Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models

TY - JOUR

T1 - Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models

AU - Wong, Wendy S W

AU - Nielsen, Rasmus

N1 - Keywords: Animals; Artificial Intelligence; Base Sequence; Chromosome Mapping; Computer Simulation; Drosophila; Markov Chains; Models, Genetic; Models, Statistical; Molecular Sequence Data; Pattern Recognition, Automated; Phylogeny; Regulatory Sequences, Nucleic Acid; Sequence Alignment; Sequence Analysis, DNA

PY - 2007

Y1 - 2007

N2 - MOTIVATION: Finding the regulatory modules for transcription factors binding is an important step in elucidating the complex molecular mechanisms underlying regulation of gene expression. There are numerous methods available for solving this problem, however, very few of them take advantage of the increasing availability of comparative genomic data. RESULTS: We develop a method for finding regulatory modules in Eukaryotic species using phylogenetic data. Using computer simulations and analysis of real data, we show that the use of phylogenetic hidden Markov model can lead to an increase in accuracy of prediction over methods that do not take advantage of the data from multiple species. AVAILABILITY: The new method is made accessible under GPL in a new publicly available JAVA program: EvoPromoter. It can be downloaded at http://sourceforge.net/projects/evopromoter/.

AB - MOTIVATION: Finding the regulatory modules for transcription factors binding is an important step in elucidating the complex molecular mechanisms underlying regulation of gene expression. There are numerous methods available for solving this problem, however, very few of them take advantage of the increasing availability of comparative genomic data. RESULTS: We develop a method for finding regulatory modules in Eukaryotic species using phylogenetic data. Using computer simulations and analysis of real data, we show that the use of phylogenetic hidden Markov model can lead to an increase in accuracy of prediction over methods that do not take advantage of the data from multiple species. AVAILABILITY: The new method is made accessible under GPL in a new publicly available JAVA program: EvoPromoter. It can be downloaded at http://sourceforge.net/projects/evopromoter/.

U2 - 10.1093/bioinformatics/btm299

DO - 10.1093/bioinformatics/btm299

M3 - Journal article

C2 - 17550911

SN - 1367-4803

VL - 23

SP - 2031

EP - 2037

JO - Bioinformatics

JF - Bioinformatics

IS - 16

ER -