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Bioinformatics Advance Access published online on March 18, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn104
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Combining Statistical Alignment and Phylogenetic Footprinting to Detect Regulatory Elements

R. Satija 1,*, L. Pachter 2 and J. Hein 1

1Department of Statistics, Oxford University, Oxford UK
2Department of Mathematics, Univeristy of California, Berkeley, CA USA

*To whom correspondence should be addressed. Rahul Satija, E-mail: satija{at}stats.ox.ac.uk


  Abstract

Motivation: Traditional alignment-based phylogenetic footprinting approaches make predictions on the basis of a single assumed alignment. The predictions are therefore highly sensitive to alignment errors or regions of alignment uncertainty. Alternatively, statistical alignment methods provide a framework for performing phylogenetic analyses by examining a distribution of alignments.

Results: We developed a novel algorithm for predicting functional elements by combining statistical alignment and phylogenetic footprinting (SAPF). SAPF simultaneously performs both alignment and annotation by combining phylogenetic footprinting techniques with an HMM transducer-based multiple alignment model, and can analyze sequence data from multiple sequences. We assessed SAPF's predictive performance on two simulated datasets and three well-annotated cis-regulatory modules from newly sequenced Drosophila genomes. The results demonstrate that removing the traditional dependence on a single alignment can significantly augment the predictive performance, especially when there is uncertainty in the alignment of functional regions.

Availability: SAPF is freely available to download online at http://www.stats.ox.ac.uk/~satija/SAPF/

Contact: satija{at}stats.ox.ac.uk

Associate Editor: Dr. Limsoon Wong

Received on January 21, 2008; revised on February 21, 2008; accepted on March 17, 2008

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