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Detection of cis-element clusters in higher eukaryotic DNA
1Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.
Bioinformatics (Oxford, England)
|October 24, 2001
Summary
We developed Cister, a hidden Markov model tool to identify DNA regulatory regions by finding cis-element clusters. This method accurately predicts regulatory targets and muscle-specific regions in the human genome.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Understanding cellular control mechanisms relies on analyzing DNA regulatory regions.
- Computational methods can accelerate the identification of these critical genomic areas.
Purpose of the Study:
- To present a novel hidden Markov model-based method for detecting regulatory regions in DNA sequences.
- To identify clusters of cis-elements within DNA to predict regulatory regions.
Main Methods:
- Utilized a hidden Markov model to search for clusters of cis-elements.
- Developed a computational tool named Cister for regulatory region prediction.
- Applied the method to known regulatory targets and muscle-specific regions.
Main Results:
- Achieved 67% sensitivity in predicting regulatory targets of the transcription factor LSF.
- Made one prediction per 33 kb of non-repetitive human genomic sequence.
- Demonstrated favorable sensitivity and prediction rates for muscle-specific regions compared to alternative methods.
Conclusions:
- Cister effectively predicts various types of regulatory regions by identifying cis-element clusters.
- The tool is user-friendly and accessible via the web.
- This approach aids in understanding gene regulation and cellular processes.