Detection of cis-element clusters in higher eukaryotic DNA.
BACKGROUND
Computational prediction and analysis of transcription regulatory regions in DNA sequences has the potential to accelerate greatly our understanding of how cellular processes are controlled. We present a hidden Markov model based method for detecting regulatory regions in DNA sequences, by searching for clusters of cis-elements.


RESULTS
When applied to regulatory targets of the transcription factor LSF, this method achieves a sensitivity of 67%, while making one prediction per 33 kb of non-repetitive human genomic sequence. When applied to muscle specific regulatory regions, we obtain a sensitivity and prediction rate that compare favorably with one of the best alternative approaches. Our method, which we call Cister, can be used to predict different varieties of regulatory region by searching for clusters of cis-elements of any type chosen by the user. Cister is simple to use and is available on the web.


BACKGROUND
http://sullivan.bu.edu/~mfrith/cister.shtml.


BACKGROUND
mfrith@bu.edu; zhiping@bu.edu.
Frith
M C
MC
Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.
Hansen
U
U
Weng
Z
Z
D000465:Algorithms; D000818:Animals; D001665:Binding Sites / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D004247:DNA / Q000235:genetics* / Q000378:metabolism; D005809:Genes, Regulator; D015894:Genome, Human; D006801:Humans; D008390:Markov Chains; D009132:Muscles / Q000378:metabolism; D011401:Promoter Regions, Genetic; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors; D004247:DNA