Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models. / Wong, Wendy S W; Nielsen, Rasmus.
I: Bioinformatics, Bind 23, Nr. 16, 2007, s. 2031-7.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
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
VL - 23
SP - 2031
EP - 2037
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 16
ER -
ID: 11529404