRM analysis in two species. The framework sums over all possible alignments of two sequences, thus accounting for alignment ambiguities in a natural way. We perform extensive tests on orthologous CRMs from two moderately diverged species Drosophila melanogaster and D. mojavensis, to demonstrate the advantages of the new approach. We show that it can overcome certain computational artifacts of traditional alignment tools and provide a different, likely more accurate, picture of cis-regulatory evolution than that obtained from existing methods. The burgeoning field of cis-regulatory evolution, which is amply supported by the availability of many related genomes, is currently thwarted by the lack of accurate alignments of regulatory regions. Our work will fill in this void and enable more reliable analysis of CRM evolution.
Year
DOI
Venue
2007
10.1371/journal.pcbi.0030216
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
hidden markov model,sequence alignment,cis regulatory module,reliability analysis,binding site,biological process
Sequence alignment,Genome,Biology,Markov chain,Exploit,Genome evolution,Bioinformatics,Probabilistic logic,Genetics,Hidden Markov model,Cis-regulatory module
Journal
Volume
Issue
ISSN
3
11
1553-734X
Citations 
PageRank 
References 
12
0.63
16
Authors
2
Name
Order
Citations
PageRank
Saurabh Sinha152948.96
Xin He2312.38