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Published May 15, 2008 | public
Journal Article

Combining statistical alignment and phylogenetic footprinting to detect regulatory elements

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 hidden Markov model (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/

Additional Information

© The Author 2008. Published by Oxford University Press. Received on January 21, 2008; revised on February 21, 2008; accepted on March 17, 2008. Advance Access publication March 18, 2008. We thank István Miklós, Rune Lyngsø and Gerton Lunter for helpful discussion. R.S. is funded by the Rhodes Trust, UK. Conflict of Interest: none declared.

Additional details

Created:
August 19, 2023
Modified:
October 24, 2023