Frith M C, Hansen U, Weng Z
Bioinformatics Program, Boston University, 44 Cummington St, Boston, MA 02215, USA.
Bioinformatics. 2001 Oct;17(10):878-89. doi: 10.1093/bioinformatics/17.10.878.
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.
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.
http://sullivan.bu.edu/~mfrith/cister.shtml.
对DNA序列中转录调控区域进行计算预测和分析，有可能极大地加速我们对细胞过程如何被控制的理解。我们提出了一种基于隐马尔可夫模型的方法，通过搜索顺式元件簇来检测DNA序列中的调控区域。
当应用于转录因子LSF的调控靶点时，该方法的灵敏度达到67%，同时每33 kb的非重复人类基因组序列进行一次预测。当应用于肌肉特异性调控区域时，我们获得的灵敏度和预测率与最佳替代方法之一相比具有优势。我们的方法称为Cister，可通过搜索用户选择的任何类型的顺式元件簇来预测不同种类的调控区域。Cister使用简单，可在网上获取使用。
http://sullivan.bu.edu/~mfrith/cister.shtml。
