BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC Export

@article{citeulike:5664256, abstract = {BACKGROUND:We have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences.RESULTS:We present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC) approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the alpha-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques.CONCLUSION:BigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from http://www.stats.ox.ac.uk/\~{}satija/BigFoot/}, author = {Satija, Rahul and Novak, Adam and Miklos, Istvan and Lyngso, Rune and Hein, Jotun}, citeulike-article-id = {5664256}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2148-9-217}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19715598}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19715598}, day = {28}, doi = {10.1186/1471-2148-9-217}, issn = {1471-2148}, journal = {BMC Evolutionary Biology}, keywords = {alignment, bayesian, conservation}, month = {August}, number = {1}, pages = {217+}, posted-at = {2009-09-03 01:39:57}, priority = {2}, title = {BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC}, url = {http://dx.doi.org/10.1186/1471-2148-9-217}, volume = {9}, year = {2009} }

TY - JOUR ID - citeulike:5664256 L3 - citeulike-article-id:5664256 N2 - BACKGROUND:We have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences.RESULTS:We present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC) approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the alpha-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques.CONCLUSION:BigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from http://www.stats.ox.ac.uk/~satija/BigFoot/ IS - 1 JF - BMC Evolutionary Biology SN - 1471-2148 TI - BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC VL - 9 SP - 217 KW - alignment KW - bayesian KW - conservation AU - Satija, Rahul AU - Novak, Adam AU - Miklos, Istvan AU - Lyngso, Rune AU - Hein, Jotun PY - 2009/08/28/ UR - http://dx.doi.org/10.1186/1471-2148-9-217 ER -