期刊
BIOINFORMATICS
卷 17, 期 10, 页码 878-889出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/17.10.878
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资金
- NCI NIH HHS [R01 CA081157, CA81157, R01 CA081157-05] Funding Source: Medline
Motivation: 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 nonrepetitive 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.
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