Simonis N, Wodak S J, Cohen G N, van Helden J
Service de Conformation des Macromolécules Biologiques et Bioinformatique, Centre de Biologie Structurale et Bioinformatique, CP 263, Université Libre de Bruxelles, Bld. du Triomphe B-1050 Bruxelles, Belgium.
Bioinformatics. 2004 Oct 12;20(15):2370-9. doi: 10.1093/bioinformatics/bth252. Epub 2004 Apr 8.
Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences.
String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs.
The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.
已经提出了几种模式发现方法来检测共调控基因上游序列中过度表达的基序，例如用于从共表达基因簇预测顺式作用元件。待分析的基因簇通常存在噪声，包含共调控基因和非共调控基因的混合物。我们提出了一种基于模式在其非编码序列中出现次数来区分共调控基因和非共调控基因的方法。
基于字符串的模式发现与判别分析相结合，以便根据假定的调控基序对基因进行分类。
通过比较在酿酒酵母的注释调控子（阳性对照）、随机基因选择（阴性对照）和高通量调控子（噪声数据）中检测到的模式的显著性来评估该方法。在注释调控子上评估分类，并使用共调控基因和随机基因的混合物评估稳健性和拒绝能力。
