Combining Statistical Alignment and Phylogenetic Footprinting to Detect Regulatory Elements.

@article{citeulike:2570497, 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@stats.ox.ac.uk.}, address = {Department of Statistics, Oxford University, Oxford UK.}, author = {Satija, R. and Pachter, L. and Hein, J. }, citeulike-article-id = {2570497}, doi = {10.1093/bioinformatics/btn104}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {alignment, annotation}, month = {March}, posted-at = {2008-05-08 09:53:45}, priority = {2}, title = {Combining Statistical Alignment and Phylogenetic Footprinting to Detect Regulatory Elements.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn104}, year = {2008} }

TY - JOUR ID - citeulike:2570497 L3 - citeulike-article-id:2570497 TI - Combining Statistical Alignment and Phylogenetic Footprinting to Detect Regulatory Elements. JF - Bioinformatics AD - Department of Statistics, Oxford University, Oxford UK. SN - 1460-2059 N2 - 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@stats.ox.ac.uk. KW - alignment KW - annotation AU - Satija, R AU - Pachter, L AU - Hein, J PY - 2008/March/18/ UR - http://dx.doi.org/10.1093/bioinformatics/btn104 ER -