Ohler U
Lehrstuhl für Mustererkennung, University of Erlangen-Nuremberg, D-91058 Erlangen, Germany.
Genome Res. 2000 Apr;10(4):539-42. doi: 10.1101/gr.10.4.539.
We describe our statistical system for promoter recognition in genomic DNA with which we took part in the Genome Annotation Assessment Project (GASP1). We applied two versions of the system: the first uses a region-based approach toward transcription start site identification, namely, interpolated Markov chains; the second was a hybrid approach combining regions and signals within a stochastic segment model. We compare the results of both versions with each other and examine how well the application on a genomic scale compares with the results we previously obtained on smaller data sets.
我们描述了用于在基因组DNA中识别启动子的统计系统,我们使用该系统参与了基因组注释评估项目(GASP1)。我们应用了该系统的两个版本:第一个版本使用基于区域的方法来识别转录起始位点,即内插马尔可夫链;第二个版本是一种混合方法,它在随机片段模型中结合了区域和信号。我们将两个版本的结果相互比较,并研究在基因组规模上的应用与我们之前在较小数据集上获得的结果相比情况如何。